1.1 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:

1.2 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

1.3 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

1.4 Cereal nutritional facts

Finally, the last dataset tht we’ll use—which was also made available through Kaggle.com—contain 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.

2.1 Drawing lines with element_line

One of the most fundamental concepts in a plot is a line. Assuming we have a starting and stopping point set already by the data we’re plotting, we can think of properties of a line that are purely aesthetic: color, thickness, and what type of line it is (solid, dotted, dashed, etc). In ggplot2, we modify these attributes (and a few others, with element_line). One examlpe of a line in a ggplot is the faint white grid underneath the data. We can modify that grid with the panel.grid argument. We’ll get into more detail about modifying the grid later, but for now we’ll use it as an example of a line. If we want to modify the color of the lines, we add element_line and specify that the color is light blue

m + theme(panel.grid = element_line(color = "lightblue"))

We can add other arguments too:

m + theme(panel.grid = element_line(color = "lightblue", size = 4, linetype = "dotted"))

In fact, if you inspect the element_line function itself, you can see that you can also add things like lineend. With this argument, you can change the ends of the line to can make the ends "round", "butt", or "square". In this example, it’s not very illustrative since the lines extend past the edge of the plotting area, but later when we come across other lines you’d like to modify, you can use that option if you’d like.

However, if you like arrows, you’ll also see that you can add them with the arrow argument. But if you thought you were done with basic ggplot2 elements, it turns out the arrow argument takes the output of the arrow() function, which allows you to modify things like the angle, length, type, and which sides of the line should get the arrow. And if that wasn’t Inception enough for you, the length argument requires the output of the unit function, in which you specify the length and the units.

m + theme(panel.grid = element_line(color = "lightblue", 
                                    arrow = arrow(angle = 20, 
                                                  length = unit(0.2, "inches"), 
                                                  ends = "both", 
                                                  type = "closed")))

This may seem like an overly complex network of functions, but the benefit is that it allows you to modify whatever you want.

2.2 Drawing rectangles with element_rect

One step up from drawing lines is to draw rectanges with element_rect. Because they consist of four lines, many of the arguments are the same as element_line.

c + theme(panel.border = element_rect(color = "darkred", size = 2, linetype = "dashed"))

Oops. Where did the plot go? So turns out rectangles aren’t just lines anymore because they create the space in the middle. So in element_rect we need to worry about what goes in the middle. By default, it seems like the color is white. What we probably want to do is remove it, or rather, make it transparent. We can do that with the fill argument:

c + theme(panel.border = element_rect(color = "darkred", size = 2, linetype = "dashed",
                                      fill = "transparent"))

So in the theme function, there are several things that are rectangles that you can modify. You can probably think of a few now like the legend or the strips in facets. We’ll use element_rect to modify those.

2.3 Drawing text with element_text

The last major element in ggplot is to draw text, which we do with element_text. As you can imagine, there are a lot of things you can change with text, like the font (“family”), it’s “face” ("bold", "italic", "bold.italic"), color, and size. In this example, we’ll modify a plot’s title.

g + theme(plot.title = element_text(family = "Palatino", face = "bold", color = "mediumpurple4", size = 20))

In addition to these attributes, we can also adjust other things. I’m also going to add the extremely handy debug = TRUE argument. This will make the plot title area yellow and will show a yellow dot where the text is anchored. So, for example, if I want to modify the angle to something onconventional, I can do so, but the result might not be very pretty.

g + theme(plot.title = element_text(family = "Palatino", face = "bold", color = "mediumpurple4", size = 20,
                                    angle = 3, debug = TRUE))

You can see that the anchor point is the top left corner of the plotting area. We may want to adjust the vertical position of this title. Both the hjust and vjust arguments take a number, ranging from 0 (left/bottom) to 1 (right/top). When we set the vertical adjustment to 0, it’ll anchor it to the bottom, which is probably what we wanted.

