Lecture 28

Bar Chart ggplot()

Byeong-Hak Choe

bchoe@geneseo.edu

SUNY Geneseo

November 11, 2024

Distribution ggplot() - Bar Chart

Bar Chart with geom_bar()

• Bar charts are used to visualize the distribution of a categorical variable.

• geom_bar() divides data into bins and count the number of observations in each bin.

Bar Chart with geom_bar()

Diamond Dataset

Bar Chart with geom_bar()

ggplot(data = diamonds,
       mapping = aes(x = cut)) + 
  geom_bar()

• geom_bar() creates a bar chart.
  • We map either the x or y aesthetic to the variable.

Bar Chart with geom_bar()

Horizontal Bar Chart

ggplot(data = diamonds,
       mapping = aes(y = cut)) + 
  geom_bar()

• Bar charts can be horizontal or vertical.
  • A horizontal bar chart is a good option for long category names.

Bar Chart with geom_bar()

Counting Occurrences of Each Category in a Categorical Variable

• The figure below demonstrates how the counting process works with geom_bar().

count(): Counting Occurrences of Each Category in a Categorical Variable

DATA.FRAME |> count(CATERIGOCAL_VARIABLE)

• The data transformation function count() is a quick and efficient way to calculate the frequency of each unique value in a categorical variable.

diamonds |> count(cut)

• diamonds |> count(cut) returns the data.frame with the two variables, cut and n:
  • n: the number of occurrences of each unique value in the cut variable in the diamonds data.frame

Bar Chart with geom_bar()

Colorful Bar Chart with the fill Aesthetic

ggplot(data = diamonds,
       mapping = 
         aes(x = cut, 
             fill = cut)) + 
  geom_bar(
    show.legend = FALSE
    ) 

• We can color a bar chart using the fill aesthetic.

count(): Counting Occurrences Across Two Categorical Variables

DATA.FRAME |> count(CATEGORICAL_VARIABLE_1, CATEGORICAL_VARIABLE_2)

• The data transformation function count() calculates the frequency of each unique combination of values across two categorical variables.

diamonds |> count(cut, clarity)

• diamonds |> count(cut, clarity) returns the data.frame with the three variables, cut, clarity, and n:
  • n: the number of occurrences of each unique combination of values in cut and clarity

Bar Chart with geom_bar()

Stacked Bar Charts with the fill Aesthetic

# Mapping the `fill` aesthetic 
# to other CATEGORICAL variable
# gives a stacked bar chart

ggplot(data = diamonds,
       mapping = 
         aes(x = cut, 
             fill = clarity)) + 
  geom_bar()

• This describes how the distribution of clarity varies by cut, with total bar height for overall count and segments for each clarity level.

Bar Chart with geom_bar()

100% Stacked Bar Charts with the fill Aesthetic & the position="fill"

ggplot(data = diamonds,
       mapping = 
         aes(x = cut, 
             fill = clarity)) + 
  geom_bar(position = "fill") +
  labs(y = "Proportion")

• This describes how the distribution of clarity varies by cut, displaying the proportion of each clarity within each cut.

Bar Chart with geom_bar()

Clustered Bar Charts with the fill Aesthetic & the position="dodge"

ggplot(data = diamonds,
       mapping = 
         aes(x = cut, 
             fill = clarity)) + 
  geom_bar(position = "dodge")

• This shows how the distribution of clarity varies by cut, with separate bars for each clarity level within each cut category.

Stacked vs. 100% Stacked vs. Clustered

Choosing the Right Bar Chart for Comparing Components and Totals

• Which type of bar chart is most effective for your data?

• Which type of bar chart best meets your visualization goals?

Choosing the Right Bar Chart

Stacked Bar Chart

• Best for showing the breakdown of subcomponents within a category alongside the overall total.
• Useful when your primary focus is on the total bar height while also showing subcomponent contributions.
• Tip: Be cautious using stacked bars if the goal is to make precise comparisons between subcomponents across different bars, as they don’t all start from the same baseline.

