6 Visualization

ggplot2:: offers a range of convenient functions for data visualization. The foundational function, ggplot(), provides the initial framework for adding supplementary layers using the + operator. In ggplot(), we define variables plotted on x- and y-axis through aes(). For example:

# without pipe
ggplot(data = iris,
       mapping = aes(x = Sepal.Length,
                     y = Sepal.Width)) +
  # additional layers...

# with pipe
iris %>% 
  ggplot(mapping = aes(x = Sepal.Length,
                       y = Sepal.Width)) +
  # additional layers...  

Please note that aes() refers to columns in the data frame. Variables names that do not exist in the data frame cannot be used.

6.0.1 Point

geom_point() : Add a point layer

# basic plot
iris %>% 
  ggplot(aes(x = Sepal.Length,
             y = Sepal.Width)) +
  geom_point()

# change color by "Species" column
iris %>% 
  ggplot(aes(x = Sepal.Length,
             y = Sepal.Width,
             color = Species)) +
  geom_point()

6.0.2 Line

geom_line() : Add a line layer

# sample data
df0 <- tibble(x = rep(1:50, 3),
              y = x * 2)

# basic plot
df0 %>% 
  ggplot(aes(x = x,
             y = y)) +
  geom_line()

6.0.3 Histogram

geom_histogram() : add a histogram layer

# basic plot; bins = 30 by default
iris %>% 
  ggplot(aes(x = Sepal.Length)) +
  geom_histogram()

# change bin width
iris %>% 
  ggplot(aes(x = Sepal.Length)) +
  geom_histogram(binwidth = 0.5)

# change bin number
iris %>% 
  ggplot(aes(x = Sepal.Length)) +
  geom_histogram(bins = 50)

6.0.4 Boxplot

geom_boxplot() : add a boxplot layer

# basic plot
iris %>% 
  ggplot(aes(x = Species,
             y = Sepal.Length)) +
  geom_boxplot()

# change fill by "Species"
iris %>% 
  ggplot(aes(x = Species,
             y = Sepal.Length,
             fill = Species)) +
  geom_boxplot()

# change fill by "Species", but consistent color
iris %>% 
  ggplot(aes(x = Species,
             y = Sepal.Length,
             fill = Species)) +
  geom_boxplot(color = "darkgrey")