6.13 Review of Basic Visualization

In this chapter, we have introduced the most commonly used plots for visualization. It is helpful to review what we have learned so far.

6.13.1 Summary of geoms

Names Funs Section
One continuous variable (e.g. cholesterol or sale_price)
Histogram geom_histogram(aes(x = cholesterol)) 6.9
Density Plot geom_density(aes(x = cholesterol)) 6.10
Boxplot geom_boxplot(aes(x = "", y = sale_price)) 6.11
One discrete variable (e.g. kit_qual)
Bar Chart geom_bar(mapping = aes(x = kit_qual)) 6.8.1
Pie Chart geom_bar(aes(x = kit_qual), stat = "identity") + coord_polar("y") 6.8.4
Two continuous variables (e.g. sugar and cholesterol)
Scatterplot geom_point(mapping = aes(x = sugar, y = cholesterol)) 6.1
Line Plot geom_line(mapping = aes(x = dt_sold, y = sale_price)) 6.5
Smoothline geom_smooth(mapping = aes(x = sugar, y = cholesterol)) 6.6
Two discrete variables (e.g. kit_qual and central_air):
Bar Chart geom_bar(mapping = aes(x = kit_qual, fill = central_air)) 6.8.3
One continuous variable and one discrete variable (e.g. kit_qual and sale_price):
Boxplot geom_boxplot(aes(x = kit_qual, y = sale_price)) 6.11.3

6.13.2 The grammatical structure of ggplot()

Next, we review the grammatical structure of ggplot().

Code Info
ggplot(data = <DATA>) + data to be used
<GEOM_FUNCTION>( geom for generating the desirable plot
mapping = aes(<MAPPINGS>), aesthetic mappings, this may include the x and y axes and other features like color, shape, fill, linetype, size, etc.
stat = <STAT>, statistical transformation, for example, when we create the errorbar
position = <POSITION>) + position, like stack, dodge, fill
<COORDINATE_FUNCTION> + such as flipping the x and y axes
<FACET_FUNCTION> + facet_wrap() and facet_grid(), create multiple plots for different subsets of the data
<SCALE_FUNCTION> + customize the x and y breaks
<THEME_FUNCTION> customize labels, title, and fonts

6.13.3 A complex ggplot() example

To conclude this chapter, let’s look at an example with all components.

library(r02pro)
library(tidyverse)
ggplot(data = na.omit(sahp)) +
  geom_bar(
    mapping = aes(x = kit_qual, fill = oa_qual > 5),
    stat = "count", 
    position = "fill") +
  coord_flip() +
  facet_wrap(vars(house_style)) +
  scale_y_continuous(breaks = seq(from = 0, to = 1, by = 0.2)) +
  theme(plot.title = element_text(size = 24, color = "magenta")) +
        xlab("Kitchen quality") +
  ylab("Proportion") +
  ggtitle('A bar chart in proportion of kitchen quality')

This plot shows a bar chart using the data sahp for the variable kit_qual, map the variable oa_qual > 5 to the fill aesthetic and with fill position, with the x and y coordinates flipped, faceted using the variable house_style, and with the breaks on the y axis, the title and its font, the label on the y axis being customized.

6.13.4 Exercises

  1. Using the gm2004 dataset from the r02pro package, create a scatterplot of sugar (x-axis) vs. cholesterol (y-axis), colored by continent, with a smoothing line added via geom_smooth(). Customize the axis labels and add a title.

  2. Using the sahp dataset, create a faceted histogram of sale_price with one panel per house_style (restrict to "1Story" and "2Story"). Customize the number of bins to 15.

  3. Recreate the complex example plot above, but replace kit_qual with house_style and facet by kit_qual instead. Add appropriate axis labels and a title.

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