+```
+
+But you can use names to both subset and extract elements:
+
+
+``` r
+xlist[["a"]]
+```
+
+``` output
+[1] "Software Carpentry"
+```
+
+The `$` function is a shorthand way for extracting elements by name:
+
+
+``` r
+xlist$data
+```
+
+``` output
+ mpg cyl disp hp drat wt qsec vs am gear carb
+Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
+Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
+Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
+Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
+Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
+Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
+```
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 5
+
+Given the following list:
+
+
+``` r
+xlist <- list(a = "Software Carpentry", b = 1:10, data = head(mtcars))
+```
+
+Using your knowledge of both list and vector subsetting, extract the number 2 from xlist.
+Hint: the number 2 is contained within the "b" item in the list.
+
+::::::::::::::: solution
+
+## Solution to challenge 5
+
+
+``` r
+xlist$b[2]
+```
+
+``` output
+[1] 2
+```
+
+
+``` r
+xlist[[2]][2]
+```
+
+``` output
+[1] 2
+```
+
+
+``` r
+xlist[["b"]][2]
+```
+
+``` output
+[1] 2
+```
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 6
+
+Given a linear model:
+
+
+``` r
+mod <- aov(pop ~ lifeExp, data=gapminder)
+```
+
+Extract the residual degrees of freedom (hint: `attributes()` will help you)
+
+::::::::::::::: solution
+
+## Solution to challenge 6
+
+
+``` r
+attributes(mod) ## `df.residual` is one of the names of `mod`
+```
+
+
+``` r
+mod$df.residual
+```
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Data frames
+
+Remember the data frames are lists underneath the hood, so similar rules
+apply. However they are also two dimensional objects:
+
+`[` with one argument will act the same way as for lists, where each list
+element corresponds to a column. The resulting object will be a data frame:
+
+
+``` r
+head(gapminder[3])
+```
+
+``` output
+ pop
+1 8425333
+2 9240934
+3 10267083
+4 11537966
+5 13079460
+6 14880372
+```
+
+Similarly, `[[` will act to extract *a single column*:
+
+
+``` r
+head(gapminder[["lifeExp"]])
+```
+
+``` output
+[1] 28.801 30.332 31.997 34.020 36.088 38.438
+```
+
+And `$` provides a convenient shorthand to extract columns by name:
+
+
+``` r
+head(gapminder$year)
+```
+
+``` output
+[1] 1952 1957 1962 1967 1972 1977
+```
+
+With two arguments, `[` behaves the same way as for matrices:
+
+
+``` r
+gapminder[1:3,]
+```
+
+``` output
+ country year pop continent lifeExp gdpPercap
+1 Afghanistan 1952 8425333 Asia 28.801 779.4453
+2 Afghanistan 1957 9240934 Asia 30.332 820.8530
+3 Afghanistan 1962 10267083 Asia 31.997 853.1007
+```
+
+If we subset a single row, the result will be a data frame (because
+the elements are mixed types):
+
+
+``` r
+gapminder[3,]
+```
+
+``` output
+ country year pop continent lifeExp gdpPercap
+3 Afghanistan 1962 10267083 Asia 31.997 853.1007
+```
+
+But for a single column the result will be a vector (this can
+be changed with the third argument, `drop = FALSE`).
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 7
+
+Fix each of the following common data frame subsetting errors:
+
+1. Extract observations collected for the year 1957
+
+
+ ``` r
+ gapminder[gapminder$year = 1957,]
+ ```
+
+2. Extract all columns except 1 through to 4
+
+
+ ``` r
+ gapminder[,-1:4]
+ ```
+
+3. Extract the rows where the life expectancy is longer the 80 years
+
+
+ ``` r
+ gapminder[gapminder$lifeExp > 80]
+ ```
+
+4. Extract the first row, and the fourth and fifth columns
+ (`continent` and `lifeExp`).
+
+
+ ``` r
+ gapminder[1, 4, 5]
+ ```
+
+5. Advanced: extract rows that contain information for the years 2002
+ and 2007
+
+
+ ``` r
+ gapminder[gapminder$year == 2002 | 2007,]
+ ```
+
+::::::::::::::: solution
+
+## Solution to challenge 7
+
+Fix each of the following common data frame subsetting errors:
+
+1. Extract observations collected for the year 1957
+
+
+ ``` r
+ # gapminder[gapminder$year = 1957,]
+ gapminder[gapminder$year == 1957,]
+ ```
+
+2. Extract all columns except 1 through to 4
+
+
+ ``` r
+ # gapminder[,-1:4]
+ gapminder[,-c(1:4)]
+ ```
+
+3. Extract the rows where the life expectancy is longer than 80 years
+
+
+ ``` r
+ # gapminder[gapminder$lifeExp > 80]
+ gapminder[gapminder$lifeExp > 80,]
+ ```
+
+4. Extract the first row, and the fourth and fifth columns
+ (`continent` and `lifeExp`).
+
+
+ ``` r
+ # gapminder[1, 4, 5]
+ gapminder[1, c(4, 5)]
+ ```
+
+5. Advanced: extract rows that contain information for the years 2002
+ and 2007
+
+
+ ``` r
+ # gapminder[gapminder$year == 2002 | 2007,]
+ gapminder[gapminder$year == 2002 | gapminder$year == 2007,]
+ gapminder[gapminder$year %in% c(2002, 2007),]
+ ```
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 8
+
+1. Why does `gapminder[1:20]` return an error? How does it differ from `gapminder[1:20, ]`?
+
+2. Create a new `data.frame` called `gapminder_small` that only contains rows 1 through 9
+ and 19 through 23. You can do this in one or two steps.
+
+::::::::::::::: solution
+
+## Solution to challenge 8
+
+1. `gapminder` is a data.frame so needs to be subsetted on two dimensions. `gapminder[1:20, ]` subsets the data to give the first 20 rows and all columns.
+
+2.
+
+``` r
+gapminder_small <- gapminder[c(1:9, 19:23),]
+```
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- Indexing in R starts at 1, not 0.
+- Access individual values by location using `[]`.
+- Access slices of data using `[low:high]`.
+- Access arbitrary sets of data using `[c(...)]`.
+- Use logical operations and logical vectors to access subsets of data.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
diff --git a/15-plot-ggplot2.md b/15-plot-ggplot2.md
new file mode 100644
index 000000000..d7ecf0a09
--- /dev/null
+++ b/15-plot-ggplot2.md
@@ -0,0 +1,555 @@
+---
+title: Creating Publication-Quality Graphics with ggplot2
+teaching: 60
+exercises: 20
+source: Rmd
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- To be able to use ggplot2 to generate publication-quality graphics.
+- To apply geometry, aesthetic, and statistics layers to a ggplot plot.
+- To manipulate the aesthetics of a plot using different colors, shapes, and lines.
+- To improve data visualization through transforming scales and paneling by group.
+- To save a plot created with ggplot to disk.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- How can I create publication-quality graphics in R?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
+
+Plotting our data is one of the best ways to
+quickly explore it and the various relationships
+between variables.
+
+There are three main plotting systems in R,
+the [base plotting system][base], the [lattice]
+package, and the [ggplot2] package.
+
+Today we'll be learning about the ggplot2 package, because
+it is the most effective for creating publication-quality
+graphics.
+
+ggplot2 is built on the grammar of graphics, the idea that any plot can be
+built from the same set of components: a **data set**,
+**mapping aesthetics**, and graphical **layers**:
+
+- **Data sets** are the data that you, the user, provide.
+
+- **Mapping aesthetics** are what connect the data to the graphics.
+ They tell ggplot2 how to use your data to affect how the graph looks,
+ such as changing what is plotted on the X or Y axis, or the size or
+ color of different data points.
+
+- **Layers** are the actual graphical output from ggplot2. Layers
+ determine what kinds of plot are shown (scatterplot, histogram, etc.),
+ the coordinate system used (rectangular, polar, others), and other
+ important aspects of the plot. The idea of layers of graphics may
+ be familiar to you if you have used image editing programs
+ like Photoshop, Illustrator, or Inkscape.
+
+Let's start off building an example using the gapminder data from earlier.
+The most basic function is `ggplot`, which lets R know that we're
+creating a new plot. Any of the arguments we give the `ggplot`
+function are the *global* options for the plot: they apply to all
+layers on the plot.
+
+
+``` r
+library("ggplot2")
+ggplot(data = gapminder)
+```
+
+Enter .
+
+```output
+Created directory '/c/Users/Vlad Dracula/.ssh'.
+Enter passphrase (empty for no passphrase):
+```
+
+Now, it is prompting Dracula for a passphrase. Since he is using his lab's laptop that other people sometimes have access to, he wants to create a passphrase.
+Be sure to use something memorable or save your passphrase somewhere, as there is no "reset my password" option.
+Note that, when typing a passphrase on a terminal, there won't be any visual feedback of your typing.
+This is normal: your passphrase will be recorded even if you see nothing changing on your screen.
+
+```output
+Enter same passphrase again:
+```
+
+After entering the same passphrase a second time, we receive the confirmation
+
+```output
+Your identification has been saved in /c/Users/Vlad Dracula/.ssh/id_ed25519
+Your public key has been saved in /c/Users/Vlad Dracula/.ssh/id_ed25519.pub
+The key fingerprint is:
+SHA256:SMSPIStNyA00KPxuYu94KpZgRAYjgt9g4BA4kFy3g1o vlad@tran.sylvan.ia
+The key's randomart image is:
++--[ED25519 256]--+
+|^B== o. |
+|%*=.*.+ |
+|+=.E =.+ |
+| .=.+.o.. |
+|.... . S |
+|.+ o |
+|+ = |
+|.o.o |
+|oo+. |
++----[SHA256]-----+
+```
+
+The "identification" is actually the private key. You should never share it. The public key is appropriately named. The "key fingerprint"
+is a shorter version of a public key.
+
+Now that we have generated the SSH keys, we will find the SSH files when we check.
+
+```bash
+ls -al ~/.ssh
+```
+
+```output
+drwxr-xr-x 1 Vlad Dracula 197121 0 Jul 16 14:48 ./
+drwxr-xr-x 1 Vlad Dracula 197121 0 Jul 16 14:48 ../
+-rw-r--r-- 1 Vlad Dracula 197121 419 Jul 16 14:48 id_ed25519
+-rw-r--r-- 1 Vlad Dracula 197121 106 Jul 16 14:48 id_ed25519.pub
+```
+
+### 3\.2 Copy the public key to GitHub
+
+Now we have a SSH key pair and we can run this command to check if GitHub can read our authentication.
+
+```bash
+ssh -T git@github.com
+```
+
+```output
+The authenticity of host 'github.com (192.30.255.112)' can't be established.
+RSA key fingerprint is SHA256:nThbg6kXUpJWGl7E1IGOCspRomTxdCARLviKw6E5SY8.
+This key is not known by any other names
+Are you sure you want to continue connecting (yes/no/[fingerprint])? y
+Please type 'yes', 'no' or the fingerprint: yes
+Warning: Permanently added 'github.com' (RSA) to the list of known hosts.
+git@github.com: Permission denied (publickey).
+```
+
+Right, we forgot that we need to give GitHub our public key!
+
+First, we need to copy the public key. Be sure to include the `.pub` at the end, otherwise you're looking at the private key.
+
+```bash
+cat ~/.ssh/id_ed25519.pub
+```
+
+```output
+ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIDmRA3d51X0uu9wXek559gfn6UFNF69yZjChyBIU2qKI vlad@tran.sylvan.ia
+```
+
+Now, going to GitHub.com, click on your profile icon in the top right corner to get the drop-down menu. Click "Settings," then on the
+settings page, click "SSH and GPG keys," on the left side "Account settings" menu. Click the "New SSH key" button on the right side. Now,
+you can add the title (Dracula uses the title "Vlad's Lab Laptop" so he can remember where the original key pair
+files are located), paste your SSH key into the field, and click the "Add SSH key" to complete the setup.
+
+Now that we've set that up, let's check our authentication again from the command line.
+
+```bash
+$ ssh -T git@github.com
+```
+
+```output
+Hi Vlad! You've successfully authenticated, but GitHub does not provide shell access.
+```
+
+Good! This output confirms that the SSH key works as intended. We are now ready to push our work to the remote repository.
+
+## 4\. Push local changes to a remote
+
+Now that authentication is setup, we can return to the remote. This command will push the changes from
+our local repository to the repository on GitHub:
+
+```bash
+$ git push origin main
+```
+
+Since Dracula set up a passphrase, it will prompt him for it. If you completed advanced settings for your authentication, it
+will not prompt for a passphrase.
+
+```output
+Enumerating objects: 16, done.
+Counting objects: 100% (16/16), done.
+Delta compression using up to 8 threads.
+Compressing objects: 100% (11/11), done.
+Writing objects: 100% (16/16), 1.45 KiB | 372.00 KiB/s, done.
+Total 16 (delta 2), reused 0 (delta 0)
+remote: Resolving deltas: 100% (2/2), done.
+To https://github.com/vlad/planets.git
+ * [new branch] main -> main
+```
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Proxy
+
+If the network you are connected to uses a proxy, there is a chance that your
+last command failed with "Could not resolve hostname" as the error message. To
+solve this issue, you need to tell Git about the proxy:
+
+```bash
+$ git config --global http.proxy http://user:password@proxy.url
+$ git config --global https.proxy https://user:password@proxy.url
+```
+
+When you connect to another network that doesn't use a proxy, you will need to
+tell Git to disable the proxy using:
+
+```bash
+$ git config --global --unset http.proxy
+$ git config --global --unset https.proxy
+```
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Password Managers
+
+If your operating system has a password manager configured, `git push` will
+try to use it when it needs your username and password. For example, this
+is the default behavior for Git Bash on Windows. If you want to type your
+username and password at the terminal instead of using a password manager,
+type:
+
+```bash
+$ unset SSH_ASKPASS
+```
+
+in the terminal, before you run `git push`. Despite the name, [Git uses
+`SSH_ASKPASS` for all credential
+entry](https://git-scm.com/docs/gitcredentials#_requesting_credentials), so
+you may want to unset `SSH_ASKPASS` whether you are using Git via SSH or
+https.
+
+You may also want to add `unset SSH_ASKPASS` at the end of your `~/.bashrc`
+to make Git default to using the terminal for usernames and passwords.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+Our local and remote repositories are now in this state:
+
+{alt='A diagram showing how "git push origin" will push changes from the local repository to the remote, making the remote repository an exact copy of the local repository.'}
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## The '-u' Flag
+
+You may see a `-u` option used with `git push` in some documentation. This
+option is synonymous with the `--set-upstream-to` option for the `git branch`
+command, and is used to associate the current branch with a remote branch so
+that the `git pull` command can be used without any arguments. To do this,
+simply use `git push -u origin main` once the remote has been set up.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+We can pull changes from the remote repository to the local one as well:
+
+```bash
+$ git pull origin main
+```
+
+```output
+From https://github.com/vlad/planets
+ * branch main -> FETCH_HEAD
+Already up-to-date.
+```
+
+Pulling has no effect in this case because the two repositories are already
+synchronized. If someone else had pushed some changes to the repository on
+GitHub, though, this command would download them to our local repository.
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## GitHub GUI
+
+Browse to your `planets` repository on GitHub.
+Underneath the Code button, find and click on the text that says "XX commits" (where "XX" is some number).
+Hover over, and click on, the three buttons to the right of each commit.
+What information can you gather/explore from these buttons?
+How would you get that same information in the shell?
+
+::::::::::::::: solution
+
+## Solution
+
+The left-most button (with the picture of a clipboard) copies the full identifier of the commit
+to the clipboard. In the shell, `git log` will show you the full commit identifier for each
+commit.
+
+When you click on the middle button, you'll see all of the changes that were made in that
+particular commit. Green shaded lines indicate additions and red ones removals. In the shell we
+can do the same thing with `git diff`. In particular, `git diff ID1..ID2` where ID1 and
+ID2 are commit identifiers (e.g. `git diff a3bf1e5..041e637`) will show the differences
+between those two commits.
+
+The right-most button lets you view all of the files in the repository at the time of that
+commit. To do this in the shell, we'd need to checkout the repository at that particular time.
+We can do this with `git checkout ID` where ID is the identifier of the commit we want to
+look at. If we do this, we need to remember to put the repository back to the right state
+afterwards!
+
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Uploading files directly in GitHub browser
+
+Github also allows you to skip the command line and upload files directly to
+your repository without having to leave the browser. There are two options.
+First you can click the "Upload files" button in the toolbar at the top of the
+file tree. Or, you can drag and drop files from your desktop onto the file
+tree. You can read more about this [on this GitHub page](https://help.github.com/articles/adding-a-file-to-a-repository/).
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## GitHub Timestamp
+
+Create a remote repository on GitHub. Push the contents of your local
+repository to the remote. Make changes to your local repository and push these
+changes. Go to the repo you just created on GitHub and check the
+[timestamps](../learners/reference.md#timestamp) of the files. How does GitHub
+record times, and why?
+
+::::::::::::::: solution
+
+## Solution
+
+GitHub displays timestamps in a human readable relative format (i.e. "22 hours ago" or "three
+weeks ago"). However, if you hover over the timestamp, you can see the exact time at which the
+last change to the file occurred.
+
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Push vs. Commit
+
+In this episode, we introduced the "git push" command.
+How is "git push" different from "git commit"?
+
+::::::::::::::: solution
+
+## Solution
+
+When we push changes, we're interacting with a remote repository to update it with the changes
+we've made locally (often this corresponds to sharing the changes we've made with others).
+Commit only updates your local repository.
+
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## GitHub License and README files
+
+In this episode we learned about creating a remote repository on GitHub, but when you initialized
+your GitHub repo, you didn't add a README.md or a license file. If you had, what do you think
+would have happened when you tried to link your local and remote repositories?
+
+::::::::::::::: solution
+
+## Solution
+
+In this case, we'd see a merge conflict due to unrelated histories. When GitHub creates a
+README.md file, it performs a commit in the remote repository. When you try to pull the remote
+repository to your local repository, Git detects that they have histories that do not share a
+common origin and refuses to merge.
+
+```bash
+$ git pull origin main
+```
+
+```output
+warning: no common commits
+remote: Enumerating objects: 3, done.
+remote: Counting objects: 100% (3/3), done.
+remote: Total 3 (delta 0), reused 0 (delta 0), pack-reused 0
+Unpacking objects: 100% (3/3), done.
+From https://github.com/vlad/planets
+ * branch main -> FETCH_HEAD
+ * [new branch] main -> origin/main
+fatal: refusing to merge unrelated histories
+```
+
+You can force git to merge the two repositories with the option `--allow-unrelated-histories`.
+Be careful when you use this option and carefully examine the contents of local and remote
+repositories before merging.
+
+```bash
+$ git pull --allow-unrelated-histories origin main
+```
+
+```output
+From https://github.com/vlad/planets
+ * branch main -> FETCH_HEAD
+Merge made by the 'recursive' strategy.
+README.md | 1 +
+1 file changed, 1 insertion(+)
+create mode 100644 README.md
+```
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- A local Git repository can be connected to one or more remote repositories.
+- Use the SSH protocol to connect to remote repositories.
+- `git push` copies changes from a local repository to a remote repository.
+- `git pull` copies changes from a remote repository to a local repository.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
diff --git a/18-collab.md b/18-collab.md
new file mode 100644
index 000000000..efaeb0eec
--- /dev/null
+++ b/18-collab.md
@@ -0,0 +1,264 @@
+---
+title: Collaborating
+teaching: 25
+exercises: 0
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- Clone a remote repository.
+- Collaborate by pushing to a common repository.
+- Describe the basic collaborative workflow.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- How can I use version control to collaborate with other people?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+For the next step, get into pairs. One person will be the "Owner" and the other
+will be the "Collaborator". The goal is that the Collaborator add changes into
+the Owner's repository. We will switch roles at the end, so both persons will
+play Owner and Collaborator.
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Practicing By Yourself
+
+If you're working through this lesson on your own, you can carry on by opening
+a second terminal window.
+This window will represent your partner, working on another computer. You
+won't need to give anyone access on GitHub, because both 'partners' are you.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+The Owner needs to give the Collaborator access. In your repository page on GitHub, click the "Settings"
+button on the right, select "Collaborators", click "Add people", and
+then enter your partner's username.
+
+{alt='A screenshot of the GitHub Collaborators settings page, which is accessed by clicking "Settings" then "Collaborators"'}
+
+To accept access to the Owner's repo, the Collaborator
+needs to go to [https://github.com/notifications](https://github.com/notifications)
+or check for email notification. Once there she can accept access to the Owner's repo.
+
+Next, the Collaborator needs to download a copy of the Owner's repository to her
+machine. This is called "cloning a repo".
+
+The Collaborator doesn't want to overwrite her own version of `planets.git`, so
+needs to clone the Owner's repository to a different location than her own
+repository with the same name.
+
+To clone the Owner's repo into her `Desktop` folder, the Collaborator enters:
+
+```bash
+$ git clone git@github.com:vlad/planets.git ~/Desktop/vlad-planets
+```
+
+Replace 'vlad' with the Owner's username.
+
+If you choose to clone without the clone path
+(`~/Desktop/vlad-planets`) specified at the end,
+you will clone inside your own planets folder!
+Make sure to navigate to the `Desktop` folder first.
+
+{alt='A diagram showing that "git clone" can create a copy of a remote GitHub repository, allowing a second person to create their own local repository that they can make changes to.'}
+
+The Collaborator can now make a change in her clone of the Owner's repository,
+exactly the same way as we've been doing before:
+
+```bash
+$ cd ~/Desktop/vlad-planets
+$ nano pluto.txt
+$ cat pluto.txt
+```
+
+```output
+It is so a planet!
+```
+
+```bash
+$ git add pluto.txt
+$ git commit -m "Add notes about Pluto"
+```
+
+```output
+ 1 file changed, 1 insertion(+)
+ create mode 100644 pluto.txt
+```
+
+Then push the change to the *Owner's repository* on GitHub:
+
+```bash
+$ git push origin main
+```
+
+```output
+Enumerating objects: 4, done.
+Counting objects: 4, done.
+Delta compression using up to 4 threads.
+Compressing objects: 100% (2/2), done.
+Writing objects: 100% (3/3), 306 bytes, done.
+Total 3 (delta 0), reused 0 (delta 0)
+To https://github.com/vlad/planets.git
+ 9272da5..29aba7c main -> main
+```
+
+Note that we didn't have to create a remote called `origin`: Git uses this
+name by default when we clone a repository. (This is why `origin` was a
+sensible choice earlier when we were setting up remotes by hand.)
+
+Take a look at the Owner's repository on GitHub again, and you should be
+able to see the new commit made by the Collaborator. You may need to refresh
+your browser to see the new commit.
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Some more about remotes
+
+In this episode and the previous one, our local repository has had
+a single "remote", called `origin`. A remote is a copy of the repository
+that is hosted somewhere else, that we can push to and pull from, and
+there's no reason that you have to work with only one. For example,
+on some large projects you might have your own copy in your own GitHub
+account (you'd probably call this `origin`) and also the main "upstream"
+project repository (let's call this `upstream` for the sake of examples).
+You would pull from `upstream` from time to
+time to get the latest updates that other people have committed.
+
+Remember that the name you give to a remote only exists locally. It's
+an alias that you choose - whether `origin`, or `upstream`, or `fred` -
+and not something intrinstic to the remote repository.
+
+The `git remote` family of commands is used to set up and alter the remotes
+associated with a repository. Here are some of the most useful ones:
+
+- `git remote -v` lists all the remotes that are configured (we already used
+ this in the last episode)
+- `git remote add [name] [url]` is used to add a new remote
+- `git remote remove [name]` removes a remote. Note that it doesn't affect the
+ remote repository at all - it just removes the link to it from the local repo.
+- `git remote set-url [name] [newurl]` changes the URL that is associated
+ with the remote. This is useful if it has moved, e.g. to a different GitHub
+ account, or from GitHub to a different hosting service. Or, if we made a typo when
+ adding it!
+- `git remote rename [oldname] [newname]` changes the local alias by which a remote
+ is known - its name. For example, one could use this to change `upstream` to `fred`.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+To download the Collaborator's changes from GitHub, the Owner now enters:
+
+```bash
+$ git pull origin main
+```
+
+```output
+remote: Enumerating objects: 4, done.
+remote: Counting objects: 100% (4/4), done.
+remote: Compressing objects: 100% (2/2), done.
+remote: Total 3 (delta 0), reused 3 (delta 0), pack-reused 0
+Unpacking objects: 100% (3/3), done.
+From https://github.com/vlad/planets
+ * branch main -> FETCH_HEAD
+ 9272da5..29aba7c main -> origin/main
+Updating 9272da5..29aba7c
+Fast-forward
+ pluto.txt | 1 +
+ 1 file changed, 1 insertion(+)
+ create mode 100644 pluto.txt
+```
+
+Now the three repositories (Owner's local, Collaborator's local, and Owner's on
+GitHub) are back in sync.
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## A Basic Collaborative Workflow
+
+In practice, it is good to be sure that you have an updated version of the
+repository you are collaborating on, so you should `git pull` before making
+our changes. The basic collaborative workflow would be:
+
+- update your local repo with `git pull origin main`,
+- make your changes and stage them with `git add`,
+- commit your changes with `git commit -m`, and
+- upload the changes to GitHub with `git push origin main`
+
+It is better to make many commits with smaller changes rather than
+of one commit with massive changes: small commits are easier to
+read and review.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Switch Roles and Repeat
+
+Switch roles and repeat the whole process.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Review Changes
+
+The Owner pushed commits to the repository without giving any information
+to the Collaborator. How can the Collaborator find out what has changed with
+command line? And on GitHub?
+
+::::::::::::::: solution
+
+## Solution
+
+On the command line, the Collaborator can use `git fetch origin main`
+to get the remote changes into the local repository, but without merging
+them. Then by running `git diff main origin/main` the Collaborator
+will see the changes output in the terminal.
+
+On GitHub, the Collaborator can go to the repository and click on
+"commits" to view the most recent commits pushed to the repository.
+
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Comment Changes in GitHub
+
+The Collaborator has some questions about one line change made by the Owner and
+has some suggestions to propose.
+
+With GitHub, it is possible to comment on the diff of a commit. Over the line of
+code to comment, a blue comment icon appears to open a comment window.
+
+The Collaborator posts her comments and suggestions using the GitHub interface.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Version History, Backup, and Version Control
+
+Some backup software can keep a history of the versions of your files. They also
+allows you to recover specific versions. How is this functionality different from version control?
+What are some of the benefits of using version control, Git and GitHub?
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- `git clone` copies a remote repository to create a local repository with a remote called `origin` automatically set up.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
diff --git a/19-conflict.md b/19-conflict.md
new file mode 100644
index 000000000..ecaad1ac6
--- /dev/null
+++ b/19-conflict.md
@@ -0,0 +1,533 @@
+---
+title: Conflicts
+teaching: 15
+exercises: 0
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- Explain what conflicts are and when they can occur.
+- Resolve conflicts resulting from a merge.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- What do I do when my changes conflict with someone else's?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+As soon as people can work in parallel, they'll likely step on each other's
+toes. This will even happen with a single person: if we are working on
+a piece of software on both our laptop and a server in the lab, we could make
+different changes to each copy. Version control helps us manage these
+[conflicts](../learners/reference.md#conflict) by giving us tools to
+[resolve](../learners/reference.md#resolve) overlapping changes.
+
+To see how we can resolve conflicts, we must first create one. The file
+`mars.txt` currently looks like this in both partners' copies of our `planets`
+repository:
+
+```bash
+$ cat mars.txt
+```
+
+```output
+Cold and dry, but everything is my favorite color
+The two moons may be a problem for Wolfman
+But the Mummy will appreciate the lack of humidity
+```
+
+Let's add a line to the collaborator's copy only:
+
+```bash
+$ nano mars.txt
+$ cat mars.txt
+```
+
+```output
+Cold and dry, but everything is my favorite color
+The two moons may be a problem for Wolfman
+But the Mummy will appreciate the lack of humidity
+This line added to Wolfman's copy
+```
+
+and then push the change to GitHub:
+
+```bash
+$ git add mars.txt
+$ git commit -m "Add a line in our home copy"
+```
+
+```output
+[main 5ae9631] Add a line in our home copy
+ 1 file changed, 1 insertion(+)
+```
+
+```bash
+$ git push origin main
+```
+
+```output
+Enumerating objects: 5, done.
+Counting objects: 100% (5/5), done.
+Delta compression using up to 8 threads
+Compressing objects: 100% (3/3), done.
+Writing objects: 100% (3/3), 331 bytes | 331.00 KiB/s, done.
+Total 3 (delta 2), reused 0 (delta 0)
+remote: Resolving deltas: 100% (2/2), completed with 2 local objects.
+To https://github.com/vlad/planets.git
+ 29aba7c..dabb4c8 main -> main
+```
+
+Now let's have the owner
+make a different change to their copy
+*without* updating from GitHub:
+
+```bash
+$ nano mars.txt
+$ cat mars.txt
+```
+
+```output
+Cold and dry, but everything is my favorite color
+The two moons may be a problem for Wolfman
+But the Mummy will appreciate the lack of humidity
+We added a different line in the other copy
+```
+
+We can commit the change locally:
+
+```bash
+$ git add mars.txt
+$ git commit -m "Add a line in my copy"
+```
+
+```output
+[main 07ebc69] Add a line in my copy
+ 1 file changed, 1 insertion(+)
+```
+
+but Git won't let us push it to GitHub:
+
+```bash
+$ git push origin main
+```
+
+```output
+To https://github.com/vlad/planets.git
+ ! [rejected] main -> main (fetch first)
+error: failed to push some refs to 'https://github.com/vlad/planets.git'
+hint: Updates were rejected because the remote contains work that you do
+hint: not have locally. This is usually caused by another repository pushing
+hint: to the same ref. You may want to first integrate the remote changes
+hint: (e.g., 'git pull ...') before pushing again.
+hint: See the 'Note about fast-forwards' in 'git push --help' for details.
+```
+
+{alt='A diagram showing a conflict that might occur when two sets of independent changes are merged'}
+
+Git rejects the push because it detects that the remote repository has new updates that have not been
+incorporated into the local branch.
+What we have to do is pull the changes from GitHub,
+[merge](../learners/reference.md#merge) them into the copy we're currently working in, and then push that.
+Let's start by pulling:
+
+```bash
+$ git pull origin main
+```
+
+```output
+remote: Enumerating objects: 5, done.
+remote: Counting objects: 100% (5/5), done.
+remote: Compressing objects: 100% (1/1), done.
+remote: Total 3 (delta 2), reused 3 (delta 2), pack-reused 0
+Unpacking objects: 100% (3/3), done.
+From https://github.com/vlad/planets
+ * branch main -> FETCH_HEAD
+ 29aba7c..dabb4c8 main -> origin/main
+Auto-merging mars.txt
+CONFLICT (content): Merge conflict in mars.txt
+Automatic merge failed; fix conflicts and then commit the result.
+```
+
+The `git pull` command updates the local repository to include those
+changes already included in the remote repository.
+After the changes from remote branch have been fetched, Git detects that changes made to the local copy
+overlap with those made to the remote repository, and therefore refuses to merge the two versions to
+stop us from trampling on our previous work. The conflict is marked in
+in the affected file:
+
+```bash
+$ cat mars.txt
+```
+
+```output
+Cold and dry, but everything is my favorite color
+The two moons may be a problem for Wolfman
+But the Mummy will appreciate the lack of humidity
+<<<<<<< HEAD
+We added a different line in the other copy
+=======
+This line added to Wolfman's copy
+>>>>>>> dabb4c8c450e8475aee9b14b4383acc99f42af1d
+```
+
+Our change is preceded by `<<<<<<< HEAD`.
+Git has then inserted `=======` as a separator between the conflicting changes
+and marked the end of the content downloaded from GitHub with `>>>>>>>`.
+(The string of letters and digits after that marker
+identifies the commit we've just downloaded.)
+
+It is now up to us to edit this file to remove these markers
+and reconcile the changes.
+We can do anything we want: keep the change made in the local repository, keep
+the change made in the remote repository, write something new to replace both,
+or get rid of the change entirely.
+Let's replace both so that the file looks like this:
+
+```bash
+$ cat mars.txt
+```
+
+```output
+Cold and dry, but everything is my favorite color
+The two moons may be a problem for Wolfman
+But the Mummy will appreciate the lack of humidity
+We removed the conflict on this line
+```
+
+To finish merging,
+we add `mars.txt` to the changes being made by the merge
+and then commit:
+
+```bash
+$ git add mars.txt
+$ git status
+```
+
+```output
+On branch main
+All conflicts fixed but you are still merging.
+ (use "git commit" to conclude merge)
+
+Changes to be committed:
+
+ modified: mars.txt
+
+```
+
+```bash
+$ git commit -m "Merge changes from GitHub"
+```
+
+```output
+[main 2abf2b1] Merge changes from GitHub
+```
+
+Now we can push our changes to GitHub:
+
+```bash
+$ git push origin main
+```
+
+```output
+Enumerating objects: 10, done.
+Counting objects: 100% (10/10), done.
+Delta compression using up to 8 threads
+Compressing objects: 100% (6/6), done.
+Writing objects: 100% (6/6), 645 bytes | 645.00 KiB/s, done.
+Total 6 (delta 4), reused 0 (delta 0)
+remote: Resolving deltas: 100% (4/4), completed with 2 local objects.
+To https://github.com/vlad/planets.git
+ dabb4c8..2abf2b1 main -> main
+```
+
+Git keeps track of what we've merged with what,
+so we don't have to fix things by hand again
+when the collaborator who made the first change pulls again:
+
+```bash
+$ git pull origin main
+```
+
+```output
+remote: Enumerating objects: 10, done.
+remote: Counting objects: 100% (10/10), done.
+remote: Compressing objects: 100% (2/2), done.
+remote: Total 6 (delta 4), reused 6 (delta 4), pack-reused 0
+Unpacking objects: 100% (6/6), done.
+From https://github.com/vlad/planets
+ * branch main -> FETCH_HEAD
+ dabb4c8..2abf2b1 main -> origin/main
+Updating dabb4c8..2abf2b1
+Fast-forward
+ mars.txt | 2 +-
+ 1 file changed, 1 insertion(+), 1 deletion(-)
+```
+
+We get the merged file:
+
+```bash
+$ cat mars.txt
+```
+
+```output
+Cold and dry, but everything is my favorite color
+The two moons may be a problem for Wolfman
+But the Mummy will appreciate the lack of humidity
+We removed the conflict on this line
+```
+
+We don't need to merge again because Git knows someone has already done that.
+
+Git's ability to resolve conflicts is very useful, but conflict resolution
+costs time and effort, and can introduce errors if conflicts are not resolved
+correctly. If you find yourself resolving a lot of conflicts in a project,
+consider these technical approaches to reducing them:
+
+- Pull from upstream more frequently, especially before starting new work
+- Use topic branches to segregate work, merging to main when complete
+- Make smaller more atomic commits
+- Push your work when it is done and encourage your team to do the same to reduce work in progress and, by extension, the chance of having conflicts
+- Where logically appropriate, break large files into smaller ones so that it is
+ less likely that two authors will alter the same file simultaneously
+
+Conflicts can also be minimized with project management strategies:
+
+- Clarify who is responsible for what areas with your collaborators
+- Discuss what order tasks should be carried out in with your collaborators so
+ that tasks expected to change the same lines won't be worked on simultaneously
+- If the conflicts are stylistic churn (e.g. tabs vs. spaces), establish a
+ project convention that is governing and use code style tools (e.g.
+ `htmltidy`, `perltidy`, `rubocop`, etc.) to enforce, if necessary
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Solving Conflicts that You Create
+
+Clone the repository created by your instructor.
+Add a new file to it,
+and modify an existing file (your instructor will tell you which one).
+When asked by your instructor,
+pull her changes from the repository to create a conflict,
+then resolve it.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Conflicts on Non-textual files
+
+What does Git do
+when there is a conflict in an image or some other non-textual file
+that is stored in version control?
+
+::::::::::::::: solution
+
+## Solution
+
+Let's try it. Suppose Dracula takes a picture of Martian surface and
+calls it `mars.jpg`.
+
+If you do not have an image file of Mars available, you can create
+a dummy binary file like this:
+
+```bash
+$ head -c 1024 /dev/urandom > mars.jpg
+$ ls -lh mars.jpg
+```
+
+```output
+-rw-r--r-- 1 vlad 57095 1.0K Mar 8 20:24 mars.jpg
+```
+
+`ls` shows us that this created a 1-kilobyte file. It is full of
+random bytes read from the special file, `/dev/urandom`.
+
+Now, suppose Dracula adds `mars.jpg` to his repository:
+
+```bash
+$ git add mars.jpg
+$ git commit -m "Add picture of Martian surface"
+```
+
+```output
+[main 8e4115c] Add picture of Martian surface
+ 1 file changed, 0 insertions(+), 0 deletions(-)
+ create mode 100644 mars.jpg
+```
+
+Suppose that Wolfman has added a similar picture in the meantime.
+His is a picture of the Martian sky, but it is *also* called `mars.jpg`.
+When Dracula tries to push, he gets a familiar message:
+
+```bash
+$ git push origin main
+```
+
+```output
+To https://github.com/vlad/planets.git
+ ! [rejected] main -> main (fetch first)
+error: failed to push some refs to 'https://github.com/vlad/planets.git'
+hint: Updates were rejected because the remote contains work that you do
+hint: not have locally. This is usually caused by another repository pushing
+hint: to the same ref. You may want to first integrate the remote changes
+hint: (e.g., 'git pull ...') before pushing again.
+hint: See the 'Note about fast-forwards' in 'git push --help' for details.
+```
+
+We've learned that we must pull first and resolve any conflicts:
+
+```bash
+$ git pull origin main
+```
+
+When there is a conflict on an image or other binary file, git prints
+a message like this:
+
+```output
+$ git pull origin main
+remote: Counting objects: 3, done.
+remote: Compressing objects: 100% (3/3), done.
