tidyverse review and updates

qmd/10_Data_Import.qmd → qmd/07_DataImport.qmd

@@ -1,22 +1,14 @@
 ---
-title: "10: Data Import with Tidyverse"
-author: "David Koppstein"
-format: 
-  revealjs:
-    toc: false
-    slide-number: true
-    chalkboard: 
-      buttons: false
-    preview-links: auto
-    logo: images/maxplanck-ie.jpg
-    footer: <https://maxplanck-ie.github.io/Rintro>
-    css: styles.css
-    theme: night
+title: "07: Data Import with Tidyverse"
+author: "Juan Caballero"
 ---
 
+```{r, child="_setup.qmd"}
+```
+
 ## Before we begin
 
--   If you haven't already, please execute the following code:
+-   If you are not using Workbench, please install the following packages:
 
 ```{r}
 #| eval: false
@@ -26,15 +18,16 @@ install.packages("dslabs")
 ```
 
 -   The first command will install a large set of packages that are designed to work together, including ggplot2.
--   The second command installs some datasets we will use from Rafael Irizarry's book, "Introduction to Data Science."
+-   The second command installs some datasets we will use from Rafael Irizarry's book, "*Introduction to Data Science*"
 
 ## Credit
 
 -   This presentation is heavily influenced by Rafael Irizarry's book [Introduction to Data Science](https://rafalab.dfci.harvard.edu/dsbook) and his corresponding course on EdX.
--   It also draws on material from previous MPI R courses given by Devon Ryan.
+-   It also draws on material from previous MPI R courses given by Devon Ryan and David Koppstein.
 
-![](images/irizarry.jpg){.absolute bottom="25" left="100" width="250"} 
-![](images/devon.jpg){.absolute bottom="25" left="800" width="250"}
+![](images/Rafael_Irizarry.jpg){.absolute bottom="25" left="100" width="250"} 
+![](images/Devon_Ryan.jpeg){.absolute bottom="25" left="450" width="250"}
+![](images/David_Koppstein.jpg){.absolute bottom="25" left="800" width="250"}
 
 ::: footer
 <https://www.edx.org/bio/rafael-irizarry>
@@ -50,15 +43,15 @@ install.packages("dslabs")
 
 ## Data Import in the Tidyverse
 
-The first step for any analysis is importing the data into a machine-readable format. The tidyverse offers the `readr::` and `readxl::` packages, as we have just recently seen.
+The first step for any analysis is importing the data into a machine-readable format. The tidyverse offers the `readr::` and `readxl::` packages.
 
 ## Data Import in the Tidyverse
 
 ```{r}
 #| echo: true
 # set path to the location for raw data files in the dslabs package and list files
 library(dslabs)
-path <- system.file("extdata", package="dslabs")
+path = system.file("extdata", package="dslabs")
 list.files(path)
 ```
 

qmd/11_Data_Wrangling.qmd → qmd/08_DataWrangling.qmd

@@ -1,18 +1,11 @@
 ---
-title: "11: Intro to Data Wrangling with Dplyr"
-author: "David Koppstein"
-format:
-  revealjs:
-    toc: false
-    slide-number: true
-    chalkboard: 
-      buttons: false
-    preview-links: auto
-    logo: images/maxplanck-ie.jpg
-    css: styles.css
-    theme: night
+title: "08: Intro to Data Wrangling with Dplyr"
+author: "Juan Caballero"
 ---
 
+```{r, child="_setup.qmd"}
+```
+
 ## Introduction to Dplyr {auto-animate="true"}
 
 Dplyr uses a functions that act as verbs to transform data frames in ways to facilitate data analysis.
@@ -356,7 +349,6 @@ murders %>% arrange(desc(rate)) %>% head()
 ```
 
 Side note 1: You can also use the `slice_max` function
-Side note 2: Why would Washington DC have a much higher murder rate than all the other states?
 
 ## Exercises with gapminder
 

qmd/12_Data_Visualization.qmd → qmd/09_DataVisualization.qmd

@@ -1,18 +1,11 @@
 ---
-title: "12: Data visualization with ggplot2"
-author: "David Koppstein"
-format: 
-  revealjs:
-    toc: false
-    slide-number: true
-    chalkboard: 
-      buttons: false
-    preview-links: auto
-    logo: images/maxplanck-ie.jpg
-    css: styles.css
-    theme: night
+title: "09: Data visualization with ggplot2"
+author: "Juan Caballero"
 ---
 
+```{r, child="_setup.qmd"}
+```
+
 ## What is ggplot2?
 
 -   ggplot2 is part of the *tidyverse*, a set of packages created by Hadley Wickham.
@@ -30,7 +23,7 @@ format:
 -   Plots in **ggplot2** consist of 3 main components:
     -   Data: The dataset being summarized
     -   Geometry: The type of plot (scatterplot, boxplot, barplot, histogram, qqplot, smooth density, etc.)
-    -   Aesthetic mapping: Variables mapped to visual cues, such as x-axis and y-axis values and color
+    -   Aesthetic mapping: Variables mapped to visual cues, such as x-axis and y-axis values and colors
 
 ## Graph components {auto-animate="true"}
 

qmd/13_Tidy_Data.qmd → qmd/10_TidyData.qmd

@@ -1,18 +1,11 @@
 ---
-title: "13: Tidy Data"
-author: "David Koppstein"
-format: 
-  revealjs:
-    toc: false
-    slide-number: true
-    chalkboard: 
-      buttons: false
-    preview-links: auto
-    logo: images/maxplanck-ie.jpg
-    css: styles.css
-    theme: night
+title: "10: Tidy Data"
+author: "Juan Caballero"
 ---
 
+```{r, child="_setup.qmd"}
+```
+
 ## What is Tidy Data? 
 
 - In tidy data, each row is an observation and each column is a different variable. 
@@ -478,15 +471,11 @@ co2_stats %>%
 
 ## Any questions?
 
-![](images/tidyr_cheat_sheet.png)
+![](images/complaining-data-science-data-science.jpg)
 
 ::: footer
 <https://github.com/rstudio/cheatsheets/blob/main/tidyr.pdf>
 :::
 
-## Thank you! 
 
-- The Koppstein lab will be starting at Uniklinik Düsseldorf in a few months
-- There is a position for a Ph.D student interested in genomic and bioinformatic methods applied to blood and brain cancers
-- E-mail [[email protected]]([email protected]) if you or anyone else are interested!