CSSS508, Week 2

class: center, top, title-slide

# CSSS508, Week 2
## Plotting with <code>ggplot2</code>
### Chuck Lanfear
### April 7, 2020 Updated: Apr 6, 2021

---

class: inverse

# But First...
## *Some useful stuff*

---

# Comments

You may have noticed that sometimes I have written code that looks like this:

```r
new.object <- 1:10 # Making vector of 1 to 10 
```
`#` is known as the *commenting symbol* in R!

Anything written on the same line *after* `#` will not be run by R.

This is useful for annotating your code to remind you (or others) what you are doing in a section.1

.footnote[
[1] In R Markdown documents, comments only work in chunks. Outside of a chunk, `#` creates **headers** like "comments" at the top of this slide.
]

---

# Saving Files

You can save an R object on your computer as a file to open later:

```r
save(new.object, file="new_object.RData")
```

You can open saved files in R as well:

```r
load("new_object.RData")
```

But where are these files being saved and loaded from?

---

# Working Directories

R saves files and looks for files to open in your current **working directory**1. You
can ask R what this is:

.footnote[[1] For a simple R function to open an Explorer / Finder window at your working directory, [see this StackOverflow response](https://stackoverflow.com/a/12135823/10277284).]

```r
getwd()
```

```
## [1] "C:/Users/cclan/OneDrive/GitHub/CSSS508/Lectures/Week2"
```

Similarly, we can set a working directory like so:

```r
setwd("C:/Users/cclan/Documents")
```

*Don't set a working directory in R Markdown documents!* They automatically set the directory they are in as the working directory.

---

# Managing Files

When managing R projects, it is normally best to give each project (such as a homework
assignment) its own folder. I use the following system:

* Every class or project has its own folder
--

* Each assignment or task has a folder inside that, which is the working directory for that item.
--

* `.Rmd` and `.R` files are named clearly and completely

For example, this presentation is located and named this:
`GitHub/CSSS508/Lectures/Week2/CSSS508_Week2_ggplot2.Rmd`

You can use whatever system you want, but be consistent so your projects are organized! 
You don't want to lose work by losing or overwriting files!

For large projects containing many files, I recommend using RStudio's built in project management system found in the top right of the RStudio window.

For journal articles I recommend Ben Marwick's [`rrtools`](https://github.com/benmarwick/rrtools) and [`huskydown`](https://github.com/benmarwick/huskydown) for UW dissertations and theses. [I made an `rrtools` demo presentation here](https://clanfear.github.io/birthtiming/inst/presentation/presentation.html#/).

---

# File Types

We mainly work with three types of file in this class:

* `.Rmd`: These are **markdown** *syntax* files, where you write code to *make documents*.

* `.R`: These are **R** *syntax* files, where you write code to process and analyze data *without making an output document*.1

.footnote[[1] While beyond the scope of this class, you can use the `source()` function to run a `.R` script file inside a `.Rmd` or `.R` file. Using this you can break a large project up into multiple files but still run it all at once!]

* `.html` or `.pdf`: These are the output documents created when you *knit* a markdown document.

Make sure you understand the difference between the uses of these file types! Please
ask for clarification if needed!

---
class: inverse

# Data and Subsetting

---

# Gapminder Data

We'll be working with data from Hans Rosling's [Gapminder](http://www.gapminder.org) project.
An excerpt of these data can be accessed through an R package called `gapminder`, cleaned and assembled by Jenny Bryan at UBC.

In the console: `install.packages("gapminder")`

Load the package and data:

```r
library(gapminder)
```

---

# Check Out Gapminder

The data frame we will work with is called `gapminder`, available once you have loaded the package. Let's see its structure:

.small[

```r
str(gapminder)
```

```
## tibble [1,704 x 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
```
]

---

# What's Interesting Here?

* **Factor** variables `country` and `continent`

+ Factors are categorical data with an underlying numeric representation
   + We'll spend a lot of time on factors later!

* Many observations: `$n=1704$` rows

* A nested/hierarchical structure: `year` in `country` in `continent`

+ These are panel data!

---
class: inverse

# Subsetting Data

---

# Installing Tidyverse

We'll want to be able to slice up this data frame into subsets (e.g. just the rows for Afghanistan, just the rows for 1997).

We will use a package called `dplyr` to do this neatly.

`dplyr` is part of the [tidyverse](http://tidyverse.org/) family of R packages that are the focus of this course.

If you have not already installed the tidyverse, type, in the console: `install.packages("tidyverse")`

This will install a *large* number of R packages we will use throughout the term, including `dplyr`.

`dplyr` is a very useful and powerful package that we will talk more about soon, but today we're just going to use it for "filtering" data.