• If you need to emphasize total values along with component contributions, use a Stacked Bar Chart

Choosing the Right Bar Chart

100% Stacked Bar Chart

• Shows the proportion of subcomponents within each category as a percentage of the total, normalizing all bars to the same height.
• Allows for easier comparison between categories for subcomponent proportions, as each segment starts from a consistent baseline.
• Ideal Use: When you want to compare relative percentages rather than absolute totals.

• For comparing relative proportions between categories, a 100% Stacked Bar Chart is ideal.

Choosing the Right Bar Chart

Clustered Bar Chart

• Plots each subcomponent as a separate bar, grouped by category, allowing for precise comparisons of each component across categories.

• Ideal Use: When your main goal is to compare individual subcomponents side by side rather than focusing on an overall total or relative percentage.

• When directly comparing individual subcomponents across categories, opt for a Clustered Bar Chart.

Bar Chart with geom_bar()

Stacked Bar Charts using the fill Aesthetic and the position = "stack"

ggplot(data = diamonds,
       mapping = 
         aes(x = cut, 
             fill = clarity)) + 
  geom_bar(position = "stack")

• The default position option is position = "stack"

Proportion Bar Chart with geom_bar()

ggplot(data = diamonds,
       mapping = 
         aes(x = cut, 
             y = after_stat(prop),
             group = 1)) + 
  geom_bar()

• after_stat(prop): Calculates the proportion of the total count.
• group = 1: Ensures the proportions are calculated over the entire data.frame, not within each group of cut

Bar Chart with geom_col()

df <- mpg |> 
  count(class)

ggplot(data = df,
       mapping = 
         aes(x = n, 
             y = class)) + 
  geom_col()

• geom_col() creates bar charts where the height of bars directly represents values in a column in a given data.frame.
  • geom_col() requires both x- and y- aesthetics.

Bar Chart with geom_col()

Sorted Bar Chart with fct_reorder(CATEGORICAL, NUMERICAL)

df <- mpg |> 
  count(class)

ggplot(data = df,
       mapping = 
         aes(x = n,
             y = 
               fct_reorder(class, n))
       ) + 
  geom_col() +
  labs(y = "Class")

• fct_reorder(CATEGORICAL, NUMERICAL): Reorders the categories of the CATEGORICAL by the median of the NUMERICAL.

Pie Charts: Alternative to Bar Charts?

• Pie charts show the proportions of a whole.
  • Each slice represents a part of the total.
  • Is a pie chart a proper alternative to a bar chart?

Pie Charts: Alternative to Bar Charts?

• Humans are better at judging lengths than angles.
  • Occasionally, using a pie chart can be a good idea.

1. Pie charts work well only if you only have a few categories—four max.

2. Pie charts work well if the goal is to emphasize simple fractions (e.g., 25%, 50%, or 75%).

Pie Charts: Alternative to Bar Charts?

3. Pie charts are not the best choice if you want audiences to compare the size of shares.

4. Pie charts are not the best choice if you want audiences to compare the distribution across categories.

Distribution of an Integer Variable

• For data visualization, integer-type variables could be treated as either categorical (discrete) or numeric (continuous), depending on the context of analysis.

• If the values of an integer-type variable means an intensity or an order, the integer variable could be numeric.

  • A variable of age integers (18, 19, 20, 21, …) could be numeric.
  • A variable of integer-valued MPG (27, 28, 29, 30, …) could be numeric.

• If not, the integer variable is categorical.

  • A variable of month integers (1, ,2, …, 12) could be categorical.

Distribution of an Integer Variable

Bar Chart for the Age variable

Histogram for the Age variable

• In ggplot, the distribution of an integer variable can appear quite similar when using geom_bar() and geom_histogram().
  • In Python or others, they can be quite different, as shown above.