Data visualization basics

Number of songs?

A reasonable person might guess that a good musical would have a lot of music. Maybe there’s a relationship between the number of songs in a musical and how much money it made at the box office. Let’s make a basic scatterplot exploring that relationship.

At minimum, we have to specify the data and the overall aesthetics (aes), which are usually at least the x (song_quantity) and y (BoxOffice) axis. One cool thing about ggplot is that you just add layers to this backbone using a + for each layer. Markers can be added using geom_point (or geom_jitter to prevent points from overlapping too much). If you save the backbone plot (or any additions thereafter) as an object (e.g., myplot), you can simply add additional layers to that object as well (illustrated below).

# specify the backbone
myplot <- ggplot(data = df, # specify data
                 aes(x = song_quantity, # specify x axis variable
                     y = BoxOffice)) # specify y axis variable
# add markers to the backbone
myplot + geom_point()

So there’s the visualized relationship between song quantity and musical quality: there isn’t one.

Runtime?

Maybe musical fans just want a good escape from their real life, so musicals that are simply longer—providing more escape—are “better”. Here’s a scatterplot exploring the relationship between runtime (length_minutes) and box office earnings (BoxOffice). I changed the point size, shape, and color just to show how.

plot2 <- ggplot(df, aes(length_minutes, BoxOffice, label = Name)) +
  geom_point(color = 'blue', # change color of points
             shape = 'triangle', # change point shape
             size = 3) # change the point size 
plot2  

If there is a relationship, it’s probably driven by that outlier that is the highest-grossing musical. Which musical is it? We could look in the dataframe to see, but this is a plotting tutorial so let’s make the plot tell us. We can add text labels to the points using geom_text and change the distance of the labels from the points using vjust and hjust. Because this adds another layer that depends on the data, we have to provide the aesthetics, using the label arguement in this case.

# add text labels with the musical name
plot2 + geom_text(aes(label = Name), vjust = -.1) 

Most those labels are pretty bunched (very ugly) up but we can see that the highest grossing musical is The Sound of Music. We can’t very well get rid of one of the most iconic musicals of all time, so let’s just keep the outlier for the sake of the exercise. The labels look terrible, though, so we should probably get rid of them (we can bring them back later in a pretty way). While we’re at it, let’s make the plot generally prettier by changing the theme and setting the axis labels to something more clear.

There are a number of ways to change the axis labels, scales, and tick marks, but the ones that seem to give the most control are scale_x_continuous and scale_y_continuous.

library(scales)
plot2_revised <- plot2 + 
  scale_x_continuous(name = "Runtime (in minutes)", # new x-axis label
                           # setting tick mark every 10 minutes
                           breaks = seq(90, 190, 10)) + 
  scale_y_continuous(name = "Gross Box Office Earnings (USD)",
                     # changes y-axis tick labels to dollar values
                     labels = dollar,
                     breaks = seq(min(df$BoxOffice), max(df$BoxOffice), 50000000))
  
plot2_revised

There are a whole bunch of preset themes in ggplot (see full list of default themes here). I like theme_classic.

plot_final <- plot2_revised + theme_classic()

plot_final

Now we’ve basically got a publication-ready plot!

Bonus: Add interactive labels

I liked being able to see which dot belonged to each musical, but the labels were too ugly. Luckily there is an interactive plotting package called plotly that has a wrapper to turn any ggplot object into an interactive plot.

library(plotly)

int_plot <- ggplotly(plot_final)

style(int_plot, hoverinfo = df$Name)

Now we can see the name of the musical when we hover over each point. That’s cool. I’ve played around with plotly a bit and it has much more functionality than I can go into here; it plays well enough with ggplot for stuff like this, but it looks like it might be worth just learning plotly too (maybe in a future blog post).