1 Introduction

In the last session, we have seen how to use ggplot2 and The Grammar of Graphics. The goal of this practical is to practices more advanced features of ggplot2.

The objectives of this session will be to:

• learn about statistical transformations
• practices position adjustments
• change the coordinate systems

The first step is to load the tidyverse.

library("tidyverse")

Like in the previous sessions, it’s good practice to create a new .R file to write your code instead of using the R terminal directly.

2 ggplot2 statistical transformations

In the previous session, we have plotted the data as they are by using the variable values as x or y coordinates, color shade, size or transparency. When dealing with categorical variables, also called factors, it can be interesting to perform some simple statistical transformations. For example, we may want to have coordinates on an axis proportional to the number of records for a given category.

We are going to use the diamonds data set included in tidyverse.

str(diamonds)

tibble [53,940 × 10] (S3: tbl_df/tbl/data.frame)
 $ carat  : num [1:53940] 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
 $ cut    : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
 $ color  : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
 $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
 $ depth  : num [1:53940] 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
 $ table  : num [1:53940] 55 61 65 58 58 57 57 55 61 61 ...
 $ price  : int [1:53940] 326 326 327 334 335 336 336 337 337 338 ...
 $ x      : num [1:53940] 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
 $ y      : num [1:53940] 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
 $ z      : num [1:53940] 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...

ggplot(data = diamonds, mapping = aes(x = cut)) + 
  geom_bar()

ggplot(data = diamonds, mapping = aes(x = cut)) + 
  stat_count()

2.3 Why stat ?

You might want to override the default stat. For example, in the following demo dataset we already have a variable for the counts per cut.

demo <- tribble(
  ~cut,         ~freq,
  "Fair",       1610,
  "Good",       4906,
  "Very Good",  12082,
  "Premium",    13791,
  "Ideal",      21551
)

(Don’t worry that you haven’t seen tribble() before. You might be able to guess at their meaning from the context, and you will learn exactly what they do soon!)

So instead of using the default geom_bar parameter stat = "count" try to use "identity"

Solution

ggplot(data = demo, mapping = aes(x = cut, y = freq)) +
  geom_bar(stat = "identity")

You might want to override the default mapping from transformed variables to aesthetics ( e.g., proportion).

ggplot(data = diamonds, mapping = aes(x = cut, y = ..prop.., group = 1)) + 
  geom_bar()

In our proportion bar chart, we need to set group = 1. Why?

Solution

ggplot(data = diamonds, mapping = aes(x = cut, y = ..prop..)) + 
  geom_bar()

If group is not used, the proportion is calculated with respect to the data that contains that field and is ultimately going to be 100% in any case. For instance, the proportion of an ideal cut in the ideal cut specific data will be 1.

2.4 More details with stat_summary

You might want to draw greater attention to the statistical transformation in your code. you might use stat_summary(), which summarize the y values for each unique x value, to draw attention to the summary that you are computing

Solution

ggplot(data = diamonds, mapping = aes(x = cut, y = depth)) + 
  stat_summary()

Set the fun.min, fun.max and fun to the min, max and median function respectively

Solution

ggplot(data = diamonds, mapping = aes(x = cut, y = depth)) + 
  stat_summary(
    fun.min = min,
    fun.max = max,
    fun = median
  )

3 Coloring area plots

Solution

ggplot(data = diamonds, mapping = aes(x = cut, color = cut)) + 
  geom_bar()

ggplot(data = diamonds, mapping = aes(x = cut, fill = cut)) + 
  geom_bar()

Solution

ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + 
  geom_bar()

4 Position adjustments

The stacking of the fill parameter is performed by the position adjustment position

Solution

ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + 
  geom_bar( position = "fill")

ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + 
  geom_bar( position = "dodge")

ggplot(data = diamonds, mapping = aes(x = cut, fill = clarity)) + 
  geom_bar( position = "jitter")

jitter is often used for plotting points when they are stacked on top of each other.

Solution

ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) + 
  geom_point()

ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) + 
  geom_jitter()

Solution

ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) + 
  geom_jitter(width = .1, height = .1)

In the geom_jitter plot that we made, we cannot really see the limits of the different clarity groups. Instead we can use the geom_violin to see their density.

Solution

ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) + 
  geom_violin()

5 Coordinate systems

Cartesian coordinate system where the x and y positions act independently to determine the location of each point. There are a number of other coordinate systems that are occasionally helpful.

ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) + 
  geom_boxplot()

Solution

ggplot(data = diamonds, mapping = aes(x = cut, y = depth, color = clarity)) + 
  geom_boxplot() +
  coord_flip()

ggplot(data = diamonds, mapping = aes(x = cut, fill = cut)) + 
  geom_bar( show.legend = FALSE,  width = 1 ) + 
  theme(aspect.ratio = 1) +
  labs(x = NULL, y = NULL)

Solution

ggplot(data = diamonds, mapping = aes(x = cut, fill = cut)) + 
  geom_bar( show.legend = FALSE,  width = 1 ) + 
  theme(aspect.ratio = 1) +
  labs(x = NULL, y = NULL) +
  coord_polar()

By combining the right geom, coordinates and faceting functions, you can build a large number of different plots to present your results.

6 See you in R.4: data transformation

7 To go further: animated plots from xls files

In order to be able to read information from a xls file, we will use the openxlsx packages. To generate animation we will use the ggannimate package. The additional gifski package will allow R to save your animation in the gif format (Graphics Interchange Format)

install.packages(c("openxlsx", "gganimate", "gifski"))

library(openxlsx)
library(gganimate)
library(gifski)

gapminder <- read.xlsx("https://can.gitbiopages.ens-lyon.fr/R_basis/session_3/gapminder.xlsx")

gapminder <- read.xlsx("gapminder.xlsx")

This dataset contains 4 variables of interest for us to display per country: - gdpPercap the GDP par capita (US$, inflation-adjusted) - lifeExp the life expectancy at birth, in years - pop the population size - contient a factor with 5 levels

Solution

ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point()

Solution

ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point() + 
  scale_x_log10()

Solution

ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point() + 
  scale_x_log10() +
  transition_time(year) +
  labs(title = 'Year: {as.integer(frame_time)}')