## Introduction

Goal: by the end of this lab, you will be able to use colorbrewer and ggthemes to customize the look of your visualization.

### Setting up

For this lab, we’re going to be using the ToothGrowth dataset, which is one of the example datasets included in R. It contains data on how fast guinea pigs’ teeth grow if you give them vitamin C supplements in various forms and at various doses. You can learn more about this dataset by typing ?ToothGrowth at the console.

Let’s take a look:

library(tidyverse)
glimpse(ToothGrowth)
## Rows: 60
## Columns: 3
## $len <dbl> 4.2, 11.5, 7.3, 5.8, 6.4, 10.0, 11.2, 11.2, 5.2, 7.0, 16.5, 16.5… ##$ supp <fct> VC, VC, VC, VC, VC, VC, VC, VC, VC, VC, VC, VC, VC, VC, VC, VC, …
## \$ dose <dbl> 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 1.0, 1.0, 1.0,…
summary(ToothGrowth)
##       len        supp         dose
##  Min.   : 4.20   OJ:30   Min.   :0.500
##  1st Qu.:13.07   VC:30   1st Qu.:0.500
##  Median :19.25           Median :1.000
##  Mean   :18.81           Mean   :1.167
##  3rd Qu.:25.27           3rd Qu.:2.000
##  Max.   :33.90           Max.   :2.000

Hmm…the dose column looks a little funny. There are only three dosage values [0.5, 1 ,2], but R is interpreting them as continuous. We can tell R that we want to convert them to categories (a.k.a. factors) using dplyr like this:

ToothGrowth <- ToothGrowth %>%
mutate(dose = factor(dose))

Now let’s look again:

summary(ToothGrowth)
##       len        supp     dose
##  Min.   : 4.20   OJ:30   0.5:20
##  1st Qu.:13.07   VC:30   1  :20
##  Median :19.25           2  :20
##  Mean   :18.81
##  3rd Qu.:25.27
##  Max.   :33.90

Much better! Now let’s draw a graph.

## Drawing a ggplot

Remember the basic recipe for building a plot with ggplot2? Don’t forget to load the library!

1. Make a boxplot of the ToothGrowth data, with x = dose, y = len, and fill = dose.
Store it in a variable called my_plot.
# sample solution
my_plot <- ggplot(ToothGrowth, aes(x = dose, y = len, fill = dose)) +
geom_boxplot()

## Changing colors manually

The default colors R selects are okay, but maybe we can do better. Let’s try using colors from the official Smith College Color Palette. We can specify the color values we want using scale_fill_manual() like this:

my_plot +
scale_fill_manual(values = c("#004f71", "#465a01", "#981d97"))

## Changing colors with RColorBrewer

That looks pretty nice, but we could spend an awful lot of time making tiny tweaks to color palettes. Luckily Cynthia Brewer over at ColorBrewer has come up with some really good ones we can borrow! Let’s load the RColorBrewer library and check it out. Note: you might need to install.packages('RColorBrewer') if you’re running R on your laptop.

library(RColorBrewer)
display.brewer.all()

Ooh, so many choices! We can now use these palettes along with scale_fill_brewer() to make perceptually-optimized plots:

my_plot +
scale_fill_brewer(palette = "Set3")

That looks a little bit… Valentine-y?

1. Try out a few other color palettes!
# sample solution
my_plot +
scale_fill_brewer(palette = "Dark2")

1. Install the nasaweather package, and load it.
# sample solution
install.packages("nasaweather")
library(nasaweather)

To make the plot easier to read, we will first filter the storms data frame to include only storms from 1995.

my_storms <- storms %>%
filter(year == 1995)
1. Plot the storms in my_storms, mapping horizontal position to longitude, vertical position to latitude, and color to wind speed. Also use a facet_wrap() to make small multiples according to the name of each storm.
# sample solution
ggplot(my_storms, aes(x = long, y = lat, color = wind)) +
geom_point() +
facet_wrap(~name)

1. Do you think a sequential or diverging color scheme is most appropriate for wind speed? Justify your answer.

## Stylizing using ggthemes

If we want even more control, we can use the ggthemes package to define not only the color palette, but the overall style of the plot as well. For example, if we want the minic the style used by the graphic design team at The Economist, we could say:

library(ggthemes)
my_plot +
theme_economist() +
scale_fill_economist()

Notice how the background changed colors, the axes were re-styled, and the legend changed positions? You can read more about available ggthemes and scales here.

## Putting it all together

Now it’s time to get creative! There are many more datasets available in R. Let’s take a look at what we’ve got to play with:

data()

You can learn more about any of the datasets by running ?<dataset> at the console (replacing <dataset> with the name of the dataset).

1. Use ggplot to draw a plot of any dataset you like, and style it.
Try combining both a theme and a color palette.
# sample solution