R Visualization Workshop

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# R Visualization Workshop
## Plotting with <code>ggplot2</code>
### Chuck Lanfear
### May 4, 2019 Updated: May 4, 2019

---

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# Setup and Data

---
# 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)
```

```
## Classes 'tbl_df', 'tbl' and 'data.frame':	1704 obs. of  6 variables:
##  $ 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  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
##  $ pop      : int  8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num  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!

---

# 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. If you're unfamiliar, see my [previous workshop](https://clanfear.github.io/Intermediate_R_Workshop/)!

`dplyr` is part of the [tidyverse](http://tidyverse.org/) family of R packages.

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` and `ggplot2`.

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

---
# Subsetting Example

For some examples to follow, I'd like to subset down only to data in China.

```r
library(dplyr)
China <- gapminder %>%
 filter(country == "China")
head(China)
```

```
## # A tibble: 6 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.
## 5 China Asia 1972 63.1 862030000 677.
## 6 China Asia 1977 64.0 943455000 741.
```

.footnote[If anything done here is unclear, let me know!]
---
class: inverse

# `ggplot2`

.center[
<img src="img/ggplot2unit.jpg" style="width: 60%;"/>
]

---

## Base R Plots

.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[
![](r_visualization_slides_files/figure-html/unnamed-chunk-5-1.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(color = "red", size = 3) +
  xlab("Year") + 
  ylab("Life expectancy") +
  ggtitle("Life expectancy in China") +
* theme_bw(base_size=18)
```
]
]

.pull-right[
![](r_visualization_slides_files/figure-html/unnamed-chunk-8-1.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

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## 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](http://docs.ggplot2.org/current/vignettes/ggplot2-specs.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.

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## Axis Labels, Points, No Background

### 1: Base Plot

.pull-left[
 .small[

```r
*ggplot(data = China,
* aes(x = year, y = lifeExp))
```
]
]
.pull-right[
![](r_visualization_slides_files/figure-html/unnamed-chunk-10-1.svg)
]

.footnote[We initialize the plot with `ggplot()` and an aesthetic.]

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