ggplot2_tutorial.Rmd

---
title : "ggplot2 Tutorial"
author  : "Murray Cadzow"
output  : html_document
---



Much of this tutorial is comprised of information from https://github.com/DataScienceSpecialization/courses/tree/master/04_ExploratoryAnalysis

and

https://www.rstudio.com/wp-content/uploads/2015/05/ggplot2-cheatsheet.pdf



## Background

-  ggplot2 is an implementation of 'The Grammar of Graphics' by Leland Wilkinson and sits alongside the __base__ and __lattice__ graphics systems in R

- The basic idea is to __map__ from data to __aesthetic__ atributes (colour, shape, size) of __geometric__ obejects (points, lines, bars)

### Base:

- build and add to the plot
- can't go backwards to adjust

### Lattice:

- entire plot created with single call
- margins/spacing automatically set since entire plot is specified at once
- really good for conditioning plots

### ggplot2:

- ggplot2 sits between __base__ and __lattice__ models for plotting
- lots of defaults specified but you can customise

## Basic Components of a ggplot2 Plot
- A _data frame_
- _aesthetic mappings_: how data are mapped to color, size 
- _geoms_: geometric objects like points, lines, shapes. 
- _facets_: for conditional plots. 
- _stats_: statistical transformations like binning, quantiles, smoothing. 
- _scales_: what scale an aesthetic map uses (example: male = red, female = blue). 
- _coordinate system_ 


## Getting started

Make sure ggplot2 is installed and if it isn't install it

```{r}
#Make sure ggplot2 is installed
if(!require("ggplot2")) {
    install.packages("ggplot2")
    library(ggplot2)
  } 

```

We'll use the iris dataset for the examples
```{r}
data(iris)
```

### qplot()
```{r}
qplot(Sepal.Length, Sepal.Width, data= iris)
```

Modify aesthetics
```{r}
qplot(Sepal.Length, Sepal.Width, data= iris, colour = Species)
```

Add a geom
```{r}
qplot(Sepal.Length, Sepal.Width, data= iris, geom = c("point","smooth"))
```

### Moving from qplot() to ggplot()

- setup plot with ggplot() and define aesthetics
- add geom for points

```{r}
ggplot(data = iris, aes(x=Sepal.Length, y =Sepal.Width)) + 
  geom_point()

ggplot(data = iris, aes(x=Sepal.Length, y =Sepal.Width)) + 
  geom_point(aes(size=Petal.Width, colour = Species)) +
  geom_hline(yintercept = mean(iris$Sepal.Width)) +
  coord_cartesian(xlim = c(0,9), ylim=c(0,5))
```

### Histogram
```{r}
ggplot(data = subset(iris, Species == "setosa"), aes(x=Sepal.Length)) + geom_histogram()

```

### Boxplots
```{r}
ggplot(data = iris, aes(x=Species, y=Sepal.Length)) + 
  geom_boxplot()

ggplot(data = iris, aes(x=Species, y=Sepal.Length)) + 
  geom_boxplot(aes(colour=Species)) + 
  geom_jitter()
```

### Facetting

- wrap
- grid

```{r}
ggplot(data = iris, aes(x=Sepal.Length, y =Sepal.Width)) + 
  geom_point() + 
  facet_grid(.~Species)

ggplot(data = iris, aes(x=Sepal.Length, y =Sepal.Width)) + 
  geom_point() + 
  facet_grid(Species~.)

ggplot(data = iris, aes(x=Sepal.Length, y =Sepal.Width)) + 
  geom_point() + 
  facet_wrap(~Species)

```


### Labels
- ggtitle()
- xlab()
- ylab()
- labs(title= "", x = "", y = "")

### Themes
- theme_bw()
- theme_grey()
- theme_classic()
- theme_minimal()

or change particular elements with __theme()__ and specify which __element__ to change

```{r}
ggplot(data = iris, aes(x=Sepal.Length, y =Sepal.Width)) + 
  geom_point() +
  ggtitle("Iris Sepal length vs Sepal width") +
  xlab("Sepal Length (mm)")+
  ylab("Sepal Width (mm)") +
  theme_bw()
```

But with power comes responsibility...

```{r}
ggplot(data = iris, aes(x=Sepal.Length, y =Sepal.Width)) + 
  geom_point(colour="yellow", shape='*', size = 10) +
  ggtitle("Iris Sepal length vs Sepal width") +
  xlab("Sepal Length (mm)")+
  ylab("Sepal Width (mm)") +
  theme_bw() +
  theme(axis.text.x = element_text(size=16, angle = 45), 
        axis.text.y = element_text(size=16), 
        axis.title.x= element_text(size=18), 
        axis.title.y = element_text(size=18),
        plot.title = element_text(colour = "purple", face='bold'),
        panel.grid.major = element_line(colour="red", linetype = "dashed", size = 3),
        panel.grid.minor = element_line(colour='blue', linetype='dotted', size=2),
        panel.background = element_rect(fill="#00FF00")) 
```

## Resources

- http://docs.ggplot2.org/current/
- http://www.statmethods.net/advgraphs/ggplot2.html
- http://zevross.com/blog/2014/08/04/beautiful-plotting-in-r-a-ggplot2-cheatsheet-3/
- https://www.rstudio.com/wp-content/uploads/2015/05/ggplot2-cheatsheet.pdf