Lecture 4: Advanced Data Visualization

A Quick Recap

We have already been writing ggplot code directly in earlier lectures. Here, we will start by saving the base plot to an object so that we can keep adding layers to it.

diving_hist <- ggplot(data = diving)

Recall that aesthetics map variables to visual properties of the plot. Examples include:

• x: the variable that will be on the x-axis
• y: the variable that will be on the y-axis
• color: the variable that categorizes data by color
• fill: the variable that controls filled regions
• shape: the variable that categorizes data by shape

You can define the aes() in the ggplot() call, which will then be used for later layers, or you can define the aes() inside a specific geom() when it only applies there. Geometric objects, or geoms, determine what gets drawn. We have already used geom_histogram(), geom_point(), and geom_line(). In this lecture, we will add a few more:

• geom_boxplot(): creates a boxplot
• geom_violin(): creates a violin plot
• geom_bar(): creates a bar plot

Putting it All Together

Let’s make a histogram of judges’ scores.

diving_hist <- 
  diving_hist + 
  geom_histogram(aes(x = JScore), binwidth = 0.25)
diving_hist

In the code above, we first overwrote diving_hist so that it is now a histogram of judges’ scores where each bin has width 0.25. In the second line, we asked R to display this object. For ggplot code, place the + at the end of a line, just like the pipe %>%.

Notice that the label for the x-axis is JScore, which is the column name from the tbl. We can change the label by adding a layer.

diving_hist <- 
  diving_hist + 
  labs(x = "Judge Score")
diving_hist

Facets

What if we want to separate the judges’ scores by country? We can use facets. Facets allow you to separate graphs by category. We do not need to redo our code for the histogram; we only need to add a facet layer to our graph diving_hist. The first argument of facet_wrap() is the column of our dataset that contains the category information. The second argument of facet_wrap() specifies the number of rows in which to display the graphs.

diving_hist <- 
  diving_hist + 
  facet_wrap("JCountry", nrow = 3)
diving_hist

To get a sense of whether a particular country’s judges are biased, it would be useful to add a reference line at the median score over all judges and countries to each facet. So, we must first calculate the overall median score, which we can do using the reframe() function. Then, we can display this median on our plots with geom_vline(), which adds a vertical line. We can also pass in a custom color argument to this line so that it will easily stand out from the rest of our values. This website provides a look at all the built-in color arguments in R: R colors

median_score <- diving %>% 
  reframe(med = median(JScore)) %>%
  pull(med)
diving_hist <- 
  diving_hist + 
  geom_vline(xintercept = median_score, color = "lightcoral")
diving_hist

Note that when calculating our median_score, the reframe function returns a tbl rather than a single value. To “pull” out the single median value from our tbl, we use the pull function.

Boxplots and violin plots

Boxplots are also an important visualization tool. We now create boxplots of the judges’ scores, separated by diving round. We can also color our boxplots according to the Round variable by including the fill argument within the aes() function. Note that if we included the fill argument outside of the aes() function, we would only be able to color all boxplots the same color (by putting fill inside of the aesthetic, we are able to “map” the variable Round to the fill color of the plot based on the values of Round).

diving_box <- ggplot(data = diving) + 
  geom_boxplot(aes(x = Round, y = JScore, fill = Round)) +
  labs(title = "Diving scores by round", x = "", y = "Judge Score", fill = "Round")
diving_box

We can also remove the unnecessary x-axis ticks and labels as the legend on the right is sufficient. We do so using the theme layer:

diving_box <- diving_box + 
  theme(axis.title.x = element_blank(), axis.text.x = element_blank(), axis.ticks.x = element_blank())
diving_box

We can also plot this data as violin plots, which take the same arguments as geom_boxplot() but use geom_violin() instead. We can also change the “look” of our plots by setting the theme. theme_minimal() is frequently used for a much cleaner look.

diving_violin <- ggplot(data = diving) + 
  geom_violin(aes(x = Round, y = JScore, fill = Round)) +
  labs(title = "Diving scores by round", x = "", y = "Judge Score", fill = "Round") +
  theme_minimal()

diving_violin

We can see that the boxplots provide more information about statistical features, such as the median and quartiles, but the violin plots provide a better sense of how the data is distributed. We can see the effect of smaller rounds having more binned scores, whereas they are spread much more evenly in the first round.