+remote: Total 3 (delta 0), reused 0 (delta 0)
+Unpacking objects: 100% (3/3), done.
+From https://github.com/vlad/planets.git
+ * branch main -> FETCH_HEAD
+ 6a67967..439dc8c main -> origin/main
+warning: Cannot merge binary files: mars.jpg (HEAD vs. 439dc8c08869c342438f6dc4a2b615b05b93c76e)
+Auto-merging mars.jpg
+CONFLICT (add/add): Merge conflict in mars.jpg
+Automatic merge failed; fix conflicts and then commit the result.
+```
+
+The conflict message here is mostly the same as it was for `mars.txt`, but
+there is one key additional line:
+
+```output
+warning: Cannot merge binary files: mars.jpg (HEAD vs. 439dc8c08869c342438f6dc4a2b615b05b93c76e)
+```
+
+Git cannot automatically insert conflict markers into an image as it does
+for text files. So, instead of editing the image file, we must check out
+the version we want to keep. Then we can add and commit this version.
+
+On the key line above, Git has conveniently given us commit identifiers
+for the two versions of `mars.jpg`. Our version is `HEAD`, and Wolfman's
+version is `439dc8c0...`. If we want to use our version, we can use
+`git checkout`:
+
+```bash
+$ git checkout HEAD mars.jpg
+$ git add mars.jpg
+$ git commit -m "Use image of surface instead of sky"
+```
+
+```output
+[main 21032c3] Use image of surface instead of sky
+```
+
+If instead we want to use Wolfman's version, we can use `git checkout` with
+Wolfman's commit identifier, `439dc8c0`:
+
+```bash
+$ git checkout 439dc8c0 mars.jpg
+$ git add mars.jpg
+$ git commit -m "Use image of sky instead of surface"
+```
+
+```output
+[main da21b34] Use image of sky instead of surface
+```
+
+We can also keep *both* images. The catch is that we cannot keep them
+under the same name. But, we can check out each version in succession
+and *rename* it, then add the renamed versions. First, check out each
+image and rename it:
+
+```bash
+$ git checkout HEAD mars.jpg
+$ git mv mars.jpg mars-surface.jpg
+$ git checkout 439dc8c0 mars.jpg
+$ mv mars.jpg mars-sky.jpg
+```
+
+Then, remove the old `mars.jpg` and add the two new files:
+
+```bash
+$ git rm mars.jpg
+$ git add mars-surface.jpg
+$ git add mars-sky.jpg
+$ git commit -m "Use two images: surface and sky"
+```
+
+```output
+[main 94ae08c] Use two images: surface and sky
+ 2 files changed, 0 insertions(+), 0 deletions(-)
+ create mode 100644 mars-sky.jpg
+ rename mars.jpg => mars-surface.jpg (100%)
+```
+
+Now both images of Mars are checked into the repository, and `mars.jpg`
+no longer exists.
+
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## A Typical Work Session
+
+You sit down at your computer to work on a shared project that is tracked in a
+remote Git repository. During your work session, you take the following
+actions, but not in this order:
+
+- *Make changes* by appending the number `100` to a text file `numbers.txt`
+- *Update remote* repository to match the local repository
+- *Celebrate* your success with some fancy beverage(s)
+- *Update local* repository to match the remote repository
+- *Stage changes* to be committed
+- *Commit changes* to the local repository
+
+In what order should you perform these actions to minimize the chances of
+conflicts? Put the commands above in order in the *action* column of the table
+below. When you have the order right, see if you can write the corresponding
+commands in the *command* column. A few steps are populated to get you
+started.
+
+| order | action . . . . . . . . . . | command . . . . . . . . . . |
+| ----- | -------------------------- | --------------------------------------------- |
+| 1 | | |
+| 2 | | `echo 100 >> numbers.txt` |
+| 3 | | |
+| 4 | | |
+| 5 | | |
+| 6 | Celebrate! | |
+
+::::::::::::::: solution
+
+## Solution
+
+| order | action . . . . . . | command . . . . . . . . . . . . . . . . . . . |
+| ----- | -------------------------- | --------------------------------------------- |
+| 1 | Update local | `git pull origin main` |
+| 2 | Make changes | `echo 100 >> numbers.txt` |
+| 3 | Stage changes | `git add numbers.txt` |
+| 4 | Commit changes | `git commit -m "Add 100 to numbers.txt"` |
+| 5 | Update remote | `git push origin main` |
+| 6 | Celebrate! | |
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- Conflicts occur when two or more people change the same lines of the same file.
+- The version control system does not allow people to overwrite each other's changes blindly, but highlights conflicts so that they can be resolved.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
diff --git a/20-dplyr.md b/20-dplyr.md
new file mode 100644
index 000000000..411e01eb4
--- /dev/null
+++ b/20-dplyr.md
@@ -0,0 +1,671 @@
+---
+title: Data Frame Manipulation with dplyr
+teaching: 40
+exercises: 15
+source: Rmd
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- To be able to use the six main data frame manipulation 'verbs' with pipes in `dplyr`.
+- To understand how `group_by()` and `summarize()` can be combined to summarize datasets.
+- Be able to analyze a subset of data using logical filtering.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- How can I manipulate data frames without repeating myself?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
+
+Manipulation of data frames means many things to many researchers: we often
+select certain observations (rows) or variables (columns), we often group the
+data by a certain variable(s), or we even calculate summary statistics. We can
+do these operations using the normal base R operations:
+
+
+``` r
+mean(gapminder$gdpPercap[gapminder$continent == "Africa"])
+```
+
+``` output
+[1] 2193.755
+```
+
+``` r
+mean(gapminder$gdpPercap[gapminder$continent == "Americas"])
+```
+
+``` output
+[1] 7136.11
+```
+
+``` r
+mean(gapminder$gdpPercap[gapminder$continent == "Asia"])
+```
+
+``` output
+[1] 7902.15
+```
+
+But this isn't very *nice* because there is a fair bit of repetition. Repeating
+yourself will cost you time, both now and later, and potentially introduce some
+nasty bugs.
+
+## The `dplyr` package
+
+Luckily, the [`dplyr`](https://cran.r-project.org/package=dplyr)
+package provides a number of very useful functions for manipulating data frames
+in a way that will reduce the above repetition, reduce the probability of making
+errors, and probably even save you some typing. As an added bonus, you might
+even find the `dplyr` grammar easier to read.
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Tip: Tidyverse
+
+`dplyr` package belongs to a broader family of opinionated R packages
+designed for data science called the "Tidyverse". These
+packages are specifically designed to work harmoniously together.
+Some of these packages will be covered along this course, but you can find more
+complete information here: [https://www.tidyverse.org/](https://www.tidyverse.org/).
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+Here we're going to cover 5 of the most commonly used functions as well as using
+pipes (`%>%`) to combine them.
+
+1. `select()`
+2. `filter()`
+3. `group_by()`
+4. `summarize()`
+5. `mutate()`
+
+If you have have not installed this package earlier, please do so:
+
+
+``` r
+install.packages('dplyr')
+```
+
+Now let's load the package:
+
+
+``` r
+library("dplyr")
+```
+
+## Using select()
+
+If, for example, we wanted to move forward with only a few of the variables in
+our data frame we could use the `select()` function. This will keep only the
+variables you select.
+
+
+``` r
+year_country_gdp <- select(gapminder, year, country, gdpPercap)
+```
+
+{alt='Diagram illustrating use of select function to select two columns of a data frame'}
+If we want to remove one column only from the `gapminder` data, for example,
+removing the `continent` column.
+
+
+``` r
+smaller_gapminder_data <- select(gapminder, -continent)
+```
+
+If we open up `year_country_gdp` we'll see that it only contains the year,
+country and gdpPercap. Above we used 'normal' grammar, but the strengths of
+`dplyr` lie in combining several functions using pipes. Since the pipes grammar
+is unlike anything we've seen in R before, let's repeat what we've done above
+using pipes.
+
+
+``` r
+year_country_gdp <- gapminder %>% select(year, country, gdpPercap)
+```
+
+To help you understand why we wrote that in that way, let's walk through it step
+by step. First we summon the gapminder data frame and pass it on, using the pipe
+symbol `%>%`, to the next step, which is the `select()` function. In this case
+we don't specify which data object we use in the `select()` function since in
+gets that from the previous pipe. **Fun Fact**: There is a good chance you have
+encountered pipes before in the shell. In R, a pipe symbol is `%>%` while in the
+shell it is `|` but the concept is the same!
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Tip: Renaming data frame columns in dplyr
+
+In Chapter 4 we covered how you can rename columns with base R by assigning a value to the output of the `names()` function.
+Just like select, this is a bit cumbersome, but thankfully dplyr has a `rename()` function.
+
+Within a pipeline, the syntax is `rename(new_name = old_name)`.
+For example, we may want to rename the gdpPercap column name from our `select()` statement above.
+
+
+``` r
+tidy_gdp <- year_country_gdp %>% rename(gdp_per_capita = gdpPercap)
+
+head(tidy_gdp)
+```
+
+``` output
+ year country gdp_per_capita
+1 1952 Afghanistan 779.4453
+2 1957 Afghanistan 820.8530
+3 1962 Afghanistan 853.1007
+4 1967 Afghanistan 836.1971
+5 1972 Afghanistan 739.9811
+6 1977 Afghanistan 786.1134
+```
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Using filter()
+
+If we now want to move forward with the above, but only with European
+countries, we can combine `select` and `filter`
+
+
+``` r
+year_country_gdp_euro <- gapminder %>%
+ filter(continent == "Europe") %>%
+ select(year, country, gdpPercap)
+```
+
+If we now want to show life expectancy of European countries but only
+for a specific year (e.g., 2007), we can do as below.
+
+
+``` r
+europe_lifeExp_2007 <- gapminder %>%
+ filter(continent == "Europe", year == 2007) %>%
+ select(country, lifeExp)
+```
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 1
+
+Write a single command (which can span multiple lines and includes pipes) that
+will produce a data frame that has the African values for `lifeExp`, `country`
+and `year`, but not for other Continents. How many rows does your data frame
+have and why?
+
+::::::::::::::: solution
+
+## Solution to Challenge 1
+
+
+``` r
+year_country_lifeExp_Africa <- gapminder %>%
+ filter(continent == "Africa") %>%
+ select(year, country, lifeExp)
+```
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+As with last time, first we pass the gapminder data frame to the `filter()`
+function, then we pass the filtered version of the gapminder data frame to the
+`select()` function. **Note:** The order of operations is very important in this
+case. If we used 'select' first, filter would not be able to find the variable
+continent since we would have removed it in the previous step.
+
+## Using group\_by()
+
+Now, we were supposed to be reducing the error prone repetitiveness of what can
+be done with base R, but up to now we haven't done that since we would have to
+repeat the above for each continent. Instead of `filter()`, which will only pass
+observations that meet your criteria (in the above: `continent=="Europe"`), we
+can use `group_by()`, which will essentially use every unique criteria that you
+could have used in filter.
+
+
+``` r
+str(gapminder)
+```
+
+``` output
+'data.frame': 1704 obs. of 6 variables:
+ $ country : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
+ $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
+ $ pop : num 8425333 9240934 10267083 11537966 13079460 ...
+ $ continent: chr "Asia" "Asia" "Asia" "Asia" ...
+ $ lifeExp : num 28.8 30.3 32 34 36.1 ...
+ $ gdpPercap: num 779 821 853 836 740 ...
+```
+
+``` r
+str(gapminder %>% group_by(continent))
+```
+
+``` output
+gropd_df [1,704 × 6] (S3: grouped_df/tbl_df/tbl/data.frame)
+ $ country : chr [1:1704] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
+ $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
+ $ pop : num [1:1704] 8425333 9240934 10267083 11537966 13079460 ...
+ $ continent: chr [1:1704] "Asia" "Asia" "Asia" "Asia" ...
+ $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
+ $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
+ - attr(*, "groups")= tibble [5 × 2] (S3: tbl_df/tbl/data.frame)
+ ..$ continent: chr [1:5] "Africa" "Americas" "Asia" "Europe" ...
+ ..$ .rows : list [1:5]
+ .. ..$ : int [1:624] 25 26 27 28 29 30 31 32 33 34 ...
+ .. ..$ : int [1:300] 49 50 51 52 53 54 55 56 57 58 ...
+ .. ..$ : int [1:396] 1 2 3 4 5 6 7 8 9 10 ...
+ .. ..$ : int [1:360] 13 14 15 16 17 18 19 20 21 22 ...
+ .. ..$ : int [1:24] 61 62 63 64 65 66 67 68 69 70 ...
+ .. ..@ ptype: int(0)
+ ..- attr(*, ".drop")= logi TRUE
+```
+
+You will notice that the structure of the data frame where we used `group_by()`
+(`grouped_df`) is not the same as the original `gapminder` (`data.frame`). A
+`grouped_df` can be thought of as a `list` where each item in the `list`is a
+`data.frame` which contains only the rows that correspond to the a particular
+value `continent` (at least in the example above).
+
+{alt='Diagram illustrating how the group by function oraganizes a data frame into groups'}
+
+## Using summarize()
+
+The above was a bit on the uneventful side but `group_by()` is much more
+exciting in conjunction with `summarize()`. This will allow us to create new
+variable(s) by using functions that repeat for each of the continent-specific
+data frames. That is to say, using the `group_by()` function, we split our
+original data frame into multiple pieces, then we can run functions
+(e.g. `mean()` or `sd()`) within `summarize()`.
+
+
+``` r
+gdp_bycontinents <- gapminder %>%
+ group_by(continent) %>%
+ summarize(mean_gdpPercap = mean(gdpPercap))
+```
+
+{alt='Diagram illustrating the use of group by and summarize together to create a new variable'}
+
+
+``` r
+continent mean_gdpPercap
+
+1 Africa 2193.755
+2 Americas 7136.110
+3 Asia 7902.150
+4 Europe 14469.476
+5 Oceania 18621.609
+```
+
+That allowed us to calculate the mean gdpPercap for each continent, but it gets
+even better.
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 2
+
+Calculate the average life expectancy per country. Which has the longest average life
+expectancy and which has the shortest average life expectancy?
+
+::::::::::::::: solution
+
+## Solution to Challenge 2
+
+
+``` r
+lifeExp_bycountry <- gapminder %>%
+ group_by(country) %>%
+ summarize(mean_lifeExp = mean(lifeExp))
+lifeExp_bycountry %>%
+ filter(mean_lifeExp == min(mean_lifeExp) | mean_lifeExp == max(mean_lifeExp))
+```
+
+``` output
+# A tibble: 2 × 2
+ country mean_lifeExp
+
+1 Iceland 76.5
+2 Sierra Leone 36.8
+```
+
+Another way to do this is to use the `dplyr` function `arrange()`, which
+arranges the rows in a data frame according to the order of one or more
+variables from the data frame. It has similar syntax to other functions from
+the `dplyr` package. You can use `desc()` inside `arrange()` to sort in
+descending order.
+
+
+``` r
+lifeExp_bycountry %>%
+ arrange(mean_lifeExp) %>%
+ head(1)
+```
+
+``` output
+# A tibble: 1 × 2
+ country mean_lifeExp
+
+1 Sierra Leone 36.8
+```
+
+``` r
+lifeExp_bycountry %>%
+ arrange(desc(mean_lifeExp)) %>%
+ head(1)
+```
+
+``` output
+# A tibble: 1 × 2
+ country mean_lifeExp
+
+1 Iceland 76.5
+```
+
+Alphabetical order works too
+
+
+``` r
+lifeExp_bycountry %>%
+ arrange(desc(country)) %>%
+ head(1)
+```
+
+``` output
+# A tibble: 1 × 2
+ country mean_lifeExp
+
+1 Zimbabwe 52.7
+```
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::
+
+The function `group_by()` allows us to group by multiple variables. Let's group by `year` and `continent`.
+
+
+``` r
+gdp_bycontinents_byyear <- gapminder %>%
+ group_by(continent, year) %>%
+ summarize(mean_gdpPercap = mean(gdpPercap))
+```
+
+``` output
+`summarise()` has grouped output by 'continent'. You can override using the
+`.groups` argument.
+```
+
+That is already quite powerful, but it gets even better! You're not limited to defining 1 new variable in `summarize()`.
+
+
+``` r
+gdp_pop_bycontinents_byyear <- gapminder %>%
+ group_by(continent, year) %>%
+ summarize(mean_gdpPercap = mean(gdpPercap),
+ sd_gdpPercap = sd(gdpPercap),
+ mean_pop = mean(pop),
+ sd_pop = sd(pop))
+```
+
+``` output
+`summarise()` has grouped output by 'continent'. You can override using the
+`.groups` argument.
+```
+
+## count() and n()
+
+A very common operation is to count the number of observations for each
+group. The `dplyr` package comes with two related functions that help with this.
+
+For instance, if we wanted to check the number of countries included in the
+dataset for the year 2002, we can use the `count()` function. It takes the name
+of one or more columns that contain the groups we are interested in, and we can
+optionally sort the results in descending order by adding `sort=TRUE`:
+
+
+``` r
+gapminder %>%
+ filter(year == 2002) %>%
+ count(continent, sort = TRUE)
+```
+
+``` output
+ continent n
+1 Africa 52
+2 Asia 33
+3 Europe 30
+4 Americas 25
+5 Oceania 2
+```
+
+If we need to use the number of observations in calculations, the `n()` function
+is useful. It will return the total number of observations in the current group rather than counting the number of observations in each group within a specific column. For instance, if we wanted to get the standard error of the life expectency per continent:
+
+
+``` r
+gapminder %>%
+ group_by(continent) %>%
+ summarize(se_le = sd(lifeExp)/sqrt(n()))
+```
+
+``` output
+# A tibble: 5 × 2
+ continent se_le
+
+1 Africa 0.366
+2 Americas 0.540
+3 Asia 0.596
+4 Europe 0.286
+5 Oceania 0.775
+```
+
+You can also chain together several summary operations; in this case calculating the `minimum`, `maximum`, `mean` and `se` of each continent's per-country life-expectancy:
+
+
+``` r
+gapminder %>%
+ group_by(continent) %>%
+ summarize(
+ mean_le = mean(lifeExp),
+ min_le = min(lifeExp),
+ max_le = max(lifeExp),
+ se_le = sd(lifeExp)/sqrt(n()))
+```
+
+``` output
+# A tibble: 5 × 5
+ continent mean_le min_le max_le se_le
+
+1 Africa 48.9 23.6 76.4 0.366
+2 Americas 64.7 37.6 80.7 0.540
+3 Asia 60.1 28.8 82.6 0.596
+4 Europe 71.9 43.6 81.8 0.286
+5 Oceania 74.3 69.1 81.2 0.775
+```
+
+## Using mutate()
+
+We can also create new variables prior to (or even after) summarizing information using `mutate()`.
+
+
+``` r
+gdp_pop_bycontinents_byyear <- gapminder %>%
+ mutate(gdp_billion = gdpPercap*pop/10^9) %>%
+ group_by(continent,year) %>%
+ summarize(mean_gdpPercap = mean(gdpPercap),
+ sd_gdpPercap = sd(gdpPercap),
+ mean_pop = mean(pop),
+ sd_pop = sd(pop),
+ mean_gdp_billion = mean(gdp_billion),
+ sd_gdp_billion = sd(gdp_billion))
+```
+
+``` output
+`summarise()` has grouped output by 'continent'. You can override using the
+`.groups` argument.
+```
+
+## Connect mutate with logical filtering: ifelse
+
+When creating new variables, we can hook this with a logical condition. A simple combination of
+`mutate()` and `ifelse()` facilitates filtering right where it is needed: in the moment of creating something new.
+This easy-to-read statement is a fast and powerful way of discarding certain data (even though the overall dimension
+of the data frame will not change) or for updating values depending on this given condition.
+
+
+``` r
+## keeping all data but "filtering" after a certain condition
+# calculate GDP only for people with a life expectation above 25
+gdp_pop_bycontinents_byyear_above25 <- gapminder %>%
+ mutate(gdp_billion = ifelse(lifeExp > 25, gdpPercap * pop / 10^9, NA)) %>%
+ group_by(continent, year) %>%
+ summarize(mean_gdpPercap = mean(gdpPercap),
+ sd_gdpPercap = sd(gdpPercap),
+ mean_pop = mean(pop),
+ sd_pop = sd(pop),
+ mean_gdp_billion = mean(gdp_billion),
+ sd_gdp_billion = sd(gdp_billion))
+```
+
+``` output
+`summarise()` has grouped output by 'continent'. You can override using the
+`.groups` argument.
+```
+
+``` r
+## updating only if certain condition is fullfilled
+# for life expectations above 40 years, the gpd to be expected in the future is scaled
+gdp_future_bycontinents_byyear_high_lifeExp <- gapminder %>%
+ mutate(gdp_futureExpectation = ifelse(lifeExp > 40, gdpPercap * 1.5, gdpPercap)) %>%
+ group_by(continent, year) %>%
+ summarize(mean_gdpPercap = mean(gdpPercap),
+ mean_gdpPercap_expected = mean(gdp_futureExpectation))
+```
+
+``` output
+`summarise()` has grouped output by 'continent'. You can override using the
+`.groups` argument.
+```
+
+## Combining `dplyr` and `ggplot2`
+
+First install and load ggplot2:
+
+
+``` r
+install.packages('ggplot2')
+```
+
+
+``` r
+library("ggplot2")
+```
+
+In the plotting lesson we looked at how to make a multi-panel figure by adding
+a layer of facet panels using `ggplot2`. Here is the code we used (with some
+extra comments):
+
+
+``` r
+# Filter countries located in the Americas
+americas <- gapminder[gapminder$continent == "Americas", ]
+# Make the plot
+ggplot(data = americas, mapping = aes(x = year, y = lifeExp)) +
+ geom_line() +
+ facet_wrap( ~ country) +
+ theme(axis.text.x = element_text(angle = 45))
+```
+
+
+ 1 Africa gdpPercap 2194.
+ 2 Africa lifeExp 48.9
+ 3 Africa pop 9916003.
+ 4 Americas gdpPercap 7136.
+ 5 Americas lifeExp 64.7
+ 6 Americas pop 24504795.
+ 7 Asia gdpPercap 7902.
+ 8 Asia lifeExp 60.1
+ 9 Asia pop 77038722.
+10 Europe gdpPercap 14469.
+11 Europe lifeExp 71.9
+12 Europe pop 17169765.
+13 Oceania gdpPercap 18622.
+14 Oceania lifeExp 74.3
+15 Oceania pop 8874672.
+```
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## From long to intermediate format with pivot\_wider()
+
+It is always good to check work. So, let's use the second `pivot` function, `pivot_wider()`, to 'widen' our observation variables back out. `pivot_wider()` is the opposite of `pivot_longer()`, making a dataset wider by increasing the number of columns and decreasing the number of rows. We can use `pivot_wider()` to pivot or reshape our `gap_long` to the original intermediate format or the widest format. Let's start with the intermediate format.
+
+The `pivot_wider()` function takes `names_from` and `values_from` arguments.
+
+To `names_from` we supply the column name whose contents will be pivoted into new
+output columns in the widened data frame. The corresponding values will be added
+from the column named in the `values_from` argument.
+
+
+``` r
+gap_normal <- gap_long %>%
+ pivot_wider(names_from = obs_type, values_from = obs_values)
+dim(gap_normal)
+```
+
+``` output
+[1] 1704 6
+```
+
+``` r
+dim(gapminder)
+```
+
+``` output
+[1] 1704 6
+```
+
+``` r
+names(gap_normal)
+```
+
+``` output
+[1] "continent" "country" "year" "gdpPercap" "lifeExp" "pop"
+```
+
+``` r
+names(gapminder)
+```
+
+``` output
+[1] "country" "year" "pop" "continent" "lifeExp" "gdpPercap"
+```
+
+Now we've got an intermediate data frame `gap_normal` with the same dimensions as
+the original `gapminder`, but the order of the variables is different. Let's fix
+that before checking if they are `all.equal()`.
+
+
+``` r
+gap_normal <- gap_normal[, names(gapminder)]
+all.equal(gap_normal, gapminder)
+```
+
+``` output
+[1] "Attributes: < Component \"class\": Lengths (3, 1) differ (string compare on first 1) >"
+[2] "Attributes: < Component \"class\": 1 string mismatch >"
+[3] "Component \"country\": 1704 string mismatches"
+[4] "Component \"pop\": Mean relative difference: 1.634504"
+[5] "Component \"continent\": 1212 string mismatches"
+[6] "Component \"lifeExp\": Mean relative difference: 0.203822"
+[7] "Component \"gdpPercap\": Mean relative difference: 1.162302"
+```
+
+``` r
+head(gap_normal)
+```
+
+``` output
+# A tibble: 6 × 6
+ country year pop continent lifeExp gdpPercap
+
+1 Algeria 1952 9279525 Africa 43.1 2449.
+2 Algeria 1957 10270856 Africa 45.7 3014.
+3 Algeria 1962 11000948 Africa 48.3 2551.
+4 Algeria 1967 12760499 Africa 51.4 3247.
+5 Algeria 1972 14760787 Africa 54.5 4183.
+6 Algeria 1977 17152804 Africa 58.0 4910.
+```
+
+``` r
+head(gapminder)
+```
+
+``` output
+ country year pop continent lifeExp gdpPercap
+1 Afghanistan 1952 8425333 Asia 28.801 779.4453
+2 Afghanistan 1957 9240934 Asia 30.332 820.8530
+3 Afghanistan 1962 10267083 Asia 31.997 853.1007
+4 Afghanistan 1967 11537966 Asia 34.020 836.1971
+5 Afghanistan 1972 13079460 Asia 36.088 739.9811
+6 Afghanistan 1977 14880372 Asia 38.438 786.1134
+```
+
+We're almost there, the original was sorted by `country`, then
+`year`.
+
+
+``` r
+gap_normal <- gap_normal %>% arrange(country, year)
+all.equal(gap_normal, gapminder)
+```
+
+``` output
+[1] "Attributes: < Component \"class\": Lengths (3, 1) differ (string compare on first 1) >"
+[2] "Attributes: < Component \"class\": 1 string mismatch >"
+```
+
+That's great! We've gone from the longest format back to the intermediate and we
+didn't introduce any errors in our code.
+
+Now let's convert the long all the way back to the wide. In the wide format, we
+will keep country and continent as ID variables and pivot the observations
+across the 3 metrics (`pop`,`lifeExp`,`gdpPercap`) and time (`year`). First we
+need to create appropriate labels for all our new variables (time\*metric
+combinations) and we also need to unify our ID variables to simplify the process
+of defining `gap_wide`.
+
+
+``` r
+gap_temp <- gap_long %>% unite(var_ID, continent, country, sep = "_")
+str(gap_temp)
+```
+
+``` output
+tibble [5,112 × 4] (S3: tbl_df/tbl/data.frame)
+ $ var_ID : chr [1:5112] "Africa_Algeria" "Africa_Algeria" "Africa_Algeria" "Africa_Algeria" ...
+ $ obs_type : chr [1:5112] "gdpPercap" "gdpPercap" "gdpPercap" "gdpPercap" ...
+ $ year : int [1:5112] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
+ $ obs_values: num [1:5112] 2449 3014 2551 3247 4183 ...
+```
+
+``` r
+gap_temp <- gap_long %>%
+ unite(ID_var, continent, country, sep = "_") %>%
+ unite(var_names, obs_type, year, sep = "_")
+str(gap_temp)
+```
+
+``` output
+tibble [5,112 × 3] (S3: tbl_df/tbl/data.frame)
+ $ ID_var : chr [1:5112] "Africa_Algeria" "Africa_Algeria" "Africa_Algeria" "Africa_Algeria" ...
+ $ var_names : chr [1:5112] "gdpPercap_1952" "gdpPercap_1957" "gdpPercap_1962" "gdpPercap_1967" ...
+ $ obs_values: num [1:5112] 2449 3014 2551 3247 4183 ...
+```
+
+Using `unite()` we now have a single ID variable which is a combination of
+`continent`,`country`,and we have defined variable names. We're now ready to
+pipe in `pivot_wider()`
+
+
+``` r
+gap_wide_new <- gap_long %>%
+ unite(ID_var, continent, country, sep = "_") %>%
+ unite(var_names, obs_type, year, sep = "_") %>%
+ pivot_wider(names_from = var_names, values_from = obs_values)
+str(gap_wide_new)
+```
+
+``` output
+tibble [142 × 37] (S3: tbl_df/tbl/data.frame)
+ $ ID_var : chr [1:142] "Africa_Algeria" "Africa_Angola" "Africa_Benin" "Africa_Botswana" ...
+ $ gdpPercap_1952: num [1:142] 2449 3521 1063 851 543 ...
+ $ gdpPercap_1957: num [1:142] 3014 3828 960 918 617 ...
+ $ gdpPercap_1962: num [1:142] 2551 4269 949 984 723 ...
+ $ gdpPercap_1967: num [1:142] 3247 5523 1036 1215 795 ...
+ $ gdpPercap_1972: num [1:142] 4183 5473 1086 2264 855 ...
+ $ gdpPercap_1977: num [1:142] 4910 3009 1029 3215 743 ...
+ $ gdpPercap_1982: num [1:142] 5745 2757 1278 4551 807 ...
+ $ gdpPercap_1987: num [1:142] 5681 2430 1226 6206 912 ...
+ $ gdpPercap_1992: num [1:142] 5023 2628 1191 7954 932 ...
+ $ gdpPercap_1997: num [1:142] 4797 2277 1233 8647 946 ...
+ $ gdpPercap_2002: num [1:142] 5288 2773 1373 11004 1038 ...
+ $ gdpPercap_2007: num [1:142] 6223 4797 1441 12570 1217 ...
+ $ lifeExp_1952 : num [1:142] 43.1 30 38.2 47.6 32 ...
+ $ lifeExp_1957 : num [1:142] 45.7 32 40.4 49.6 34.9 ...
+ $ lifeExp_1962 : num [1:142] 48.3 34 42.6 51.5 37.8 ...
+ $ lifeExp_1967 : num [1:142] 51.4 36 44.9 53.3 40.7 ...
+ $ lifeExp_1972 : num [1:142] 54.5 37.9 47 56 43.6 ...
+ $ lifeExp_1977 : num [1:142] 58 39.5 49.2 59.3 46.1 ...
+ $ lifeExp_1982 : num [1:142] 61.4 39.9 50.9 61.5 48.1 ...
+ $ lifeExp_1987 : num [1:142] 65.8 39.9 52.3 63.6 49.6 ...
+ $ lifeExp_1992 : num [1:142] 67.7 40.6 53.9 62.7 50.3 ...
+ $ lifeExp_1997 : num [1:142] 69.2 41 54.8 52.6 50.3 ...
+ $ lifeExp_2002 : num [1:142] 71 41 54.4 46.6 50.6 ...
+ $ lifeExp_2007 : num [1:142] 72.3 42.7 56.7 50.7 52.3 ...
+ $ pop_1952 : num [1:142] 9279525 4232095 1738315 442308 4469979 ...
+ $ pop_1957 : num [1:142] 10270856 4561361 1925173 474639 4713416 ...
+ $ pop_1962 : num [1:142] 11000948 4826015 2151895 512764 4919632 ...
+ $ pop_1967 : num [1:142] 12760499 5247469 2427334 553541 5127935 ...
+ $ pop_1972 : num [1:142] 14760787 5894858 2761407 619351 5433886 ...
+ $ pop_1977 : num [1:142] 17152804 6162675 3168267 781472 5889574 ...
+ $ pop_1982 : num [1:142] 20033753 7016384 3641603 970347 6634596 ...
+ $ pop_1987 : num [1:142] 23254956 7874230 4243788 1151184 7586551 ...
+ $ pop_1992 : num [1:142] 26298373 8735988 4981671 1342614 8878303 ...
+ $ pop_1997 : num [1:142] 29072015 9875024 6066080 1536536 10352843 ...
+ $ pop_2002 : num [1:142] 31287142 10866106 7026113 1630347 12251209 ...
+ $ pop_2007 : num [1:142] 33333216 12420476 8078314 1639131 14326203 ...
+```
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 3
+
+Take this 1 step further and create a `gap_ludicrously_wide` format data by pivoting over countries, year and the 3 metrics?
+**Hint** this new data frame should only have 5 rows.
+
+::::::::::::::: solution
+
+## Solution to Challenge 3
+
+
+``` r
+gap_ludicrously_wide <- gap_long %>%
+ unite(var_names, obs_type, year, country, sep = "_") %>%
+ pivot_wider(names_from = var_names, values_from = obs_values)
+```
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+Now we have a great 'wide' format data frame, but the `ID_var` could be more
+usable, let's separate it into 2 variables with `separate()`
+
+
+``` r
+gap_wide_betterID <- separate(gap_wide_new, ID_var, c("continent", "country"), sep="_")
+gap_wide_betterID <- gap_long %>%
+ unite(ID_var, continent, country, sep = "_") %>%
+ unite(var_names, obs_type, year, sep = "_") %>%
+ pivot_wider(names_from = var_names, values_from = obs_values) %>%
+ separate(ID_var, c("continent","country"), sep = "_")
+str(gap_wide_betterID)
+```
+
+``` output
+tibble [142 × 38] (S3: tbl_df/tbl/data.frame)
+ $ continent : chr [1:142] "Africa" "Africa" "Africa" "Africa" ...
+ $ country : chr [1:142] "Algeria" "Angola" "Benin" "Botswana" ...
+ $ gdpPercap_1952: num [1:142] 2449 3521 1063 851 543 ...
+ $ gdpPercap_1957: num [1:142] 3014 3828 960 918 617 ...
+ $ gdpPercap_1962: num [1:142] 2551 4269 949 984 723 ...
+ $ gdpPercap_1967: num [1:142] 3247 5523 1036 1215 795 ...
+ $ gdpPercap_1972: num [1:142] 4183 5473 1086 2264 855 ...
+ $ gdpPercap_1977: num [1:142] 4910 3009 1029 3215 743 ...
+ $ gdpPercap_1982: num [1:142] 5745 2757 1278 4551 807 ...
+ $ gdpPercap_1987: num [1:142] 5681 2430 1226 6206 912 ...
+ $ gdpPercap_1992: num [1:142] 5023 2628 1191 7954 932 ...
+ $ gdpPercap_1997: num [1:142] 4797 2277 1233 8647 946 ...
+ $ gdpPercap_2002: num [1:142] 5288 2773 1373 11004 1038 ...
+ $ gdpPercap_2007: num [1:142] 6223 4797 1441 12570 1217 ...
+ $ lifeExp_1952 : num [1:142] 43.1 30 38.2 47.6 32 ...
+ $ lifeExp_1957 : num [1:142] 45.7 32 40.4 49.6 34.9 ...
+ $ lifeExp_1962 : num [1:142] 48.3 34 42.6 51.5 37.8 ...
+ $ lifeExp_1967 : num [1:142] 51.4 36 44.9 53.3 40.7 ...
+ $ lifeExp_1972 : num [1:142] 54.5 37.9 47 56 43.6 ...
+ $ lifeExp_1977 : num [1:142] 58 39.5 49.2 59.3 46.1 ...
+ $ lifeExp_1982 : num [1:142] 61.4 39.9 50.9 61.5 48.1 ...
+ $ lifeExp_1987 : num [1:142] 65.8 39.9 52.3 63.6 49.6 ...
+ $ lifeExp_1992 : num [1:142] 67.7 40.6 53.9 62.7 50.3 ...
+ $ lifeExp_1997 : num [1:142] 69.2 41 54.8 52.6 50.3 ...
+ $ lifeExp_2002 : num [1:142] 71 41 54.4 46.6 50.6 ...
+ $ lifeExp_2007 : num [1:142] 72.3 42.7 56.7 50.7 52.3 ...
+ $ pop_1952 : num [1:142] 9279525 4232095 1738315 442308 4469979 ...
+ $ pop_1957 : num [1:142] 10270856 4561361 1925173 474639 4713416 ...
+ $ pop_1962 : num [1:142] 11000948 4826015 2151895 512764 4919632 ...
+ $ pop_1967 : num [1:142] 12760499 5247469 2427334 553541 5127935 ...
+ $ pop_1972 : num [1:142] 14760787 5894858 2761407 619351 5433886 ...
+ $ pop_1977 : num [1:142] 17152804 6162675 3168267 781472 5889574 ...
+ $ pop_1982 : num [1:142] 20033753 7016384 3641603 970347 6634596 ...
+ $ pop_1987 : num [1:142] 23254956 7874230 4243788 1151184 7586551 ...
+ $ pop_1992 : num [1:142] 26298373 8735988 4981671 1342614 8878303 ...
+ $ pop_1997 : num [1:142] 29072015 9875024 6066080 1536536 10352843 ...
+ $ pop_2002 : num [1:142] 31287142 10866106 7026113 1630347 12251209 ...
+ $ pop_2007 : num [1:142] 33333216 12420476 8078314 1639131 14326203 ...
+```
+
+``` r
+all.equal(gap_wide, gap_wide_betterID)
+```
+
+``` output
+[1] "Attributes: < Component \"class\": Lengths (1, 3) differ (string compare on first 1) >"
+[2] "Attributes: < Component \"class\": 1 string mismatch >"
+```
+
+There and back again!
+
+## Other great resources
+
+- [R for Data Science](https://r4ds.hadley.nz/) (online book)
+- [Data Wrangling Cheat sheet](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) (pdf file)
+- [Introduction to tidyr](https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html) (online documentation)
+- [Data wrangling with R and RStudio](https://www.rstudio.com/resources/webinars/data-wrangling-with-r-and-rstudio/) (online video)
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- Use the `tidyr` package to change the layout of data frames.
+- Use `pivot_longer()` to go from wide to longer layout.
