library(ggplot2)

2.1 Amount of sugar per McDonald’s category

For some of the workshop, I’ll work with the McDonald’s menu items dataset, which you can access from my wesite. To simplify things a little bit, I’ll take a subset—just four of the nine categories. To make that subset, I’ll use the subset function.

menu <- read.csv("http://joeystanley.com/downloads/menu.csv")
menu_subset <- subset(menu, Category %in% c("Smoothies & Shakes", "Desserts", "Beverages", "Snacks & Sides"))

The default plot will just be the distribution of the number of sugars in each of the four remaining categories. For the color, I’ll use Paul Tol’s themes, which I access using the package ggthemes.

m <- ggplot(menu_subset, aes(Category, Sugars, fill = Category)) +
    geom_boxplot(size = 0.75) +
    geom_jitter(color = "gray15") + 
    ggthemes::scale_fill_ptol()

So now, to plot it, all I need to do is call m.

m

Conveniently for us, if I want to make changes to the plot, I can just add additional lines of ggplot2 code to the p and it’ll work out like normal. In other words…

m + ggtitle("A Default Plot")

…is shorthand for…

ggplot(menu_subset, aes(Category, Sugars, fill = Category)) +
    geom_boxplot(size = 0.75) +
    geom_jitter(color = "gray15") + 
    ggthemes::scale_fill_ptol() + 
    ggtitle("A Default Plot")

Both will produce this plot:

2.2 Stranger Things ratings

Another dataset I’ll use is the Stranger Things dataset that was used in a previous workshop. Like before, I’ll change the season column to a factor too. For the default plot here, s, I’ll make the same scatterplot of the number of votes the episode got on IMDB by the average rating. I’ll make the dots bigger so they’re easier to see. And for fun, I’ll use a Wes Anderson theme inspired by the movie Fantastic Mr. Fox.

stranger <- read.csv("http://joeystanley.com/data/stranger.csv")
stranger$season = factor(stranger$season)
s <- ggplot(stranger, aes(votes, rating, color = season)) + 
    geom_point(size = 4) + 
    scale_color_manual(values = wesanderson::wes_palette("FantasticFox1")) + 
    labs(title = "Stranger Things episodes",
         subtitle = "Average rating by number of votes on IMDB")
s

2.3 Girlnames

The last is a dataset of the top 25 most common baby girl names in the US in 2017. I’ll read it in and prep it as I did in the intro workshop, except I’ll reverse the order of the girlnames and will make a horizontal bar chart. I’ll also only keep the top 15 just so there aren’t so many bars and color it using Color Brewer.

girlnames <- read.delim("http://joeystanley.com/data/girlnames.txt", sep = "\t")
girlnames <- girlnames[1:15,]
girlnames$name = factor(girlnames$name, levels = rev(girlnames$name))
g <- ggplot(girlnames, aes(name, n, fill = n)) + 
    geom_bar(stat = "identity") + 
    scale_fill_distiller(type = "seq", palette = "Purples") + 
    coord_flip() + 
    labs(title = "Top 15 baby girl names in the US in 2017",
         caption = "Data Source: Social Security data via the babynames package")
g

2.4 Cereal nutritional facts

Finally, the last dataset tht we’ll use—which was also made available through Kaggle.com—contains nutritional information from about 80 different kinds of cereal. We’ll load it in, make a few changes as before, and make a faceted plot showing the amount of fiber per cereal and its rating, split up by brand.

cereal <- read.csv("http://joeystanley.com/data/cereal.csv")
cereal$mfr <- forcats::fct_recode(cereal$mfr,
                                  "Kellogg's" = "K",
                                  "General Mills" = "G",
                                  "Post" = "P",
                                  "Quaker Oats" = "Q",
                                  "Ralston Purina" = "R",
                                  "Nabisco" = "N")
c <- ggplot(cereal, aes(fiber, rating)) + 
    geom_text(aes(label = name),
              check_overlap = TRUE, vjust = "inward", hjust = "inward") + 
    facet_wrap(~mfr)
c

Note that within facet_wrap, I’ve added a couple extra arguments (check_overlap, vjust, and hjust). I recently learned about these from Hadley Wichkham’s book-in-progress, ggplot2 (version 3) which you can view here. They ensure that labels don’t overlap (removing some if necessary), and then make sure they don’t spill off over the edges of the plot. Very handy.

3.1 theme_gray()

By default, ggplot2 will display using a theme that is generally pretty good. The creators have taken some liberties to decide a few things, some founded upon principles of good data visualization and others that are more opinionated. In Hadley Wickham’s words:

The theme is designed to put the data forward while supporting comparisons, following the advice of (Tufte 2006; Brewer 1994; Carr 2002, 1994; Carr and Sun 1999). We can still see the gridlines to aid in the judgement of position (Cleveland 1993a), but they have little visual impact and we can easily ‘tune’ them out. The grey background gives the plot a similar typographic colour to the text, ensuring that the graphics fit in with the flow of a document without jumping out with a bright white background. Finally, the grey background creates a continuous field of colour which ensures that the plot is perceived as a single visual entity.

Using the defaut theme will produce beautiful plots. Here’s what that would look like:

m
s
g
c

3.2 Other themes in ggplot2

Fortunately, if you don’t like the look of the gray background, you can easily switch to one of the other seven these that ggplot2 has bult in.

My go-to theme is called theme_bw() for just “black and white.” The biggest difference is that now the background is white instead of gray. But it also adds a thin black border around the whole thing and has faint grey grid lines instead of white ones.

m + theme_bw()
g + theme_bw()
s + theme_bw()
c + theme_bw()

The linedraw theme uses only pure black lines, rather than any gray ones, which makes it kind of look a little vintage. In facets is where I see the biggest change: the boxes at the top (called strips) are black.

m + theme_linedraw()
g + theme_linedraw()
s + theme_linedraw()
c + theme_linedraw()

The light theme is very similar to bw. The biggest difference is the outside box is lighter. In facets, the strips are darker and the text is colored white.

m + theme_light()
g + theme_light()
s + theme_light()
c + theme_light()

The classic theme has no grid and the top and right parts of the box are gone too, just leaving the x and y axes. The facet strips are empty white boxes now.

m + theme_classic()
g + theme_classic()
s + theme_classic()
c + theme_classic()

If you really like the gray, you can go for the dark theme, which has a darker background and a darker gray for the grid lines. In the girl names plot, you can see that the lighter colors pop out more effectively, but the middle ones blend in with the background a little bit, which is something to keep in mind when you add color to your plots.

m + theme_dark()
g + theme_dark()
s + theme_dark()
c + theme_dark()

The minimal theme is even simpler than light. It doens’t have the outside border but it does retain the inside grid. The strips are gone entirely, leaving just the text behind, which is a little confusing with this plot.

m + theme_minimal()
g + theme_minimal()
s + theme_minimal()
c + theme_minimal()

Finally, you can go completely blank with void. This may seem a little weird, but it does have useful purposes, like when creating maps.

m + theme_void()
g + theme_void()
s + theme_void()
c + theme_void()