g + theme(plot.title = element_text(family = "Palatino", face = "bold", color = "mediumpurple4", size = 20,
                                    angle = 3, vjust = 0, debug = TRUE))

If you want to center your plot, you can do so by setting hjust to 0.5.

g + theme(plot.title = element_text(family = "Palatino", face = "bold", color = "mediumpurple4", size = 20,
                                    hjust = 0.5, debug = TRUE))

Note that you can use values outside of the range [0,1] and they’ll move around just as you expect:

g + theme(plot.title = element_text(family = "Palatino", face = "bold", color = "mediumpurple4", size = 20,
                                    vjust = -2, hjust = 2, debug = TRUE))

Finally, you may want to adjust the spacing around the text. To illustrate lineheight, I’ll change the title to something with two lines so you can get a better picture of what it’s doing. So if I really want to add double spacing, I can set it to 2. If I want to do something more subtle, maybe like 1.15.

g + labs(title = "Here is a\nTwo-lined Title",
         subtitle = "This is the subtitle") + 
    theme(plot.title = element_text(family = "Palatino", face = "bold", color = "mediumpurple4", size = 20,
                                    lineheight = 2, debug = TRUE))

For titles, this often won’t be necessary, but for other text elements in your plot, the lineheight may be useful.

The last thing is the margin, which takes the output of the margin function. In it, you specify how large the margins should be around your title area. The order is top, right, bottom, left, which you can maybe remember with the mneumonic “trouble”. For the title, left and right margins don’t mean much, but you can adjust how far the title is from the top of the graph or from the plotting area. Of course, if you want it right up against the plotting area, you can set the bottom margin to zero.

g + theme(plot.title = element_text(family = "Palatino", face = "bold", color = "mediumpurple4", size = 20,
                                    margin = margin(1, 0, 0, 0, unit = "cm"), debug = TRUE))

So that’s the element_text function. It’s powerful because you can control a lot of things with it.

2.4 Drawing nothing with element_blank

The final category of ggplot elements is element_blank and it’s pretty simple: it draws nothing. An important detail though is that not only does draw nothing, but it doesn’t even reserve space for that thing. So in the Stranger Things plot, the x- and y-axis labels are useful, but if we wanted to purposely make the plot confusing, we could remove them.

s + theme(axis.title = element_blank())

So if there’s something in your plot by default and you want to remove it, you just simply use element_blank and it’ll zap it from existence.

3.1 Axis

The arguments that control a plot’s axis are the following.

1. axis.title
   • axis.title.x
     • axis.title.x.top
     • axis.title.x.bottom
   • axis.title.y
     • axis.title.y.left,
     • axis.title.y.right,
2. axis.text
   • axis.text.x
     • axis.text.x.top
     • axis.text.x.bottom
   • axis.text.y
     • axis.text.y.left
     • axis.text.y.right
3. axis.ticks
   • axis.ticks.x
     • axis.ticks.x.top
     • axis.ticks.x.bottom
   • axis.ticks.y
     • axis.ticks.y.left
     • axis.ticks.y.right,
   • axis.ticks.length
     • axis.ticks.length.x
       • axis.ticks.length.x.top
       • axis.ticks.length.x.bottom,
     • axis.ticks.length.y
       • axis.ticks.length.y.left
       • axis.ticks.length.y.right
4. axis.line
   • axis.line.x
     • axis.line.x.top
     • axis.line.x.bottom
   • axis.line.y
     • axis.line.y.left
     • axis.line.y.right

As you can see, they fall nicely into a bit of a hierarchy. The main ones are the title, text, ticks, and line. Within each of those, there are separate functions for the x- and y-axis. Within those, you can modify the top/bottom or left/right properties.