+- Use `pivot_wider()` to go from long to wider layout.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
diff --git a/22-quarto.md b/22-quarto.md
new file mode 100644
index 000000000..a04b52df6
--- /dev/null
+++ b/22-quarto.md
@@ -0,0 +1,659 @@
+---
+title: Producing Reports With Quarto
+teaching: 60
+exercises: 15
+source: Rmd
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- Understand the value of writing reproducible reports
+- Learn how to recognise and compile the basic components of an Quarto file
+- Become familiar with R code chunks, and understand their purpose, structure and options
+- Demonstrate the use of inline chunks for weaving R outputs into text blocks, for example when discussing the results of some calculations
+- Be aware of alternative output formats to which a Quarto file can be exported
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- How can I integrate software and reports?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
+## Data analysis reports
+
+Data analysts tend to write a lot of reports, describing their
+analyses and results, for their collaborators or to document their
+work for future reference.
+
+Many new users begin by first writing a single R script containing all of their
+work, and then share the analysis by emailing the script and various graphs
+as attachments. But this can be cumbersome, requiring a lengthy discussion to
+explain which attachment was which result.
+
+Writing formal reports with Word or [LaTeX](https://www.latex-project.org/)
+can simplify this process by incorporating both the analysis report and output graphs
+into a single document. But tweaking formatting to make figures look correct
+and fixing obnoxious page breaks can be tedious and lead to a lengthy "whack-a-mole"
+game of fixing new mistakes resulting from a single formatting change.
+
+Creating a report as a web page (which is an html file) using [Quarto](https://quarto.org/docs/get-started/hello/rstudio.html)
+makes things easier.
+The report can be one long stream, so tall figures that wouldn't ordinarily fit on
+one page can be kept at full size and easier to read, since the reader can simply
+keep scrolling. Additionally, the formatting of a Quarto document is simple and easy to modify,
+allowing you to spend more time on your analyses instead of writing reports.
+
+::: callout
+You might also have heard about [R Markdown](https://rmarkdown.rstudio.com/) as a literate
+programming tool. In a lot of ways, Quarto is the
+[next-generation version of R Markdown](https://quarto.org/docs/faq/rmarkdown.html)
+with more advanced features and multi-language support. However, at its core, Quarto still
+works the same as R Markdown when it comes to R-based documents and R Markdown files (`.Rmd`)
+are still compatible with Quarto. Much of what we cover in this episode is thus also valid
+for traditional R Markdown files.
+:::
+
+
+## Literate programming
+
+Ideally, such analysis reports are *reproducible* documents: If an
+error is discovered, or if some additional subjects are added to the
+data, you can just re-compile the report and get the new or corrected
+results rather than having to reconstruct figures, paste them into
+a Word document, and hand-edit various detailed results.
+
+The key R package here is [`knitr`](https://yihui.name/knitr/). It allows you
+to create a document that is a mixture of text and chunks of
+code. When the document is processed by `knitr`, chunks of code will
+be executed, and graphs or other results will be inserted into the final document.
+
+This sort of idea has been called "literate programming".
+
+When rendering a document, `knitr` will execute the R code in each chunk and
+creates a new markdown (`.md`) document, which will include both the regular text
+and output from the executed code chunks. This markdown file is then converted to the final
+output format with [pandoc](http://pandoc.org/). This whole process is handled for you
+by the *Render* button in the RStudio IDE.
+
+## Creating a Quarto document
+
+Within RStudio, click `File → New File → Quarto document...` and
+you'll get a dialog box like this:
+
+{alt='Screenshot of the New Quarto Document dialogue box in RStudio'}
+
+You can stick with the default (HTML output), but give it a title.
+
+## Basic components of R Markdown
+
+The initial chunk of text (header) contains instructions for Quarto to specify what kind of document will be created, and the options chosen. You can use the header to give your document a title, author, date, and tell it what type of output you want
+to produce. In this case, we're creating an `html` document.
+
+```yaml
+---
+title: "My Quarto document"
+author: "John Doe"
+format: html
+editor: visual
+---
+```
+
+You can delete any of those fields if you don't want them
+included. The double-quotes aren't strictly *necessary* in this case.
+They're mostly needed if you want to include a colon in the title.
+
+RStudio creates the document with some example text to get you
+started. Note below that there are chunks like
+
+````{verbatim}
+```{r}
+1 + 1
+```
+````
+
+These are chunks of R code that will be executed by `knitr` and replaced
+by their results. More on this later.
+
+## Markdown
+
+Markdown is a system for writing web pages by marking up the text much
+as you would in an email rather than writing html code. The marked-up
+text gets *converted* to html, replacing the marks with the proper
+html code.
+
+For now, let's delete all of the stuff that's there and write a bit of
+markdown.
+
+You make things **bold** using two asterisks, like this: `**bold**`,
+and you make things *italics* by using underscores, like this:
+`_italics_`.
+
+You can make a bulleted list by writing a list with hyphens or
+asterisks with a space between the list and other text, like this:
+
+```markdown
+A list:
+
+* bold with double-asterisks
+* italics with underscores
+* code-type font with backticks
+```
+
+or like this:
+
+```markdown
+A second list:
+
+- bold with double-asterisks
+- italics with underscores
+- code-type font with backticks
+```
+
+Each will appear as:
+
+- bold with double-asterisks
+- italics with underscores
+- code-type font with backticks
+
+You can use whatever method you prefer, but *be consistent*. This maintains the
+readability of your code.
+
+You can make a numbered list by just using numbers. You can even use the
+same number over and over if you want:
+
+```markdown
+1. bold with double-asterisks
+1. italics with underscores
+1. code-type font with backticks
+```
+
+This will appear as:
+
+1. bold with double-asterisks
+2. italics with underscores
+3. code-type font with backticks
+
+You can make section headers of different sizes by initiating a line
+with some number of `#` symbols:
+
+```markdown
+# Title
+
+## Main section
+
+### Sub-section
+
+#### Sub-sub section
+```
+
+You *compile* the R Markdown document to an html webpage by clicking
+the "Render" button in the upper-left. Or using the keyboard shortcut
+Shift \+Ctrl \+K on Windows and Linux,
+or Shift \+Cmd \+K on Mac.
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 1
+
+Create a new Quarto document. Delete all of the R code chunks
+and write a bit of Markdown (some sections, some italicized
+text, and an itemized list).
+
+Convert the document to a webpage.
+
+::::::::::::::: solution
+
+## Solution to Challenge 1
+
+In RStudio, select `File > New file > Quarto Document...`
+
+Delete the placeholder text and add the following:
+
+```markdown
+# Introduction
+
+## Background on Data
+
+This report uses the *gapminder* dataset, which has columns that include:
+
+* country
+* continent
+* year
+* lifeExp
+* pop
+* gdpPercap
+
+## Background on Methods
+
+```
+
+Then click the 'Render' button on the toolbar to generate an html document (webpage).
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## A bit more Markdown
+
+You can make a hyperlink like this:
+`[Carpentries Home Page](https://carpentries.org/)`.
+
+You can include an image file like this: ``
+
+You can do subscripts (e.g., F~2~) with `F~2~` and superscripts (e.g.,
+F^2^) with `F^2^`.
+
+If you know how to write equations in
+[LaTeX](https://www.latex-project.org/), you can use `$ $` and `$$ $$` to insert math equations, like
+`$E = mc^2$` and
+
+```
+$$y = \mu + \sum_{i=1}^p \beta_i x_i + \epsilon$$
+```
+
+which will show as
+
+$$y = \mu + \sum_{i=1}^p \beta_i x_i + \epsilon$$
+
+
+You can review Markdown syntax by navigating to the
+"Markdown Quick Reference" under the "Help" field in the
+toolbar at the top of RStudio.
+
+## R code chunks
+
+The real power of Quarto comes from
+mixing markdown with chunks of code. When
+processed, the R code will be executed; if they produce figures, the
+figures will be inserted in the final document.
+
+The main code chunks look like this:
+
+````{verbatim}
+```{r}
+#| label: load_data
+gapminder <- read.csv("gapminder.csv")
+```
+````
+
+That is, you place a chunk of R code between \`\`\`{r}
+and \`\`\`. You should give each chunk
+a unique name, by inserting the line `#| label: label_name`
+as they will help you to fix errors and, if any graphs are
+produced, the file names are based on the name of the code chunk that
+produced them. You can create code chunks quickly in RStudio using the shortcuts
+Ctrl \+Alt \+I on Windows and Linux,
+or Cmd \+Option \+I on Mac.
+
+::: spoiler
+### Code chunk options in R Markdown
+In R Markdown, you add chunk labels by including them within the \`\`\`{r} line like so:
+
+````{verbatim}
+```{r label_data}
+gapminder <- read.csv("gapminder.csv")
+```
+````
+:::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 2
+
+Add code chunks to:
+
+- Load the ggplot2 package
+- Read the gapminder data
+- Create a plot
+
+::::::::::::::: solution
+
+## Solution to Challenge 2
+
+````{verbatim}
+```{r}
+#| label: libraries
+library(ggplot2)
+```
+````
+
+````{verbatim}
+```{r}
+#| label: load-gapminder-data
+gapminder <- read.csv("gapminder.csv")
+```
+````
+
+````{verbatim}
+```{r}
+#| label: make-plot
+plot(lifeExp ~ year, data = gapminder)
+```
+````
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## How things get compiled
+
+When you press the "Render" button, the Quarto document is
+processed by [`knitr`](https://yihui.name/knitr) and a plain Markdown
+document is produced (as well as, potentially, a set of figure files): the R code is executed
+and replaced by both the input and the output; if figures are
+produced, links to those figures are included.
+
+The Markdown and figure documents are then processed by the tool
+[`pandoc`](https://pandoc.org/), which converts the Markdown file into an
+html file, with the figures embedded.
+
+{alt='Schematic of the Quarto rendering process'}
+
+## Chunk options
+
+There are a variety of options to affect how the code chunks are
+treated. Here are some examples:
+
+- Use `echo=FALSE` to avoid having the code itself shown.
+- Use `results="hide"` to avoid having any results printed.
+- Use `eval=FALSE` to have the code shown but not evaluated.
+- Use `warning=FALSE` and `message=FALSE` to hide any warnings or
+ messages produced.
+- Use `fig.height` and `fig.width` to control the size of the figures
+ produced (in inches).
+
+So you might write:
+
+````{verbatim}
+```{r}
+#| label: load_libraries
+#| echo: false
+#| message: false
+library(dplyr)
+library(ggplot2)
+```
+````
+
+Often there will be particular options that you'll want to use
+repeatedly; for this, you can set *global* chunk options in the files YAML header like so:
+
+```yaml
+---
+...
+knitr:
+ opts_chunk:
+ message: false
+ warning: false
+ echo: false
+ results: "hide"
+ fig.path: "Figs/
+ fig.width: 11
+---
+```
+
+The `fig.path` option defines where the figures will be saved. The `/`
+here is really important; without it, the figures would be saved in
+the standard place but just with names that begin with `Figs`.
+
+If you have multiple R Markdown files in a common directory, you might
+want to use `fig.path` to define separate prefixes for the figure file
+names, like `fig.path="Figs/cleaning-"` and `fig.path="Figs/analysis-"`.
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 3
+
+Use chunk options to control the size of a figure and to hide the
+code.
+
+::::::::::::::: solution
+
+## Solution to Challenge 3
+
+````{verbatim}
+```{r}
+#| label: faitful-plot
+#| echo: false
+#| fig.width: 3
+plot(faitful)
+```
+````
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+You can review all of the `R` chunk options by navigating to
+the "R Markdown Cheat Sheet" under the "Cheatsheets" section
+of the "Help" field in the toolbar at the top of RStudio.
+
+## Inline R code
+
+You can make *every* number in your report reproducible. Use
+\`r and \` for an in-line code chunk,
+like so: ``` ``r "r round(some_value, 2)"`` ```. The code will be
+executed and replaced with the *value* of the result.
+
+Don't let these in-line chunks get split across lines.
+
+Perhaps precede the paragraph with a larger code chunk that does
+calculations and defines variables, with `include=FALSE` for that larger
+chunk (which is the same as `echo=FALSE` and `results="hide"`).
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 4
+
+Try out a bit of in-line R code.
+
+::::::::::::::: solution
+
+## Solution to Challenge 4
+
+Here's some inline code to determine that 2 + 2 = `r 2+2`:
+
+```{verbatim}
+Here's some inline code to determine that 2 + 2 = `r 2+2`:
+```
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Other output options
+
+You can also convert R Markdown to a PDF or a Word document.
+Change the `format: ` field in the YAML header to `pdf` or `docx`.
+For an overview of all the available output formats, see the
+[Quarto documentation](https://quarto.org/docs/output-formats/all-formats.html)
+
+## Parameterised reports
+
+Literate programming with tools like Quarto and R Markdown is very powerful in
+that it allows you to generate analysis reports in a reproducible manner.
+This makes it very easy to update your work and alter the input parameters within the report
+You can take this one step further, by parametrising the reports themselves.
+This is very useful in a number of cases, for example:
+
+* Running the same analysis on different datasets
+* Generating multiple reports for different groups of the data (e.g. geographic location or time periods)
+* Controlling the `knitr` options; e.g. you might want to show the code in some reports but not in others
+
+### Including parameters in a Quarto document
+
+Including parameters in a Quarto document (or R Markdown, which follows the same syntax) can be done
+by adding the `params` field to they YAML header. This field can hold a list of multiple parameters.
+
+For example, imagine we want to analyse the life expectancy of different countries, using the
+`gapminder` dataset, but we want a separate report for each country. To achieve this, we set
+the YAML header for our Quarto document as follows:
+
+```yaml
+---
+title: "Life Expectancy Report"
+format: html
+execute:
+ echo: false
+ warning: false
+ message: false
+params:
+ country: "Afghanistan"
+---
+```
+
+We can then reference this parameter anywhere in the R code in our report by accessing the
+`params` object. To calculate the life expectancy for just the country defined by the `params`,
+we can do:
+
+````{verbatim}
+```{r}
+library(tidyverse)
+library(gapminder)
+```
+
+```{r}
+life_expectancy <-
+ gapminder |>
+ select(country, year, lifeExp) |>
+ filter(country == params$country)
+```
+````
+
+The last line in the code chunk above uses the `params` object to filter the `gapminder` dataset.
+Note that we can also `params` in inline R code snippets. So we can generate a heading that will
+change based on the country parameter:
+
+````{verbatim}
+## Life Expectancy in `r params$country`
+
+```{r}
+#| label: !expr paste0("life-expectancy-plot", params$country)
+ggplot(life_expectancy, aes(year, lifeExp)) +
+ geom_line() +
+ theme_minimal()
+```
+````
+
+In fact, we can even use this in the main document title in the YAML header, as it also accepts
+R code expressions:
+
+```yaml
+---
+title: "Life Expectancy Report for `r params$country`"
+...
+```
+
+### Rendering Quarto documents from within R
+
+Of course, manually editing the YAML header every time you want to generate a report
+isn't much better than manually editing the report itself. The real power of parameterised
+reports is when we render them *programmatically*. This can be done using the
+[`{quarto}` R package](https://github.com/quarto-dev/quarto-r), which provides
+the `quarto_render()` function. This function takes a Quarto file and any execution parameters
+as input.
+
+So to generate the life expectancy report for Afghanistan, we can write a script with the following
+code:
+
+```r
+# render-report.R
+library(quarto)
+quarto_render("life_expectancy_report.qmd", execute_params = list(country = "Afghanistan"))
+```
+
+And now for the real magic, we can modify our script to render a report for a list of countries
+of interest from the `gapminder` dataset.
+
+```{r}
+# render-all-reports.R
+library(quarto)
+countries <- c("Afghanistan", "Belgium", "India", "United Kingdom")
+for (country in countries) {
+ quarto_render(
+ input = "life_expectancy_report.qmd",
+ output_file = paste0("life_expectancy_", country, ".html"),
+ execute_params = list(country = country)
+ )
+}
+```
+
+After running this script, we should have the following files in our working directory:
+
+```
+.
+├── life_expectancy_Afghanistan.html
+├── life_expectancy_Belgium.html
+├── life_expectancy_India.html
+├── life_expectancy_United Kingdom.html
+├── life_expectancy_report.qmd
+└── life_expectancy_report_files
+```
+
+::: callout
+**WARNING**: although this will work and generate the correct output files,
+you might notice that each report will show the _exact same plot_, which is unexpected.
+This is an [issue](https://github.com/quarto-dev/quarto-r/issues/205) with the Quarto R package.
+
+With R Markdown we don't have this issue, to render we would use the following code:
+
+```r
+rmarkdown::render(
+ input = "life_expectency_report.Rmd",
+ output_file = paste0("life_expectancy_", country, ".html"),
+ params = list(country = country)
+)
+```
+
+:::
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Tip: Creating PDF documents
+
+Creating .pdf documents may require installation of some extra software. The R
+package `tinytex` provides some tools to help make this process easier for R users.
+With `tinytex` installed, run `tinytex::install_tinytex()` to install the required
+software (you'll only need to do this once) and then when you knit to pdf `tinytex`
+will automatically detect and install any additional LaTeX packages that are needed to
+produce the pdf document. Visit the [tinytex website](https://yihui.org/tinytex/)
+for more information.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Tip: Visual markdown editing in RStudio
+
+RStudio versions 1.4 and later include visual markdown editing mode.
+In visual editing mode, markdown expressions (like `**bold words**`) are
+transformed to the formatted appearance (**bold words**) as you type.
+This mode also includes a toolbar at the top with basic formatting buttons,
+similar to what you might see in common word processing software programs.
+You can turn visual editing on and off by pressing
+the {alt='Icon for turning on and off the visual editing mode in RStudio, which looks like a pair of compasses'}
+button in the top right corner of your R Markdown document.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Resources
+
+- [Knitr in a knutshell tutorial](https://kbroman.org/knitr_knutshell)
+- [Dynamic Documents with R and knitr](https://www.amazon.com/exec/obidos/ASIN/1482203537/7210-20) (book)
+- [R Markdown documentation](https://rmarkdown.rstudio.com)
+- [R Markdown cheat sheet](https://www.rstudio.com/wp-content/uploads/2016/03/rmarkdown-cheatsheet-2.0.pdf)
+- [Getting started with R Markdown](https://www.rstudio.com/resources/webinars/getting-started-with-r-markdown/)
+- [R Markdown: The Definitive Guide](https://bookdown.org/yihui/rmarkdown/) (book by Rstudio team)
+- [Reproducible Reporting](https://www.rstudio.com/resources/webinars/reproducible-reporting/)
+- [The Ecosystem of R Markdown](https://www.rstudio.com/resources/webinars/the-ecosystem-of-r-markdown/)
+- [Introducing Bookdown](https://www.rstudio.com/resources/webinars/introducing-bookdown/)
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- Mix reporting written in R Markdown with software written in R.
+- Specify chunk options to control formatting.
+- Use `knitr` to convert these documents into PDF and other formats.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
diff --git a/23-statistics.md b/23-statistics.md
new file mode 100644
index 000000000..d4929984b
--- /dev/null
+++ b/23-statistics.md
@@ -0,0 +1,35019 @@
+---
+title: "Basic Statistics: describing, modelling and reporting"
+teaching: 60
+exercises: 20
+source: Rmd
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- To be able to describe the different types of data
+- To be able to do basic data exploration of a real dataset
+- To be able to calculate descriptive statistics
+- To be able to perform statistical inference on a dataset
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- How can I detect the type of data I have?
+- How can I make meaningful summaries of my data?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Content
+
+- Types of Data
+- Exploring your dataset
+- Descriptive Statistics
+- Inferential Statistics
+
+## Data
+
+
+``` r
+# We will need these libraries and this data later.
+library(tidyverse)
+library(ggplot2)
+
+# loading data
+lon_dims_imd_2019 <- read.csv("data/English_IMD_2019_Domains_rebased_London_by_CDRC.csv")
+
+# create a binary membership variable for City of London (for later examples)
+lon_dims_imd_2019 <- lon_dims_imd_2019 %>% mutate(city = la19nm == "City of London")
+```
+
+We are going to use the data from the Consumer Data Research Centre, specifically the London IMD 2019 (English IMD 2019 Domains rebased) data.
+Atribution: Data provided by the Consumer Data Research Centre, an ESRC Data Investment: ES/L011840/1, ES/L011891/1
+
+The statistical unit areas used to provide indices of relative deprivation across the country are Lower layer Super Output Areas (LSOAs), dimensions of depravation include income, employment, education, health, crime, barriers to housing and services, and the living environment.
+We have added a variable *city* indicating if an LSOA is within the City of London, or not.
+
+## The big picture
+
+- Research often seeks to answer a question about a larger population by collecting data on a small sample
+- Data collection:
+ - Many variables
+ - For each person/unit.
+- This procedure, *sampling*, must be controlled so as to ensure **representative** data.
+
+## Descriptive and inferential statistics
+
+::: callout
+Just as data in general are of different types - for example numeric vs text data - statistical data are assigned to different *levels of measure*. The level of measure determines how we can describe and model the data.
+:::
+
+# Describing data
+
+- Continuous variables
+- Discrete variables
+
+::: callout
+How do we convey information on what your data looks like, using numbers or figures?
+:::
+
+### Describing continuous data.
+
+First establish the distribution of the data. You can visualise this with a histogram.
+
+
+``` r
+ggplot(lon_dims_imd_2019, aes(x = barriers_london_rank)) +
+ geom_histogram()
+```
+
+``` output
+`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+```
+
+
+ 1 Barking and Dagenham 7786.
+ 2 Barnet 17049.
+ 3 Bexley 18592.
+ 4 Brent 11500.
+ 5 Bromley 20826.
+ 6 Camden 14359.
+ 7 City of London 19800.
+ 8 Croydon 14686.
+ 9 Ealing 13718.
+10 Enfield 11403.
+# ℹ 23 more rows
+```
+
+
+Is the difference between the income ranks statistically significant?
+
+## t-test
+
+### Assumptions of a t-test
+
+- One independent categorical variable with 2 groups and one dependent continuous variable
+
+- The dependent variable is approximately normally distributed in each group
+
+- The observations are independent of each other
+
+- For students' original t-statistic, that the variances in both groups are more or less equal. This constraint should probably be abandoned in favour of always using a conservative test.
+
+## Doing a t-test
+
+
+``` r
+t.test(health_london_rank ~ city, data = lon_dims_imd_2019)$statistic
+```
+
+``` output
+ t
+-0.5183242
+```
+
+``` r
+t.test(health_london_rank ~ city, data = lon_dims_imd_2019)$parameter
+```
+
+``` output
+ df
+5.015827
+```
+
+Notice that the summary()** of the test contains more data than is output by default.
+
+
+Write a paragraph in markdown format reporting this test result including the t-statistic, the degrees of freedom, the confidence interval and the p-value to 4 places. To do this include your r code **inline** with your text, rather than in an R code chunk.
+
+### t-test result
+
+Testing supported the rejection of the null hypothesis that there is no difference between mean health rank of City of London and non-City of London areas (**t**=-0.5183, **df**= 5.0158,
+**p**= 0.6263).
+
+(Can you get p to display to four places? Cf *format()*.)
+
+## More than two levels of IV
+
+While the t-test is sufficient where there are two levels of the IV, for situations where there are more than two, we use the **ANOVA** family of procedures. To show this, we will create a variable that subsets our data by *per capita GDP* levels. If the ANOVA result is statistically significant, we will use a post-hoc test method to do pairwise comparisons (here Tukey's Honest Significant Differences.)
+
+
+``` r
+anovamodel <- aov(lon_dims_imd_2019$health_london_rank ~ lon_dims_imd_2019$la19nm)
+summary(anovamodel)
+```
+
+``` output
+ Df Sum Sq Mean Sq F value Pr(>F)
+lon_dims_imd_2019$la19nm 32 1.156e+11 3.614e+09 94.3 <2e-16 ***
+Residuals 4802 1.840e+11 3.832e+07
+---
+Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+```
+
+``` r
+TukeyHSD(anovamodel)
+```
+
+``` output
+ Tukey multiple comparisons of means
+ 95% family-wise confidence level
+
+Fit: aov(formula = lon_dims_imd_2019$health_london_rank ~ lon_dims_imd_2019$la19nm)
+
+$`lon_dims_imd_2019$la19nm`
+ diff lwr
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+Redbridge-Lambeth 14035.53747 0.0000000
+Richmond upon Thames-Lambeth 18463.58650 0.0000000
+Southwark-Lambeth 3378.84999 0.9999985
+Sutton-Lambeth 12029.15810 0.0000000
+Tower Hamlets-Lambeth 2175.98721 1.0000000
+Waltham Forest-Lambeth 7179.94554 0.0000001
+Wandsworth-Lambeth 9652.58364 0.0000000
+Westminster-Lambeth 12463.20343 0.0000000
+Merton-Lewisham 11303.65105 0.0000000
+Newham-Lewisham 3656.22670 0.9997498
+Redbridge-Lewisham 12708.20434 0.0000000
+Richmond upon Thames-Lewisham 17133.36339 0.0000000
+Southwark-Lewisham 2051.77078 1.0000000
+Sutton-Lewisham 10699.38092 0.0000000
+Tower Hamlets-Lewisham 847.71349 0.7600126
+Waltham Forest-Lewisham 5851.67183 0.0026373
+Wandsworth-Lewisham 8326.12255 0.0000000
+Westminster-Lewisham 11133.91627 0.0000000
+Newham-Merton -4646.78195 0.0000000
+Redbridge-Merton 4404.24025 0.9629607
+Richmond upon Thames-Merton 8811.18912 0.0000000
+Southwark-Merton -6250.61341 0.0000000
+Sutton-Merton 2380.04353 1.0000000
+Tower Hamlets-Merton -7462.13148 0.0000000
+Waltham Forest-Merton -2458.17315 0.0000000
+Wandsworth-Merton 27.57051 0.0569064
+Westminster-Merton 2817.68506 1.0000000
+Redbridge-Newham 11648.04386 0.0000000
+Richmond upon Thames-Newham 16071.50710 0.0000000
+Southwark-Newham 991.75889 0.9038487
+Sutton-Newham 9637.78685 0.0000000
+Tower Hamlets-Newham -212.99796 0.0165858
+Waltham Forest-Newham 4790.96038 0.4441260
+Wandsworth-Newham 7266.47206 0.0000000
+Westminster-Newham 10072.61013 0.0000000
+Richmond upon Thames-Redbridge 7042.64479 0.0000183
+Southwark-Redbridge -8035.96088 0.0000000
+Sutton-Redbridge 609.08720 0.4435823
+Tower Hamlets-Redbridge -9241.15063 0.0000000
+Waltham Forest-Redbridge -4237.19230 0.0000000
+Wandsworth-Redbridge -1761.02429 0.0000001
+Westminster-Redbridge 1044.08901 0.8899974
+Southwark-Richmond upon Thames -11956.45964 0.0000000
+Sutton-Richmond upon Thames -3330.11422 0.0000000
+Tower Hamlets-Richmond upon Thames -13169.86317 0.0000000
+Waltham Forest-Richmond upon Thames -8165.90484 0.0000000
+Wandsworth-Richmond upon Thames -5677.31474 0.0000000
+Westminster-Richmond upon Thames -2891.67551 0.0000000
+Sutton-Southwark 11227.13749 0.0000000
+Tower Hamlets-Southwark 1375.99537 0.9962196
+Waltham Forest-Southwark 6379.95370 0.0000789
+Wandsworth-Southwark 8855.03616 0.0000000
+Westminster-Southwark 11661.84418 0.0000000
+Tower Hamlets-Sutton -6818.93444 0.0000000
+Waltham Forest-Sutton -1814.97611 0.0000004
+Wandsworth-Sutton 671.68939 0.5298477
+Westminster-Sutton 3460.35540 1.0000000
+Waltham Forest-Tower Hamlets 7774.54329 0.0000000
+Wandsworth-Tower Hamlets 10254.74085 0.0000000
+Westminster-Tower Hamlets 13053.55326 0.0000000
+Wandsworth-Waltham Forest 5250.78251 0.0531872
+Westminster-Waltham Forest 8049.59492 0.0000000
+Westminster-Wandsworth 5295.92117 0.0973035
+```
+
+# Regression Modelling
+
+The most common use of regression modelling is to explore the relationship between two continuous variables, for example between `Income_london_rank` and `health_london_rank` in our data. We can first determine whether there is any significant correlation between the values, and if there is, plot the relationship.
+
+
+``` r
+cor.test(lon_dims_imd_2019$Income_london_rank, lon_dims_imd_2019$health_london_rank)
+```
+
+``` output
+
+ Pearson's product-moment correlation
+
+data: lon_dims_imd_2019$Income_london_rank and lon_dims_imd_2019$health_london_rank
+t = 92.907, df = 4833, p-value < 2.2e-16
+alternative hypothesis: true correlation is not equal to 0
+95 percent confidence interval:
+ 0.7903110 0.8105571
+sample estimates:
+ cor
+0.8006626
+```
+
+``` r
+ggplot(lon_dims_imd_2019, aes(Income_london_rank, health_london_rank)) +
+ geom_point() +
+ geom_smooth()
+```
+
+``` output
+`geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
+```
+
+
+1 (Intercept) 16916. 227. 74.4 0
+2 livingEnv_london_rank 0.343 0.0192 17.9 1.63e-69
+```
+The row names have been moved into a column called term, and the column names are simple and consistent (and can be accessed using $).
+
+Information about the model can be explored with 'augment'. The function augments the original data with information from the model,
+such as the fitted values and residuals for each of the original points in the regression.
+
+
+
+``` r
+augment(reg_LivEnv_health)
+```
+
+``` output
+# A tibble: 4,835 × 8
+ health_london_rank livingEnv_london_rank .fitted .resid .hat .sigma
+
+ 1 32113 7789 19588. 12525. 0.000250 7624.
+ 2 29705 13070 21400. 8305. 0.000252 7625.
+ 3 17600 4092 18320. -720. 0.000458 7626.
+ 4 17907 9397 20140. -2233. 0.000213 7626.
+ 5 21581 10629 20562. 1019. 0.000207 7626.
+ 6 16414 11162 20745. -4331. 0.000210 7626.
+ 7 12334 8672 19891. -7557. 0.000226 7625.
+ 8 9661 9611 20213. -10552. 0.000211 7625.
+ 9 16050 2269 17694. -1644. 0.000624 7626.
+10 18178 4309 18394. -216. 0.000441 7626.
+# ℹ 4,825 more rows
+# ℹ 2 more variables: .cooksd , .std.resid
+```
+
+Some of the data presented by 'augment' will be discussed in the episode Linear Regression Diagnostics.
+
+Summary statistics are computed for the entire regression, such as R^2 and the F-statistic can be accessed with the 'glance' function:
+
+
+
+``` r
+glance(reg_LivEnv_health)
+```
+
+``` output
+# A tibble: 1 × 12
+ r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
+
+1 0.0622 0.0621 7625. 321. 1.63e-69 1 -50081. 100167. 100187.
+# ℹ 3 more variables: deviance , df.residual , nobs
+```
+### Generalised linear models
+
+We can also use the 'broom' functions to present data from Generalised linear and non-linear models.
+For example, if we wanted to explore the Income Rank in relation to whether or not an area was within the City of London.
+
+
+``` r
+# add a variable to indicate whether or not an area is within the City of London
+lon_dims_imd_2019 <- lon_dims_imd_2019 %>% mutate(city = la19nm == "City of London")
+
+# create a Generalised Linear Model
+glmlondims <- glm(city ~ Income_london_rank, lon_dims_imd_2019, family = "binomial")
+summary(glmlondims)
+```
+
+``` output
+
+Call:
+glm(formula = city ~ Income_london_rank, family = "binomial",
+ data = lon_dims_imd_2019)
+
+Coefficients:
+ Estimate Std. Error z value Pr(>|z|)
+(Intercept) -7.868e+00 1.004e+00 -7.835 4.68e-15 ***
+Income_london_rank 6.888e-05 4.635e-05 1.486 0.137
+---
+Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+(Dispersion parameter for binomial family taken to be 1)
+
+ Null deviance: 92.295 on 4834 degrees of freedom
+Residual deviance: 90.062 on 4833 degrees of freedom
+AIC: 94.062
+
+Number of Fisher Scoring iterations: 10
+```
+
+Use of 'tidy':
+
+``` r
+tidy(glmlondims)
+```
+
+``` output
+# A tibble: 2 × 5
+ term estimate std.error statistic p.value
+
+1 (Intercept) -7.87 1.00 -7.84 4.68e-15
+2 Income_london_rank 0.0000689 0.0000463 1.49 1.37e- 1
+```
+
+Use of 'augment':
+
+``` r
+augment(glmlondims)
+```
+
+``` output
+# A tibble: 4,835 × 8
+ city Income_london_rank .fitted .resid .hat .sigma .cooksd .std.resid
+
+ 1 TRUE 32831 -5.61 3.35 0.00194 0.128 2.65e-1 3.35
+ 2 TRUE 29901 -5.81 3.41 0.00115 0.127 1.93e-1 3.41
+ 3 TRUE 18510 -6.59 3.63 0.000233 0.126 8.51e-2 3.63
+ 4 TRUE 6029 -7.45 3.86 0.000332 0.125 2.87e-1 3.86
+ 5 FALSE 14023 -6.90 -0.0448 0.000239 0.137 1.20e-7 -0.0448
+ 6 FALSE 6261 -7.44 -0.0343 0.000330 0.137 9.72e-8 -0.0343
+ 7 FALSE 3382 -7.64 -0.0311 0.000360 0.137 8.70e-8 -0.0311
+ 8 FALSE 7506 -7.35 -0.0358 0.000315 0.137 1.01e-7 -0.0358
+ 9 FALSE 8902 -7.25 -0.0376 0.000298 0.137 1.05e-7 -0.0376
+10 FALSE 9033 -7.25 -0.0378 0.000296 0.137 1.06e-7 -0.0378
+# ℹ 4,825 more rows
+```
+
+Use of 'glance':
+
+``` r
+glance(glmlondims)
+```
+
+``` output
+# A tibble: 1 × 8
+ null.deviance df.null logLik AIC BIC deviance df.residual nobs
+
+1 92.3 4834 -45.0 94.1 107. 90.1 4833 4835
+```
+You will notice that the statistics computed by 'glance' are different for glm objects than for lm (e.g. deviance rather than R^2).
+
+### Hypothesis Testing
+
+The tidy function can also be applied a range of hypotheses tests, such as built-in functions like t.test, cor.test, and wilcox.test.
+
+### t-test
+
+``` r
+tt <- t.test(Income_london_rank ~ city, lon_dims_imd_2019)
+tidy(tt)
+```
+
+``` output
+# A tibble: 1 × 10
+ estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high
+
+1 -5344. 14456. 19800. -1.24 0.270 5.01 -16407. 5719.
+# ℹ 2 more variables: method , alternative
+```
+Some cases might have fewer columns (for example, no confidence interval).
+
+Wilcox test:
+
+``` r
+wt <- wilcox.test(Income_london_rank ~ city, lon_dims_imd_2019)
+tidy(wt)
+```
+
+``` output
+# A tibble: 1 × 4
+ statistic p.value method alternative
+
+1 9836. 0.174 Wilcoxon rank sum test with continuity correcti… two.sided
+```
+
+Since the 'tidy' output is already only one row, glance returns the same output:
+
+``` r
+# t-test
+glance(tt)
+```
+
+``` output
+# A tibble: 1 × 10
+ estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high
+
+1 -5344. 14456. 19800. -1.24 0.270 5.01 -16407. 5719.
+# ℹ 2 more variables: method , alternative
+```
+
+``` r
+# Wilcox test
+glance(wt)
+```
+
+``` output
+# A tibble: 1 × 4
+ statistic p.value method alternative
+
+1 9836. 0.174 Wilcoxon rank sum test with continuity correcti… two.sided
+```
+The chisq.test enables use to investigate whether changes in one categorical variable are related to changes in another categorical variable.
+
+The 'augment' method is defined only for chi-squared tests, since there is no meaningful sense, for other tests,
+in which a hypothesis test produces output about each initial data point.
+
+
+``` r
+# convert IDAOP_london_decile to a factor so it is not interprested as continuous data
+lon_dims_imd_2019$IDAOP_london_decile <- factor(lon_dims_imd_2019$IDAOP_london_decile)
+
+# xtabs creates a frequency table of IMD deciles within London borooughs
+chit <- chisq.test(xtabs(~ la19nm + IDAOP_london_decile, data = lon_dims_imd_2019))
+```
+
+``` warning
+Warning in chisq.test(xtabs(~la19nm + IDAOP_london_decile, data =
+lon_dims_imd_2019)): Chi-squared approximation may be incorrect
+```
+
+``` r
+tidy(chit)
+```
+
+``` output
+# A tibble: 1 × 4
+ statistic p.value parameter method
+
+1 2841. 0 288 Pearson's Chi-squared test
+```
+
+
+``` r
+augment(chit)
+```
+
+``` output
+# A tibble: 330 × 9
+ la19nm IDAOP_london_decile .observed .prop .row.prop .col.prop .expected
+
+ 1 Barking … 1 6 1.24e-3 0.0545 0.0124 11.0
+ 2 Barnet 1 6 1.24e-3 0.0284 0.0124 21.1
+ 3 Bexley 1 0 0 0 0 14.6
+ 4 Brent 1 12 2.48e-3 0.0694 0.0248 17.3
+ 5 Bromley 1 2 4.14e-4 0.0102 0.00414 19.7
+ 6 Camden 1 12 2.48e-3 0.0902 0.0248 13.3
+ 7 City of … 1 0 0 0 0 0.599
+ 8 Croydon 1 8 1.65e-3 0.0364 0.0166 22.0
+ 9 Ealing 1 11 2.28e-3 0.0561 0.0228 19.6
+10 Enfield 1 9 1.86e-3 0.0492 0.0186 18.3
+# ℹ 320 more rows
+# ℹ 2 more variables: .resid , .std.resid
+```
+There are a number of underlying assumptions of a Chi-Square test, these are:
+* Independence: The Chi-Square test assumes that the observations in the data are independent of each other. This means that the outcome of one observation should not influence the outcome of another.
+* Random Sampling: The data should be obtained through random sampling to ensure that it is representative of the population from which it was drawn.
+* Expected Frequency: The Chi-Square test assumes that the expected frequency count for each cell in the contingency table should be at least 5. If this assumption is not met, the test results may not be reliable.
