Bivariate data analysis and bivariate data vizualisation

This page will show you how you can analyze and visualize 2 variables and their relationships using R.

Explanation

Contingency tables (a.k.a. crosstabs) with tabyl

Use the tabyl() function from the janitor package. The first line creates the table. The rest formats the table (giving it a title, adding totals etc…).

library(janitor)
mtcars %>% 
  tabyl(am, gear, show_na = FALSE) %>% 
  adorn_title("combined") %>% #both var names in the title
  adorn_totals("col") %>% #column totals
  adorn_totals("row") %>% #row totals
  adorn_percentages("col") %>% #columnwise, rowwise (row), or total percentages (all)
  adorn_pct_formatting(digits = 1) %>%
  adorn_ns() #show the numbers of cases

Box plots for different groups using ggplot

For a boxplot use geom_boxplot():

# A boxplot comparing different species is having two variables in the aesthetics

iris %>% 
  ggplot(aes(x = Species, y = Petal.Width)) +
  geom_boxplot()

Scatterplots with ggplot

A scatter plot in the ggplot package will be done with the geom geom_point(). Notice that the aesthetics in the first layer now contain two variables.

mtcars %>% 
  ggplot(aes(x = cyl, y = mpg)) + 
  geom_point()

If you want to add a linear regression line, add an extra ‘geom’ (geom_smooth).

mtcars %>% 
  ggplot(aes(x = cyl, y = mpg)) + 
  geom_point() +
  geom_smooth(method = "lm", se = F)

If you want to visualize different groups in the scatterplot, you can add extra variables to the aesthetics. In addition to x and y, you can use color (the “outside” color of points), fill (the “inside” color of the points), shape (of points) and size.

mtcars %>% 
  ggplot(aes(x = cyl, 
             y = mpg, 
             color = factor(gear)
             )
         ) + 
  geom_point()

Faceting

For different subplots, use the layer facet_wrap(~ ) as default. This is another way to introduce a third variable.

mpg %>% 
  ggplot(aes(x = factor(cyl), y = hwy)) +
  geom_boxplot() + 
  facet_wrap(~ year)

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