Read in data

Let’s start again by reading in the data from yesterday using the read_csv() function after loading the tidyverse:

library(tidyverse)
nba_stats <- read_csv("http://www.stat.cmu.edu/cmsac/sure/2022/materials/data/sports/intro_r/nba_2022_player_stats.csv")
## 
## ── Column specification ────────────────────────────────────────────────────────────
## cols(
##   .default = col_double(),
##   player = col_character(),
##   position = col_character(),
##   team = col_character()
## )
## ℹ Use `spec()` for the full column specifications.

Peaking at the data

Write code that displays the column names of nba_stats. Also, look at the first six rows of your dataset to get an idea of what these variables look like. Which variables are quantitative, and which are categorical?

# INSERT CODE HERE

Always make a bar chart…

Now we’ll use the ggplot() function to create a bar plot of the position variable. To make things easier, we provide the code for you to do this below; just uncomment the code and run it to create the bar plot. In what follows, you must answer some questions about the code and plot.

# Create the bar plot of position:
# nba_stats %>%
#   ggplot(aes(x = position)) +
#   geom_bar(fill = "darkblue") +
#   labs(title = "Number of NBA players by position",
#        x = "Position", y = "Number of players",
#        caption = "Source: Basketball-Reference.com")

Answer the following questions about the code and plot:

More area plots (but bar plots are better!)

Now we’ll make a few other area plots:

Your goal for this part is to create each of these plots. These plots can be created by copy-and-pasting the bar plot code from above and modifying it slightly. Follow these directions to create each of these plots:

# PUT YOUR SPINE CHART CODE HERE
# PUT YOUR PIE CHART CODE HERE
# PUT YOUR ROSE DIAGRAM CODE HERE

Notes on colors in plots

Three types of color scales to work with:

  1. Qualitative: distinguishing discrete items that don’t have an order (nominal categorical). Colors should be distinct and equal with none standing out unless otherwise desired for emphasis.
  1. Sequential: when data values are mapped to one shade, e.g., for an ordered categorical variable or low to high continuous variable
  1. Divergent: think of it as two sequential scales with a natural midpoint midpoint could represent 0 (assuming +/- values) or 50% if your data spans the full scale

Options for ggplot2 colors

The default color scheme is pretty bad to put it bluntly, but ggplot2 has ColorBrewer built in which makes it easy to customize your color scales. For instance, we can make a scatterplot with three_pointers on the y-axis and offensive_rebounds on the x-axis and using the geom_point() layer with each point colored by position:

nba_stats %>%
  ggplot(aes(x = offensive_rebounds, y = three_pointers,
             color = position)) +
  geom_point(alpha = 0.5) +
  labs(x = "Offensive rebounds", y = "Three-pointers",
       color = "Position") +
  theme_bw()

What does alpha change? We can change the color plot for this plot using scale_color_brewer() function:

nba_stats %>%
  ggplot(aes(x = offensive_rebounds, y = three_pointers,
             color = position)) +
  geom_point(alpha = 0.5) +
  scale_color_brewer(palette = "Set2") +
  labs(x = "Offensive rebounds", y = "Three-pointers",
       color = "Position") +
  theme_bw()

Which do you prefer, the default palette or this new one? You can check out more color palettes here.

Something you should keep in mind is to pick a color-blind friendly palette. One simple way to do this is by using the ggthemes package (you need to install it first before running this code!) which has color-blind friendly palettes included:

nba_stats %>%
  ggplot(aes(x = offensive_rebounds, y = three_pointers,
             color = position)) +
  geom_point(alpha = 0.5) +
  # Notice I did not use library(ggthemes) to do this... just '::'
  ggthemes::scale_color_colorblind() +
  labs(x = "Offensive rebounds", y = "Three-pointers",
       color = "Position") +
  theme_bw()

In terms of displaying color from low to high, the viridis scales are excellent choices (and are also color-blind friendly!). For instance, we can map another continuous variable (minutes_played) to the color:

nba_stats %>%
  ggplot(aes(x = offensive_rebounds, y = three_pointers,
             color = minutes_played)) +
  geom_point(alpha = 0.5) +
  scale_color_viridis_c() +
  labs(x = "Offensive rebounds", y = "Three-pointers",
       color = "Minutes played") +
  theme_bw()

What does this reveal about the plot? What happens if you delete scale_color_viridis_c() + from above? Which do you prefer?