+
+As we have received a warning about the reliability of our test, it is likely that one of these assumptions has not been met,
+and that this is not a suitable test for this data.
+
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 3
+
+Use broom to amend the display of your model outputs.
+
+Which function(s) did you use and why?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+### Conventions
+There are some conventions that enable consistency across the broom functions, these are:
+* The output of the tidy, augment and glance functions is always a tibble.
+* The output never has rownames. This ensures that you can combine it with other tidy outputs without fear of losing information (since rownames in R cannot contain duplicates).
+* Some column names are kept consistent, so that they can be combined across different models and so that you know what to expect (in contrast to asking “is it pval or PValue?” every time).
diff --git a/25-linreg-diagnostics.md b/25-linreg-diagnostics.md
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--- /dev/null
+++ b/25-linreg-diagnostics.md
@@ -0,0 +1,242 @@
+---
+title: Assumption Diagnostics and Regression Trouble Shooting
+teaching: 80
+exercises: 20
+source: Rmd
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- To be able to check assumptions for linear regression models
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- How can I check that my data is suitable for use in a linear regression model?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Content
+
+- Residual diagnostics
+- Heteroskedasticity Check
+- Multicollinearity Check
+- Identification of Influential Points
+
+## Data
+
+
+``` r
+# We will need these libraries and this data later.
+library(ggplot2)
+library(olsrr)
+library(car)
+
+lon_dims_imd_2019 <- read.csv("data/English_IMD_2019_Domains_rebased_London_by_CDRC.csv")
+```
+
+For accurate model interpretation and prediction, there are a number of assumptions about linear regression models that need to be verified.
+These are:
+* Linear Relationship: The core premise of multiple linear regression is the existence of a linear relationship between the dependent (outcome) variable and the independent variables. T
+* Multivariate Normality: The analysis assumes that the residuals (the differences between observed and predicted values) are normally distributed. This assumption can be assessed by examining histograms or Q-Q plots of the residuals, or through statistical tests such as the Kolmogorov-Smirnov test.
+* No Multicollinearity: It is essential that the independent variables are not too highly correlated with each other, a condition known as multicollinearity. This can be checked using:
+ + Correlation matrices, where correlation coefficients should ideally be below 0.80.
+ + Variance Inflation Factor (VIF), with VIF values above 10 indicating problematic multicollinearity.
+* Homoscedasticity: The variance of error terms (residuals) should be consistent across all levels of the independent variables.
+
+We can perform a number of tests to see if the assumptions are met.
+
+## Residual Diagnostics
+
+### Residual QQ Plot
+Plot for detecting violation of normality assumption.
+
+``` r
+model1 <- lm(health_london_rank ~ livingEnv_london_rank + barriers_london_rank + la19nm, data = lon_dims_imd_2019)
+
+ols_plot_resid_qq(model1)
+```
+
+
+
+
+
+ image/svg+xml
+
+
+
+
+ c("a" , "b" , "c" ) c("a" , "c" ) FALSE TRUE ?? ?? c("a" , "b" , "c" ) c("a" , "c" ) FALSE TRUE c("a" ... FALSE != !=
+
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+\documentclass[convert]{standalone}
+
+\usepackage{tikz}
+\usepackage{colortbl}
+\renewcommand{\familydefault}{\sfdefault}
+
+\begin{document}
+
+\begin{tikzpicture}
+
+% Headings
+
+\node (INPUT-LABEL) at (0, 5) {Input Data};
+\node (GROUP-LABEL) at (3, 5) {Split};
+\node (SUMMARY-LABEL) at (6, 5) {Apply};
+\node (OUTPUT-LABEL) at (9, 5) {Combine};
+
+
+% Data Nodes
+
+\node (INPUT) at (0, 2) {
+
+ \begin{tabular}{| c | r |}
+ \hline
+ \rowcolor[gray]{.7}
+ x & y \\ \hline
+ a & 2 \\ \hline
+ a & 4 \\ \hline
+ b & 0 \\ \hline
+ b & 5 \\ \hline
+ c & 5 \\ \hline
+ c & 10 \\ \hline
+ \end{tabular}
+
+};
+
+\node (GROUP-A) at (3, 4) {
+
+ \begin{tabular}{| c | r |}
+ \hline
+ \rowcolor[gray]{.7}
+ x & y \\ \hline
+ a & 2 \\ \hline
+ a & 4 \\ \hline
+ \end{tabular}
+
+};
+
+\node (GROUP-B) at (3, 2) {
+
+ \begin{tabular}{| c | r |}
+ \hline
+ \rowcolor[gray]{.7}
+ x & y \\ \hline
+ b & 0 \\ \hline
+ b & 5 \\ \hline
+ \end{tabular}
+
+};
+
+\node (GROUP-C) at (3, 0) {
+
+ \begin{tabular}{| c | r |}
+ \hline
+ \rowcolor[gray]{.7}
+ x & y \\ \hline
+ c & 5 \\ \hline
+ c & 10 \\ \hline
+ \end{tabular}
+
+};
+
+\node (SUMMARY-A) at (6, 4) {
+
+ \begin{tabular}{| c | r |}
+ \hline
+ \rowcolor[gray]{.7}
+ x & y \\ \hline
+ a & 3.0 \\ \hline
+ \end{tabular}
+
+};
+
+\node (SUMMARY-B) at (6, 2) {
+
+ \begin{tabular}{| c | r |}
+ \hline
+ \rowcolor[gray]{.7}
+ x & y \\ \hline
+ b & 2.5 \\ \hline
+ \end{tabular}
+
+};
+
+\node (SUMMARY-C) at (6, 0) {
+
+ \begin{tabular}{| c | r |}
+ \hline
+ \rowcolor[gray]{.7}
+ x & y \\ \hline
+ c & 7.5 \\ \hline
+ \end{tabular}
+
+};
+
+\node (OUPUT) at (9, 2) {
+
+ \begin{tabular}{| c | r |}
+ \hline
+ \rowcolor[gray]{.7}
+ x & y \\ \hline
+ a & 3.0 \\ \hline
+ b & 2.5 \\ \hline
+ c & 7.5 \\ \hline
+ \end{tabular}
+
+};
+
+
+% Arrows
+
+\draw[->, to path={-> (\tikztotarget)}]
+ (INPUT) edge (GROUP-A)
+ (INPUT) edge (GROUP-B)
+ (INPUT) edge (GROUP-C)
+
+ (GROUP-A) edge (SUMMARY-A)
+ (GROUP-B) edge (SUMMARY-B)
+ (GROUP-C) edge (SUMMARY-C)
+
+ (SUMMARY-A) edge (OUPUT)
+ (SUMMARY-B) edge (OUPUT)
+ (SUMMARY-C) edge (OUPUT)
+;
+
+\end{tikzpicture}
+
+\end{document}
+
+%------------------------
+% References
+% https://tex.stackexchange.com/questions/251642/draw-arrows-between-nodes-with-tikz
+% https://tex.stackexchange.com/questions/11866/compile-a-latex-document-into-a-png-image-thats-as-short-as-possible
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+++ b/fig/12-plyr-fig2.tex
@@ -0,0 +1,64 @@
+\documentclass[convert]{standalone}
+
+\usepackage{array}
+\usepackage{multirow}
+\usepackage{rotating}
+\usepackage{colortbl}
+\renewcommand{\familydefault}{\sfdefault}
+\renewcommand{\arraystretch}{2.2}
+
+\begin{document}
+
+\begin{tabular}{crccccc}
+
+&
+& \multicolumn{4}{c}{Output}
+\\
+
+& %\cellcolor[gray]{0.7}
+& \cellcolor[gray]{0.7}array
+& \cellcolor[gray]{0.7}data frame
+& \cellcolor[gray]{0.7}list
+& \cellcolor[gray]{0.7}nothing
+\\
+
+& \cellcolor[gray]{0.7}array
+& aaply
+& adply
+& alply
+& a\_ply
+\\
+
+& \cellcolor[gray]{0.7}data frame
+& daply
+& ddply
+& dlply
+& d\_ply
+\\
+
+& \cellcolor[gray]{0.7} list
+& laply
+& ldply
+& llply
+& l\_ply
+\\
+
+& \cellcolor[gray]{0.7}n replicates
+& raply
+& rdply
+& rlply
+& r\_ply
+\\
+
+\multirow{-5}{*}{\rotatebox[origin=c]{90}{Input}}
+& \cellcolor[gray]{0.7}function arguments
+& maply
+& mdply
+& mlply
+& m\_ply
+\\
+
+\end{tabular}
+
+
+\end{document}
diff --git a/fig/12-plyr-generate-figures.sh b/fig/12-plyr-generate-figures.sh
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index 000000000..9236d34e0
--- /dev/null
+++ b/fig/12-plyr-generate-figures.sh
@@ -0,0 +1,10 @@
+#! /bin/bash
+
+pdflatex -shell-escape 12-plyr-fig1.tex
+
+rm 12-plyr-fig1.aux 12-plyr-fig1.log 12-plyr-fig1.pdf
+
+pdflatex -shell-escape 12-plyr-fig2.tex
+
+rm 12-plyr-fig2.aux 12-plyr-fig2.log 12-plyr-fig2.pdf
+
diff --git a/fig/13-dplyr-fig1.png b/fig/13-dplyr-fig1.png
new file mode 100644
index 000000000..7f3067a3c
Binary files /dev/null and b/fig/13-dplyr-fig1.png differ
diff --git a/fig/13-dplyr-fig2.png b/fig/13-dplyr-fig2.png
new file mode 100644
index 000000000..caa86d462
Binary files /dev/null and b/fig/13-dplyr-fig2.png differ
diff --git a/fig/13-dplyr-fig3.png b/fig/13-dplyr-fig3.png
new file mode 100644
index 000000000..ae00ce386
Binary files /dev/null and b/fig/13-dplyr-fig3.png differ
diff --git a/fig/13-dplyr-generate-figures.R b/fig/13-dplyr-generate-figures.R
new file mode 100644
index 000000000..4c3f4b223
--- /dev/null
+++ b/fig/13-dplyr-generate-figures.R
@@ -0,0 +1,383 @@
+# export figures manually
+library(DiagrammeR)
+##################################### 13-dplyr-fig2.png #####################################)
+grViz('digraph html {
+ table1 [shape=none, margin=0,label=<
+
+
+ a
+ b
+ c
+ d
+
+
+
+
+
+
+
+
+
+
+
>];
+
+ table2 [shape=none, margin=0, label=<
+
+
+ a
+ c
+
+
+
+
+
+
+
+
+
+
+
>];
+
+ table1:f1:s -> table2:f1:s
+ table1:f0:n -> table2:f0:n
+
+ subgraph {
+ rank = same; table1; table2;
+ }
+
+ labelloc="t";
+ fontname="Courier";
+ label="select(data.frame, a, c)";
+ }
+ ')
+
+##################################### 13-dplyr-fig2.png #####################################
+grViz('digraph html {
+ rankdir=LR;
+ table1 [shape=none, margin=0,label=<
+
+
+ a
+ b
+ c
+ d
+
+
+
+ 1
+
+
+
+ 1
+
+
+
+ 2
+
+
+
+ 2
+
+
+
+ 3
+
+
+
+ 3
+
+
>];
+ table2 [shape=none, margin=0,label=<
+
+
+ a
+ b
+ c
+ d
+
+
+
+ 1
+
+
+
+ 1
+
+
+
>];
+ table3 [shape=none, margin=0,label=<
+
+
+ a
+ b
+ c
+ d
+
+
+
+ 2
+
+
+
+ 2
+
+
>];
+ table4 [shape=none, margin=0,label=<
+
+
+ a
+ b
+ c
+ d
+
+
+
+ 3
+
+
+
+ 3
+
+
>];
+
+ table1:f0 -> table2:f0
+ table1:f1 -> table3:f1
+ table1:f2 -> table4:f2
+
+
+ subgraph {
+ rank = same; table2; table3 ;table4;
+ }
+
+ labelloc="t";
+ fontname="Courier";
+ label="gapminder %>%\\l\tgroup_by(a)";
+ }
+ ')
+
+##################################### 13-dplyr-fig3.png #####################################
+grViz('digraph html {
+ rankdir=LR;
+
+ table1 [shape=none, margin=0,label=<
+
+
+ a
+ b
+ c
+ d
+
+
+
+ 1
+
+
+
+ 1
+
+
+
+ 2
+
+
+
+ 2
+
+
+
+ 3
+
+
+
+ 3
+
+
>];
+
+ table2 [shape=none, margin=0,label=<
+
+
+ a
+ b
+ c
+ d
+
+
+
+ 1
+
+
+
+ 1
+
+
>];
+
+ table3 [shape=none, margin=0,label=<
+
+
+ a
+ b
+ c
+ d
+
+
+
+ 2
+
+
+
+ 2
+
+
>];
+
+ table4 [shape=none, margin=0,label=<
+
+
+ a
+ b
+ c
+ d
+
+
+
+ 3
+
+
+
+ 3
+
+
>];
+
+ table5 [shape=none, margin=0,label=<
+
+
+ a
+ mean_b
+
+
+
+ 1
+
+
+
+ 2
+
+
+
+ 3
+
+
>];
+
+
+ table1:f0 -> table2:f0
+ table1:f1 -> table3:f1
+ table1:f2 -> table4:f2
+ table2:f3:n -> table5:f0
+ table3:f3 -> table5:f1
+ table4:f3 -> table5:f2:w
+
+ subgraph {
+ table1; table2; table3 ;table4; table5
+ }
+
+ subgraph {
+ rank = same; table2; table3; table4;
+ }
+
+ labelloc="t";
+ fontname="Courier";
+ label="gapminder %>%\\l\tgroup_by(a) %>%\\l\tsummarize(mean_b=mean(b))\\l";
+ }
+ ')
diff --git a/fig/14-tidyr-fig1.png b/fig/14-tidyr-fig1.png
new file mode 100644
index 000000000..4ce006667
Binary files /dev/null and b/fig/14-tidyr-fig1.png differ
diff --git a/fig/14-tidyr-fig2.png b/fig/14-tidyr-fig2.png
new file mode 100644
index 000000000..7287d0194
Binary files /dev/null and b/fig/14-tidyr-fig2.png differ
diff --git a/fig/14-tidyr-fig3.png b/fig/14-tidyr-fig3.png
new file mode 100644
index 000000000..4c13aa57d
Binary files /dev/null and b/fig/14-tidyr-fig3.png differ
diff --git a/fig/14-tidyr-fig3.svg b/fig/14-tidyr-fig3.svg
new file mode 100644
index 000000000..6f756ef15
--- /dev/null
+++ b/fig/14-tidyr-fig3.svg
@@ -0,0 +1,269 @@
+
+
+
+
+ID
+a1
+a2
+a3
+ID
+2
+1
+3
+2
+1
+3
+2
+1
+3
+2
+1
+3
+a1
+a2
+a3
+a1
+a2
+a3
+a1
+a2
+a3
+key
+value
+wide format
+long format
+pivot_longer(data, cols = c("a1", "a2", "a3"), names_to = "key", values_to = "value")
+ID
+a1
+a2
+a3
+2
+1
+3
+ID
+2
+1
+3
+ID
+2
+1
+3
+ID
+a1
+a2
+a3
+2
+1
+3
+ID
+2
+1
+3
+ID
+2
+1
+3
+ID
+2
+1
+3
+ID
+2
+1
+3
+ID
+2
+1
+3
+a1
+a2
+a3
+a1
+a2
+a3
+a1
+a2
+a3
+
+
+
+
+
+
+separate by selected columns
+
+
+
+
+
+convert column names to column
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+name columns with key and value arguments
diff --git a/fig/14-tidyr-fig4.png b/fig/14-tidyr-fig4.png
new file mode 100644
index 000000000..fd5d68c64
Binary files /dev/null and b/fig/14-tidyr-fig4.png differ
diff --git a/fig/14-tidyr-generate-figures.R b/fig/14-tidyr-generate-figures.R
new file mode 100644
index 000000000..9ed954ae4
--- /dev/null
+++ b/fig/14-tidyr-generate-figures.R
@@ -0,0 +1,385 @@
+# export figures manually
+library(DiagrammeR)
+##################################### 14-tidyr-fig1.png #####################################
+grViz('digraph html {
+
+ table1 [shape=none, margin=0, label=<
+
+
+ ID
+ a1
+ a2
+ a3
+
+
+
+
+
+
+
+
+
+
+
>];
+
+ table2 [shape=none, margin=0,label=<
+
+
+ ID
+ ID2
+ A
+
+
+
+ 1
+ a1
+
+
+
+ 2
+ a1
+
+
+
+ 3
+ a1
+
+
+
+ 1
+ a2
+
+
+
+ 2
+ a2
+
+
+
+ 3
+ a2
+
+
+
+ 1
+ a3
+
+
+
+ 2
+ a3
+
+
+
+ 3
+ a3
+
+
>];
+
+ subgraph {
+ rank = same; table1; table2;
+ }
+
+
+ labelloc="t";
+ fontname="Courier";
+ label="wide vs long";
+ }
+ ')
+
+##################################### 14-tidyr-fig2.png #####################################
+grViz('digraph html {
+ table1 [shape=none, margin=0, label=<
+
+
+ continent
+ country
+ gdpPercap_1952
+ gdpPercap_1957
+ gdpPercap_...
+ lifeExp_1952
+ lifeExp_1957
+ lifeExp_...
+ pop_1952
+ pop_1957
+ pop_...
+
+
+
+ Africa
+ Algeria
+
+
+
+ Africa
+ Angola
+
+
+
+ ...
+ ...
+
+
>];
+
+ labelloc="t";
+ fontname="Courier";
+ label="wide format";
+ }
+ ')
+
+##################################### 14-tidyr-fig3.png #####################################
+grViz('digraph html {
+
+ table1 [shape=none, margin=0, label=<
+
+
+ continent
+ country
+ obstype_year
+ obs_value
+
+
+
+ Africa
+ Algeria
+ gdpPercap_1952
+
+
+
+ Africa
+ Algeria
+ gdpPercap_1957
+
+
+
+ Africa
+ Algeria
+ gdpPercap_...
+
+
+
+ Africa
+ Algeria
+ lifeExp_1952
+
+
+
+ Africa
+ Algeria
+ lifeExp_1957
+
+
+
+ Africa
+ Algeria
+ lifeExp_...
+
+
+
+ Africa
+ Algeria
+ pop_1952
+
+
+
+ Africa
+ Algeria
+ pop_1957
+
+
+
+ Africa
+ Algeria
+ pop_...
+
+
+
+ Africa
+ Angola
+ gdpPercap_1952
+
+
+
+ Africa
+ Angola
+ gdpPercap_1957
+
+
+
+ Africa
+ Angola
+ gdpPercap_...
+
+
+
+ Africa
+ Angola
+ lifeExp_1952
+
+
+
+ Africa
+ Angola
+ lifeExp_1957
+
+
+
+ Africa
+ Angola
+ lifeExp_...
+
+
+
+ Africa
+ Angola
+ pop_1952
+
+
+
+ Africa
+ Angola
+ pop_1957
+
+
+
+ Africa
+ Angola
+ pop_...
+
+
+
+ Africa
+ ...
+ gdpPercap_1952
+
+
+
+ Africa
+ ...
+ gdpPercap_1957
+
+
+
+ Africa
+ ...
+ gdpPercap_...
+
+
+
+ Africa
+ ...
+ lifeExp_1952
+
+
+
+ Africa
+ ...
+ lifeExp_1957
+
+
+
+ Africa
+ ...
+ lifeExp_...
+
+
+
+ Africa
+ ...
+ pop_1952
+
+
+
+ Africa
+ ...
+ pop_1957
+
+
+
+ Africa
+ ...
+ pop_...
+
+
+
>];
+
+ labelloc="t";
+ fontname="Courier";
+ label="long format";
+ }
+ ')
diff --git a/fig/15-plot-ggplot2-rendered-axis-scale-1.png b/fig/15-plot-ggplot2-rendered-axis-scale-1.png
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+ image/svg+xml
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new file mode 100644
index 000000000..71b9dcae0
--- /dev/null
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+
+
+
+image/svg+xml .git
+FILE1.txt
+FILE2.txt
+git commit
+staging area
+repository
+git add FILE2.txt
+git add FILE1.txt
+image/svg+xml .git
+https://github.com/vlad/planets.git
+.git
+~/vlad/planets
+git add
+git commit
+staging area
+repository
+
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+ .git
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+ HEAD
+ HEAD~1
+ HEAD~2
+ 892134f
+ f22b25e
+ 3a54f76
+
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+ git restore -s HEAD~1 . or git restore -s f22b25e .
+ repository
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diff --git a/fig/git-staging-area.svg b/fig/git-staging-area.svg
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+ git commit
+ staging area
+ repository
+
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diff --git a/fig/git_staging.svg b/fig/git_staging.svg
new file mode 100644
index 000000000..3db5b3b5a
--- /dev/null
+++ b/fig/git_staging.svg
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+
+
+
+image/svg+xml a2129cb 892134f 59e230a 3a54f76 43fe423 branch main staging area git add file2.txt git add file1.txt HEAD HEAD~1 HEAD~2 git commit git restore file1.txt image/svg+xml .git origin https://github.com/vlad/planets.git ~/vlad/planets
+git add
+git commit
+staging area
+repository
+.git https://github.com/vlad/planets.git
+repository
+git clone
+.git origin https://github.com/vlad/planets.git ~/wolfman/planets
+git add
+git commit
+staging area
+repository
+image/svg+xml .git origin https://github.com/vlad/planets.git ~/vlad/planets
+git add
+git commit
+staging area
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+.git https://github.com/vlad/planets.git
+repository
+git push origin
+
+
+
+
+
+
+ image/svg+xml
+
+
+
+
+
+
+
+
+ Users
+
+ tmp
+
+
+
+ bin
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+
+ data
+
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+ /
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+ imhotep
+
+ larry
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+ nelle
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+ c("a" , "a" , "a" ) c("a" , "c" ) TRUE FALSE ?? ?? c("a" , "a" , "a" ) c("a" , "c" ) TRUE FALSE c("a" ... TRUE
+
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+++ b/index.md
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+---
+site: sandpaper::sandpaper_site
+---
+
+*an introduction to R for non-programmers using gapminder data*
+
+The goal of this lesson is to teach novice programmers to write modular code
+and best practices for using R for data analysis. R is commonly used in many
+scientific disciplines for statistical analysis and its array of third-party
+packages. We find that many scientists who come to Software Carpentry workshops
+use R and want to learn more. The emphasis of these materials is to give
+attendees a strong foundation in the fundamentals of R, and to teach best
+practices for scientific computing: breaking down analyses into modular units,
+task automation, and encapsulation.
+
+Note that this workshop will focus on teaching the fundamentals of the
+programming language R, and will not teach statistical analysis.
+
+The lesson contains more material than can be taught in a day. The [instructor notes page](instructors/instructor-notes.md) has some suggested lesson plans suitable for a one or half day workshop.
+
+A variety of third party packages are used throughout this workshop. These
+are not necessarily the best, nor are they comprehensive, but they are
+packages we find useful, and have been chosen primarily for their
+usability.
+
+:::::::::::::::::::::::::::::::::::::::::: prereq
+
+## Prerequisites
+
+Understand that computers store data and instructions (programs, scripts etc.) in files.
+Files are organised in directories (folders).
+Know how to access files not in the working directory by specifying the path.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
diff --git a/instructor-notes.md b/instructor-notes.md
new file mode 100644
index 000000000..83d1925ef
--- /dev/null
+++ b/instructor-notes.md
@@ -0,0 +1,135 @@
+---
+title: Instructor Notes
+---
+
+## Timing
+
+Leave about 30 minutes at the start of each workshop and another 15 mins
+at the start of each session for technical difficulties like WiFi and
+installing things (even if you asked students to install in advance, longer if
+not).
+
+## Lesson Plans
+
+The lesson contains much more material than can be taught in a day.
+Instructors will need to pick an appropriate subset of episodes to use
+in a standard one day course.
+
+Some suggested paths through the material are:
+
+(suggested by [@liz-is](https://github.com/swcarpentry/r-novice-gapminder/issues/104#issuecomment-276529213))
+
+- 01 Introduction to R and RStudio
+- 04 Data Structures
+- 05 Exploring Data Frames ("Realistic example" section onwards)
+- 08 Creating Publication-Quality Graphics with ggplot2
+- 10 Functions Explained
+- 13 Dataframe Manipulation with dplyr
+- 15 Producing Reports With knitr
+
+(suggested by [@naupaka](https://github.com/swcarpentry/r-novice-gapminder/issues/104#issuecomment-312547509))
+
+- 01 Introduction to R and RStudio
+- 02 Project Management With RStudio
+- 03 Seeking Help
+- 04 Data Structures
+- 05 Exploring Data Frames
+- 06 Subsetting Data
+- 09 Vectorization
+- 08 Creating Publication-Quality Graphics with ggplot2 *OR*
+ 13 Dataframe Manipulation with dplyr
+- 15 Producing Reports With knitr
+
+A half day course could consist of (suggested by [@karawoo](https://github.com/swcarpentry/r-novice-gapminder/issues/104#issuecomment-277599864)):
+
+- 01 Introduction to R and RStudio
+- 04 Data Structures (only creating vectors with `c()`)
+- 05 Exploring Data Frames ("Realistic example" section onwards)
+- 06 Subsetting Data (excluding factor, matrix and list subsetting)
+- 08 Creating Publication-Quality Graphics with ggplot2
+
+## Setting up git in RStudio
+
+There can be difficulties linking git to RStudio depending on the
+operating system and the version of the operating system. To make sure
+Git is properly installed and configured, the learners should go to
+the Options window in the RStudio application.
+
+- **Mac OS X:**
+ - Go RStudio -> Preferences... -> Git/SVN
+ - Check and see whether there is a path to a file in the "Git executable" window. If not, the next challenge is figuring out where Git is located.
+ - In the terminal enter `which git` and you will get a path to the git executable. In the "Git executable" window you may have difficulties finding the directory since OS X hides many of the operating system files. While the file selection window is open, pressing "Command-Shift-G" will pop up a text entry box where you will be able to type or paste in the full path to your git executable: e.g. /usr/bin/git or whatever else it might be.
+- **Windows:**
+ - Go Tools -> Global options... -> Git/SVN
+ - If you use the Software Carpentry Installer, then 'git.exe' should be installed at `C:/Program Files/Git/bin/git.exe`.
+
+To prevent the learners from having to re-enter their password each time they push a commit to GitHub, this command (which can be run from a bash prompt) will make it so they only have to enter their password once:
+
+```bash
+$ git config --global credential.helper 'cache --timeout=10000000'
+```
+
+## RStudio Color Preview
+
+RStudio has a feature to preview the color for certain named colors and hexadecimal colors. This may confuse or distract learners (and instructors) who are not expecting it.
+
+Mainly, this is likely to come up during the episode on "Data Structures" with the following code block:
+
+```r
+cats <- data.frame(coat = c("calico", "black", "tabby"),
+ weight = c(2.1, 5.0, 3.2),
+ likes_string = c(1, 0, 1))
+```
+
+This option can be turned off and on in the following menu setting:
+Tools -> Global Options -> Code -> Display -> Enable preview of named and hexadecimal colors (under "Syntax")
+
+## Pulling in Data
+
+The easiest way to get the data used in this lesson during a workshop is to have
+attendees download the raw data from [gapminder-data] and
+[gapminder-data-wide].
+
+Attendees can use the `File - Save As` dialog in their browser to save the file.
+
+## Overall
+
+Make sure to emphasize good practices: put code in scripts, and make
+sure they're version controlled. Encourage students to create script
+files for challenges.
+
+If you're working in a cloud environment, get them to upload the
+gapminder data after the second lesson.
+
+Make sure to emphasize that matrices are vectors underneath the hood
+and data frames are lists underneath the hood: this will explain a
+lot of the esoteric behaviour encountered in basic operations.
+
+Vector recycling and function stacks are probably best explained
+with diagrams on a whiteboard.
+
+Be sure to actually go through examples of an R help page: help files
+can be intimidating at first, but knowing how to read them is tremendously
+useful.
+
+Be sure to show the CRAN task views, look at one of the topics.
+
+There's a lot of content: move quickly through the earlier lessons. Their
+extensiveness is mostly for purposes of learning by osmosis: so that their
+memory will trigger later when they encounter a problem or some esoteric behaviour.
+
+Key lessons to take time on:
+
+- Data subsetting - conceptually difficult for novices
+- Functions - learners especially struggle with this
+- Data structures - worth being thorough, but you can go through it quickly.
+
+Don't worry about being correct or knowing the material back-to-front. Use
+mistakes as teaching moments: the most vital skill you can impart is how to
+debug and recover from unexpected errors.
+
+[gapminder-data]: data/gapminder_data.csv
+[gapminder-data-wide]: data/gapminder_wide.csv
+
+
+
diff --git a/learner-profiles.md b/learner-profiles.md
new file mode 100644
index 000000000..434e335aa
--- /dev/null
+++ b/learner-profiles.md
@@ -0,0 +1,5 @@
+---
+title: FIXME
+---
+
+This is a placeholder file. Please add content here.
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+"profiles/learner-profiles.md" "60b93493cf1da06dfd63255d73854461" "site/built/learner-profiles.md" "2024-09-01"
+"renv/profiles/lesson-requirements/renv.lock" "8d46557ca0574488ea9135029f5f02bf" "site/built/renv.lock" "2024-09-01"
diff --git a/reference.md b/reference.md
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+++ b/reference.md
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+---
+title: 'Reference'
+---
+
+## Reference
+
+## [Introduction to R and RStudio](episodes/01-rstudio-intro.Rmd)
+
+- Use the escape key to cancel incomplete commands or running code
+ (Ctrl+C) if you're using R from the shell.
+- Basic arithmetic operations follow standard order of precedence:
+ - Brackets: `(`, `)`
+ - Exponents: `^` or `**`
+ - Divide: `/`
+ - Multiply: `*`
+ - Add: `+`
+ - Subtract: `-`
+- Scientific notation is available, e.g: `2e-3`
+- Anything to the right of a `#` is a comment, R will ignore this!
+- Functions are denoted by `function_name()`. Expressions inside the
+ brackets are evaluated before being passed to the function, and
+ functions can be nested.
+- Mathematical functions: `exp`, `sin`, `log`, `log10`, `log2` etc.
+- Comparison operators: `<`, `<=`, `>`, `>=`, `==`, `!=`
+- Use `all.equal` to compare numbers!
+- `<-` is the assignment operator. Anything to the right is evaluate, then
+ stored in a variable named to the left.
+- `ls` lists all variables and functions you've created
+- `rm` can be used to remove them
+- When assigning values to function arguments, you *must* use `=`.
+
+## [Project management with RStudio](episodes/02-project-intro.Rmd)
+
+- To create a new project, go to File -> New Project
+- Install the `packrat` package to create self-contained projects
+- `install.packages` to install packages from CRAN
+- `library` to load a package into R
+- `packrat::status` to check whether all packages referenced in your
+ scripts have been installed.
+
+## [Seeking help](episodes/03-seeking-help.Rmd)
+
+- To access help for a function type `?function_name` or `help(function_name)`
+- Use quotes for special operators e.g. `?"+"`
+- Use fuzzy search if you can't remember a name '??search\_term'
+- [CRAN task views](https://cran.at.r-project.org/web/views) are a good starting point.
+- [Stack Overflow](https://stackoverflow.com/) is a good place to get help with your code.
+ - `?dput` will dump data you are working from so others can load it easily.
+ - `sessionInfo()` will give details of your setup that others may need for debugging.
+
+## [Data structures](episodes/04-data-structures-part1.Rmd)
+
+Individual values in R must be one of 5 **data types**, multiple values can be grouped in **data structures**.
+
+**Data types**
+
+- `typeof(object)` gives information about an items data type.
+
+- There are 5 main data types:
+
+ - `?numeric` real (decimal) numbers
+ - `?integer` whole numbers only
+ - `?character` text
+ - `?complex` complex numbers
+ - `?logical` TRUE or FALSE values
+
+ **Special types:**
+
+ - `?NA` missing values
+ - `?NaN` "not a number" for undefined values (e.g. `0/0`).
+ - `?Inf`, `-Inf` infinity.
+ - `?NULL` a data structure that doesn't exist
+
+ `NA` can occur in any atomic vector. `NaN`, and `Inf` can only
+ occur in complex, integer or numeric type vectors. Atomic vectors
+ are the building blocks for all other data structures. A `NULL` value
+ will occur in place of an entire data structure (but can occur as list
+ elements).
+
+**Basic data structures in R:**
+
+- atomic `?vector` (can only contain one type)
+- `?list` (containers for other objects)
+- `?data.frame` two dimensional objects whose columns can contain different types of data
+- `?matrix` two dimensional objects that can contain only one type of data.
+- `?factor` vectors that contain predefined categorical data.
+- `?array` multi-dimensional objects that can only contain one type of data
+
+Remember that matrices are really atomic vectors underneath the hood, and that
+data.frames are really lists underneath the hood (this explains some of the weirder
+behaviour of R).
+
+**[Vectors](episodes/04-data-structures-part1.Rmd)**
+
+- `?vector()` All items in a vector must be the same type.
+- Items can be converted from one type to another using *coercion*.
+- The concatenate function 'c()' will append items to a vector.
+- `seq(from=0, to=1, by=1)` will create a sequence of numbers.
+- Items in a vector can be named using the `names()` function.
+
+**[Factors](episodes/04-data-structures-part1.Rmd)**
+
+- `?factor()` Factors are a data structure designed to store categorical data.
+- `levels()` shows the valid values that can be stored in a vector of type factor.
+
+**[Lists](episodes/04-data-structures-part1.Rmd)**
+
+- `?list()` Lists are a data structure designed to store data of different types.
+
+**[Matrices](episodes/04-data-structures-part1.Rmd)**
+
+- `?matrix()` Matrices are a data structure designed to store 2-dimensional data.
+
+**[Data Frames](episodes/05-data-structures-part2.Rmd)**
+
+- `?data.frame` is a key data structure. It is a `list` of `vectors`.
+- `cbind()` will add a column (vector) to a data.frame.
+- `rbind()` will add a row (list) to a data.frame.
+
+**Useful functions for querying data structures:**
+
+- `?str` structure, prints out a summary of the whole data structure
+- `?typeof` tells you the type inside an atomic vector
+- `?class` what is the data structure?
+- `?head` print the first `n` elements (rows for two-dimensional objects)
+- `?tail` print the last `n` elements (rows for two-dimensional objects)
+- `?rownames`, `?colnames`, `?dimnames` retrieve or modify the row names
+ and column names of an object.
+- `?names` retrieve or modify the names of an atomic vector or list (or
+ columns of a data.frame).
+- `?length` get the number of elements in an atomic vector
+- `?nrow`, `?ncol`, `?dim` get the dimensions of a n-dimensional object
+ (Won't work on atomic vectors or lists).
+
+## [Exploring Data Frames](episodes/05-data-structures-part2.Rmd)
+
+- `read.csv` to read in data in a regular structure
+ - `sep` argument to specify the separator
+ - "," for comma separated
+ - "\\t" for tab separated
+ - Other arguments:
+ - `header=TRUE` if there is a header row
+
+## [Subsetting data](episodes/06-data-subsetting.Rmd)
+
+- Elements can be accessed by:
+
+ - Index
+ - Name
+ - Logical vectors
+
+- `[` single square brackets:
+
+ - *extract* single elements or *subset* vectors
+ - e.g.`x[1]` extracts the first item from vector x.
+ - *extract* single elements of a list. The returned value will be another `list()`.
+ - *extract* columns from a data.frame
+
+- `[` with two arguments to:
+
+ - *extract* rows and/or columns of
+ - matrices
+ - data.frames
+ - e.g. `x[1,2]` will extract the value in row 1, column 2.
+ - e.g. `x[2,:]` will extract the entire second column of values.
+
+- `[[` double square brackets to extract items from lists.
+
+- `$` to access columns or list elements by name
+
+- negative indices skip elements
+
+## [Control flow](episodes/07-control-flow.Rmd)
+
+- Use `if` condition to start a conditional statement, `else if` condition to provide
+ additional tests, and `else` to provide a default
+- The bodies of the branches of conditional statements must be indented.
+- Use `==` to test for equality.
+- `%in%` will return a `TRUE`/`FALSE` indicating if there is a match between an element and a vector.
+- `X && Y` is only true if both X and Y are `TRUE`.
+- `X || Y` is true if either X or Y, or both, are `TRUE`.
+- Zero is considered `FALSE`; all other numbers are considered `TRUE`
+- Nest loops to operate on multi-dimensional data.
+
+## [Creating publication quality graphics](episodes/08-plot-ggplot2.Rmd)
+
+- figures can be created with the grammar of graphics:
+ - `library(ggplot2)`
+ - `ggplot` to create the base figure
+ - `aes`thetics specify the data axes, shape, color, and data size
+ - `geom`etry functions specify the type of plot, e.g. `point`, `line`, `density`, `box`
+ - `geom`etry functions also add statistical transforms, e.g. `geom_smooth`
+ - `scale` functions change the mapping from data to aesthetics
+ - `facet` functions stratify the figure into panels
+ - `aes`thetics apply to individual layers, or can be set for the whole plot
+ inside `ggplot`.
+ - `theme` functions change the overall look of the plot
+ - order of layers matters!
+ - `ggsave` to save a figure.
+
+## [Vectorization](episodes/09-vectorization.Rmd)
+
+- Most functions and operations apply to each element of a vector
+- `*` applies element-wise to matrices
+- `%*%` for true matrix multiplication
+- `any()` will return `TRUE` if any element of a vector is `TRUE`
+- `all()` will return `TRUE` if *all* elements of a vector are `TRUE`
+
+## [Functions explained](episodes/10-functions.Rmd)
+
+- `?"function"`
+- Put code whose parameters change frequently in a function, then call it with
+ different parameter values to customize its behavior.