Barplots

We can also create barplots using geom_bar. By mapping fill=JCountry inside our aes(), we can map each bar’s color to the country for that judge.

bar <- ggplot(data = diving) +
  geom_bar(aes(x = JCountry, fill = JCountry)) + 
  labs(x = "Judge Country") +
  theme_minimal() +
  theme(legend.position = "none") 
bar

Scatterplots

Now let’s turn back to scatterplots, which were introduced in Lecture 2. We plot judges’ score versus rank of the diver. As we expect, divers with better (lower-numbered) ranks tend to receive higher scores.

scatter_raw = ggplot(data = diving) +
  geom_point(aes(x = Rank, y = JScore, color = Country)) +
  labs(x = "Diver Rank", y = "Judge Score") + 
  theme_minimal()

scatter_raw

We can see that this plot is a little messy and hard to interpret, which may commonly happen with scatterplots when multiple rows correspond to the same thing, such as a diver. We can improve on this visual by grouping per diver and using reframe() to calculate the mean of the judge scores, ranks, and difficulties for each diver. We also want to save the country for visualizations, so we use the first() command within reframe().

diving_grouped <- diving %>% 
  group_by(Diver) %>% 
  reframe(JScore_mean = mean(JScore), 
          Rank_mean = mean(Rank),
          Difficulty_mean = mean(Difficulty),
          Country = first(Country)) 

head(diving_grouped)  

## # A tibble: 6 × 5
##   Diver              JScore_mean Rank_mean Difficulty_mean Country
##   <chr>                    <dbl>     <dbl>           <dbl> <chr>  
## 1 ABALLI Jesus-Iory         6.61      22              3.08 CUB    
## 2 AHRENS Stefan             7.29      10.6            2.74 GER    
## 3 AKHMETBEKOV Damir         4.32      41              2.95 KAZ    
## 4 ALCALA Maria-Jose         5.53      30              2.84 MEX    
## 5 ALEKSEEVA Svetlana        6.87      16              2.37 BLR    
## 6 ALLY Tony                 6.92      11.3            2.79 GBR

This will allow us to plot a single point per diver, which will make the plot much easier to interpret.

scatter = ggplot(data = diving_grouped) +
  geom_point(aes(x = Rank_mean, y = JScore_mean, color = Country), size = 2) +
  labs(x = "Diver Rank", y = "Judge Score") + 
  theme_minimal()

scatter

We can see here the much clearer negative relationship between judge score and diver rank.

Lines

We can also add an abline to our scatterplot–that is, a line where we specify the y-intercept (intercept) and the slope (slope):

scatter <- scatter + 
  geom_abline(intercept = 8.5, slope = -0.1, color = "red")
scatter

Note: geom_abline is different from geom_line: geom_line “connects the dots” between your data and so doesn’t have to be a straight line, whereas geom_abline draws a straight line with the specified slope and y-intercept. A common usage is to add a visual reference line to a scatterplot.

Other geom for lines are:

• geom_vline: to add a vertical line to a plot
• geom_hline: to add a horizontal line to a plot

Stats

We can also specify a layer using stat_, which stands for statistical transformation. This is useful if we want to plot a summary statistic of our data, such as a mean or median. By using a stat_ layer, we do not have to compute this summary statistic beforehand–ggplot will compute the summary statistic for us and then plot the result.

For example, suppose we want to plot the means of each judge’s score and provide error bars of one standard error on either side of the mean. We could use reframe and group_by to find the mean and standard errors for each judge, or we could just use a stat_ layer!

The layer stat_summary() computes and then plots a user-specified summary statistic. We choose the option mean_se to calculate the means and standard errors of the scores of each judge.

As always, we set up the plot by calling ggplot, specifying data = diving and then providing the aes. In this case, we want the judge on the x-axis and their scores on the y-axis. We then add our stat_summary layer.

judges <- ggplot(data = diving, aes(x = Judge, y = JScore)) + 
  stat_summary(fun.data = mean_se) + 
  labs(y = "Judge Score")
judges

We can see that the judges’ names are bunched together… we can make them much more readable by rotating the x-axis labels by 45 degrees using the theme layer:

judges_rotate <- judges + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
judges_rotate

We can also completely flip the coordinates to make a horizontal plot!

judges <- judges + coord_flip()
judges