Notes on themes

You might have noticed above have various changes to the theme of plots for customization. You will constantly be changing the theme of your plots to optimize the display. Fortunately, there are a number of built-in themes you can use to start with rather than the default theme_gray():

nba_stats %>%
  ggplot(aes(x = offensive_rebounds, y = three_pointers,
             color = minutes_played)) +
  geom_point(alpha = 0.5) +
  scale_color_viridis_c() +
  labs(x = "Offensive rebounds", y = "Three-pointers",
       color = "Minutes played") +
  theme_gray()

For instance, Prof Yurko will constantly use theme_bw() many times throughout the summer:

nba_stats %>%
  ggplot(aes(x = offensive_rebounds, y = three_pointers,
             color = minutes_played)) +
  geom_point(alpha = 0.5) +
  scale_color_viridis_c() +
  labs(x = "Offensive rebounds", y = "Three-pointers",
       color = "Minutes played") +
  theme_bw()

There are options such as theme_minimal():

nba_stats %>%
  ggplot(aes(x = offensive_rebounds, y = three_pointers,
             color = minutes_played)) +
  geom_point(alpha = 0.5) +
  scale_color_viridis_c() +
  labs(x = "Offensive rebounds", y = "Three-pointers",
       color = "Minutes played") +
  theme_minimal()

or theme_classic():

nba_stats %>%
  ggplot(aes(x = offensive_rebounds, y = three_pointers,
             color = minutes_played)) +
  geom_point(alpha = 0.5) +
  scale_color_viridis_c() +
  labs(x = "Offensive rebounds", y = "Three-pointers",
       color = "Minutes played") +
  theme_classic()

There are also packages with popular, such as the ggthemes package which includes, for example, theme_economist():

library(ggthemes)
nba_stats %>%
  ggplot(aes(x = offensive_rebounds, y = three_pointers,
             color = minutes_played)) +
  geom_point(alpha = 0.5) +
  scale_color_viridis_c() +
  labs(x = "Offensive rebounds", y = "Three-pointers",
       color = "Minutes played") +
  theme_economist()

and theme_fivethirtyeight() to name a couple:

nba_stats %>%
  ggplot(aes(x = offensive_rebounds, y = three_pointers,
             color = minutes_played)) +
  geom_point(alpha = 0.5) +
  scale_color_viridis_c() +
  labs(x = "Offensive rebounds", y = "Three-pointers",
       color = "Minutes played") +
  theme_fivethirtyeight()

With any theme you have picked, you can then modify specific components directly using the theme() layer. There are many aspects of the plot’s theme to modify, such as my decision to move the legend to the bottom of the figure, drop the legend title, and increase the font size for the y-axis:

nba_stats %>%
  ggplot(aes(x = offensive_rebounds, y = three_pointers,
             color = minutes_played)) +
  geom_point(alpha = 0.5) +
  scale_color_viridis_c() +
  labs(x = "Offensive rebounds", y = "Three-pointers",
       title = "Joint distribution of three-pointers and offensive rebounds",
       subtitle = "NBA statistics from 2021-2022 season",
       color = "Minutes played") +
  theme_bw() +
  theme(legend.position = "bottom",
        legend.title = element_blank(),
        axis.text.y = element_text(size = 14),
        axis.text.x = element_text(size = 6))

If you’re tired of explicitly customizing every plot in the same way all the time, then you should make a custom theme. It’s quite easy to make a custom theme for ggplot2 and of course there are an incredible number of ways to customize your theme. In the code chunk, I modify the theme_bw() theme using the %+replace% argument to make my new theme named my_theme() - which is stored as a function:

my_theme <- function () {
  # Start with the base font size
  theme_bw(base_size = 10) %+replace%
    theme(
      panel.background  = element_blank(),
      plot.background = element_rect(fill = "transparent", color = NA), 
      legend.position = "bottom",
      legend.background = element_rect(fill = "transparent", color = NA),
      legend.key = element_rect(fill = "transparent", color = NA),
      axis.ticks = element_blank(),
      panel.grid.major = element_line(color = "grey90", size = 0.3), 
      panel.grid.minor = element_blank(),
      plot.title = element_text(size = 18, 
                                hjust = 0, vjust = 0.5, 
                                face = "bold", 
                                margin = margin(b = 0.2, unit = "cm")),
      plot.subtitle = element_text(size = 12, hjust = 0, 
                                   vjust = 0.5, 
                                   margin = margin(b = 0.2, unit = "cm")),
      plot.caption = element_text(size = 7, hjust = 1,
                                  face = "italic", 
                                  margin = margin(t = 0.1, unit = "cm")),
      axis.text.x = element_text(size = 13),
      axis.text.y = element_text(size = 13)
    )
}

Now I can go ahead and my plot from before with this theme:

nba_stats %>%
  ggplot(aes(x = offensive_rebounds, y = three_pointers,
             color = minutes_played)) +
  geom_point(alpha = 0.5) +
  scale_color_viridis_c() +
  labs(x = "Offensive rebounds", y = "Three-pointers",
       title = "Joint distribution of three-pointers and offensive rebounds",
       subtitle = "NBA statistics from 2021-2022 season",
       color = "Minutes played") +
  my_theme()