Bar plots

Introduction to Data Visualization with ggplot2

Rick Scavetta

Founder, Scavetta Academy

Bar Plots, with a categorical X-axis

Use geom_bar() or geom_col()

Geom	Stat	Action
`geom_bar()`	"count"	Counts the number of cases at each x position
`geom_col()`	"identity"	Plot actual values

All positions from before are available
Two types
- Absolute counts
- Distributions

Bar Plots, with a categorical X-axis

Use geom_bar() or geom_col()

Geom	Stat	Action
`geom_bar()`	"count"	Counts the number of cases at each x position
`geom_col()`	"identity"	Plot actual values

Bar Plots, with a categorical X-axis

Use geom_bar() or geom_col()

Geom	Stat	Action
`geom_bar()`	"count"	Counts the number of cases at each x position
`geom_col()`	"identity"	Plot actual values

All positions from before are available
Two types
- Absolute counts
- Distributions

Habits of mammals

str(sleep)

'data.frame':    76 obs. of  3 variables:
 $ vore : Factor w/ 4 levels "carni","herbi",..: 1 4 2 4 2 2 1 1 2 2 ...
 $ total: num  12.1 17 14.4 14.9 4 14.4 8.7 10.1 3 5.3 ...
 $ rem  : num  NA 1.8 2.4 2.3 0.7 2.2 1.4 2.9 NA 0.6 ...

Bar plot

ggplot(sleep, aes(vore)) + 
  geom_bar()

Plotting distributions instead of absolute counts

# Calculate Descriptive Statistics:
iris %>% 
  select(Species, Sepal.Width) %>% 
  pivot_longer(!Species, names_to = "key", 
  values_to = "value") %>%
  group_by(Species) %>% 
  summarise(avg = mean(value), 
            stdev = sd(value))
   -> iris_summ_long

iris_summ_long

Species	avg	stdev
setosa	3.43	0.38
versicolor	2.77	0.31
virginica	2.97	0.32

Plotting distributions

ggplot(iris_summ_long, aes(x = Species, 
                           y = avg)) + 
  geom_col() +
  geom_errorbar(aes(ymin = avg - stdev, 
                    ymax = avg + stdev), 
                width = 0.1)

Let's practice!

Introduction to Data Visualization with ggplot2