This hierarchy is not just there to make things easier to remember. Instead, the hierarchy is built into the functions themselves. So if you modify something in a higher level, it’ll percolate to the lower arguments of the hierarchy automatically. Let’s see how that works.

s + theme(axis.title = element_text(color = "brown", face = "bold", size = 20))

s + theme(axis.title = element_text(face = "bold", size = 20),
          axis.title.x = element_text(color = "red"),
          axis.title.y = element_text(color = "blue"))

s_sec_axis <- s + scale_x_continuous(sec.axis = dup_axis()) + 
    scale_y_continuous(sec.axis = sec_axis(~ ., name = "rating", breaks = seq(0, 10, 0.5)))

s_sec_axis + 
    # All axes titles should be bold and size 20 by default
    theme(axis.title = element_text(face = "bold", size = 20),
          
          # The x axis titles will be red
          axis.title.x = element_text(color = "red"),
          # Remove the bottom one entirely
          axis.title.x.bottom = element_blank(),
          # The top one should be smaller, but still red and bold
          axis.title.x.top = element_text(size = 15),
          
          # The y axis titles will be blue
          axis.title.y = element_text(color = "blue"),
          # The left one will be horizontal and top-aligned
          axis.title.y.left = element_text(angle = 0, vjust = 1),
          # The right one will be bottom aligned and have the debug option on
          axis.title.y.right = element_text(hjust = 1, debug = TRUE))

s + theme(axis.title.x = element_text(margin = margin(t = 0.5, unit = "cm")))

s + theme(axis.text = element_text(debug = TRUE))

s + scale_x_continuous(sec.axis = dup_axis()) + 
    scale_y_continuous(sec.axis = sec_axis(~ ., name = "rating", breaks = seq(0, 10, 0.5))) + 
    
    # All axes texts should be bold and size 20 by default
    theme(axis.text = element_text(face = "bold", size = 20),
          
          # The x axis texts will be red
          axis.text.x = element_text(color = "red"),
          # Remove the bottom one entirely
          axis.text.x.bottom = element_blank(),
          # The top one should be smaller, but still red and bold
          axis.text.x.top = element_text(size = 15),
          
          # The y axis texts will be blue
          axis.text.y = element_text(color = "blue"),
          # The left one will be horizontal and top-aligned
          axis.text.y.left = element_text(angle = 0, vjust = 1),
          # The right one will be bottom aligned and have the debug option on
          axis.text.y.right = element_text(hjust = 1, debug = TRUE))

m + theme(axis.line = element_line(color = "orange", size = 2))

m + theme(axis.line = element_line(color = "orange", size = 2),
          axis.line.y = element_line(linetype = "dashed"))

m + theme(axis.line = element_line(color = "orange", size = 2),
          axis.line.y = element_line(linetype = "dashed"),
          axis.line.x = element_blank())

3.2 Legend

The next plot element that we can control with theme is the legend. Looking at the list of arguments that pertain to the legend, you can see that there are a lot:

1. legend.background
2. legend.margin
3. legend.spacing
   • legend.spacing.x
   • legend.spacing.y
4. legend.key
   • legend.key.size
     • legend.key.height
     • legend.key.width
5. legend.text
   • legend.text.align
6. legend.title
   • legend.title.align
7. legend.position
8. legend.direction
9. legend.justification
10. legend.box
    • legend.box.just
    • legend.box.margin
    • legend.box.background
    • legend.box.spacing

I go through these in what seem to me to be a somewhat logical order, starting with ones that people might use the most and moving toward those that require certain plot elements for them to show up.

m + theme(legend.title = element_text(face = "bold", color = "gray20", size = 15),
          legend.title.align = 0.5)

m + theme(legend.text = element_text(family = "Courier", color = "magenta", angle = 10),
          legend.text.align = 1)

m + theme(legend.title = element_text(family = "Palatino", face = "bold", color = "gray20", size = 15),
          legend.title.align = 1,
          legend.text = element_text(family = "Palatino", color = "gray30", size = 12),
          legend.text.align = 1)

m + theme(legend.key = element_rect(color = "salmon"))

m + theme(legend.key.size = unit(2, "cm"))