+- The last line of a function is returned, or you can use `return` explicitly
+- Any code written in the body of the function will preferably look for variables defined inside the function.
+- Document Why, then What, then lastly How (if the code isn't self explanatory)
+
+## [Writing data](episodes/11-writing-data.Rmd)
+
+- `write.table` to write out objects in regular format
+- set `quote=FALSE` so that text isn't wrapped in `"` marks
+
+## [Dataframe manipulation with dplyr](episodes/12-dplyr.Rmd)
+
+- `library(dplyr)`
+- `?select` to extract variables by name.
+- `?filter` return rows with matching conditions.
+- `?group_by` group data by one of more variables.
+- `?summarize` summarize multiple values to a single value.
+- `?mutate` add new variables to a data.frame.
+- Combine operations using the `?"%>%"` pipe operator.
+
+## [Dataframe manipulation with tidyr](episodes/13-tidyr.Rmd)
+
+- `library(tidyr)`
+- `?pivot_longer` convert data from *wide* to *long* format.
+- `?pivot_wider` convert data from *long* to *wide* format.
+- `?separate` split a single value into multiple values.
+- `?unite` merge multiple values into a single value.
+
+## [Producing reports with knitr](episodes/14-knitr-markdown.Rmd)
+
+- Value of reproducible reports
+- Basics of Markdown
+- R code chunks
+- Chunk options
+- Inline R code
+- Other output formats
+
+## [Best practices for writing good code](episodes/15-wrap-up.Rmd)
+
+- Program defensively, i.e., assume that errors are going to arise, and write code to detect them when they do.
+- Write tests before writing code in order to help determine exactly what that code is supposed to do.
+- Know what code is supposed to do before trying to debug it.
+- Make it fail every time.
+- Make it fail fast.
+- Change one thing at a time, and for a reason.
+- Keep track of what you've done.
+- Be humble
+
+## Glossary
+
+[argument]{#argument}
+: A value given to a function or program when it runs.
+The term is often used interchangeably (and inconsistently) with [parameter](#parameter).
+
+[assign]{#assign}
+: To give a value a name by associating a variable with it.
+
+[body]{#body}
+: (of a function): the statements that are executed when a function runs.
+
+[comment]{#comment}
+: A remark in a program that is intended to help human readers understand what is going on,
+but is ignored by the computer.
+Comments in Python, R, and the Unix shell start with a `#` character and run to the end of the line;
+comments in SQL start with `--`,
+and other languages have other conventions.
+
+[comma-separated values]{#comma-separated-values}
+: (CSV) A common textual representation for tables
+in which the values in each row are separated by commas.
+
+[delimiter]{#delimiter}
+: A character or characters used to separate individual values,
+such as the commas between columns in a [CSV](#comma-separated-values) file.
+
+[documentation]{#documentation}
+: Human-language text written to explain what software does,
+how it works, or how to use it.
+
+[floating-point number]{#floating-point-number}
+: A number containing a fractional part and an exponent.
+See also: [integer](#integer).
+
+[for loop]{#for-loop}
+: A loop that is executed once for each value in some kind of set, list, or range.
+See also: [while loop](#while-loop).
+
+[index]{#index}
+: A subscript that specifies the location of a single value in a collection,
+such as a single pixel in an image.
+
+[integer]{#integer}
+: A whole number, such as -12343. See also: [floating-point number](#floating-point-number).
+
+[library]{#library}
+: In R, the directory(ies) where [packages](#package) are stored.
+
+[package]{#package}
+: A collection of R functions, data and compiled code in a well-defined format. Packages are stored in a [library](#library) and loaded using the library() function.
+
+[parameter]{#parameter}
+: A variable named in the function's declaration that is used to hold a value passed into the call.
+The term is often used interchangeably (and inconsistently) with [argument](#argument).
+
+[return statement]{#return-statement}
+: A statement that causes a function to stop executing and return a value to its caller immediately.
+
+[sequence]{#sequence}
+: A collection of information that is presented in a specific order.
+
+[shape]{#shape}
+: An array's dimensions, represented as a vector.
+For example, a 5×3 array's shape is `(5,3)`.
+
+[string]{#string}
+: Short for "character string",
+a [sequence](#sequence) of zero or more characters.
+
+[syntax error]{#syntax-error}
+: A programming error that occurs when statements are in an order or contain characters
+not expected by the programming language.
+
+[type]{#type}
+: The classification of something in a program (for example, the contents of a variable)
+as a kind of number (e.g. [floating-point number](#floating-point-number), [integer](#integer)), [string](#string),
+or something else. In R the command typeof() is used to query a variables type.
+
+[while loop]{#while-loop}
+: A loop that keeps executing as long as some condition is true.
+See also: [for loop](#for-loop).
+
+
diff --git a/results/lifeExp.png b/results/lifeExp.png
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index 000000000..0637a624a
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+---
+title: Setup
+---
+
+This lesson assumes you have R and RStudio installed on your computer.
+
+- [Download and install the latest version of R](https://www.r-project.org/).
+- [Download and install RStudio](https://www.rstudio.com/products/rstudio/download/#download). RStudio is an application (an integrated development environment or IDE) that facilitates the use of R and offers a number of nice additional features. You will need the free Desktop version for your computer.
+
+
+
+Here we called `ggplot` and told it what data we want to show on
+our figure. This is not enough information for `ggplot` to actually
+draw anything. It only creates a blank slate for other elements
+to be added to.
+
+Now we're going to add in the **mapping aesthetics** using the
+`aes` function. `aes` tells `ggplot` how variables in the **data**
+map to *aesthetic* properties of the figure, such as which columns
+of the data should be used for the **x** and **y** locations.
+
+
+``` r
+ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp))
+```
+
+
+
+Here we told `ggplot` we want to plot the "gdpPercap" column of the
+gapminder data frame on the x-axis, and the "lifeExp" column on the
+y-axis. Notice that we didn't need to explicitly pass `aes` these
+columns (e.g. `x = gapminder[, "gdpPercap"]`), this is because
+`ggplot` is smart enough to know to look in the **data** for that column!
+
+The final part of making our plot is to tell `ggplot` how we want to
+visually represent the data. We do this by adding a new **layer**
+to the plot using one of the **geom** functions.
+
+
+``` r
+ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
+ geom_point()
+```
+
+
+
+Here we used `geom_point`, which tells `ggplot` we want to visually
+represent the relationship between **x** and **y** as a scatterplot of points.
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 1
+
+Modify the example so that the figure shows how life expectancy has
+changed over time:
+
+
+``` r
+ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) + geom_point()
+```
+
+Hint: the gapminder dataset has a column called "year", which should appear
+on the x-axis.
+
+::::::::::::::: solution
+
+## Solution to challenge 1
+
+Here is one possible solution:
+
+
+``` r
+ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp)) + geom_point()
+```
+
+
+
+
+
+Instead of adding a `geom_point` layer, we've added a `geom_line` layer.
+
+However, the result doesn't look quite as we might have expected: it seems to be jumping around a lot in each continent. Let's try to separate the data by country, plotting one line for each country:
+
+
+``` r
+ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, group=country, color=continent)) +
+ geom_line()
+```
+
+
+
+We've added the **group** *aesthetic*, which tells `ggplot` to draw a line for each
+country.
+
+But what if we want to visualize both lines and points on the plot? We can
+add another layer to the plot:
+
+
+``` r
+ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, group=country, color=continent)) +
+ geom_line() + geom_point()
+```
+
+
+
+It's important to note that each layer is drawn on top of the previous layer. In
+this example, the points have been drawn *on top of* the lines. Here's a
+demonstration:
+
+
+``` r
+ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, group=country)) +
+ geom_line(mapping = aes(color=continent)) + geom_point()
+```
+
+
+
+In this example, the *aesthetic* mapping of **color** has been moved from the
+global plot options in `ggplot` to the `geom_line` layer so it no longer applies
+to the points. Now we can clearly see that the points are drawn on top of the
+lines.
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Tip: Setting an aesthetic to a value instead of a mapping
+
+So far, we've seen how to use an aesthetic (such as **color**) as a *mapping* to a variable in the data. For example, when we use `geom_line(mapping = aes(color=continent))`, ggplot will give a different color to each continent. But what if we want to change the color of all lines to blue? You may think that `geom_line(mapping = aes(color="blue"))` should work, but it doesn't. Since we don't want to create a mapping to a specific variable, we can move the color specification outside of the `aes()` function, like this: `geom_line(color="blue")`.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 3
+
+Switch the order of the point and line layers from the previous example. What
+happened?
+
+::::::::::::::: solution
+
+## Solution to challenge 3
+
+The lines now get drawn over the points!
+
+
+``` r
+ggplot(data = gapminder, mapping = aes(x=year, y=lifeExp, group=country)) +
+ geom_point() + geom_line(mapping = aes(color=continent))
+```
+
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Transformations and statistics
+
+ggplot2 also makes it easy to overlay statistical models over the data. To
+demonstrate we'll go back to our first example:
+
+
+``` r
+ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
+ geom_point()
+```
+
+
+
+Currently it's hard to see the relationship between the points due to some strong
+outliers in GDP per capita. We can change the scale of units on the x axis using
+the *scale* functions. These control the mapping between the data values and
+visual values of an aesthetic. We can also modify the transparency of the
+points, using the *alpha* function, which is especially helpful when you have
+a large amount of data which is very clustered.
+
+
+``` r
+ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
+ geom_point(alpha = 0.5) + scale_x_log10()
+```
+
+
+
+
+We can make the line thicker by *setting* the **linewidth** aesthetic in the
+`geom_smooth` layer:
+
+
+``` r
+ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
+ geom_point(alpha = 0.5) + scale_x_log10() + geom_smooth(method="lm", linewidth=1.5)
+```
+
+``` output
+`geom_smooth()` using formula = 'y ~ x'
+```
+
+
+
+There are two ways an *aesthetic* can be specified. Here we *set* the **linewidth** aesthetic by passing it as an argument to `geom_smooth` and it is applied the same to the whole `geom`. Previously in the lesson we've used the `aes` function to define a *mapping* between data variables and their visual representation.
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 4a
+
+Modify the color and size of the points on the point layer in the previous
+example.
+
+Hint: do not use the `aes` function.
+
+Hint: the equivalent of `linewidth` for points is `size`.
+
+::::::::::::::: solution
+
+## Solution to challenge 4a
+
+Here a possible solution:
+Notice that the `color` argument is supplied outside of the `aes()` function.
+This means that it applies to all data points on the graph and is not related to
+a specific variable.
+
+
+``` r
+ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
+ geom_point(size=3, color="orange") + scale_x_log10() +
+ geom_smooth(method="lm", linewidth=1.5)
+```
+
+``` output
+`geom_smooth()` using formula = 'y ~ x'
+```
+
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 4b
+
+Modify your solution to Challenge 4a so that the
+points are now a different shape and are colored by continent with new
+trendlines. Hint: The color argument can be used inside the aesthetic.
+
+::::::::::::::: solution
+
+## Solution to challenge 4b
+
+Here is a possible solution:
+Notice that supplying the `color` argument inside the `aes()` functions enables you to
+connect it to a certain variable. The `shape` argument, as you can see, modifies all
+data points the same way (it is outside the `aes()` call) while the `color` argument which
+is placed inside the `aes()` call modifies a point's color based on its continent value.
+
+
+``` r
+ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp, color = continent)) +
+ geom_point(size=3, shape=17) + scale_x_log10() +
+ geom_smooth(method="lm", linewidth=1.5)
+```
+
+``` output
+`geom_smooth()` using formula = 'y ~ x'
+```
+
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Multi-panel figures
+
+Earlier we visualized the change in life expectancy over time across all
+countries in one plot. Alternatively, we can split this out over multiple panels
+by adding a layer of **facet** panels.
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Tip
+
+We start by making a subset of data including only countries located
+in the Americas. This includes 25 countries, which will begin to
+clutter the figure. Note that we apply a "theme" definition to rotate
+the x-axis labels to maintain readability. Nearly everything in
+ggplot2 is customizable.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
+``` r
+americas <- gapminder[gapminder$continent == "Americas",]
+ggplot(data = americas, mapping = aes(x = year, y = lifeExp)) +
+ geom_line() +
+ facet_wrap( ~ country) +
+ theme(axis.text.x = element_text(angle = 45))
+```
+
+
+
+The `facet_wrap` layer took a "formula" as its argument, denoted by the tilde
+(~). This tells R to draw a panel for each unique value in the country column
+of the gapminder dataset.
+
+## Modifying text
+
+To clean this figure up for a publication we need to change some of the text
+elements. The x-axis is too cluttered, and the y axis should read
+"Life expectancy", rather than the column name in the data frame.
+
+We can do this by adding a couple of different layers. The **theme** layer
+controls the axis text, and overall text size. Labels for the axes, plot
+title and any legend can be set using the `labs` function. Legend titles
+are set using the same names we used in the `aes` specification. Thus below
+the color legend title is set using `color = "Continent"`, while the title
+of a fill legend would be set using `fill = "MyTitle"`.
+
+
+``` r
+ggplot(data = americas, mapping = aes(x = year, y = lifeExp, color=continent)) +
+ geom_line() + facet_wrap( ~ country) +
+ labs(
+ x = "Year", # x axis title
+ y = "Life expectancy", # y axis title
+ title = "Figure 1", # main title of figure
+ color = "Continent" # title of legend
+ ) +
+ theme(axis.text.x = element_text(angle = 90, hjust = 1))
+```
+
+
+
+## Exporting the plot
+
+The `ggsave()` function allows you to export a plot created with ggplot. You can specify the dimension and resolution of your plot by adjusting the appropriate arguments (`width`, `height` and `dpi`) to create high quality graphics for publication. In order to save the plot from above, we first assign it to a variable `lifeExp_plot`, then tell `ggsave` to save that plot in `png` format to a directory called `results`. (Make sure you have a `results/` folder in your working directory.)
+
+
+
+
+``` r
+lifeExp_plot <- ggplot(data = americas, mapping = aes(x = year, y = lifeExp, color=continent)) +
+ geom_line() + facet_wrap( ~ country) +
+ labs(
+ x = "Year", # x axis title
+ y = "Life expectancy", # y axis title
+ title = "Figure 1", # main title of figure
+ color = "Continent" # title of legend
+ ) +
+ theme(axis.text.x = element_text(angle = 90, hjust = 1))
+
+ggsave(filename = "results/lifeExp.png", plot = lifeExp_plot, width = 12, height = 10, dpi = 300, units = "cm")
+```
+
+There are two nice things about `ggsave`. First, it defaults to the last plot, so if you omit the `plot` argument it will automatically save the last plot you created with `ggplot`. Secondly, it tries to determine the format you want to save your plot in from the file extension you provide for the filename (for example `.png` or `.pdf`). If you need to, you can specify the format explicitly in the `device` argument.
+
+This is a taste of what you can do with ggplot2. RStudio provides a
+really useful [cheat sheet][cheat] of the different layers available, and more
+extensive documentation is available on the [ggplot2 website][ggplot-doc]. All RStudio cheat sheets are available from the [RStudio website][cheat_all].
+Finally, if you have no idea how to change something, a quick Google search will
+usually send you to a relevant question and answer on Stack Overflow with reusable
+code to modify!
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 5
+
+Generate boxplots to compare life expectancy between the different continents during the available years.
+
+Advanced:
+
+- Rename y axis as Life Expectancy.
+- Remove x axis labels.
+
+::::::::::::::: solution
+
+## Solution to Challenge 5
+
+Here a possible solution:
+`xlab()` and `ylab()` set labels for the x and y axes, respectively
+The axis title, text and ticks are attributes of the theme and must be modified within a `theme()` call.
+
+
+``` r
+ggplot(data = gapminder, mapping = aes(x = continent, y = lifeExp, fill = continent)) +
+ geom_boxplot() + facet_wrap(~year) +
+ ylab("Life Expectancy") +
+ theme(axis.title.x=element_blank(),
+ axis.text.x = element_blank(),
+ axis.ticks.x = element_blank())
+```
+
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+[base]: https://www.statmethods.net/graphs/index.html
+[lattice]: https://www.statmethods.net/advgraphs/trellis.html
+[ggplot2]: https://www.statmethods.net/advgraphs/ggplot2.html
+[cheat]: https://www.rstudio.org/links/data_visualization_cheat_sheet
+[cheat_all]: https://www.rstudio.com/resources/cheatsheets/
+[ggplot-doc]: https://ggplot2.tidyverse.org/reference/
+
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- Use `ggplot2` to create plots.
+- Think about graphics in layers: aesthetics, geometry, statistics, scale transformation, and grouping.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
diff --git a/16-writing-data.md b/16-writing-data.md
new file mode 100644
index 000000000..d28fcdf9c
--- /dev/null
+++ b/16-writing-data.md
@@ -0,0 +1,217 @@
+---
+title: Writing Data
+teaching: 10
+exercises: 10
+source: Rmd
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- To be able to write out plots and data from R.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- How can I save plots and data created in R?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
+
+## Saving plots
+
+You have already seen how to save the most recent plot you create in `ggplot2`,
+using the command `ggsave`. As a refresher:
+
+
+``` r
+ggsave("My_most_recent_plot.pdf")
+```
+
+You can save a plot from within RStudio using the 'Export' button
+in the 'Plot' window. This will give you the option of saving as a
+.pdf or as .png, .jpg or other image formats.
+
+Sometimes you will want to save plots without creating them in the
+'Plot' window first. Perhaps you want to make a pdf document with
+multiple pages: each one a different plot, for example. Or perhaps
+you're looping through multiple subsets of a file, plotting data from
+each subset, and you want to save each plot, but obviously can't stop
+the loop to click 'Export' for each one.
+
+In this case you can use a more flexible approach. The function
+`pdf` creates a new pdf device. You can control the size and resolution
+using the arguments to this function.
+
+
+``` r
+pdf("Life_Exp_vs_time.pdf", width=12, height=4)
+ggplot(data=gapminder, aes(x=year, y=lifeExp, colour=country)) +
+ geom_line() +
+ theme(legend.position = "none")
+
+# You then have to make sure to turn off the pdf device!
+
+dev.off()
+```
+
+Open up this document and have a look.
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 1
+
+Rewrite your 'pdf' command to print a second
+page in the pdf, showing a facet plot (hint: use `facet_grid`)
+of the same data with one panel per continent.
+
+::::::::::::::: solution
+
+## Solution to challenge 1
+
+
+``` r
+pdf("Life_Exp_vs_time.pdf", width = 12, height = 4)
+p <- ggplot(data = gapminder, aes(x = year, y = lifeExp, colour = country)) +
+ geom_line() +
+ theme(legend.position = "none")
+p
+p + facet_grid(~continent)
+dev.off()
+```
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+The commands `jpeg`, `png` etc. are used similarly to produce
+documents in different formats.
+
+## Writing data
+
+At some point, you'll also want to write out data from R.
+
+We can use the `write.table` function for this, which is
+very similar to `read.table` from before.
+
+Let's create a data-cleaning script, for this analysis, we
+only want to focus on the gapminder data for Australia:
+
+
+``` r
+aust_subset <- gapminder[gapminder$country == "Australia",]
+
+write.table(aust_subset,
+ file="cleaned-data/gapminder-aus.csv",
+ sep=","
+)
+```
+
+Let's switch back to the shell to take a look at the data to make sure it looks
+OK:
+
+
+``` bash
+head cleaned-data/gapminder-aus.csv
+```
+
+``` output
+"country","year","pop","continent","lifeExp","gdpPercap"
+"61","Australia",1952,8691212,"Oceania",69.12,10039.59564
+"62","Australia",1957,9712569,"Oceania",70.33,10949.64959
+"63","Australia",1962,10794968,"Oceania",70.93,12217.22686
+"64","Australia",1967,11872264,"Oceania",71.1,14526.12465
+"65","Australia",1972,13177000,"Oceania",71.93,16788.62948
+"66","Australia",1977,14074100,"Oceania",73.49,18334.19751
+"67","Australia",1982,15184200,"Oceania",74.74,19477.00928
+"68","Australia",1987,16257249,"Oceania",76.32,21888.88903
+"69","Australia",1992,17481977,"Oceania",77.56,23424.76683
+```
+
+Hmm, that's not quite what we wanted. Where did all these
+quotation marks come from? Also the row numbers are
+meaningless.
+
+Let's look at the help file to work out how to change this
+behaviour.
+
+
+``` r
+?write.table
+```
+
+By default R will wrap character vectors with quotation marks
+when writing out to file. It will also write out the row and
+column names.
+
+Let's fix this:
+
+
+``` r
+write.table(
+ gapminder[gapminder$country == "Australia",],
+ file="cleaned-data/gapminder-aus.csv",
+ sep=",", quote=FALSE, row.names=FALSE
+)
+```
+
+Now lets look at the data again using our shell skills:
+
+
+``` bash
+head cleaned-data/gapminder-aus.csv
+```
+
+``` output
+country,year,pop,continent,lifeExp,gdpPercap
+Australia,1952,8691212,Oceania,69.12,10039.59564
+Australia,1957,9712569,Oceania,70.33,10949.64959
+Australia,1962,10794968,Oceania,70.93,12217.22686
+Australia,1967,11872264,Oceania,71.1,14526.12465
+Australia,1972,13177000,Oceania,71.93,16788.62948
+Australia,1977,14074100,Oceania,73.49,18334.19751
+Australia,1982,15184200,Oceania,74.74,19477.00928
+Australia,1987,16257249,Oceania,76.32,21888.88903
+Australia,1992,17481977,Oceania,77.56,23424.76683
+```
+
+That looks better!
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 2
+
+Write a data-cleaning script file that subsets the gapminder
+data to include only data points collected since 1990.
+
+Use this script to write out the new subset to a file
+in the `cleaned-data/` directory.
+
+::::::::::::::: solution
+
+## Solution to challenge 2
+
+
+``` r
+write.table(
+ gapminder[gapminder$year > 1990, ],
+ file = "cleaned-data/gapminder-after1990.csv",
+ sep = ",", quote = FALSE, row.names = FALSE
+)
+```
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- Save plots from RStudio using the 'Export' button.
+- Use `write.table` to save tabular data.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
diff --git a/17-github.md b/17-github.md
new file mode 100644
index 000000000..9df4b3ab6
--- /dev/null
+++ b/17-github.md
@@ -0,0 +1,556 @@
+---
+title: Remotes in GitHub
+teaching: 45
+exercises: 0
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- Explain what remote repositories are and why they are useful.
+- Push to or pull from a remote repository.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- How do I share my changes with others on the web?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+Version control really comes into its own when we begin to collaborate with
+other people. We already have most of the machinery we need to do this; the
+only thing missing is to copy changes from one repository to another.
+
+Systems like Git allow us to move work between any two repositories. In
+practice, though, it's easiest to use one copy as a central hub, and to keep it
+on the web rather than on someone's laptop. Most programmers use hosting
+services like [GitHub](https://github.com), [Bitbucket](https://bitbucket.org) or
+[GitLab](https://gitlab.com/) to hold those main copies; we'll explore the pros
+and cons of this in a [later episode](13-hosting.md).
+
+Let's start by sharing the changes we've made to our current project with the
+world. To this end we are going to create a *remote* repository that will be linked to our *local* repository.
+
+## 1\. Create a remote repository
+
+Log in to [GitHub](https://github.com), then click on the icon in the top right corner to
+create a new repository called `planets`:
+
+{alt='The first step in creating a repository on GitHub: clicking the "create new" button'}
+
+Name your repository "planets" and then click "Create Repository".
+
+Note: Since this repository will be connected to a local repository, it needs to be empty. Leave
+"Initialize this repository with a README" unchecked, and keep "None" as options for both "Add
+.gitignore" and "Add a license." See the "GitHub License and README files" exercise below for a full
+explanation of why the repository needs to be empty.
+
+{alt='The second step in creating a repository on GitHub: filling out the new repository form to provide the repository name, and specify that neither a readme nor a license should be created'}
+
+As soon as the repository is created, GitHub displays a page with a URL and some
+information on how to configure your local repository:
+
+{alt='The summary page displayed by GitHub after a new repository has been created. It contains instructions for configuring the new GitHub repository as a git remote'}
+
+This effectively does the following on GitHub's servers:
+
+```bash
+$ mkdir planets
+$ cd planets
+$ git init
+```
+
+If you remember back to the earlier [episode](04-changes.md) where we added and
+committed our earlier work on `mars.txt`, we had a diagram of the local repository
+which looked like this:
+
+{alt='A diagram showing how "git add" registers changes in the staging area, while "git commit" moves changes from the staging area to the repository'}
+
+Now that we have two repositories, we need a diagram like this:
+
+{alt='A diagram illustrating how the GitHub "planets" repository is also a git repository like our local repository, but that it is currently empty'}
+
+Note that our local repository still contains our earlier work on `mars.txt`, but the
+remote repository on GitHub appears empty as it doesn't contain any files yet.
+
+## 2\. Connect local to remote repository
+
+Now we connect the two repositories. We do this by making the
+GitHub repository a [remote](../learners/reference.md#remote) for the local repository.
+The home page of the repository on GitHub includes the URL string we need to
+identify it:
+
+{alt='Clicking the "Copy to Clipboard" button on GitHub to obtain the repository\'s URL'}
+
+Click on the 'SSH' link to change the [protocol](../learners/reference.md#protocol) from HTTPS to SSH.
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## HTTPS vs. SSH
+
+We use SSH here because, while it requires some additional configuration, it is a
+security protocol widely used by many applications. The steps below describe SSH at a
+minimum level for GitHub.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+{alt='A screenshot showing that clicking on "SSH" will make GitHub provide the SSH URL for a repository instead of the HTTPS URL'}
+
+Copy that URL from the browser, go into the local `planets` repository, and run
+this command:
+
+```bash
+$ git remote add origin git@github.com:vlad/planets.git
+```
+
+Make sure to use the URL for your repository rather than Vlad's: the only
+difference should be your username instead of `vlad`.
+
+`origin` is a local name used to refer to the remote repository. It could be called
+anything, but `origin` is a convention that is often used by default in git
+and GitHub, so it's helpful to stick with this unless there's a reason not to.
+
+We can check that the command has worked by running `git remote -v`:
+
+```bash
+$ git remote -v
+```
+
+```output
+origin git@github.com:vlad/planets.git (fetch)
+origin git@github.com:vlad/planets.git (push)
+```
+
+We'll discuss remotes in more detail in the next episode, while
+talking about how they might be used for collaboration.
+
+## 3\. SSH Background and Setup
+
+Before Dracula can connect to a remote repository, he needs to set up a way for his computer to authenticate with GitHub so it knows it's him trying to connect to his remote repository.
+
+We are going to set up the method that is commonly used by many different services to authenticate access on the command line. This method is called Secure Shell Protocol (SSH). SSH is a cryptographic network protocol that allows secure communication between computers using an otherwise insecure network.
+
+SSH uses what is called a key pair. This is two keys that work together to validate access. One key is publicly known and called the public key, and the other key called the private key is kept private. Very descriptive names.
+
+You can think of the public key as a padlock, and only you have the key (the private key) to open it. You use the public key where you want a secure method of communication, such as your GitHub account. You give this padlock, or public key, to GitHub and say "lock the communications to my account with this so that only computers that have my private key can unlock communications and send git commands as my GitHub account."
+
+What we will do now is the minimum required to set up the SSH keys and add the public key to a GitHub account.
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Advanced SSH
+
+A supplemental episode in this lesson discusses SSH and key pairs in more depth and detail.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+The first thing we are going to do is check if this has already been done on the computer you're on. Because generally speaking, this setup only needs to happen once and then you can forget about it.
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Keeping your keys secure
+
+You shouldn't really forget about your SSH keys, since they keep your account secure. It's good
+practice to audit your secure shell keys every so often. Especially if you are using multiple
+computers to access your account.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+We will run the list command to check what key pairs already exist on your computer.
+
+```bash
+ls -al ~/.ssh
+```
+
+Your output is going to look a little different depending on whether or not SSH has ever been set up on the computer you are using.
+
+Dracula has not set up SSH on his computer, so his output is
+
+```output
+ls: cannot access '/c/Users/Vlad Dracula/.ssh': No such file or directory
+```
+
+If SSH has been set up on the computer you're using, the public and private key pairs will be listed. The file names are either `id_ed25519`/`id_ed25519.pub` or `id_rsa`/`id_rsa.pub` depending on how the key pairs were set up.
+Since they don't exist on Dracula's computer, he uses this command to create them.
+
+### 3\.1 Create an SSH key pair
+
+To create an SSH key pair Vlad uses this command, where the `-t` option specifies which type of algorithm to use and `-C` attaches a comment to the key (here, Vlad's email):
+
+```bash
+$ ssh-keygen -t ed25519 -C "vlad@tran.sylvan.ia"
+```
+
+If you are using a legacy system that doesn't support the Ed25519 algorithm, use:
+`$ ssh-keygen -t rsa -b 4096 -C "your_email@example.com"`
+
+```output
+Generating public/private ed25519 key pair.
+Enter file in which to save the key (/c/Users/Vlad Dracula/.ssh/id_ed25519):
+```
+
+We want to use the default file, so just press Enter.
+
+```output
+Created directory '/c/Users/Vlad Dracula/.ssh'.
+Enter passphrase (empty for no passphrase):
+```
+
+Now, it is prompting Dracula for a passphrase. Since he is using his lab's laptop that other people sometimes have access to, he wants to create a passphrase.
+Be sure to use something memorable or save your passphrase somewhere, as there is no "reset my password" option.
+Note that, when typing a passphrase on a terminal, there won't be any visual feedback of your typing.
+This is normal: your passphrase will be recorded even if you see nothing changing on your screen.
+
+```output
+Enter same passphrase again:
+```
+
+After entering the same passphrase a second time, we receive the confirmation
+
+```output
+Your identification has been saved in /c/Users/Vlad Dracula/.ssh/id_ed25519
+Your public key has been saved in /c/Users/Vlad Dracula/.ssh/id_ed25519.pub
+The key fingerprint is:
+SHA256:SMSPIStNyA00KPxuYu94KpZgRAYjgt9g4BA4kFy3g1o vlad@tran.sylvan.ia
+The key's randomart image is:
++--[ED25519 256]--+
+|^B== o. |
+|%*=.*.+ |
+|+=.E =.+ |
+| .=.+.o.. |
+|.... . S |
+|.+ o |
+|+ = |
+|.o.o |
+|oo+. |
++----[SHA256]-----+
+```
+
+The "identification" is actually the private key. You should never share it. The public key is appropriately named. The "key fingerprint"
+is a shorter version of a public key.
+
+Now that we have generated the SSH keys, we will find the SSH files when we check.
+
+```bash
+ls -al ~/.ssh
+```
+
+```output
+drwxr-xr-x 1 Vlad Dracula 197121 0 Jul 16 14:48 ./
+drwxr-xr-x 1 Vlad Dracula 197121 0 Jul 16 14:48 ../
+-rw-r--r-- 1 Vlad Dracula 197121 419 Jul 16 14:48 id_ed25519
+-rw-r--r-- 1 Vlad Dracula 197121 106 Jul 16 14:48 id_ed25519.pub
+```
+
+### 3\.2 Copy the public key to GitHub
+
+Now we have a SSH key pair and we can run this command to check if GitHub can read our authentication.
+
+```bash
+ssh -T git@github.com
+```
+
+```output
+The authenticity of host 'github.com (192.30.255.112)' can't be established.
+RSA key fingerprint is SHA256:nThbg6kXUpJWGl7E1IGOCspRomTxdCARLviKw6E5SY8.
+This key is not known by any other names
+Are you sure you want to continue connecting (yes/no/[fingerprint])? y
+Please type 'yes', 'no' or the fingerprint: yes
+Warning: Permanently added 'github.com' (RSA) to the list of known hosts.
+git@github.com: Permission denied (publickey).
+```
+
+Right, we forgot that we need to give GitHub our public key!
+
+First, we need to copy the public key. Be sure to include the `.pub` at the end, otherwise you're looking at the private key.
+
+```bash
+cat ~/.ssh/id_ed25519.pub
+```
+
+```output
+ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIDmRA3d51X0uu9wXek559gfn6UFNF69yZjChyBIU2qKI vlad@tran.sylvan.ia
+```
+
+Now, going to GitHub.com, click on your profile icon in the top right corner to get the drop-down menu. Click "Settings," then on the
+settings page, click "SSH and GPG keys," on the left side "Account settings" menu. Click the "New SSH key" button on the right side. Now,
+you can add the title (Dracula uses the title "Vlad's Lab Laptop" so he can remember where the original key pair
+files are located), paste your SSH key into the field, and click the "Add SSH key" to complete the setup.
+
+Now that we've set that up, let's check our authentication again from the command line.
+
+```bash
+$ ssh -T git@github.com
+```
+
+```output
+Hi Vlad! You've successfully authenticated, but GitHub does not provide shell access.
+```
+
+Good! This output confirms that the SSH key works as intended. We are now ready to push our work to the remote repository.
+
+## 4\. Push local changes to a remote
+
+Now that authentication is setup, we can return to the remote. This command will push the changes from
+our local repository to the repository on GitHub:
+
+```bash
+$ git push origin main
+```
+
+Since Dracula set up a passphrase, it will prompt him for it. If you completed advanced settings for your authentication, it
+will not prompt for a passphrase.
+
+```output
+Enumerating objects: 16, done.
+Counting objects: 100% (16/16), done.
+Delta compression using up to 8 threads.
+Compressing objects: 100% (11/11), done.
+Writing objects: 100% (16/16), 1.45 KiB | 372.00 KiB/s, done.
+Total 16 (delta 2), reused 0 (delta 0)
+remote: Resolving deltas: 100% (2/2), done.
+To https://github.com/vlad/planets.git
+ * [new branch] main -> main
+```
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Proxy
+
+If the network you are connected to uses a proxy, there is a chance that your
+last command failed with "Could not resolve hostname" as the error message. To
+solve this issue, you need to tell Git about the proxy:
+
+```bash
+$ git config --global http.proxy http://user:password@proxy.url
+$ git config --global https.proxy https://user:password@proxy.url
+```
+
+When you connect to another network that doesn't use a proxy, you will need to
+tell Git to disable the proxy using:
+
+```bash
+$ git config --global --unset http.proxy
+$ git config --global --unset https.proxy
+```
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Password Managers
+
+If your operating system has a password manager configured, `git push` will
+try to use it when it needs your username and password. For example, this
+is the default behavior for Git Bash on Windows. If you want to type your
+username and password at the terminal instead of using a password manager,
+type:
+
+```bash
+$ unset SSH_ASKPASS
+```
+
+in the terminal, before you run `git push`. Despite the name, [Git uses
+`SSH_ASKPASS` for all credential
+entry](https://git-scm.com/docs/gitcredentials#_requesting_credentials), so
+you may want to unset `SSH_ASKPASS` whether you are using Git via SSH or
+https.
+
+You may also want to add `unset SSH_ASKPASS` at the end of your `~/.bashrc`
+to make Git default to using the terminal for usernames and passwords.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+Our local and remote repositories are now in this state:
+
+{alt='A diagram showing how "git push origin" will push changes from the local repository to the remote, making the remote repository an exact copy of the local repository.'}
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## The '-u' Flag
+
+You may see a `-u` option used with `git push` in some documentation. This
+option is synonymous with the `--set-upstream-to` option for the `git branch`
+command, and is used to associate the current branch with a remote branch so
+that the `git pull` command can be used without any arguments. To do this,
+simply use `git push -u origin main` once the remote has been set up.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+We can pull changes from the remote repository to the local one as well:
+
+```bash
+$ git pull origin main
+```
+
+```output
+From https://github.com/vlad/planets
+ * branch main -> FETCH_HEAD
+Already up-to-date.
+```
+
+Pulling has no effect in this case because the two repositories are already
+synchronized. If someone else had pushed some changes to the repository on
+GitHub, though, this command would download them to our local repository.
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## GitHub GUI
+
+Browse to your `planets` repository on GitHub.
+Underneath the Code button, find and click on the text that says "XX commits" (where "XX" is some number).
+Hover over, and click on, the three buttons to the right of each commit.
+What information can you gather/explore from these buttons?
+How would you get that same information in the shell?
+
+::::::::::::::: solution
+
+## Solution
+
+The left-most button (with the picture of a clipboard) copies the full identifier of the commit
+to the clipboard. In the shell, `git log` will show you the full commit identifier for each
+commit.
+
+When you click on the middle button, you'll see all of the changes that were made in that
+particular commit. Green shaded lines indicate additions and red ones removals. In the shell we
+can do the same thing with `git diff`. In particular, `git diff ID1..ID2` where ID1 and
+ID2 are commit identifiers (e.g. `git diff a3bf1e5..041e637`) will show the differences
+between those two commits.
+
+The right-most button lets you view all of the files in the repository at the time of that
+commit. To do this in the shell, we'd need to checkout the repository at that particular time.
+We can do this with `git checkout ID` where ID is the identifier of the commit we want to
+look at. If we do this, we need to remember to put the repository back to the right state
+afterwards!
+
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Uploading files directly in GitHub browser
+
+Github also allows you to skip the command line and upload files directly to
+your repository without having to leave the browser. There are two options.
+First you can click the "Upload files" button in the toolbar at the top of the
+file tree. Or, you can drag and drop files from your desktop onto the file
+tree. You can read more about this [on this GitHub page](https://help.github.com/articles/adding-a-file-to-a-repository/).
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## GitHub Timestamp
+
+Create a remote repository on GitHub. Push the contents of your local
+repository to the remote. Make changes to your local repository and push these
+changes. Go to the repo you just created on GitHub and check the
+[timestamps](../learners/reference.md#timestamp) of the files. How does GitHub
+record times, and why?
+
+::::::::::::::: solution
+
+## Solution
+
+GitHub displays timestamps in a human readable relative format (i.e. "22 hours ago" or "three
+weeks ago"). However, if you hover over the timestamp, you can see the exact time at which the
+last change to the file occurred.
+
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Push vs. Commit
+
+In this episode, we introduced the "git push" command.
+How is "git push" different from "git commit"?
