9  Summary bar graphs

This graph requires:

✅ a numeric (continuous) variable

✅ a categorical variable

9.1 Description

Bar graphs can summarize data by showing a statistic for each category. The x-axis lists the categories, and the y-axis shows the values. They can include error bars to show variation or confidence intervals.

In ggplot2, we can create summary bar graphs with geom_bar().

9.2 Set up

PACKAGES:

Install packages.

install.packages("palmerpenguins")
library(palmerpenguins) 
library(ggplot2)

DATA:

Artwork by Allison Horst

Remove the missing values from body_mass_g and island in the palmerpenguins::penguins data and convert body mass in grams to kilograms (body_mass_kg).

We’ll also reduce the number of columns in the penguins data for clarity.

peng_sum_col <- palmerpenguins::penguins |> 
    dplyr::select(body_mass_g, island) |> 
    tidyr::drop_na() |> 
    # divide the mass value by 1000
    dplyr::mutate(
        body_mass_kg = body_mass_g / 1000
    ) |> 
    dplyr::group_by(island) |> 
    dplyr::summarise(
      avg_bmi_kg = mean(body_mass_kg)
    ) |> 
    dplyr::ungroup()
dplyr::glimpse(peng_sum_col)
#> Rows: 3
#> Columns: 2
#> $ island     <fct> Biscoe, Dream, Torgersen
#> $ avg_bmi_kg <dbl> 4.716018, 3.712903, 3.706373

9.3 Grammar

CODE:

• Create labels with labs()

• Initialize the graph with ggplot() and provide data

• Map island to x and avg_bmi_kg to y

• Inside the aes() of geom_col(), map island to fill

• Outside the aes() of geom_col(), remove the legend with show.legend = FALSE

labs_sum_col <- labs(
    title = "Average Penguin Body Mass",
    subtitle = "What is the average penguin BMI per Island?",
    x = "Island",
    y = "Average Penguin Body Mass (kg)")
ggp2_sum_col <- ggplot(data = peng_sum_col,
    aes(x = island,
        y = avg_bmi_kg)) +
    geom_col(aes(fill = island), 
        show.legend = FALSE)  
ggp2_sum_col + 
    labs_sum_col

GRAPH:

9.4 More Info

Below is more information on geom_bar() vs. geom_col().

9.4.1 Identity vs. Count

• The geom_bar() geom will also create grouped bar graphs, but we have to switch the stat argument to "identity".

ggplot(data = peng_sum_col,
    aes(x = island,
        y = avg_bmi_kg)) +
    geom_col(aes(fill = island), 
        show.legend = FALSE,
        stat = "identity")  +
    labs_sum_col

9.4.2 geom_bar() vs. geom_col()

• geom_bar() will map a categorical variable to the x or y and display counts for the discrete levels (see stat_count() for more info)

• geom_col() will map both x and y aesthetics, and is used when we want to display numerical (quantitative) values across the levels of a categorical variable. geom_col() assumes these values have been created in their own column (see stat_identity() for more info)