---

# Loading dplyr

```r
library(dplyr)
```

```
## 
## Attaching package: 'dplyr'
```

```
## The following objects are masked from 'package:stats':
## 
##     filter, lag
```

```
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
```

---

# Wait, was that an error?

When you load packages in R that have functions sharing the same name as functions you already have, the more recently loaded functions overwrite the previous ones ("masks them").

This **message** is just letting you know that. To avoid showing this in your R Markdown file, add `message=FALSE` or `include=FALSE` to your chunk options when loading packages.

Sometimes you may get a **warning message** when loading packages---usually because you aren't running the latest version of R:

```
Warning message:
package `gapminder' was built under R version 3.5.3 
```
Chunk options `message=FALSE` or `include=FALSE` will hide this. *Update R* to deal with it completely!

---

# `magrittr` and Pipes

`dplyr` allows us to use `magrittr`1 operators (`%>%`) to "pipe" data between functions. So instead of nesting functions like this:

.footnote[[1] [Ceci n'est pas un pipe](https://en.wikipedia.org/wiki/The_Treachery_of_Images)]

```r
log(mean(gapminder$pop))
```

```
## [1] 17.20333
```

We can pipe them like this:

```r
gapminder$pop %>% mean() %>% log()
```

```
## [1] 17.20333
```

Read this as, "send `gapminder$pop` to `mean()`, then send the output of that to `log()`."
In essence, pipes read "left to right" while nested functions read "inside to out."
This may be confusing... we'll cover it more later!

---

# `filter` Data Frames

```r
gapminder %>% filter(country == "Algeria")
```

```
## # A tibble: 12 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Algeria Africa 1952 43.1 9279525 2449.
## 2 Algeria Africa 1957 45.7 10270856 3014.
## 3 Algeria Africa 1962 48.3 11000948 2551.
## 4 Algeria Africa 1967 51.4 12760499 3247.
## 5 Algeria Africa 1972 54.5 14760787 4183.
## 6 Algeria Africa 1977 58.0 17152804 4910.
## 7 Algeria Africa 1982 61.4 20033753 5745.
## 8 Algeria Africa 1987 65.8 23254956 5681.
## 9 Algeria Africa 1992 67.7 26298373 5023.
## 10 Algeria Africa 1997 69.2 29072015 4797.
## 11 Algeria Africa 2002 71.0 31287142 5288.
## 12 Algeria Africa 2007 72.3 33333216 6223.
```

What is this doing?

---

# How Expressions Work

What does `country == "Algeria"` actually do?

```r
head(gapminder$country == "Algeria", 50) # display first 50 elements
```

```
##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
## [34]  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [45] FALSE FALSE FALSE FALSE FALSE FALSE
```

It returns a vector of `TRUE` or `FALSE` values.

When used with the subset operator (`[]`), elements for which a `TRUE` is given are returned while those corresponding to `FALSE` are dropped.

---

# Logical Operators

We used `==` for testing "equals": `country == "Algeria"`.

There are many other [logical operators](http://www.statmethods.net/management/operators.html):

* `!=`: not equal to
--

* `>`, `>=`, `<`, `<=`: less than, less than or equal to, etc.
--

* `%in%`: used with checking equal to one of several values

Or we can combine multiple logical conditions:

* `&`: both conditions need to hold (AND)
--

* `|`: at least one condition needs to hold (OR)
--

* `!`: inverts a logical condition (`TRUE` becomes `FALSE`, `FALSE` becomes `TRUE`)

We'll use these a lot so don't worry too much right now!

---

# Multiple Conditions Example

```r
gapminder %>%
    filter(country == "Oman" & year > 1980)
```

```
## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Oman Asia 1982 62.7 1301048 12955.
## 2 Oman Asia 1987 67.7 1593882 18115.
## 3 Oman Asia 1992 71.2 1915208 18617.
## 4 Oman Asia 1997 72.5 2283635 19702.
## 5 Oman Asia 2002 74.2 2713462 19775.
## 6 Oman Asia 2007 75.6 3204897 22316.
```

---
# Multiple Conditions

.pull-left[

### And: `&`

```r
gapminder %>% 
  filter(country == "Oman" &
         year > 1980)
```

.image-100[
![](img/country_and_year.svg)
]

*Give me rows where the country is Oman **and** the year is after 1980.*

]

.pull-right[

### Or: `|`

```r
gapminder %>%
  filter(country == "Oman" |
         year > 1980)
```

.image-100[
![](img/country_or_year.svg)
]

*Give me rows where the country is Oman **or** the year is after 1980... or **both**.*

]