+
+::::::::::::::: solution
+
+## Solution
+
+When we push changes, we're interacting with a remote repository to update it with the changes
+we've made locally (often this corresponds to sharing the changes we've made with others).
+Commit only updates your local repository.
+
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## GitHub License and README files
+
+In this episode we learned about creating a remote repository on GitHub, but when you initialized
+your GitHub repo, you didn't add a README.md or a license file. If you had, what do you think
+would have happened when you tried to link your local and remote repositories?
+
+::::::::::::::: solution
+
+## Solution
+
+In this case, we'd see a merge conflict due to unrelated histories. When GitHub creates a
+README.md file, it performs a commit in the remote repository. When you try to pull the remote
+repository to your local repository, Git detects that they have histories that do not share a
+common origin and refuses to merge.
+
+```bash
+$ git pull origin main
+```
+
+```output
+warning: no common commits
+remote: Enumerating objects: 3, done.
+remote: Counting objects: 100% (3/3), done.
+remote: Total 3 (delta 0), reused 0 (delta 0), pack-reused 0
+Unpacking objects: 100% (3/3), done.
+From https://github.com/vlad/planets
+ * branch main -> FETCH_HEAD
+ * [new branch] main -> origin/main
+fatal: refusing to merge unrelated histories
+```
+
+You can force git to merge the two repositories with the option `--allow-unrelated-histories`.
+Be careful when you use this option and carefully examine the contents of local and remote
+repositories before merging.
+
+```bash
+$ git pull --allow-unrelated-histories origin main
+```
+
+```output
+From https://github.com/vlad/planets
+ * branch main -> FETCH_HEAD
+Merge made by the 'recursive' strategy.
+README.md | 1 +
+1 file changed, 1 insertion(+)
+create mode 100644 README.md
+```
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- A local Git repository can be connected to one or more remote repositories.
+- Use the SSH protocol to connect to remote repositories.
+- `git push` copies changes from a local repository to a remote repository.
+- `git pull` copies changes from a remote repository to a local repository.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
diff --git a/18-collab.md b/18-collab.md
new file mode 100644
index 000000000..efaeb0eec
--- /dev/null
+++ b/18-collab.md
@@ -0,0 +1,264 @@
+---
+title: Collaborating
+teaching: 25
+exercises: 0
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- Clone a remote repository.
+- Collaborate by pushing to a common repository.
+- Describe the basic collaborative workflow.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- How can I use version control to collaborate with other people?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+For the next step, get into pairs. One person will be the "Owner" and the other
+will be the "Collaborator". The goal is that the Collaborator add changes into
+the Owner's repository. We will switch roles at the end, so both persons will
+play Owner and Collaborator.
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Practicing By Yourself
+
+If you're working through this lesson on your own, you can carry on by opening
+a second terminal window.
+This window will represent your partner, working on another computer. You
+won't need to give anyone access on GitHub, because both 'partners' are you.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+The Owner needs to give the Collaborator access. In your repository page on GitHub, click the "Settings"
+button on the right, select "Collaborators", click "Add people", and
+then enter your partner's username.
+
+{alt='A screenshot of the GitHub Collaborators settings page, which is accessed by clicking "Settings" then "Collaborators"'}
+
+To accept access to the Owner's repo, the Collaborator
+needs to go to [https://github.com/notifications](https://github.com/notifications)
+or check for email notification. Once there she can accept access to the Owner's repo.
+
+Next, the Collaborator needs to download a copy of the Owner's repository to her
+machine. This is called "cloning a repo".
+
+The Collaborator doesn't want to overwrite her own version of `planets.git`, so
+needs to clone the Owner's repository to a different location than her own
+repository with the same name.
+
+To clone the Owner's repo into her `Desktop` folder, the Collaborator enters:
+
+```bash
+$ git clone git@github.com:vlad/planets.git ~/Desktop/vlad-planets
+```
+
+Replace 'vlad' with the Owner's username.
+
+If you choose to clone without the clone path
+(`~/Desktop/vlad-planets`) specified at the end,
+you will clone inside your own planets folder!
+Make sure to navigate to the `Desktop` folder first.
+
+{alt='A diagram showing that "git clone" can create a copy of a remote GitHub repository, allowing a second person to create their own local repository that they can make changes to.'}
+
+The Collaborator can now make a change in her clone of the Owner's repository,
+exactly the same way as we've been doing before:
+
+```bash
+$ cd ~/Desktop/vlad-planets
+$ nano pluto.txt
+$ cat pluto.txt
+```
+
+```output
+It is so a planet!
+```
+
+```bash
+$ git add pluto.txt
+$ git commit -m "Add notes about Pluto"
+```
+
+```output
+ 1 file changed, 1 insertion(+)
+ create mode 100644 pluto.txt
+```
+
+Then push the change to the *Owner's repository* on GitHub:
+
+```bash
+$ git push origin main
+```
+
+```output
+Enumerating objects: 4, done.
+Counting objects: 4, done.
+Delta compression using up to 4 threads.
+Compressing objects: 100% (2/2), done.
+Writing objects: 100% (3/3), 306 bytes, done.
+Total 3 (delta 0), reused 0 (delta 0)
+To https://github.com/vlad/planets.git
+ 9272da5..29aba7c main -> main
+```
+
+Note that we didn't have to create a remote called `origin`: Git uses this
+name by default when we clone a repository. (This is why `origin` was a
+sensible choice earlier when we were setting up remotes by hand.)
+
+Take a look at the Owner's repository on GitHub again, and you should be
+able to see the new commit made by the Collaborator. You may need to refresh
+your browser to see the new commit.
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Some more about remotes
+
+In this episode and the previous one, our local repository has had
+a single "remote", called `origin`. A remote is a copy of the repository
+that is hosted somewhere else, that we can push to and pull from, and
+there's no reason that you have to work with only one. For example,
+on some large projects you might have your own copy in your own GitHub
+account (you'd probably call this `origin`) and also the main "upstream"
+project repository (let's call this `upstream` for the sake of examples).
+You would pull from `upstream` from time to
+time to get the latest updates that other people have committed.
+
+Remember that the name you give to a remote only exists locally. It's
+an alias that you choose - whether `origin`, or `upstream`, or `fred` -
+and not something intrinstic to the remote repository.
+
+The `git remote` family of commands is used to set up and alter the remotes
+associated with a repository. Here are some of the most useful ones:
+
+- `git remote -v` lists all the remotes that are configured (we already used
+ this in the last episode)
+- `git remote add [name] [url]` is used to add a new remote
+- `git remote remove [name]` removes a remote. Note that it doesn't affect the
+ remote repository at all - it just removes the link to it from the local repo.
+- `git remote set-url [name] [newurl]` changes the URL that is associated
+ with the remote. This is useful if it has moved, e.g. to a different GitHub
+ account, or from GitHub to a different hosting service. Or, if we made a typo when
+ adding it!
+- `git remote rename [oldname] [newname]` changes the local alias by which a remote
+ is known - its name. For example, one could use this to change `upstream` to `fred`.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+To download the Collaborator's changes from GitHub, the Owner now enters:
+
+```bash
+$ git pull origin main
+```
+
+```output
+remote: Enumerating objects: 4, done.
+remote: Counting objects: 100% (4/4), done.
+remote: Compressing objects: 100% (2/2), done.
+remote: Total 3 (delta 0), reused 3 (delta 0), pack-reused 0
+Unpacking objects: 100% (3/3), done.
+From https://github.com/vlad/planets
+ * branch main -> FETCH_HEAD
+ 9272da5..29aba7c main -> origin/main
+Updating 9272da5..29aba7c
+Fast-forward
+ pluto.txt | 1 +
+ 1 file changed, 1 insertion(+)
+ create mode 100644 pluto.txt
+```
+
+Now the three repositories (Owner's local, Collaborator's local, and Owner's on
+GitHub) are back in sync.
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## A Basic Collaborative Workflow
+
+In practice, it is good to be sure that you have an updated version of the
+repository you are collaborating on, so you should `git pull` before making
+our changes. The basic collaborative workflow would be:
+
+- update your local repo with `git pull origin main`,
+- make your changes and stage them with `git add`,
+- commit your changes with `git commit -m`, and
+- upload the changes to GitHub with `git push origin main`
+
+It is better to make many commits with smaller changes rather than
+of one commit with massive changes: small commits are easier to
+read and review.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Switch Roles and Repeat
+
+Switch roles and repeat the whole process.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Review Changes
+
+The Owner pushed commits to the repository without giving any information
+to the Collaborator. How can the Collaborator find out what has changed with
+command line? And on GitHub?
+
+::::::::::::::: solution
+
+## Solution
+
+On the command line, the Collaborator can use `git fetch origin main`
+to get the remote changes into the local repository, but without merging
+them. Then by running `git diff main origin/main` the Collaborator
+will see the changes output in the terminal.
+
+On GitHub, the Collaborator can go to the repository and click on
+"commits" to view the most recent commits pushed to the repository.
+
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Comment Changes in GitHub
+
+The Collaborator has some questions about one line change made by the Owner and
+has some suggestions to propose.
+
+With GitHub, it is possible to comment on the diff of a commit. Over the line of
+code to comment, a blue comment icon appears to open a comment window.
+
+The Collaborator posts her comments and suggestions using the GitHub interface.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Version History, Backup, and Version Control
+
+Some backup software can keep a history of the versions of your files. They also
+allows you to recover specific versions. How is this functionality different from version control?
+What are some of the benefits of using version control, Git and GitHub?
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- `git clone` copies a remote repository to create a local repository with a remote called `origin` automatically set up.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
diff --git a/19-conflict.md b/19-conflict.md
new file mode 100644
index 000000000..ecaad1ac6
--- /dev/null
+++ b/19-conflict.md
@@ -0,0 +1,533 @@
+---
+title: Conflicts
+teaching: 15
+exercises: 0
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- Explain what conflicts are and when they can occur.
+- Resolve conflicts resulting from a merge.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- What do I do when my changes conflict with someone else's?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+As soon as people can work in parallel, they'll likely step on each other's
+toes. This will even happen with a single person: if we are working on
+a piece of software on both our laptop and a server in the lab, we could make
+different changes to each copy. Version control helps us manage these
+[conflicts](../learners/reference.md#conflict) by giving us tools to
+[resolve](../learners/reference.md#resolve) overlapping changes.
+
+To see how we can resolve conflicts, we must first create one. The file
+`mars.txt` currently looks like this in both partners' copies of our `planets`
+repository:
+
+```bash
+$ cat mars.txt
+```
+
+```output
+Cold and dry, but everything is my favorite color
+The two moons may be a problem for Wolfman
+But the Mummy will appreciate the lack of humidity
+```
+
+Let's add a line to the collaborator's copy only:
+
+```bash
+$ nano mars.txt
+$ cat mars.txt
+```
+
+```output
+Cold and dry, but everything is my favorite color
+The two moons may be a problem for Wolfman
+But the Mummy will appreciate the lack of humidity
+This line added to Wolfman's copy
+```
+
+and then push the change to GitHub:
+
+```bash
+$ git add mars.txt
+$ git commit -m "Add a line in our home copy"
+```
+
+```output
+[main 5ae9631] Add a line in our home copy
+ 1 file changed, 1 insertion(+)
+```
+
+```bash
+$ git push origin main
+```
+
+```output
+Enumerating objects: 5, done.
+Counting objects: 100% (5/5), done.
+Delta compression using up to 8 threads
+Compressing objects: 100% (3/3), done.
+Writing objects: 100% (3/3), 331 bytes | 331.00 KiB/s, done.
+Total 3 (delta 2), reused 0 (delta 0)
+remote: Resolving deltas: 100% (2/2), completed with 2 local objects.
+To https://github.com/vlad/planets.git
+ 29aba7c..dabb4c8 main -> main
+```
+
+Now let's have the owner
+make a different change to their copy
+*without* updating from GitHub:
+
+```bash
+$ nano mars.txt
+$ cat mars.txt
+```
+
+```output
+Cold and dry, but everything is my favorite color
+The two moons may be a problem for Wolfman
+But the Mummy will appreciate the lack of humidity
+We added a different line in the other copy
+```
+
+We can commit the change locally:
+
+```bash
+$ git add mars.txt
+$ git commit -m "Add a line in my copy"
+```
+
+```output
+[main 07ebc69] Add a line in my copy
+ 1 file changed, 1 insertion(+)
+```
+
+but Git won't let us push it to GitHub:
+
+```bash
+$ git push origin main
+```
+
+```output
+To https://github.com/vlad/planets.git
+ ! [rejected] main -> main (fetch first)
+error: failed to push some refs to 'https://github.com/vlad/planets.git'
+hint: Updates were rejected because the remote contains work that you do
+hint: not have locally. This is usually caused by another repository pushing
+hint: to the same ref. You may want to first integrate the remote changes
+hint: (e.g., 'git pull ...') before pushing again.
+hint: See the 'Note about fast-forwards' in 'git push --help' for details.
+```
+
+{alt='A diagram showing a conflict that might occur when two sets of independent changes are merged'}
+
+Git rejects the push because it detects that the remote repository has new updates that have not been
+incorporated into the local branch.
+What we have to do is pull the changes from GitHub,
+[merge](../learners/reference.md#merge) them into the copy we're currently working in, and then push that.
+Let's start by pulling:
+
+```bash
+$ git pull origin main
+```
+
+```output
+remote: Enumerating objects: 5, done.
+remote: Counting objects: 100% (5/5), done.
+remote: Compressing objects: 100% (1/1), done.
+remote: Total 3 (delta 2), reused 3 (delta 2), pack-reused 0
+Unpacking objects: 100% (3/3), done.
+From https://github.com/vlad/planets
+ * branch main -> FETCH_HEAD
+ 29aba7c..dabb4c8 main -> origin/main
+Auto-merging mars.txt
+CONFLICT (content): Merge conflict in mars.txt
+Automatic merge failed; fix conflicts and then commit the result.
+```
+
+The `git pull` command updates the local repository to include those
+changes already included in the remote repository.
+After the changes from remote branch have been fetched, Git detects that changes made to the local copy
+overlap with those made to the remote repository, and therefore refuses to merge the two versions to
+stop us from trampling on our previous work. The conflict is marked in
+in the affected file:
+
+```bash
+$ cat mars.txt
+```
+
+```output
+Cold and dry, but everything is my favorite color
+The two moons may be a problem for Wolfman
+But the Mummy will appreciate the lack of humidity
+<<<<<<< HEAD
+We added a different line in the other copy
+=======
+This line added to Wolfman's copy
+>>>>>>> dabb4c8c450e8475aee9b14b4383acc99f42af1d
+```
+
+Our change is preceded by `<<<<<<< HEAD`.
+Git has then inserted `=======` as a separator between the conflicting changes
+and marked the end of the content downloaded from GitHub with `>>>>>>>`.
+(The string of letters and digits after that marker
+identifies the commit we've just downloaded.)
+
+It is now up to us to edit this file to remove these markers
+and reconcile the changes.
+We can do anything we want: keep the change made in the local repository, keep
+the change made in the remote repository, write something new to replace both,
+or get rid of the change entirely.
+Let's replace both so that the file looks like this:
+
+```bash
+$ cat mars.txt
+```
+
+```output
+Cold and dry, but everything is my favorite color
+The two moons may be a problem for Wolfman
+But the Mummy will appreciate the lack of humidity
+We removed the conflict on this line
+```
+
+To finish merging,
+we add `mars.txt` to the changes being made by the merge
+and then commit:
+
+```bash
+$ git add mars.txt
+$ git status
+```
+
+```output
+On branch main
+All conflicts fixed but you are still merging.
+ (use "git commit" to conclude merge)
+
+Changes to be committed:
+
+ modified: mars.txt
+
+```
+
+```bash
+$ git commit -m "Merge changes from GitHub"
+```
+
+```output
+[main 2abf2b1] Merge changes from GitHub
+```
+
+Now we can push our changes to GitHub:
+
+```bash
+$ git push origin main
+```
+
+```output
+Enumerating objects: 10, done.
+Counting objects: 100% (10/10), done.
+Delta compression using up to 8 threads
+Compressing objects: 100% (6/6), done.
+Writing objects: 100% (6/6), 645 bytes | 645.00 KiB/s, done.
+Total 6 (delta 4), reused 0 (delta 0)
+remote: Resolving deltas: 100% (4/4), completed with 2 local objects.
+To https://github.com/vlad/planets.git
+ dabb4c8..2abf2b1 main -> main
+```
+
+Git keeps track of what we've merged with what,
+so we don't have to fix things by hand again
+when the collaborator who made the first change pulls again:
+
+```bash
+$ git pull origin main
+```
+
+```output
+remote: Enumerating objects: 10, done.
+remote: Counting objects: 100% (10/10), done.
+remote: Compressing objects: 100% (2/2), done.
+remote: Total 6 (delta 4), reused 6 (delta 4), pack-reused 0
+Unpacking objects: 100% (6/6), done.
+From https://github.com/vlad/planets
+ * branch main -> FETCH_HEAD
+ dabb4c8..2abf2b1 main -> origin/main
+Updating dabb4c8..2abf2b1
+Fast-forward
+ mars.txt | 2 +-
+ 1 file changed, 1 insertion(+), 1 deletion(-)
+```
+
+We get the merged file:
+
+```bash
+$ cat mars.txt
+```
+
+```output
+Cold and dry, but everything is my favorite color
+The two moons may be a problem for Wolfman
+But the Mummy will appreciate the lack of humidity
+We removed the conflict on this line
+```
+
+We don't need to merge again because Git knows someone has already done that.
+
+Git's ability to resolve conflicts is very useful, but conflict resolution
+costs time and effort, and can introduce errors if conflicts are not resolved
+correctly. If you find yourself resolving a lot of conflicts in a project,
+consider these technical approaches to reducing them:
+
+- Pull from upstream more frequently, especially before starting new work
+- Use topic branches to segregate work, merging to main when complete
+- Make smaller more atomic commits
+- Push your work when it is done and encourage your team to do the same to reduce work in progress and, by extension, the chance of having conflicts
+- Where logically appropriate, break large files into smaller ones so that it is
+ less likely that two authors will alter the same file simultaneously
+
+Conflicts can also be minimized with project management strategies:
+
+- Clarify who is responsible for what areas with your collaborators
+- Discuss what order tasks should be carried out in with your collaborators so
+ that tasks expected to change the same lines won't be worked on simultaneously
+- If the conflicts are stylistic churn (e.g. tabs vs. spaces), establish a
+ project convention that is governing and use code style tools (e.g.
+ `htmltidy`, `perltidy`, `rubocop`, etc.) to enforce, if necessary
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Solving Conflicts that You Create
+
+Clone the repository created by your instructor.
+Add a new file to it,
+and modify an existing file (your instructor will tell you which one).
+When asked by your instructor,
+pull her changes from the repository to create a conflict,
+then resolve it.
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Conflicts on Non-textual files
+
+What does Git do
+when there is a conflict in an image or some other non-textual file
+that is stored in version control?
+
+::::::::::::::: solution
+
+## Solution
+
+Let's try it. Suppose Dracula takes a picture of Martian surface and
+calls it `mars.jpg`.
+
+If you do not have an image file of Mars available, you can create
+a dummy binary file like this:
+
+```bash
+$ head -c 1024 /dev/urandom > mars.jpg
+$ ls -lh mars.jpg
+```
+
+```output
+-rw-r--r-- 1 vlad 57095 1.0K Mar 8 20:24 mars.jpg
+```
+
+`ls` shows us that this created a 1-kilobyte file. It is full of
+random bytes read from the special file, `/dev/urandom`.
+
+Now, suppose Dracula adds `mars.jpg` to his repository:
+
+```bash
+$ git add mars.jpg
+$ git commit -m "Add picture of Martian surface"
+```
+
+```output
+[main 8e4115c] Add picture of Martian surface
+ 1 file changed, 0 insertions(+), 0 deletions(-)
+ create mode 100644 mars.jpg
+```
+
+Suppose that Wolfman has added a similar picture in the meantime.
+His is a picture of the Martian sky, but it is *also* called `mars.jpg`.
+When Dracula tries to push, he gets a familiar message:
+
+```bash
+$ git push origin main
+```
+
+```output
+To https://github.com/vlad/planets.git
+ ! [rejected] main -> main (fetch first)
+error: failed to push some refs to 'https://github.com/vlad/planets.git'
+hint: Updates were rejected because the remote contains work that you do
+hint: not have locally. This is usually caused by another repository pushing
+hint: to the same ref. You may want to first integrate the remote changes
+hint: (e.g., 'git pull ...') before pushing again.
+hint: See the 'Note about fast-forwards' in 'git push --help' for details.
+```
+
+We've learned that we must pull first and resolve any conflicts:
+
+```bash
+$ git pull origin main
+```
+
+When there is a conflict on an image or other binary file, git prints
+a message like this:
+
+```output
+$ git pull origin main
+remote: Counting objects: 3, done.
+remote: Compressing objects: 100% (3/3), done.
+remote: Total 3 (delta 0), reused 0 (delta 0)
+Unpacking objects: 100% (3/3), done.
+From https://github.com/vlad/planets.git
+ * branch main -> FETCH_HEAD
+ 6a67967..439dc8c main -> origin/main
+warning: Cannot merge binary files: mars.jpg (HEAD vs. 439dc8c08869c342438f6dc4a2b615b05b93c76e)
+Auto-merging mars.jpg
+CONFLICT (add/add): Merge conflict in mars.jpg
+Automatic merge failed; fix conflicts and then commit the result.
+```
+
+The conflict message here is mostly the same as it was for `mars.txt`, but
+there is one key additional line:
+
+```output
+warning: Cannot merge binary files: mars.jpg (HEAD vs. 439dc8c08869c342438f6dc4a2b615b05b93c76e)
+```
+
+Git cannot automatically insert conflict markers into an image as it does
+for text files. So, instead of editing the image file, we must check out
+the version we want to keep. Then we can add and commit this version.
+
+On the key line above, Git has conveniently given us commit identifiers
+for the two versions of `mars.jpg`. Our version is `HEAD`, and Wolfman's
+version is `439dc8c0...`. If we want to use our version, we can use
+`git checkout`:
+
+```bash
+$ git checkout HEAD mars.jpg
+$ git add mars.jpg
+$ git commit -m "Use image of surface instead of sky"
+```
+
+```output
+[main 21032c3] Use image of surface instead of sky
+```
+
+If instead we want to use Wolfman's version, we can use `git checkout` with
+Wolfman's commit identifier, `439dc8c0`:
+
+```bash
+$ git checkout 439dc8c0 mars.jpg
+$ git add mars.jpg
+$ git commit -m "Use image of sky instead of surface"
+```
+
+```output
+[main da21b34] Use image of sky instead of surface
+```
+
+We can also keep *both* images. The catch is that we cannot keep them
+under the same name. But, we can check out each version in succession
+and *rename* it, then add the renamed versions. First, check out each
+image and rename it:
+
+```bash
+$ git checkout HEAD mars.jpg
+$ git mv mars.jpg mars-surface.jpg
+$ git checkout 439dc8c0 mars.jpg
+$ mv mars.jpg mars-sky.jpg
+```
+
+Then, remove the old `mars.jpg` and add the two new files:
+
+```bash
+$ git rm mars.jpg
+$ git add mars-surface.jpg
+$ git add mars-sky.jpg
+$ git commit -m "Use two images: surface and sky"
+```
+
+```output
+[main 94ae08c] Use two images: surface and sky
+ 2 files changed, 0 insertions(+), 0 deletions(-)
+ create mode 100644 mars-sky.jpg
+ rename mars.jpg => mars-surface.jpg (100%)
+```
+
+Now both images of Mars are checked into the repository, and `mars.jpg`
+no longer exists.
+
+
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## A Typical Work Session
+
+You sit down at your computer to work on a shared project that is tracked in a
+remote Git repository. During your work session, you take the following
+actions, but not in this order:
+
+- *Make changes* by appending the number `100` to a text file `numbers.txt`
+- *Update remote* repository to match the local repository
+- *Celebrate* your success with some fancy beverage(s)
+- *Update local* repository to match the remote repository
+- *Stage changes* to be committed
+- *Commit changes* to the local repository
+
+In what order should you perform these actions to minimize the chances of
+conflicts? Put the commands above in order in the *action* column of the table
+below. When you have the order right, see if you can write the corresponding
+commands in the *command* column. A few steps are populated to get you
+started.
+
+| order | action . . . . . . . . . . | command . . . . . . . . . . |
+| ----- | -------------------------- | --------------------------------------------- |
+| 1 | | |
+| 2 | | `echo 100 >> numbers.txt` |
+| 3 | | |
+| 4 | | |
+| 5 | | |
+| 6 | Celebrate! | |
+
+::::::::::::::: solution
+
+## Solution
+
+| order | action . . . . . . | command . . . . . . . . . . . . . . . . . . . |
+| ----- | -------------------------- | --------------------------------------------- |
+| 1 | Update local | `git pull origin main` |
+| 2 | Make changes | `echo 100 >> numbers.txt` |
+| 3 | Stage changes | `git add numbers.txt` |
+| 4 | Commit changes | `git commit -m "Add 100 to numbers.txt"` |
+| 5 | Update remote | `git push origin main` |
+| 6 | Celebrate! | |
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- Conflicts occur when two or more people change the same lines of the same file.
+- The version control system does not allow people to overwrite each other's changes blindly, but highlights conflicts so that they can be resolved.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
diff --git a/20-dplyr.md b/20-dplyr.md
new file mode 100644
index 000000000..411e01eb4
--- /dev/null
+++ b/20-dplyr.md
@@ -0,0 +1,671 @@
+---
+title: Data Frame Manipulation with dplyr
+teaching: 40
+exercises: 15
+source: Rmd
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- To be able to use the six main data frame manipulation 'verbs' with pipes in `dplyr`.
+- To understand how `group_by()` and `summarize()` can be combined to summarize datasets.
+- Be able to analyze a subset of data using logical filtering.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- How can I manipulate data frames without repeating myself?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
+
+Manipulation of data frames means many things to many researchers: we often
+select certain observations (rows) or variables (columns), we often group the
+data by a certain variable(s), or we even calculate summary statistics. We can
+do these operations using the normal base R operations:
+
+
+``` r
+mean(gapminder$gdpPercap[gapminder$continent == "Africa"])
+```
+
+``` output
+[1] 2193.755
+```
+
+``` r
+mean(gapminder$gdpPercap[gapminder$continent == "Americas"])
+```
+
+``` output
+[1] 7136.11
+```
+
+``` r
+mean(gapminder$gdpPercap[gapminder$continent == "Asia"])
+```
+
+``` output
+[1] 7902.15
+```
+
+But this isn't very *nice* because there is a fair bit of repetition. Repeating
+yourself will cost you time, both now and later, and potentially introduce some
+nasty bugs.
+
+## The `dplyr` package
+
+Luckily, the [`dplyr`](https://cran.r-project.org/package=dplyr)
+package provides a number of very useful functions for manipulating data frames
+in a way that will reduce the above repetition, reduce the probability of making
+errors, and probably even save you some typing. As an added bonus, you might
+even find the `dplyr` grammar easier to read.
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Tip: Tidyverse
+
+`dplyr` package belongs to a broader family of opinionated R packages
+designed for data science called the "Tidyverse". These
+packages are specifically designed to work harmoniously together.
+Some of these packages will be covered along this course, but you can find more
+complete information here: [https://www.tidyverse.org/](https://www.tidyverse.org/).
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+Here we're going to cover 5 of the most commonly used functions as well as using
+pipes (`%>%`) to combine them.
+
+1. `select()`
+2. `filter()`
+3. `group_by()`
+4. `summarize()`
+5. `mutate()`
+
+If you have have not installed this package earlier, please do so:
+
+
+``` r
+install.packages('dplyr')
+```
+
+Now let's load the package:
+
+
+``` r
+library("dplyr")
+```
+
+## Using select()
+
+If, for example, we wanted to move forward with only a few of the variables in
+our data frame we could use the `select()` function. This will keep only the
+variables you select.
+
+
+``` r
+year_country_gdp <- select(gapminder, year, country, gdpPercap)
+```
+
+{alt='Diagram illustrating use of select function to select two columns of a data frame'}
+If we want to remove one column only from the `gapminder` data, for example,
+removing the `continent` column.
+
+
+``` r
+smaller_gapminder_data <- select(gapminder, -continent)
+```
+
+If we open up `year_country_gdp` we'll see that it only contains the year,
+country and gdpPercap. Above we used 'normal' grammar, but the strengths of
+`dplyr` lie in combining several functions using pipes. Since the pipes grammar
+is unlike anything we've seen in R before, let's repeat what we've done above
+using pipes.
+
+
+``` r
+year_country_gdp <- gapminder %>% select(year, country, gdpPercap)
+```
+
+To help you understand why we wrote that in that way, let's walk through it step
+by step. First we summon the gapminder data frame and pass it on, using the pipe
+symbol `%>%`, to the next step, which is the `select()` function. In this case
+we don't specify which data object we use in the `select()` function since in
+gets that from the previous pipe. **Fun Fact**: There is a good chance you have
+encountered pipes before in the shell. In R, a pipe symbol is `%>%` while in the
+shell it is `|` but the concept is the same!
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Tip: Renaming data frame columns in dplyr
+
+In Chapter 4 we covered how you can rename columns with base R by assigning a value to the output of the `names()` function.
+Just like select, this is a bit cumbersome, but thankfully dplyr has a `rename()` function.
+
+Within a pipeline, the syntax is `rename(new_name = old_name)`.
+For example, we may want to rename the gdpPercap column name from our `select()` statement above.
+
+
+``` r
+tidy_gdp <- year_country_gdp %>% rename(gdp_per_capita = gdpPercap)
+
+head(tidy_gdp)
+```
+
+``` output
+ year country gdp_per_capita
+1 1952 Afghanistan 779.4453
+2 1957 Afghanistan 820.8530
+3 1962 Afghanistan 853.1007
+4 1967 Afghanistan 836.1971
+5 1972 Afghanistan 739.9811
+6 1977 Afghanistan 786.1134
+```
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Using filter()
+
+If we now want to move forward with the above, but only with European
+countries, we can combine `select` and `filter`
+
+
+``` r
+year_country_gdp_euro <- gapminder %>%
+ filter(continent == "Europe") %>%
+ select(year, country, gdpPercap)
+```
+
+If we now want to show life expectancy of European countries but only
+for a specific year (e.g., 2007), we can do as below.
+
+
+``` r
+europe_lifeExp_2007 <- gapminder %>%
+ filter(continent == "Europe", year == 2007) %>%
+ select(country, lifeExp)
+```
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 1
+
+Write a single command (which can span multiple lines and includes pipes) that
+will produce a data frame that has the African values for `lifeExp`, `country`
+and `year`, but not for other Continents. How many rows does your data frame
+have and why?
+
+::::::::::::::: solution
+
+## Solution to Challenge 1
+
+
+``` r
+year_country_lifeExp_Africa <- gapminder %>%
+ filter(continent == "Africa") %>%
+ select(year, country, lifeExp)
+```
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+As with last time, first we pass the gapminder data frame to the `filter()`
+function, then we pass the filtered version of the gapminder data frame to the
+`select()` function. **Note:** The order of operations is very important in this
+case. If we used 'select' first, filter would not be able to find the variable
+continent since we would have removed it in the previous step.
+
+## Using group\_by()
+
+Now, we were supposed to be reducing the error prone repetitiveness of what can
+be done with base R, but up to now we haven't done that since we would have to
+repeat the above for each continent. Instead of `filter()`, which will only pass
+observations that meet your criteria (in the above: `continent=="Europe"`), we
+can use `group_by()`, which will essentially use every unique criteria that you
+could have used in filter.
+
+
+``` r
+str(gapminder)
+```
+
+``` output
+'data.frame': 1704 obs. of 6 variables:
+ $ country : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
+ $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
+ $ pop : num 8425333 9240934 10267083 11537966 13079460 ...
+ $ continent: chr "Asia" "Asia" "Asia" "Asia" ...
+ $ lifeExp : num 28.8 30.3 32 34 36.1 ...
+ $ gdpPercap: num 779 821 853 836 740 ...
+```
+
+``` r
+str(gapminder %>% group_by(continent))
+```
+
+``` output
+gropd_df [1,704 × 6] (S3: grouped_df/tbl_df/tbl/data.frame)
+ $ country : chr [1:1704] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
+ $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
+ $ pop : num [1:1704] 8425333 9240934 10267083 11537966 13079460 ...
+ $ continent: chr [1:1704] "Asia" "Asia" "Asia" "Asia" ...
+ $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
+ $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
+ - attr(*, "groups")= tibble [5 × 2] (S3: tbl_df/tbl/data.frame)
+ ..$ continent: chr [1:5] "Africa" "Americas" "Asia" "Europe" ...
+ ..$ .rows : list
+
+This code makes the right plot but it also creates an intermediate variable
+(`americas`) that we might not have any other uses for. Just as we used
+`%>%` to pipe data along a chain of `dplyr` functions we can use it to pass data
+to `ggplot()`. Because `%>%` replaces the first argument in a function we don't
+need to specify the `data =` argument in the `ggplot()` function. By combining
+`dplyr` and `ggplot2` functions we can make the same figure without creating any
+new variables or modifying the data.
+
+
+``` r
+gapminder %>%
+ # Filter countries located in the Americas
+ filter(continent == "Americas") %>%
+ # Make the plot
+ ggplot(mapping = aes(x = year, y = lifeExp)) +
+ geom_line() +
+ facet_wrap( ~ country) +
+ theme(axis.text.x = element_text(angle = 45))
+```
+
+
+
+More examples of using the function `mutate()` and the `ggplot2` package.
+
+
+``` r
+gapminder %>%
+ # extract first letter of country name into new column
+ mutate(startsWith = substr(country, 1, 1)) %>%
+ # only keep countries starting with A or Z
+ filter(startsWith %in% c("A", "Z")) %>%
+ # plot lifeExp into facets
+ ggplot(aes(x = year, y = lifeExp, colour = continent)) +
+ geom_line() +
+ facet_wrap(vars(country)) +
+ theme_minimal()
+```
+
+
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Advanced Challenge
+
+Calculate the average life expectancy in 2002 of 2 randomly selected countries
+for each continent. Then arrange the continent names in reverse order.
+**Hint:** Use the `dplyr` functions `arrange()` and `sample_n()`, they have
+similar syntax to other dplyr functions.
+
+::::::::::::::: solution
+
+## Solution to Advanced Challenge
+
+
+``` r
+lifeExp_2countries_bycontinents <- gapminder %>%
+ filter(year==2002) %>%
+ group_by(continent) %>%
+ sample_n(2) %>%
+ summarize(mean_lifeExp=mean(lifeExp)) %>%
+ arrange(desc(mean_lifeExp))
+```
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Other great resources
+
+- [R for Data Science](https://r4ds.hadley.nz/) (online book)
+- [Data Wrangling Cheat sheet](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) (pdf file)
+- [Introduction to dplyr](https://dplyr.tidyverse.org/) (online documentation)
+- [Data wrangling with R and RStudio](https://www.rstudio.com/resources/webinars/data-wrangling-with-r-and-rstudio/) (online video)
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- Use the `dplyr` package to manipulate data frames.
+- Use `select()` to choose variables from a data frame.
+- Use `filter()` to choose data based on values.
+- Use `group_by()` and `summarize()` to work with subsets of data.
+- Use `mutate()` to create new variables.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
diff --git a/21-tidyr.md b/21-tidyr.md
new file mode 100644
index 000000000..9c49eb6b3
--- /dev/null
+++ b/21-tidyr.md
@@ -0,0 +1,605 @@
+---
+title: Data Frame Manipulation with tidyr
+teaching: 30
+exercises: 15
+source: Rmd
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- To understand the concepts of 'longer' and 'wider' data frame formats and be able to convert between them with `tidyr`.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- How can I change the layout of a data frame?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
+
+Researchers often want to reshape their data frames from 'wide' to 'longer'
+layouts, or vice-versa. The 'long' layout or format is where:
+
+- each column is a variable
+- each row is an observation
+
+In the purely 'long' (or 'longest') format, you usually have 1 column for the observed variable and the other columns are ID variables.
+
+For the 'wide' format each row is often a site/subject/patient and you have
+multiple observation variables containing the same type of data. These can be
+either repeated observations over time, or observation of multiple variables (or
+a mix of both). You may find data input may be simpler or some other
+applications may prefer the 'wide' format. However, many of `R`'s functions have
+been designed assuming you have 'longer' formatted data. This tutorial will help you
+efficiently transform your data shape regardless of original format.
+
+{alt='Diagram illustrating the difference between a wide versus long layout of a data frame'}
+
+Long and wide data frame layouts mainly affect readability. For humans, the wide format is often more intuitive since we can often see more of the data on the screen due
+to its shape. However, the long format is more machine readable and is closer
+to the formatting of databases. The ID variables in our data frames are similar to
+the fields in a database and observed variables are like the database values.
+
+## Getting started
+
+First install the packages if you haven't already done so (you probably
+installed dplyr in the previous lesson):
+
+
+``` r
+#install.packages("tidyr")
+#install.packages("dplyr")
+```
+
+Load the packages
+
+
+``` r
+library("tidyr")
+library("dplyr")
+```
+
+First, lets look at the structure of our original gapminder data frame:
+
+
+``` r
+str(gapminder)
+```
+
+``` output
+'data.frame': 1704 obs. of 6 variables:
+ $ country : chr "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
+ $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
+ $ pop : num 8425333 9240934 10267083 11537966 13079460 ...
+ $ continent: chr "Asia" "Asia" "Asia" "Asia" ...
+ $ lifeExp : num 28.8 30.3 32 34 36.1 ...
+ $ gdpPercap: num 779 821 853 836 740 ...
+```
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 1
+
+Is gapminder a purely long, purely wide, or some intermediate format?
+
+::::::::::::::: solution
+
+## Solution to Challenge 1
+
+The original gapminder data.frame is in an intermediate format. It is not
+purely long since it had multiple observation variables
+(`pop`,`lifeExp`,`gdpPercap`).
+
+:::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+Sometimes, as with the gapminder dataset, we have multiple types of observed
+data. It is somewhere in between the purely 'long' and 'wide' data formats. We
+have 3 "ID variables" (`continent`, `country`, `year`) and 3 "Observation
+variables" (`pop`,`lifeExp`,`gdpPercap`). This intermediate format can be
+preferred despite not having ALL observations in 1 column given that all 3
+observation variables have different units. There are few operations that would
+need us to make this data frame any longer (i.e. 4 ID variables and 1
+Observation variable).
+
+While using many of the functions in R, which are often vector based, you
+usually do not want to do mathematical operations on values with different
+units. For example, using the purely long format, a single mean for all of the
+values of population, life expectancy, and GDP would not be meaningful since it
+would return the mean of values with 3 incompatible units. The solution is that
+we first manipulate the data either by grouping (see the lesson on `dplyr`), or
+we change the structure of the data frame. **Note:** Some plotting functions in
+R actually work better in the wide format data.
+
+## From wide to long format with pivot\_longer()
+
+Until now, we've been using the nicely formatted original gapminder dataset, but
+'real' data (i.e. our own research data) will never be so well organized. Here
+let's start with the wide formatted version of the gapminder dataset.
+
+> Download the wide version of the gapminder data from [this link to a csv file](data/gapminder_wide.csv)
+> and save it in your data folder.
+
+We'll load the data file and look at it. Note: we don't want our continent and
+country columns to be factors, so we use the stringsAsFactors argument for
+`read.csv()` to disable that.
+
+
+``` r
+gap_wide <- read.csv("data/gapminder_wide.csv", stringsAsFactors = FALSE)
+str(gap_wide)
+```
+
+``` output
+'data.frame': 142 obs. of 38 variables:
+ $ continent : chr "Africa" "Africa" "Africa" "Africa" ...
+ $ country : chr "Algeria" "Angola" "Benin" "Botswana" ...
+ $ gdpPercap_1952: num 2449 3521 1063 851 543 ...
+ $ gdpPercap_1957: num 3014 3828 960 918 617 ...
+ $ gdpPercap_1962: num 2551 4269 949 984 723 ...
+ $ gdpPercap_1967: num 3247 5523 1036 1215 795 ...
+ $ gdpPercap_1972: num 4183 5473 1086 2264 855 ...
+ $ gdpPercap_1977: num 4910 3009 1029 3215 743 ...
+ $ gdpPercap_1982: num 5745 2757 1278 4551 807 ...
+ $ gdpPercap_1987: num 5681 2430 1226 6206 912 ...
+ $ gdpPercap_1992: num 5023 2628 1191 7954 932 ...
+ $ gdpPercap_1997: num 4797 2277 1233 8647 946 ...
+ $ gdpPercap_2002: num 5288 2773 1373 11004 1038 ...
+ $ gdpPercap_2007: num 6223 4797 1441 12570 1217 ...
+ $ lifeExp_1952 : num 43.1 30 38.2 47.6 32 ...
+ $ lifeExp_1957 : num 45.7 32 40.4 49.6 34.9 ...
+ $ lifeExp_1962 : num 48.3 34 42.6 51.5 37.8 ...
+ $ lifeExp_1967 : num 51.4 36 44.9 53.3 40.7 ...
+ $ lifeExp_1972 : num 54.5 37.9 47 56 43.6 ...
+ $ lifeExp_1977 : num 58 39.5 49.2 59.3 46.1 ...
+ $ lifeExp_1982 : num 61.4 39.9 50.9 61.5 48.1 ...
+ $ lifeExp_1987 : num 65.8 39.9 52.3 63.6 49.6 ...
+ $ lifeExp_1992 : num 67.7 40.6 53.9 62.7 50.3 ...
+ $ lifeExp_1997 : num 69.2 41 54.8 52.6 50.3 ...
+ $ lifeExp_2002 : num 71 41 54.4 46.6 50.6 ...
+ $ lifeExp_2007 : num 72.3 42.7 56.7 50.7 52.3 ...
+ $ pop_1952 : num 9279525 4232095 1738315 442308 4469979 ...
+ $ pop_1957 : num 10270856 4561361 1925173 474639 4713416 ...
+ $ pop_1962 : num 11000948 4826015 2151895 512764 4919632 ...
+ $ pop_1967 : num 12760499 5247469 2427334 553541 5127935 ...
+ $ pop_1972 : num 14760787 5894858 2761407 619351 5433886 ...
+ $ pop_1977 : num 17152804 6162675 3168267 781472 5889574 ...
+ $ pop_1982 : num 20033753 7016384 3641603 970347 6634596 ...
+ $ pop_1987 : num 23254956 7874230 4243788 1151184 7586551 ...
+ $ pop_1992 : num 26298373 8735988 4981671 1342614 8878303 ...
+ $ pop_1997 : num 29072015 9875024 6066080 1536536 10352843 ...
+ $ pop_2002 : int 31287142 10866106 7026113 1630347 12251209 7021078 15929988 4048013 8835739 614382 ...
+ $ pop_2007 : int 33333216 12420476 8078314 1639131 14326203 8390505 17696293 4369038 10238807 710960 ...
+```
+
+{alt='Diagram illustrating the wide format of the gapminder data frame'}
+
+To change this very wide data frame layout back to our nice, intermediate (or longer) layout, we will use one of the two available `pivot` functions from the `tidyr` package. To convert from wide to a longer format, we will use the `pivot_longer()` function. `pivot_longer()` makes datasets longer by increasing the number of rows and decreasing the number of columns, or 'lengthening' your observation variables into a single variable.
+
+{alt='Diagram illustrating how pivot longer reorganizes a data frame from a wide to long format'}
+
+
+``` r
+gap_long <- gap_wide %>%
+ pivot_longer(
+ cols = c(starts_with('pop'), starts_with('lifeExp'), starts_with('gdpPercap')),
+ names_to = "obstype_year", values_to = "obs_values"
+ )
+str(gap_long)
+```
+
+``` output
+tibble [5,112 × 4] (S3: tbl_df/tbl/data.frame)
+ $ continent : chr [1:5112] "Africa" "Africa" "Africa" "Africa" ...
+ $ country : chr [1:5112] "Algeria" "Algeria" "Algeria" "Algeria" ...
+ $ obstype_year: chr [1:5112] "pop_1952" "pop_1957" "pop_1962" "pop_1967" ...
+ $ obs_values : num [1:5112] 9279525 10270856 11000948 12760499 14760787 ...
+```
+
+Here we have used piping syntax which is similar to what we were doing in the
+previous lesson with dplyr. In fact, these are compatible and you can use a mix
+of tidyr and dplyr functions by piping them together.
+
+We first provide to `pivot_longer()` a vector of column names that will be
+pivoted into longer format. We could type out all the observation variables, but
+as in the `select()` function (see `dplyr` lesson), we can use the `starts_with()`
+argument to select all variables that start with the desired character string.
+`pivot_longer()` also allows the alternative syntax of using the `-` symbol to
+identify which variables are not to be pivoted (i.e. ID variables).
+
+The next arguments to `pivot_longer()` are `names_to` for naming the column that
+will contain the new ID variable (`obstype_year`) and `values_to` for naming the
+new amalgamated observation variable (`obs_value`). We supply these new column
+names as strings.
+
+{alt='Diagram illustrating the long format of the gapminder data'}
+
+
+``` r
+gap_long <- gap_wide %>%
+ pivot_longer(
+ cols = c(-continent, -country),
+ names_to = "obstype_year", values_to = "obs_values"
+ )
+str(gap_long)
+```
+
+``` output
+tibble [5,112 × 4] (S3: tbl_df/tbl/data.frame)
+ $ continent : chr [1:5112] "Africa" "Africa" "Africa" "Africa" ...
+ $ country : chr [1:5112] "Algeria" "Algeria" "Algeria" "Algeria" ...
+ $ obstype_year: chr [1:5112] "gdpPercap_1952" "gdpPercap_1957" "gdpPercap_1962" "gdpPercap_1967" ...
+ $ obs_values : num [1:5112] 2449 3014 2551 3247 4183 ...
+```
+
+That may seem trivial with this particular data frame, but sometimes you have 1
+ID variable and 40 observation variables with irregular variable names. The
+flexibility is a huge time saver!
+
+Now `obstype_year` actually contains 2 pieces of information, the observation
+type (`pop`,`lifeExp`, or `gdpPercap`) and the `year`. We can use the
+`separate()` function to split the character strings into multiple variables
+
+
+``` r
+gap_long <- gap_long %>% separate(obstype_year, into = c('obs_type', 'year'), sep = "_")
+gap_long$year <- as.integer(gap_long$year)
+```
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 2
+
+Using `gap_long`, calculate the mean life expectancy, population, and gdpPercap for each continent.
+**Hint:** use the `group_by()` and `summarize()` functions we learned in the `dplyr` lesson
+
+::::::::::::::: solution
+
+## Solution to Challenge 2
+
+
+``` r
+gap_long %>% group_by(continent, obs_type) %>%
+ summarize(means=mean(obs_values))
+```
+
+``` output
+`summarise()` has grouped output by 'continent'. You can override using the
+`.groups` argument.
+```
+
+``` output
+# A tibble: 15 × 3
+# Groups: continent [5]
+ continent obs_type means
+ 
+
+What is the distribution of this data?
+
+### What is the distribution of population?
+
+The raw values are difficult to visualise, so we can take the log of the values and log those. Try this command
+
+
+``` r
+ggplot(lon_dims_imd_2019, aes(x = log(barriers_london_rank))) +
+ geom_histogram()
+```
+
+``` output
+`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+```
+
+
+
+What is the distribution of this data?
+
+## Parametric vs non-parametric analysis
+
+- Parametric analysis assumes that
+ - The data follows a known distribution
+ - It can be described using *parameters*
+ - Examples of distributions include, normal, Poisson, exponential.
+- Non parametric data
+ - The data can't be said to follow a known distribution
+
+::::::::::::::::::::::::::::::::::::: instructor
+Emphasise that parametric is not equal to normal.
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+### Describing parametric and non-parametric data
+
+How do you use numbers to convey what your data looks like.
+
+- Parametric data
+ - Use the parameters that describe the distribution.
+ - For a Gaussian (normal) distribution - use mean and standard deviation
+ - For a Poisson distribution - use average event rate
+ - etc.
+- Non Parametric data
+ - Use the median (the middle number when they are ranked from lowest to highest) and the interquartile range (the number 75% of the way up the list when ranked minus the number 25% of the way)
+- You can use the command `summary(data_frame_name)` to get these numbers for each variable.
+
+## Mean versus standard deviation
+
+- What does standard deviation mean?
+- Both graphs have the same mean (center), but the second one has data which is more spread out.
+
+
+``` r
+# small standard deviation
+dummy_1 <- rnorm(1000, mean = 10, sd = 0.5)
+dummy_1 <- as.data.frame(dummy_1)
+ggplot(dummy_1, aes(x = dummy_1)) +
+ geom_histogram()
+```
+
+``` output
+`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+```
+
+
+
+``` r
+# larger standard deviation
+dummy_2 <- rnorm(1000, mean = 10, sd = 200)
+dummy_2 <- as.data.frame(dummy_2)
+ggplot(dummy_2, aes(x = dummy_2)) +
+ geom_histogram()
+```
+
+``` output
+`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
+```
+
+
+
+::::::::::::::::::::::::::::::::::::: instructor
+Get them to plot the graphs. Explain that we are generating random data from different distributions and plotting them.
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+### Calculating mean and standard deviation
+
+
+``` r
+mean(lon_dims_imd_2019$barriers_london_rank, na.rm = TRUE)
+```
+
+``` output
+[1] 2418
+```
+
+Calculate the standard deviation and confirm that it is the square root of the variance:
+
+
+``` r
+sdbarriers <- sd(lon_dims_imd_2019$barriers_london_rank, na.rm = TRUE)
+print(sdbarriers)
+```
+
+``` output
+[1] 1395.889
+```
+
+``` r
+varbarriers <- var(lon_dims_imd_2019$barriers_london_rank, na.rm = TRUE)
+print(varbarriers)
+```
+
+``` output
+[1] 1948505
+```
+
+``` r
+sqrt(varbarriers) == sdbarriers
+```
+
+``` output
+[1] TRUE
+```
+
+The `na.rm` argument tells R to ignore missing values in the variable.
+
+### Calculating median and interquartile range
+
+
+``` r
+median(lon_dims_imd_2019$barriers_london_rank, na.rm = TRUE)
+```
+
+``` output
+[1] 2418
+```
+
+
+``` r
+IQR(lon_dims_imd_2019$barriers_london_rank, na.rm = TRUE)
+```
+
+``` output
+[1] 2417
+```
+
+Again, we ignore the missing values.
+
+## Describing discrete data
+
+- Frequencies
+
+
+``` r
+table(lon_dims_imd_2019$la19nm)
+```
+
+``` output
+
+ Barking and Dagenham Barnet Bexley
+ 110 211 146
+ Brent Bromley Camden
+ 173 197 133
+ City of London Croydon Ealing
+ 6 220 196
+ Enfield Greenwich Hackney
+ 183 151 144
+Hammersmith and Fulham Haringey Harrow
+ 113 145 137
+ Havering Hillingdon Hounslow
+ 150 161 142
+ Islington Kensington and Chelsea Kingston upon Thames
+ 123 103 98
+ Lambeth Lewisham Merton
+ 178 169 124
+ Newham Redbridge Richmond upon Thames
+ 164 161 115
+ Southwark Sutton Tower Hamlets
+ 166 121 144
+ Waltham Forest Wandsworth Westminster
+ 144 179 128
+```
+
+- Proportions
+
+
+``` r
+areastable <- table(lon_dims_imd_2019$la19nm)
+prop.table(areastable)
+```
+
+``` output
+
+ Barking and Dagenham Barnet Bexley
+ 0.022750776 0.043640124 0.030196484
+ Brent Bromley Camden
+ 0.035780765 0.040744571 0.027507756
+ City of London Croydon Ealing
+ 0.001240951 0.045501551 0.040537746
+ Enfield Greenwich Hackney
+ 0.037849018 0.031230610 0.029782834
+Hammersmith and Fulham Haringey Harrow
+ 0.023371251 0.029989659 0.028335057
+ Havering Hillingdon Hounslow
+ 0.031023785 0.033298862 0.029369183
+ Islington Kensington and Chelsea Kingston upon Thames
+ 0.025439504 0.021302999 0.020268873
+ Lambeth Lewisham Merton
+ 0.036814891 0.034953464 0.025646329
+ Newham Redbridge Richmond upon Thames
+ 0.033919338 0.033298862 0.023784902
+ Southwark Sutton Tower Hamlets
+ 0.034332989 0.025025853 0.029782834
+ Waltham Forest Wandsworth Westminster
+ 0.029782834 0.037021717 0.026473630
+```
+
+Contingency tables of frequencies can also be tabulated with **table()**. For example:
+
+
+``` r
+table(
+ lon_dims_imd_2019$la19nm,
+ lon_dims_imd_2019$IDAOP_london_decile
+)
+```
+
+``` output
+
+ 1 2 3 4 5 6 7 8 9 10
+ Barking and Dagenham 6 11 23 25 22 12 7 4 0 0
+ Barnet 6 7 13 15 18 32 29 37 29 25
+ Bexley 0 3 2 5 11 11 15 24 30 45
+ Brent 12 19 24 28 43 18 17 11 1 0
+ Bromley 2 3 6 9 10 12 20 19 41 75
+ Camden 12 19 14 18 14 10 9 16 11 10
+ City of London 0 0 1 0 0 0 0 1 0 4
+ Croydon 8 7 16 25 23 20 29 24 29 39
+ Ealing 11 18 22 23 24 23 31 21 18 5
+ Enfield 9 19 27 22 26 17 16 22 18 7
+ Greenwich 19 20 11 19 17 14 20 14 12 5
+ Hackney 65 44 17 13 3 0 2 0 0 0
+ Hammersmith and Fulham 13 10 22 14 12 14 10 11 6 1
+ Haringey 30 32 25 14 8 5 12 9 8 2
+ Harrow 2 6 8 8 20 24 26 17 15 11
+ Havering 0 2 4 8 7 16 17 21 37 38
+ Hillingdon 1 5 6 9 13 26 28 24 20 29
+ Hounslow 2 8 12 16 19 25 23 21 14 2
+ Islington 29 27 26 17 8 7 6 3 0 0
+ Kensington and Chelsea 13 9 9 5 5 11 5 10 15 21
+ Kingston upon Thames 1 1 2 3 2 8 12 19 22 28
+ Lambeth 34 33 26 26 22 16 9 9 3 0
+ Lewisham 14 17 21 25 31 20 14 17 10 0
+ Merton 1 2 7 10 10 19 14 21 25 15
+ Newham 44 57 30 17 8 7 1 0 0 0
+ Redbridge 3 5 19 23 24 25 19 20 13 10
+ Richmond upon Thames 0 0 0 3 2 5 11 16 32 46
+ Southwark 37 39 21 18 15 12 7 5 6 6
+ Sutton 0 1 5 6 5 8 11 22 30 33
+ Tower Hamlets 83 17 16 6 7 4 5 4 1 1
+ Waltham Forest 8 16 21 20 19 23 14 12 7 4
+ Wandsworth 3 17 12 24 24 27 32 18 15 7
+ Westminster 15 10 15 10 11 13 12 12 15 15
+```
+
+Which leads quite naturally to the consideration of any association between the observed frequencies.
+
+# Inferential statistics
+
+## Meaningful analysis
+
+- What is your hypothesis - what is your null hypothesis?
+
+::: callout
+Always: the level of the independent variable has no effect on the level of the dependent variable.
+:::
+
+- What type of variables (data type) do you have?
+
+- What are the assumptions of the test you are using?
+
+- Interpreting the result
+
+## Testing significance
+
+- p-value
+
+- \<0.05
+
+- 0.03-0.049
+
+ - Would benefit from further testing.
+
+**0.05** is not a magic number.
+
+## Comparing means
+
+It all starts with a hypothesis
+
+- Null hypothesis
+ - "There is no difference in mean height between men and women" $$mean\_height\_men - mean\_height\_women = 0$$
+- Alternate hypothesis
+ - "There is a difference in mean height between men and women"
+
+## More on hypothesis testing
+
+- The null hypothesis (H0) assumes that the true mean difference (μd) is equal to zero.
+
+- The two-tailed alternative hypothesis (H1) assumes that μd is not equal to zero.
+
+- The upper-tailed alternative hypothesis (H1) assumes that μd is greater than zero.
+
+- The lower-tailed alternative hypothesis (H1) assumes that μd is less than zero.
+
+- Remember: hypotheses are never about data, they are about the processes which produce the data. The value of μd is unknown. The goal of hypothesis testing is to determine the hypothesis (null or alternative) with which the data are more consistent.
+
+## Comparing means
+
+Is there an absolute difference between the income ranks of the Lower-layer Super Output Areas?
+
+
+``` r
+lon_dims_imd_2019 %>%
+ group_by(la19nm) %>%
+ summarise(avg = mean(Income_london_rank)) %>%
+ arrange(la19nm, .locale = "en")
+```
+
+``` output
+# A tibble: 33 × 2
+ la19nm avg
+ 
+
+Having decided that a further investigation of this relationship is worthwhile, we can create a linear model with the function `lm()`.
+
+
+``` r
+modelone <- lm(lon_dims_imd_2019$Income_london_rank ~ lon_dims_imd_2019$health_london_rank)
+summary(modelone)
+```
+
+``` output
+
+Call:
+lm(formula = lon_dims_imd_2019$Income_london_rank ~ lon_dims_imd_2019$health_london_rank)
+
+Residuals:
+ Min 1Q Median 3Q Max
+-15354 -3547 -102 3458 24528
+
+Coefficients:
+ Estimate Std. Error t value Pr(>|t|)
+(Intercept) -3.223e+03 2.039e+02 -15.80 <2e-16 ***
+lon_dims_imd_2019$health_london_rank 8.634e-01 9.293e-03 92.91 <2e-16 ***
+---
+Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 5087 on 4833 degrees of freedom
+Multiple R-squared: 0.6411, Adjusted R-squared: 0.641
+F-statistic: 8632 on 1 and 4833 DF, p-value: < 2.2e-16
+```
+
+## Regression with a categorical IV (the t-test)
+
+Run the following code chunk and compare the results to the t-test conducted earlier.
+
+
+``` r
+lon_dims_imd_2019 %>%
+ mutate(city = factor(city))
+```
+
+``` output
+ ls11cd la19nm IDAOP_london_rank IDAOP_london_decile
+1 E01000001 City of London 32,820 10
+2 E01000002 City of London 31,938 10
+3 E01000003 City of London 16,377 8
+4 E01000005 City of London 3,885 3
+5 E01000006 Barking and Dagenham 12,934 7
+6 E01000007 Barking and Dagenham 2,514 2
+7 E01000008 Barking and Dagenham 893 1
+8 E01000009 Barking and Dagenham 2,238 2
+9 E01000010 Barking and Dagenham 531 1
+10 E01000011 Barking and Dagenham 2,747 2
+11 E01000012 Barking and Dagenham 3,554 3
+12 E01000013 Barking and Dagenham 6,152 4
+13 E01000014 Barking and Dagenham 5,474 4
+14 E01000015 Barking and Dagenham 4,299 3
+15 E01000016 Barking and Dagenham 6,054 4
+16 E01000017 Barking and Dagenham 4,966 4
+17 E01000018 Barking and Dagenham 7,710 5
+18 E01000019 Barking and Dagenham 5,121 4
+19 E01000020 Barking and Dagenham 3,361 3
+20 E01000021 Barking and Dagenham 2,973 2
+21 E01000022 Barking and Dagenham 8,010 5
+22 E01000023 Barking and Dagenham 3,920 3
+23 E01000024 Barking and Dagenham 5,988 4
+24 E01000025 Barking and Dagenham 5,223 4
+25 E01000027 Barking and Dagenham 8,003 5
+26 E01000028 Barking and Dagenham 1,337 1
+27 E01000029 Barking and Dagenham 14,665 7
+28 E01000030 Barking and Dagenham 6,752 4
+29 E01000031 Barking and Dagenham 10,229 6
+30 E01000032 Barking and Dagenham 10,623 6
+31 E01000033 Barking and Dagenham 6,836 4
+32 E01000034 Barking and Dagenham 4,183 3
+33 E01000035 Barking and Dagenham 10,771 6
+34 E01000036 Barking and Dagenham 17,831 8
+35 E01000037 Barking and Dagenham 19,150 8
+36 E01000038 Barking and Dagenham 12,816 7
+37 E01000039 Barking and Dagenham 10,237 6
+38 E01000040 Barking and Dagenham 4,009 3
+39 E01000041 Barking and Dagenham 8,307 5
+40 E01000042 Barking and Dagenham 2,664 2
+41 E01000043 Barking and Dagenham 7,080 5
+42 E01000044 Barking and Dagenham 11,619 6
+43 E01000045 Barking and Dagenham 6,039 4
+44 E01000046 Barking and Dagenham 6,970 5
+45 E01000048 Barking and Dagenham 542 1
+46 E01000049 Barking and Dagenham 2,013 2
+47 E01000050 Barking and Dagenham 3,780 3
+48 E01000051 Barking and Dagenham 4,347 3
+49 E01000052 Barking and Dagenham 4,952 4
+50 E01000053 Barking and Dagenham 3,562 3
+51 E01000054 Barking and Dagenham 8,518 5
+52 E01000055 Barking and Dagenham 5,627 4
+53 E01000056 Barking and Dagenham 7,170 5
+54 E01000057 Barking and Dagenham 9,204 5
+55 E01000058 Barking and Dagenham 3,947 3
+56 E01000059 Barking and Dagenham 6,592 4
+57 E01000060 Barking and Dagenham 6,146 4
+58 E01000061 Barking and Dagenham 876 1
+59 E01000062 Barking and Dagenham 9,404 6
+60 E01000063 Barking and Dagenham 9,933 6
+61 E01000064 Barking and Dagenham 7,753 5
+62 E01000065 Barking and Dagenham 4,265 3
+63 E01000066 Barking and Dagenham 6,256 4
+64 E01000067 Barking and Dagenham 7,362 5
+65 E01000068 Barking and Dagenham 6,374 4
+66 E01000069 Barking and Dagenham 11,375 6
+67 E01000070 Barking and Dagenham 11,616 6
+68 E01000071 Barking and Dagenham 7,099 5
+69 E01000072 Barking and Dagenham 4,356 3
+70 E01000073 Barking and Dagenham 3,775 3
+71 E01000074 Barking and Dagenham 5,611 4
+72 E01000075 Barking and Dagenham 8,331 5
+73 E01000076 Barking and Dagenham 5,967 4
+74 E01000077 Barking and Dagenham 4,001 3
+75 E01000078 Barking and Dagenham 7,609 5
+76 E01000079 Barking and Dagenham 3,522 3
+77 E01000080 Barking and Dagenham 7,102 5
+78 E01000081 Barking and Dagenham 8,809 5
+79 E01000082 Barking and Dagenham 8,412 5
+80 E01000083 Barking and Dagenham 7,666 5
+81 E01000084 Barking and Dagenham 5,988 4
+82 E01000085 Barking and Dagenham 4,473 3
+83 E01000086 Barking and Dagenham 2,071 2
+84 E01000087 Barking and Dagenham 5,755 4
+85 E01000088 Barking and Dagenham 11,461 6
+86 E01000089 Barking and Dagenham 10,978 6
+87 E01000090 Barking and Dagenham 13,059 7
+88 E01000091 Barking and Dagenham 4,494 3
+89 E01000092 Barking and Dagenham 2,433 2
+90 E01000093 Barking and Dagenham 3,602 3
+91 E01000094 Barking and Dagenham 4,696 3
+92 E01000095 Barking and Dagenham 5,578 4
+93 E01000096 Barking and Dagenham 7,216 5
+94 E01000097 Barking and Dagenham 3,003 2
+95 E01000098 Barking and Dagenham 3,976 3
+96 E01000099 Barking and Dagenham 4,653 3
+97 E01000100 Barking and Dagenham 7,748 5
+98 E01000101 Barking and Dagenham 3,708 3
+99 E01000102 Barking and Dagenham 2,897 2
+100 E01000103 Barking and Dagenham 5,718 4
+101 E01000104 Barking and Dagenham 5,031 4
+102 E01000105 Barking and Dagenham 2,176 2
+103 E01000106 Barking and Dagenham 3,214 3
+104 E01000107 Barking and Dagenham 13,271 7
+105 E01000108 Barking and Dagenham 15,136 8
+106 E01000109 Barking and Dagenham 14,500 7
+107 E01000110 Barking and Dagenham 16,839 8
+108 E01000111 Barking and Dagenham 6,411 4
+109 E01000112 Barking and Dagenham 7,486 5
+110 E01000113 Barking and Dagenham 10,921 6
+111 E01000114 Barking and Dagenham 14,710 7
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+4695 FALSE
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+4702 FALSE
+4703 FALSE
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+4707 FALSE
+4708 FALSE
+4709 FALSE
+4710 FALSE
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+4713 FALSE
+4714 FALSE
+4715 FALSE
+4716 FALSE
+4717 FALSE
+4718 FALSE
+4719 FALSE
+4720 FALSE
+4721 FALSE
+4722 FALSE
+4723 FALSE
+4724 FALSE
+4725 FALSE
+4726 FALSE
+4727 FALSE
+4728 FALSE
+4729 FALSE
+4730 FALSE
+4731 FALSE
+4732 FALSE
+4733 FALSE
+4734 FALSE
+4735 FALSE
+4736 FALSE
+4737 FALSE
+4738 FALSE
+4739 FALSE
+4740 FALSE
+4741 FALSE
+4742 FALSE
+4743 FALSE
+4744 FALSE
+4745 FALSE
+4746 FALSE
+4747 FALSE
+4748 FALSE
+4749 FALSE
+4750 FALSE
+4751 FALSE
+4752 FALSE
+4753 FALSE
+4754 FALSE
+4755 FALSE
+4756 FALSE
+4757 FALSE
+4758 FALSE
+4759 FALSE
+4760 FALSE
+4761 FALSE
+ [ reached 'max' / getOption("max.print") -- omitted 74 rows ]
+```
+
+``` r
+modelttest <- lm(lon_dims_imd_2019$health_london_rank ~ lon_dims_imd_2019$city)
+
+summary(modelttest)
+```
+
+``` output
+
+Call:
+lm(formula = lon_dims_imd_2019$health_london_rank ~ lon_dims_imd_2019$city)
+
+Residuals:
+ Min 1Q Median 3Q Max
+-19251.6 -6392.6 367.4 6853.4 12362.4
+
+Coefficients:
+ Estimate Std. Error t value Pr(>|t|)
+(Intercept) 20481.6 113.3 180.75 <2e-16 ***
+lon_dims_imd_2019$cityTRUE 1478.2 3216.6 0.46 0.646
+---
+Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 7874 on 4833 degrees of freedom
+Multiple R-squared: 4.37e-05, Adjusted R-squared: -0.0001632
+F-statistic: 0.2112 on 1 and 4833 DF, p-value: 0.6458
+```
+
+## Regression with a categorical IV (ANOVA)
+
+Use the `lm()` function to model the relationship between `lon_dims_imd_2019$la19nm`
+and `lon_dims_imd_2019$health_london_rank`. Compare the results with the ANOVA carried out earlier.
+
+## Break
+
+- We will explore regression models in more detail during the rest of the day
diff --git a/24-linear-reg-broom.md b/24-linear-reg-broom.md
new file mode 100644
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+---
+title: "Linear Regression and Broom"
+teaching: 80
+exercises: 20
+source: Rmd
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- To be able to explore relationships between variables
+- To be able to calculate predicted variables and residuals
+- To be able to construct linear regression models
+- To be able to present model outcomes using Broom
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- How can I explore relationships between variables in my data?
+- How can I present model outputs in an easier to read way?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Content
+
+- Linear Regression Models
+- Use of Log transform
+- Use of Categorical Variables
+- Use of Broom
+
+## Data
+
+
+``` r
+# We will need these libraries and this data later.
+library(ggplot2)
+library(tidyverse)
+library(lmtest)
+library(sandwich)
+library(broom)
+
+lon_dims_imd_2019 <- read.csv("data/English_IMD_2019_Domains_rebased_London_by_CDRC.csv")
+```
+
+We are going to use the data from the Consumer Data Research Centre, specifically the London IMD 2019 (English IMD 2019 Domains rebased).
+
+Atribution: Data provided by the Consumer Data Research Centre, an ESRC Data Investment: ES/L011840/1, ES/L011891/1
+
+The statistical unit areas across the country are Lower layer Super Output Areas (LSOAs). We will explore the relationships between the different dimensions of the Indices of Multiple Deprivation.
+
+## Linear Regression
+
+Linear Regression enables use to to explore the the linear relationship of the dependent variable Y and independent variable(s) X(s).
+We are going to explore the linear relationship between the Health Deprivation and Disability Domain and the Living Environment Deprivation Domain.
+
+The Health Deprivation and Disability Domain measures the risk of premature death and the impairment of quality of life through poor physical or mental health. The domain measures morbidity, disability and premature mortality but not aspects of behaviour or environment that may be predictive of future health deprivation.
+
+The Living Environment Deprivation Domain measures the quality of the local environment. The indicators fall into two sub-domains. The ‘indoors’ living environment measures the quality of housing; while the ‘outdoors’ living environment contains measures of air quality and road traffic accidents.
+
+Reference:
+ McLennan, David et al. The English Indices of Deprivation 2019 : Technical Report. Ministry of Housing, Communities and Local Government, 2019. Print.
+
+
+### Simple Linear Regression
+In the simple linear regression example we have only one dependent variable (health_london_rank) and one independent variable (livingEnv_london_rank).
+
+
+``` r
+reg_LivEnv_health <- lm(health_london_rank ~ livingEnv_london_rank, data = lon_dims_imd_2019)
+# We put the dependent variable to the left of the '~' and the independent variable(s) to the right
+# and we tell R which dataset we are referring to.
+
+summary(reg_LivEnv_health)
+```
+
+``` output
+
+Call:
+lm(formula = health_london_rank ~ livingEnv_london_rank, data = lon_dims_imd_2019)
+
+Residuals:
+ Min 1Q Median 3Q Max
+-21549.6 -5948.2 609.1 6239.2 15792.2
+
+Coefficients:
+ Estimate Std. Error t value Pr(>|t|)
+(Intercept) 1.692e+04 2.274e+02 74.40 <2e-16 ***
+livingEnv_london_rank 3.430e-01 1.915e-02 17.91 <2e-16 ***
+---
+Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 7625 on 4833 degrees of freedom
+Multiple R-squared: 0.06225, Adjusted R-squared: 0.06205
+F-statistic: 320.8 on 1 and 4833 DF, p-value: < 2.2e-16
+```
+
+From the result of this analysis, we can see that the Living Environment Deprivation Domain rank has a significant(small p-value, general rule of thumb <0.05) and positive relationship(positive coefficient) with the Health Deprivation and Disability Domain rank.
+
+One way of interpreting the result is: One unit increase in the Living Environment rank is related to around 0.343 (3.430e-01) points increase of the Health Deprivation and Disability rank.
+
+R-square shows the amount of variance of Y explained by X. In this case the Living Environment rank explains 6.225% of the variance in the Health Deprivation and Disability rank. Adj R2(6.205%) shows the same as R2 but adjusted by the # of cases and # of variables. When the # of variables is small and the # of cases is very large then Adj R2 is closer to R2.
+
+### Log transform
+
+If your data is skwewed, it can be useful to transform a variable to it's log form when doing the regression.
+You can either transform the variable beforehand or do so in the equation.
+
+
+``` r
+reg_logbarriers_health <- lm(health_london_rank ~ log(barriers_london_rank), data = lon_dims_imd_2019)
+
+summary(reg_logbarriers_health)
+```
+
+``` output
+
+Call:
+lm(formula = health_london_rank ~ log(barriers_london_rank),
+ data = lon_dims_imd_2019)
+
+Residuals:
+ Min 1Q Median 3Q Max
+-20379.3 -5611.7 774.9 5988.6 23828.5
+
+Coefficients:
+ Estimate Std. Error t value Pr(>|t|)
+(Intercept) -1349.4 798.3 -1.69 0.091 .
+log(barriers_london_rank) 2917.0 105.7 27.59 <2e-16 ***
+---
+Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 7319 on 4833 degrees of freedom
+Multiple R-squared: 0.1361, Adjusted R-squared: 0.1359
+F-statistic: 761.1 on 1 and 4833 DF, p-value: < 2.2e-16
+```
+The interpretation of the log-transformed variable is a bit different.
+In this example only the predictor variable is log tranformed, therefore to interpret the slope coefficient we divide it by 100 (2917.0/100=29.170).
+
+If the dependent/response variable is solely log-transformed. Exponentiate the coefficient. This gives the multiplicative factor for every one-unit increase in the independent variable. Example: the coefficient is 0.198. exp(0.198) = 1.218962. For every one-unit increase in the independent variable, our dependent variable increases by a factor of about 1.22, or 22%. Recall that multiplying a number by 1.22 is the same as increasing the number by 22%. Likewise, multiplying a number by, say 0.84, is the same as decreasing the number by 1 – 0.84 = 0.16, or 16%.
+
+If both are transformed. nterpret the coefficient as the percent increase in the dependent variable for every 1% increase in the independent variable. Example: the coefficient is 0.198. For every 1% increase in the independent variable, our dependent variable increases by about 0.20%. For x percent increase, calculate 1.x to the power of the coefficient, subtract 1, and multiply by 100. Example: For every 20% increase in the independent variable, our dependent variable increases by about (1.20 0.198 - 1) * 100 = 3.7 percent.
+
+### Predicted values and Residuals
+
+We can expand our simple linear regression example to incorporate the Barriers to Housing and Services Domain rank.
+The Barriers to Housing and Services Domain measures the physical and financial accessibility of housing and local services. The indicators fall into two sub-domains: ‘geographical barriers’, which relate to the physical proximity of local services, and ‘wider barriers’ which includes issues relating to access to housing, such as affordability.
+
+
+``` r
+reg_LivEnv_barriers_health <- lm(
+ health_london_rank ~ livingEnv_london_rank + barriers_london_rank,
+ data = lon_dims_imd_2019
+)
+
+summary(reg_LivEnv_barriers_health)
+```
+
+``` output
+
+Call:
+lm(formula = health_london_rank ~ livingEnv_london_rank + barriers_london_rank,
+ data = lon_dims_imd_2019)
+
+Residuals:
+ Min 1Q Median 3Q Max
+-21685.8 -4834.9 546.5 5142.4 18971.7
+
+Coefficients:
+ Estimate Std. Error t value Pr(>|t|)
+(Intercept) 1.169e+04 2.502e+02 46.74 <2e-16 ***
+livingEnv_london_rank 2.620e-01 1.721e-02 15.22 <2e-16 ***
+barriers_london_rank 2.508e+00 7.059e-02 35.54 <2e-16 ***
+---
+Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 6790 on 4832 degrees of freedom
+Multiple R-squared: 0.2566, Adjusted R-squared: 0.2562
+F-statistic: 833.7 on 2 and 4832 DF, p-value: < 2.2e-16
+```
+
+After running the regression model, we can access the model predicted values and the residuals compared to the real observations.