---

# Saving a Subset

If we think a particular subset will be used repeatedly, we can save it and give it a name like any other object:

```r
China <- gapminder %>% filter(country == "China")
head(China, 4)
```

```
## # A tibble: 4 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 China Asia 1952 44 556263527 400.
## 2 China Asia 1957 50.5 637408000 576.
## 3 China Asia 1962 44.5 665770000 488.
## 4 China Asia 1967 58.4 754550000 613.
```

---
class: inverse

# `ggplot2`

.center[
<img src="https://github.com/clanfear/CSSS508/raw/master/Lectures/Week2/img/ggplot2unit.jpg" style="width: 60%;"/>
]

---

## Base R Plots from Last Week

.pull-left[
 .small[

```r
plot(lifeExp ~ year, 
     data = China, 
     xlab = "Year", 
     ylab = "Life expectancy",
     main = "Life expectancy in China", 
     col = "red", 
     cex.lab = 1.5,
     cex.main= 1.5,
     pch = 16)
```
 ]
]

.pull-right[
![](CSSS508_Week2_ggplot2_files/figure-html/unnamed-chunk-18-1.png)
]

---
# `ggplot2`

An alternative way of plotting many prefer (myself included)1 uses the `ggplot2` package in R, which is part of the `tidyverse`.

.footnote[[1] [Though this is not without debate](http://simplystatistics.org/2016/02/11/why-i-dont-use-ggplot2/)]

```r
library(ggplot2)
```

The core idea underlying this package is the [**layered grammar of graphics**](https://doi.org/10.1198/jcgs.2009.07098): we can break up elements of a plot into pieces and combine them.

---
## Chinese Life Expectancy in `ggplot`

.pull-left[
 .small[

```r
ggplot(data = China, 
       aes(x = year, y = lifeExp)) +
    geom_point()
```
]
]

.pull-right[
![](CSSS508_Week2_ggplot2_files/figure-html/unnamed-chunk-21-1.svg)
]

---
# Structure of a ggplot

`ggplot2` graphics objects consist of two primary components:

1. **Layers**, the components of a graph.

* We *add* layers to a `ggplot2` object using `+`.
   * This includes lines, shapes, and text.

2. **Aesthetics**, which determine how the layers appear.

* We *set* aesthetics using *arguments* (e.g. `color="red"`) inside layer functions.
   * This includes locations, colors, and sizes.
   * Aesthetics also determine how data *map* to appearances.

---

# Layers

**Layers** are the components of the graph, such as:

* `ggplot()`: initializes `ggplot2` object, specifies input data
* `geom_point()`: layer of scatterplot points
* `geom_line()`: layer of lines
* `ggtitle()`, `xlab()`, `ylab()`: layers of labels
* `facet_wrap()`: layer creating separate panels stratified by some factor wrapping around
* `facet_grid()`: same idea, but can split by two variables along rows and columns (e.g. `facet_grid(gender ~ age_group)`)
* `theme_bw()`: replace default gray background with black-and-white

Layers are separated by a `+` sign. For clarity, I usually put each layer on a new line, unless it takes few or no arguments (e.g. `xlab()`, `ylab()`, `theme_bw()`).

---

# Aesthetics

**Aesthetics** control the appearance of the layers:

* `x`, `y`: `$x$` and `$y$` coordinate values to use
* `color`: set color of elements based on some data value
* `group`: describe which points are conceptually grouped together for the plot (often used with lines)
* `size`: set size of points/lines based on some data value
* `alpha`: set transparency based on some data value

---

## Aesthetics: Setting vs. mapping

Layers take arguments to control their appearance, such as point/line colors or transparency (`alpha` between 0 and 1).

* Arguments like `color`, `size`, `linetype`, `shape`, `fill`, and `alpha` can be used directly on the layers (**setting aesthetics**), e.g. `geom_point(color = "red")`. See the [`ggplot2` documentation](https://ggplot2.tidyverse.org/reference/index.html) for options. These *don't depend on the data*.

* Arguments inside `aes()` (**mapping aesthetics**) will *depend on the data*, e.g. `geom_point(aes(color = continent))`.

* `aes()` in the `ggplot()` layer gives overall aesthetics to use in other layers, but can be changed on individual layers (including switching `x` or `y` to different variables)

This may seem pedantic, but precise language makes searching for help easier.

Now let's see all this jargon in action.

---
## Axis Labels, Points, No Background

### 1: Base Plot

.pull-left[
 .small[

```r
*ggplot(data = China,
* aes(x = year, y = lifeExp))
```
]
]
.pull-right[
![](CSSS508_Week2_ggplot2_files/figure-html/unnamed-chunk-23-1.svg)
]

.footnote[Initialize the plot with `ggplot()` and `x` and `y` aesthetics **mapped** to variables.]

---