+
+
+``` r
+# first we fit the predictions
+health_rank_pred <- fitted(reg_LivEnv_barriers_health)
+health_rank_pred <- as.data.frame(health_rank_pred)
+
+# now we add the residual values too
+health_rank_resid <- residuals(reg_LivEnv_barriers_health)
+health_rank_pred$resid <- health_rank_resid
+
+# We can thenview the predictions and residuals
+head(health_rank_pred)
+```
+
+``` output
+ health_rank_pred resid
+1 20454.30 11658.702
+2 24260.97 5444.031
+3 15233.92 2366.080
+4 16671.36 1235.645
+5 15719.80 5861.196
+6 14981.47 1432.528
+```
+
+
+``` r
+# You can view the full data in RStudio with the View() function
+View(health_rank_pred)
+```
+
+### Robust Regression
+
+We can run the robust standard error regressions(control for heteroskedasticity, meaning unequal variances):
+
+
+``` r
+reg_LivEnv_barriers_health$robse <- vcovHC(reg_LivEnv_barriers_health, type = "HC1")
+coeftest(reg_LivEnv_barriers_health, reg_LivEnv_barriers_health$robse)
+```
+
+``` output
+
+t test of coefficients:
+
+ Estimate Std. Error t value Pr(>|t|)
+(Intercept) 1.1694e+04 2.3776e+02 49.182 < 2.2e-16 ***
+livingEnv_london_rank 2.6197e-01 1.7458e-02 15.006 < 2.2e-16 ***
+barriers_london_rank 2.5085e+00 6.4394e-02 38.955 < 2.2e-16 ***
+---
+Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+```
+
+In addition, we can access the cluster-robust standard errors regression results:
+
+
+``` r
+# cluster-robust standard errors
+coeftest(reg_LivEnv_barriers_health, reg_LivEnv_barriers_health$clse)
+```
+
+``` output
+
+t test of coefficients:
+
+ Estimate Std. Error t value Pr(>|t|)
+(Intercept) 1.1694e+04 2.5018e+02 46.741 < 2.2e-16 ***
+livingEnv_london_rank 2.6197e-01 1.7207e-02 15.225 < 2.2e-16 ***
+barriers_london_rank 2.5085e+00 7.0588e-02 35.537 < 2.2e-16 ***
+---
+Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+```
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 1
+
+Use the `gapminder` data to create a linear model between two continuous variables.
+
+Discuss your question and your findings.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+### Regression with Categorical independent variables
+
+We will explore the use of categorical independent variables in linear regression in this episode.
+When the dependent variable is a categorical variable, you may consider the alternatives of linear regression like logit regression and multinomial regression.
+
+
+``` r
+# As a categorical variable we have added la19nm, these are the names of the London boroughs
+reg_cat_var <- lm(health_london_rank ~ livingEnv_london_rank + barriers_london_rank + la19nm, data = lon_dims_imd_2019)
+
+summary(reg_cat_var)
+```
+
+``` output
+
+Call:
+lm(formula = health_london_rank ~ livingEnv_london_rank + barriers_london_rank +
+ la19nm, data = lon_dims_imd_2019)
+
+Residuals:
+ Min 1Q Median 3Q Max
+-21694.3 -3423.6 248.1 3691.8 19321.5
+
+Coefficients:
+ Estimate Std. Error t value Pr(>|t|)
+(Intercept) 1.105e+04 5.429e+02 20.346 < 2e-16 ***
+livingEnv_london_rank 3.595e-02 1.802e-02 1.996 0.046036 *
+barriers_london_rank 3.149e+00 7.888e-02 39.924 < 2e-16 ***
+la19nmBarnet 8.400e+03 6.508e+02 12.908 < 2e-16 ***
+la19nmBexley 1.416e+03 7.186e+02 1.970 0.048856 *
+la19nmBrent 7.765e+03 6.545e+02 11.863 < 2e-16 ***
+la19nmBromley 5.208e+03 6.789e+02 7.670 2.06e-14 ***
+la19nmCamden -1.842e+03 7.426e+02 -2.480 0.013184 *
+la19nmCity of London 4.742e+03 2.251e+03 2.107 0.035185 *
+la19nmCroydon 7.488e+02 6.388e+02 1.172 0.241192
+la19nmEaling 3.697e+03 6.459e+02 5.724 1.11e-08 ***
+la19nmEnfield 7.344e+03 6.501e+02 11.297 < 2e-16 ***
+la19nmGreenwich -2.221e+03 6.873e+02 -3.232 0.001238 **
+la19nmHackney -2.725e+03 6.811e+02 -4.000 6.43e-05 ***
+la19nmHammersmith and Fulham -3.907e+03 7.438e+02 -5.252 1.57e-07 ***
+la19nmHaringey 1.019e+03 6.877e+02 1.481 0.138580
+la19nmHarrow 9.993e+03 7.053e+02 14.168 < 2e-16 ***
+la19nmHavering -3.945e+02 7.317e+02 -0.539 0.589781
+la19nmHillingdon 8.459e+02 6.937e+02 1.219 0.222791
+la19nmHounslow 2.462e+03 6.890e+02 3.573 0.000356 ***
+la19nmIslington -8.217e+03 7.305e+02 -11.248 < 2e-16 ***
+la19nmKensington and Chelsea 9.191e+03 7.473e+02 12.299 < 2e-16 ***
+la19nmKingston upon Thames 4.899e+03 7.804e+02 6.277 3.75e-10 ***
+la19nmLambeth -6.270e+03 6.830e+02 -9.180 < 2e-16 ***
+la19nmLewisham -1.991e+03 6.675e+02 -2.983 0.002869 **
+la19nmMerton 3.387e+02 7.474e+02 0.453 0.650434
+la19nmNewham 4.209e+03 6.617e+02 6.361 2.19e-10 ***
+la19nmRedbridge 4.420e+03 6.914e+02 6.392 1.79e-10 ***
+la19nmRichmond upon Thames 4.469e+03 7.740e+02 5.774 8.22e-09 ***
+la19nmSouthwark -3.363e+03 6.731e+02 -4.996 6.05e-07 ***
+la19nmSutton -1.729e+02 7.537e+02 -0.229 0.818563
+la19nmTower Hamlets -5.862e+03 6.968e+02 -8.414 < 2e-16 ***
+la19nmWaltham Forest 2.026e+03 6.854e+02 2.956 0.003137 **
+la19nmWandsworth 1.087e+01 6.797e+02 0.016 0.987246
+la19nmWestminster 5.683e+02 7.472e+02 0.761 0.446958
+---
+Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 5362 on 4800 degrees of freedom
+Multiple R-squared: 0.5395, Adjusted R-squared: 0.5363
+F-statistic: 165.4 on 34 and 4800 DF, p-value: < 2.2e-16
+```
+
+R automatically recognizes la19nm as a factor and treats it accordingly.
+The missing one in the coefficient summary (Barking and Dagenham) is treated as a base line, therefore the value is 0.
+However, we can also modify our model to show for all:
+
+
+``` r
+reg_cat_var_showall <- lm(
+ health_london_rank ~ 0 + livingEnv_london_rank + barriers_london_rank + la19nm,
+ data = lon_dims_imd_2019
+)
+
+summary(reg_cat_var_showall)
+```
+
+``` output
+
+Call:
+lm(formula = health_london_rank ~ 0 + livingEnv_london_rank +
+ barriers_london_rank + la19nm, data = lon_dims_imd_2019)
+
+Residuals:
+ Min 1Q Median 3Q Max
+-21694.3 -3423.6 248.1 3691.8 19321.5
+
+Coefficients:
+ Estimate Std. Error t value Pr(>|t|)
+livingEnv_london_rank 3.595e-02 1.802e-02 1.996 0.046 *
+barriers_london_rank 3.149e+00 7.888e-02 39.924 < 2e-16 ***
+la19nmBarking and Dagenham 1.105e+04 5.429e+02 20.346 < 2e-16 ***
+la19nmBarnet 1.945e+04 4.764e+02 40.820 < 2e-16 ***
+la19nmBexley 1.246e+04 5.876e+02 21.209 < 2e-16 ***
+la19nmBrent 1.881e+04 4.526e+02 41.564 < 2e-16 ***
+la19nmBromley 1.625e+04 5.444e+02 29.858 < 2e-16 ***
+la19nmCamden 9.205e+03 5.783e+02 15.918 < 2e-16 ***
+la19nmCity of London 1.579e+04 2.198e+03 7.184 7.78e-13 ***
+la19nmCroydon 1.180e+04 4.574e+02 25.789 < 2e-16 ***
+la19nmEaling 1.474e+04 4.388e+02 33.601 < 2e-16 ***
+la19nmEnfield 1.839e+04 4.549e+02 40.431 < 2e-16 ***
+la19nmGreenwich 8.825e+03 5.233e+02 16.865 < 2e-16 ***
+la19nmHackney 8.322e+03 4.675e+02 17.802 < 2e-16 ***
+la19nmHammersmith and Fulham 7.140e+03 5.675e+02 12.582 < 2e-16 ***
+la19nmHaringey 1.207e+04 4.848e+02 24.886 < 2e-16 ***
+la19nmHarrow 2.104e+04 5.667e+02 37.127 < 2e-16 ***
+la19nmHavering 1.065e+04 6.178e+02 17.243 < 2e-16 ***
+la19nmHillingdon 1.189e+04 5.541e+02 21.464 < 2e-16 ***
+la19nmHounslow 1.351e+04 5.050e+02 26.750 < 2e-16 ***
+la19nmIslington 2.830e+03 5.500e+02 5.145 2.78e-07 ***
+la19nmKensington and Chelsea 2.024e+04 5.491e+02 36.859 < 2e-16 ***
+la19nmKingston upon Thames 1.595e+04 6.476e+02 24.624 < 2e-16 ***
+la19nmLambeth 4.777e+03 4.657e+02 10.257 < 2e-16 ***
+la19nmLewisham 9.055e+03 4.543e+02 19.931 < 2e-16 ***
+la19nmMerton 1.139e+04 5.935e+02 19.182 < 2e-16 ***
+la19nmNewham 1.526e+04 4.467e+02 34.149 < 2e-16 ***
+la19nmRedbridge 1.547e+04 5.307e+02 29.141 < 2e-16 ***
+la19nmRichmond upon Thames 1.552e+04 6.355e+02 24.414 < 2e-16 ***
+la19nmSouthwark 7.684e+03 4.662e+02 16.481 < 2e-16 ***
+la19nmSutton 1.087e+04 6.274e+02 17.332 < 2e-16 ***
+la19nmTower Hamlets 5.184e+03 5.107e+02 10.151 < 2e-16 ***
+la19nmWaltham Forest 1.307e+04 4.831e+02 27.057 < 2e-16 ***
+la19nmWandsworth 1.106e+04 4.905e+02 22.543 < 2e-16 ***
+la19nmWestminster 1.161e+04 5.649e+02 20.560 < 2e-16 ***
+---
+Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 5362 on 4800 degrees of freedom
+Multiple R-squared: 0.9407, Adjusted R-squared: 0.9403
+F-statistic: 2177 on 35 and 4800 DF, p-value: < 2.2e-16
+```
+### Categorical variables with interaction terms
+
+Sometimes we are interested in how a variable interacts with another variable.
+We can explore any interactions between locations (la19nm) and the living environment and barrier ranks.
+
+
+``` r
+reg_cat_var_int <- lm(health_london_rank ~ la19nm * (livingEnv_london_rank + barriers_london_rank), data = lon_dims_imd_2019)
+
+summary(reg_cat_var_int)
+```
+
+``` output
+
+Call:
+lm(formula = health_london_rank ~ la19nm * (livingEnv_london_rank +
+ barriers_london_rank), data = lon_dims_imd_2019)
+
+Residuals:
+ Min 1Q Median 3Q Max
+-19227.9 -3254.4 297.6 3372.8 17891.5
+
+Coefficients:
+ Estimate Std. Error
+(Intercept) 1.300e+04 1.431e+03
+la19nmBarnet 9.411e+03 1.909e+03
+la19nmBexley -5.447e+03 2.464e+03
+la19nmBrent 3.068e+03 1.866e+03
+la19nmBromley 1.577e+03 2.107e+03
+la19nmCamden -1.547e+04 3.565e+03
+la19nmCity of London -8.405e+02 5.283e+03
+la19nmCroydon -3.824e+03 1.679e+03
+la19nmEaling 2.124e+03 1.841e+03
+la19nmEnfield 2.420e+03 1.750e+03
+la19nmGreenwich -3.383e+03 2.268e+03
+la19nmHackney -1.803e+03 1.937e+03
+la19nmHammersmith and Fulham -1.123e+04 2.389e+03
+la19nmHaringey -4.527e+02 1.975e+03
+la19nmHarrow 7.800e+03 2.476e+03
+la19nmHavering -8.133e+03 2.519e+03
+la19nmHillingdon 7.792e+02 2.030e+03
+la19nmHounslow 4.939e+03 2.064e+03
+la19nmIslington -4.614e+03 2.592e+03
+la19nmKensington and Chelsea 1.264e+04 2.185e+03
+la19nmKingston upon Thames 5.297e+03 2.972e+03
+la19nmLambeth -6.973e+03 2.276e+03
+la19nmLewisham -4.745e+03 1.912e+03
+la19nmMerton -8.433e+03 2.793e+03
+la19nmNewham 3.468e+03 1.786e+03
+la19nmRedbridge 4.339e+03 2.156e+03
+la19nmRichmond upon Thames 3.822e+03 4.288e+03
+la19nmSouthwark -6.830e+03 1.923e+03
+la19nmSutton -8.242e+03 2.600e+03
+la19nmTower Hamlets -2.705e+03 2.163e+03
+la19nmWaltham Forest 2.070e+03 1.814e+03
+la19nmWandsworth 6.438e+02 2.060e+03
+la19nmWestminster -1.063e+04 3.281e+03
+livingEnv_london_rank -1.900e-01 1.321e-01
+barriers_london_rank 3.620e+00 8.023e-01
+la19nmBarnet:livingEnv_london_rank 1.641e-01 1.553e-01
+la19nmBexley:livingEnv_london_rank 5.286e-01 1.644e-01
+la19nmBrent:livingEnv_london_rank 5.219e-01 1.663e-01
+la19nmBromley:livingEnv_london_rank 4.577e-01 1.492e-01
+la19nmCamden:livingEnv_london_rank -4.599e-02 1.783e-01
+la19nmCity of London:livingEnv_london_rank 3.448e-01 9.482e-01
+la19nmCroydon:livingEnv_london_rank 4.524e-01 1.417e-01
+la19nmEaling:livingEnv_london_rank 2.053e-01 1.624e-01
+la19nmEnfield:livingEnv_london_rank 5.217e-01 1.556e-01
+la19nmGreenwich:livingEnv_london_rank 2.230e-01 1.620e-01
+la19nmHackney:livingEnv_london_rank -2.882e-01 1.817e-01
+la19nmHammersmith and Fulham:livingEnv_london_rank 1.915e-01 2.025e-01
+la19nmHaringey:livingEnv_london_rank -9.428e-02 2.048e-01
+la19nmHarrow:livingEnv_london_rank 3.559e-01 1.774e-01
+la19nmHavering:livingEnv_london_rank 5.238e-01 1.584e-01
+la19nmHillingdon:livingEnv_london_rank 3.026e-01 1.572e-01
+la19nmHounslow:livingEnv_london_rank -3.776e-02 1.691e-01
+la19nmIslington:livingEnv_london_rank -4.471e-01 1.967e-01
+la19nmKensington and Chelsea:livingEnv_london_rank -1.673e+00 2.725e-01
+la19nmKingston upon Thames:livingEnv_london_rank 2.551e-01 1.748e-01
+la19nmLambeth:livingEnv_london_rank -1.901e-01 2.037e-01
+la19nmLewisham:livingEnv_london_rank 1.834e-01 1.687e-01
+la19nmMerton:livingEnv_london_rank 5.430e-01 1.761e-01
+la19nmNewham:livingEnv_london_rank -4.372e-03 1.627e-01
+la19nmRedbridge:livingEnv_london_rank 2.310e-01 1.655e-01
+la19nmRichmond upon Thames:livingEnv_london_rank -2.672e-03 1.876e-01
+la19nmSouthwark:livingEnv_london_rank 2.786e-01 1.656e-01
+la19nmSutton:livingEnv_london_rank 5.311e-01 1.576e-01
+la19nmTower Hamlets:livingEnv_london_rank 1.954e-01 1.629e-01
+la19nmWaltham Forest:livingEnv_london_rank -2.538e-02 1.729e-01
+la19nmWandsworth:livingEnv_london_rank -1.552e-01 1.637e-01
+la19nmWestminster:livingEnv_london_rank -9.646e-01 2.107e-01
+la19nmBarnet:barriers_london_rank -1.334e+00 8.565e-01
+la19nmBexley:barriers_london_rank -4.421e-01 8.661e-01
+la19nmBrent:barriers_london_rank -7.477e-01 9.505e-01
+la19nmBromley:barriers_london_rank -1.240e+00 8.666e-01
+la19nmCamden:barriers_london_rank 2.994e+00 1.172e+00
+la19nmCity of London:barriers_london_rank 1.001e+00 3.050e+00
+la19nmCroydon:barriers_london_rank -6.200e-01 8.595e-01
+la19nmEaling:barriers_london_rank -5.816e-01 8.604e-01
+la19nmEnfield:barriers_london_rank -6.740e-01 8.776e-01
+la19nmGreenwich:barriers_london_rank -8.003e-01 8.842e-01
+la19nmHackney:barriers_london_rank 4.611e-01 1.427e+00
+la19nmHammersmith and Fulham:barriers_london_rank 1.442e+00 9.425e-01
+la19nmHaringey:barriers_london_rank 4.613e-01 9.154e-01
+la19nmHarrow:barriers_london_rank -1.413e+00 9.202e-01
+la19nmHavering:barriers_london_rank -3.766e-01 9.029e-01
+la19nmHillingdon:barriers_london_rank -1.592e+00 8.683e-01
+la19nmHounslow:barriers_london_rank -1.445e+00 9.061e-01
+la19nmIslington:barriers_london_rank -7.852e-01 1.031e+00
+la19nmKensington and Chelsea:barriers_london_rank 1.076e+00 1.007e+00
+la19nmKingston upon Thames:barriers_london_rank -1.257e+00 9.816e-01
+la19nmLambeth:barriers_london_rank -3.110e-01 9.491e-01
+la19nmLewisham:barriers_london_rank 1.004e-01 9.045e-01
+la19nmMerton:barriers_london_rank 4.934e-01 9.399e-01
+la19nmNewham:barriers_london_rank 2.625e+00 2.260e+00
+la19nmRedbridge:barriers_london_rank -1.116e+00 8.944e-01
+la19nmRichmond upon Thames:barriers_london_rank -1.884e-01 1.104e+00
+la19nmSouthwark:barriers_london_rank 5.627e-02 9.074e-01
+la19nmSutton:barriers_london_rank -5.571e-02 9.399e-01
+la19nmTower Hamlets:barriers_london_rank -2.394e+00 9.808e-01
+la19nmWaltham Forest:barriers_london_rank -5.478e-01 9.237e-01
+la19nmWandsworth:barriers_london_rank -2.874e-01 8.705e-01
+la19nmWestminster:barriers_london_rank 3.494e+00 1.111e+00
+ t value Pr(>|t|)
+(Intercept) 9.089 < 2e-16 ***
+la19nmBarnet 4.929 8.57e-07 ***
+la19nmBexley -2.210 0.027119 *
+la19nmBrent 1.645 0.100112
+la19nmBromley 0.749 0.454155
+la19nmCamden -4.338 1.47e-05 ***
+la19nmCity of London -0.159 0.873612
+la19nmCroydon -2.277 0.022851 *
+la19nmEaling 1.154 0.248528
+la19nmEnfield 1.382 0.166890
+la19nmGreenwich -1.491 0.135952
+la19nmHackney -0.931 0.352136
+la19nmHammersmith and Fulham -4.700 2.68e-06 ***
+la19nmHaringey -0.229 0.818706
+la19nmHarrow 3.150 0.001640 **
+la19nmHavering -3.228 0.001254 **
+la19nmHillingdon 0.384 0.701039
+la19nmHounslow 2.393 0.016735 *
+la19nmIslington -1.780 0.075114 .
+la19nmKensington and Chelsea 5.787 7.61e-09 ***
+la19nmKingston upon Thames 1.782 0.074808 .
+la19nmLambeth -3.064 0.002197 **
+la19nmLewisham -2.482 0.013106 *
+la19nmMerton -3.019 0.002549 **
+la19nmNewham 1.942 0.052165 .
+la19nmRedbridge 2.013 0.044191 *
+la19nmRichmond upon Thames 0.891 0.372776
+la19nmSouthwark -3.552 0.000387 ***
+la19nmSutton -3.169 0.001538 **
+la19nmTower Hamlets -1.251 0.211119
+la19nmWaltham Forest 1.141 0.253966
+la19nmWandsworth 0.313 0.754638
+la19nmWestminster -3.240 0.001203 **
+livingEnv_london_rank -1.438 0.150471
+barriers_london_rank 4.511 6.60e-06 ***
+la19nmBarnet:livingEnv_london_rank 1.057 0.290593
+la19nmBexley:livingEnv_london_rank 3.215 0.001312 **
+la19nmBrent:livingEnv_london_rank 3.139 0.001705 **
+la19nmBromley:livingEnv_london_rank 3.068 0.002169 **
+la19nmCamden:livingEnv_london_rank -0.258 0.796425
+la19nmCity of London:livingEnv_london_rank 0.364 0.716133
+la19nmCroydon:livingEnv_london_rank 3.191 0.001425 **
+la19nmEaling:livingEnv_london_rank 1.264 0.206349
+la19nmEnfield:livingEnv_london_rank 3.354 0.000803 ***
+la19nmGreenwich:livingEnv_london_rank 1.376 0.168758
+la19nmHackney:livingEnv_london_rank -1.586 0.112711
+la19nmHammersmith and Fulham:livingEnv_london_rank 0.946 0.344403
+la19nmHaringey:livingEnv_london_rank -0.460 0.645286
+la19nmHarrow:livingEnv_london_rank 2.005 0.044971 *
+la19nmHavering:livingEnv_london_rank 3.307 0.000951 ***
+la19nmHillingdon:livingEnv_london_rank 1.925 0.054317 .
+la19nmHounslow:livingEnv_london_rank -0.223 0.823335
+la19nmIslington:livingEnv_london_rank -2.273 0.023082 *
+la19nmKensington and Chelsea:livingEnv_london_rank -6.137 9.08e-10 ***
+la19nmKingston upon Thames:livingEnv_london_rank 1.459 0.144509
+la19nmLambeth:livingEnv_london_rank -0.934 0.350602
+la19nmLewisham:livingEnv_london_rank 1.087 0.277108
+la19nmMerton:livingEnv_london_rank 3.082 0.002065 **
+la19nmNewham:livingEnv_london_rank -0.027 0.978565
+la19nmRedbridge:livingEnv_london_rank 1.396 0.162864
+la19nmRichmond upon Thames:livingEnv_london_rank -0.014 0.988639
+la19nmSouthwark:livingEnv_london_rank 1.683 0.092533 .
+la19nmSutton:livingEnv_london_rank 3.370 0.000759 ***
+la19nmTower Hamlets:livingEnv_london_rank 1.200 0.230337
+la19nmWaltham Forest:livingEnv_london_rank -0.147 0.883300
+la19nmWandsworth:livingEnv_london_rank -0.948 0.343019
+la19nmWestminster:livingEnv_london_rank -4.578 4.80e-06 ***
+la19nmBarnet:barriers_london_rank -1.558 0.119396
+la19nmBexley:barriers_london_rank -0.510 0.609807
+la19nmBrent:barriers_london_rank -0.787 0.431493
+la19nmBromley:barriers_london_rank -1.431 0.152500
+la19nmCamden:barriers_london_rank 2.555 0.010647 *
+la19nmCity of London:barriers_london_rank 0.328 0.742734
+la19nmCroydon:barriers_london_rank -0.721 0.470718
+la19nmEaling:barriers_london_rank -0.676 0.499110
+la19nmEnfield:barriers_london_rank -0.768 0.442499
+la19nmGreenwich:barriers_london_rank -0.905 0.365429
+la19nmHackney:barriers_london_rank 0.323 0.746543
+la19nmHammersmith and Fulham:barriers_london_rank 1.530 0.126056
+la19nmHaringey:barriers_london_rank 0.504 0.614335
+la19nmHarrow:barriers_london_rank -1.535 0.124794
+la19nmHavering:barriers_london_rank -0.417 0.676607
+la19nmHillingdon:barriers_london_rank -1.834 0.066734 .
+la19nmHounslow:barriers_london_rank -1.595 0.110827
+la19nmIslington:barriers_london_rank -0.762 0.446220
+la19nmKensington and Chelsea:barriers_london_rank 1.068 0.285397
+la19nmKingston upon Thames:barriers_london_rank -1.280 0.200487
+la19nmLambeth:barriers_london_rank -0.328 0.743140
+la19nmLewisham:barriers_london_rank 0.111 0.911614
+la19nmMerton:barriers_london_rank 0.525 0.599625
+la19nmNewham:barriers_london_rank 1.161 0.245561
+la19nmRedbridge:barriers_london_rank -1.247 0.212307
+la19nmRichmond upon Thames:barriers_london_rank -0.171 0.864559
+la19nmSouthwark:barriers_london_rank 0.062 0.950560
+la19nmSutton:barriers_london_rank -0.059 0.952740
+la19nmTower Hamlets:barriers_london_rank -2.440 0.014708 *
+la19nmWaltham Forest:barriers_london_rank -0.593 0.553200
+la19nmWandsworth:barriers_london_rank -0.330 0.741265
+la19nmWestminster:barriers_london_rank 3.146 0.001667 **
+---
+Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 5162 on 4736 degrees of freedom
+Multiple R-squared: 0.5789, Adjusted R-squared: 0.5702
+F-statistic: 66.45 on 98 and 4736 DF, p-value: < 2.2e-16
+```
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge 2
+
+Using the `gapminder` data to create a linear model between a categorical and a continuous variable .
+
+Discuss your question and your findings.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Broom
+
+The 'broom' package offers an alternative way of presenting the output of statistical analysis.
+It centers around three S3 methods, each of which take common objects produced by R statistical functions (lm, t.test, nls, etc) and convert them into a tibble.
+
+These are:
+* tidy: constructs a tibble that summarizes the model’s statistical findings. This includes coefficients and p-values for each term in a regression, per-cluster information in clustering applications, or per-test information for multtest functions.
+* augment: add columns to the original data that was modeled. This includes predictions, residuals, and cluster assignments.
+* glance: construct a concise one-row summary of the model. This typically contains values such as R^2, adjusted R^2, and residual standard error that are computed once for the entire model.
+
+
+Let's revisit our initial linear model:
+
+``` r
+reg_LivEnv_health <- lm(health_london_rank ~ livingEnv_london_rank, data = lon_dims_imd_2019)
+
+summary(reg_LivEnv_health)
+```
+
+``` output
+
+Call:
+lm(formula = health_london_rank ~ livingEnv_london_rank, data = lon_dims_imd_2019)
+
+Residuals:
+ Min 1Q Median 3Q Max
+-21549.6 -5948.2 609.1 6239.2 15792.2
+
+Coefficients:
+ Estimate Std. Error t value Pr(>|t|)
+(Intercept) 1.692e+04 2.274e+02 74.40 <2e-16 ***
+livingEnv_london_rank 3.430e-01 1.915e-02 17.91 <2e-16 ***
+---
+Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+
+Residual standard error: 7625 on 4833 degrees of freedom
+Multiple R-squared: 0.06225, Adjusted R-squared: 0.06205
+F-statistic: 320.8 on 1 and 4833 DF, p-value: < 2.2e-16
+```
+
+There is a lot of useful information, but it not available in a way so that you can combine it with other models or do further analysis.
+We can convert this to tabular data using the 'tidy' function.
+
+
+``` r
+tidy(reg_LivEnv_health)
+```
+
+``` output
+# A tibble: 2 × 5
+ term estimate std.error statistic p.value
+ 
+
+### Residual Normality Test
+Test for detecting violation of normality assumption.
+
+``` r
+ols_test_normality(model1)
+```
+
+``` output
+-----------------------------------------------
+ Test Statistic pvalue
+-----------------------------------------------
+Shapiro-Wilk 0.9968 0.0000
+Kolmogorov-Smirnov 0.0238 0.0084
+Cramer-von Mises 404.803 0.0000
+Anderson-Darling 4.1283 0.0000
+-----------------------------------------------
+```
+
+Correlation between observed residuals and expected residuals under normality.
+
+``` r
+ols_test_correlation(model1)
+```
+
+``` output
+[1] 0.9983931
+```
+From these tests we can see that our assumptions are seemingly correct.
+
+#### Residual vs Fitted Values Plot
+We can create a scatter plot of residuals on the y axis and fitted values on the x axis to detect non-linearity, unequal error variances, and outliers.
+
+Characteristics of a well behaved residual vs fitted plot:
+* The residuals spread randomly around the 0 line indicating that the relationship is linear.
+* The residuals form an approximate horizontal band around the 0 line indicating homogeneity of error variance.
+* No one residual is visibly away from the random pattern of the residuals indicating that there are no outliers.
+
+
+``` r
+ols_plot_resid_fit(model1)
+```
+
+
+
+### Residual Histogram
+Additionally, we can create a histogram of residuals for detecting violation of normality assumption.
+
+
+``` r
+ols_plot_resid_hist(model1)
+```
+
+
+
+## Additional Diagnostics
+In addition the residual diagnostics, we can also assess our model for Heteroskedasticity, Multicollinearity and any Influential/High leverage points.
+
+### Heteroskedasticity Check
+
+We can use the ncvTest() function to test for equal variances.
+
+
+``` r
+ncvTest(model1)
+```
+
+``` output
+Non-constant Variance Score Test
+Variance formula: ~ fitted.values
+Chisquare = 52.92299, Df = 1, p = 3.4689e-13
+```
+The p-value here is low, indicating that this model may have a problem of unequal variances.
+
+### Multicollinearity Check
+We want to know whether we have too many variables that have high correlation with each other.
+If the value of GVIF is greater than 4, it suggests collinearity.
+
+
+``` r
+vif(model1)
+```
+
+``` output
+ GVIF Df GVIF^(1/(2*Df))
+livingEnv_london_rank 1.790038 1 1.337923
+barriers_london_rank 2.038530 1 1.427771
+la19nm 3.583479 32 1.020143
+```
+
+### Outlier Identification
+We can identify outliers in our data by creating a QQPlot and running statistical tests on the data.
+The outlierTest() reports the Bonferroni p-values for testing each observation in turn to be a mean-shift outlier, based Studentized residuals in
+linear (t-tests), generalized linear models (normal tests), and linear mixed models.
+
+
+``` r
+# Create a qqPlot
+qqPlot(model1, id.n = 2)
+```
+
+
+
+``` output
+[1] 4612 4721
+```
+
+``` r
+# Test for outliers
+outlierTest(model1)
+```
+
+``` output
+No Studentized residuals with Bonferroni p < 0.05
+Largest |rstudent|:
+ rstudent unadjusted p-value Bonferroni p
+4612 -4.069694 4.7832e-05 0.23127
+```
+
+The null hypothesis for the Bonferonni adjusted outlier test is the observation is an outlier. Here observation related to ‘4612’ is an outlier.
+In our data '4612' is difficult to identify as it may refer to either a Living Environment or a Barriers rank.
+
+### Identifying Influence Points
+
+We can identify potential influential points via an Influence Plot, these may be Ouliers and/or High Leverage points.
+A point can be considered influential if it's exclusion causes substantial change in the estimated regression.
+
+An influence plot summarises the 3 metrics for influence analysis in one single glance.
+The X-axis plots the leverage, which is normalised between 0 and 1.
+The Y-axis plots the studentized residuals, which can be positive or negative.
+The size of the circles for each data point reflect its Cook’s distance, degree of influence.
+
+Vertical reference lines are drawn at twice and three times the average hat value, horizontal reference lines at -2, 0, and 2 on the Studentized-residual scale.
+
+``` r
+# Influence Plots
+influencePlot(model1)
+```
+
+
+
+``` output
+ StudRes Hat CookD
+1 1.5548629 0.166808357 0.0138248622
+2 0.4024213 0.167706627 0.0009324887
+4612 -4.0696939 0.008281120 0.0039386753
+4675 -1.1177591 0.166718323 0.0071416294
+4676 -0.2645859 0.167101314 0.0004013628
+4721 3.6227234 0.007981167 0.0030092149
+```
+Identified influential points are returned in a data frame with the hat values, Studentized residuals and Cook's distance of the identified points.
+If no points are identified, nothing is returned.
+
+Influence points can be further explored with an Influence Index Plot which provides index plots of influence and related diagnostics for a regression model.
+
+
+``` r
+influenceIndexPlot(model1)
+```
+
+
+
+If an observation is influential then that observation can change the fit of the linear model.
+
+An observation may be influential because:
+1. Someone made a recording error
+2. Someone made a fundamental mistake collecting the observation
+3. The data point is perfectly valid, in which case the model cannot account for the behaviour.
+
+If the case is 1 or 2, then you can remove the point (or correct it).
+If it's 3, it's not worth deleting a valid point; the data may be better suited a non-linear model.
+
+::::::::::::::::::::::::::::::::::::::: challenge
+
+## Challenge
+
+Choose 3 of the of the regression diagnostics, check that the assumptions are met for the linear models you have created using the `gapminder` data.
+
+State what you tested and discuss your findings.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
diff --git a/27-wrap-up.md b/27-wrap-up.md
new file mode 100644
index 000000000..0ab7afb7a
--- /dev/null
+++ b/27-wrap-up.md
@@ -0,0 +1,114 @@
+---
+title: Writing Good Software
+teaching: 15
+exercises: 0
+source: Rmd
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- Describe best practices for writing R and explain the justification for each.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- How can I write software that other people can use?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Structure your project folder
+
+Keep your project folder structured, organized and tidy, by creating subfolders for your code files, manuals, data, binaries, output plots, etc. It can be done completely manually, or with the help of RStudio's `New Project` functionality, or a designated package, such as `ProjectTemplate`.
+
+::::::::::::::::::::::::::::::::::::::::: callout
+
+## Tip: ProjectTemplate - a possible solution
+
+One way to automate the management of projects is to install the third-party package, `ProjectTemplate`.
+This package will set up an ideal directory structure for project management.
+This is very useful as it enables you to have your analysis pipeline/workflow organised and structured.
+Together with the default RStudio project functionality and Git you will be able to keep track of your
+work as well as be able to share your work with collaborators.
+
+1. Install `ProjectTemplate`.
+2. Load the library
+3. Initialise the project:
+
+
+``` r
+install.packages("ProjectTemplate")
+library("ProjectTemplate")
+create.project("../my_project_2", merge.strategy = "allow.non.conflict")
+```
+
+For more information on ProjectTemplate and its functionality visit the
+home page [ProjectTemplate](https://projecttemplate.net/index.html)
+
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+## Make code readable
+
+The most important part of writing code is making it readable and understandable.
+You want someone else to be able to pick up your code and be able to understand
+what it does: more often than not this someone will be you 6 months down the line,
+who will otherwise be cursing past-self.
+
+## Documentation: tell us what and why, not how
+
+When you first start out, your comments will often describe what a command does,
+since you're still learning yourself and it can help to clarify concepts and
+remind you later. However, these comments aren't particularly useful later on
+when you don't remember what problem your code is trying to solve. Try to also
+include comments that tell you *why* you're solving a problem, and *what* problem
+that is. The *how* can come after that: it's an implementation detail you ideally
+shouldn't have to worry about.
+
+## Keep your code modular
+
+Our recommendation is that you should separate your functions from your analysis
+scripts, and store them in a separate file that you `source` when you open the R
+session in your project. This approach is nice because it leaves you with an
+uncluttered analysis script, and a repository of useful functions that can be
+loaded into any analysis script in your project. It also lets you group related
+functions together easily.
+
+## Break down problem into bite size pieces
+
+When you first start out, problem solving and function writing can be daunting
+tasks, and hard to separate from code inexperience. Try to break down your
+problem into digestible chunks and worry about the implementation details later:
+keep breaking down the problem into smaller and smaller functions until you
+reach a point where you can code a solution, and build back up from there.
+
+## Know that your code is doing the right thing
+
+Make sure to test your functions!
+
+## Don't repeat yourself
+
+Functions enable easy reuse within a project. If you see blocks of similar
+lines of code through your project, those are usually candidates for being
+moved into functions.
+
+If your calculations are performed through a series of functions, then the
+project becomes more modular and easier to change. This is especially the case
+for which a particular input always gives a particular output.
+
+## Remember to be stylish
+
+Apply consistent style to your code.
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- Keep your project folder structured, organized and tidy.
+- Document what and why, not how.
+- Break programs into short single-purpose functions.
+- Write re-runnable tests.
+- Don't repeat yourself.
+- Be consistent in naming, indentation, and other aspects of style.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
new file mode 100644
index 000000000..f19b80495
--- /dev/null
+++ b/CODE_OF_CONDUCT.md
@@ -0,0 +1,13 @@
+---
+title: "Contributor Code of Conduct"
+---
+
+As contributors and maintainers of this project,
+we pledge to follow the [The Carpentries Code of Conduct][coc].
+
+Instances of abusive, harassing, or otherwise unacceptable behavior
+may be reported by following our [reporting guidelines][coc-reporting].
+
+
+[coc-reporting]: https://docs.carpentries.org/topic_folders/policies/incident-reporting.html
+[coc]: https://docs.carpentries.org/topic_folders/policies/code-of-conduct.html
diff --git a/LICENSE.md b/LICENSE.md
new file mode 100644
index 000000000..7632871ff
--- /dev/null
+++ b/LICENSE.md
@@ -0,0 +1,79 @@
+---
+title: "Licenses"
+---
+
+## Instructional Material
+
+All Carpentries (Software Carpentry, Data Carpentry, and Library Carpentry)
+instructional material is made available under the [Creative Commons
+Attribution license][cc-by-human]. The following is a human-readable summary of
+(and not a substitute for) the [full legal text of the CC BY 4.0
+license][cc-by-legal].
+
+You are free:
+
+- to **Share**---copy and redistribute the material in any medium or format
+- to **Adapt**---remix, transform, and build upon the material
+
+for any purpose, even commercially.
+
+The licensor cannot revoke these freedoms as long as you follow the license
+terms.
+
+Under the following terms:
+
+- **Attribution**---You must give appropriate credit (mentioning that your work
+ is derived from work that is Copyright (c) The Carpentries and, where
+ practical, linking to