Categorical Data

R Packages

  • csucistats
  • tidyverse

The Great American Coffee Taste Test

  • The Great American Coffee Taste Test

  • Categorical Data

  • Continguency Tables

  • Bar Plots

  • Cross-Tabulation

  • Pie Charts

  • Theming

Background

In October 2023, James Hoffman and Cometeer held the “Great American Coffee Taste Test” on YouTube, asking viewers to fill out a survey and coffee ordered from Cometeer.

Data

The data is part of DSLC Tidy Tuesday program where data sets are provided to help data science learners how to create graphics.

Information on the data sets variables (columns) can be found here.

Data

Code 
library(rcistats)
library(tidyverse)
library(ThemePark)
library(DT)

coffee <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-05-14/coffee_survey.csv")
datatable(slice_sample(coffee, n = 10), options = list(dom = "p", pageLength = 5))

Categorical Data

  • The Great American Coffee Taste Test

  • Categorical Data

  • Continguency Tables

  • Bar Plots

  • Cross-Tabulation

  • Pie Charts

  • Theming

Categorical Data

Categorical data are data recordings that represented a category.

Data may be recorded as a “character” or “string” data.

Data may be recorded as a whole number, with an attached code book indicating the categories each number belongs to.

Examples of Categorical Data

  • Are you a student?

  • What city do you live in?

  • What is your major?

Likert Scale

Likert scales are the rating systems you may have answered in surveys.

  1. Strongly Disagree
  2. Disagree
  3. Neutral
  4. Agree
  5. Strongly Agree

Likert Scales

Likert scales may be treated as numerical data if the jumps between scales are equal.

Summarizing Categorical Data

Once we have the data, how do we summarize it to other people.

Continguency Tables

  • The Great American Coffee Taste Test

  • Categorical Data

  • Continguency Tables

  • Bar Plots

  • Cross-Tabulation

  • Pie Charts

  • Theming

Continguency Tables

Continguency tables display how often a category is seen in the data.

There are two types of statistics that are reported in a table, the frequency and proportion.

Frequencey

Frequency represents the count of observing a specific category in your sample.

#>  [1] NA  "2" "3" "2" "1" "3" "2" "2" "4" "2"

Proportions (relative frequencey)

Proportions represent the percentage that the category represents the sample.

This allows you to generalize your sample to the population, regardless of sample size.

Continguency Tables in R

The variable caffeine indicates how much caffeine a participant prefers.

Code 
cat_stats(coffee$caffeine)

Bar Plots

  • The Great American Coffee Taste Test

  • Categorical Data

  • Continguency Tables

  • Bar Plots

  • Cross-Tabulation

  • Pie Charts

  • Theming

Plotting in R

Plotting in R can be done via the ggplot2, a powerful library based on the Grammar of Graphics.

Plotting in R

  1. You need to create a base plot using the ggplot()
  2. Use the + to change the look of the base plot
  3. Indicate how to transform the base plot to the desired plot
  4. geom_*
  5. stat_*
  6. Change the look of the plot with other functions
  7. Use a theme_* function to add a theme to the plot

Bar Plots

Bar Plots can be used to display the frequency or proportions on the data.

Frequency Bar Plots in R

Code 
ggplot(data = DATA, aes(x = VARIABLE)) +
  geom_bar()

Frequency Bar Plots in R

Code 
ggplot(coffee, aes(caffeine)) +
  geom_bar()

Relative Frequency Bar Plots in R

Code 
ggplot(data = DATA, aes(x = VARIABLE, y = after_stat(prop), group = 1)) +
  geom_bar()

Relative Frequency Bar Plots in R

Code 
ggplot(coffee, aes(caffeine, after_stat(prop), group = 1)) +
  geom_bar()

Cross-Tabulation

  • The Great American Coffee Taste Test

  • Categorical Data

  • Continguency Tables

  • Bar Plots

  • Cross-Tabulation

  • Pie Charts

  • Theming

Data

The variable taste indicates if the participants like the taste of coffee.

Code 
cat_stats(coffee$taste)

Cross-Tabulation

Cross-tabulations, also known as contingency tables, are statistical tools used to analyze the relationship between two or more categorical variables by displaying their frequency distribution in a table format. Each cell in the table represents the count or frequency of observations that fall into a particular combination of categories for the variables.

Key Features of Cross-Tabulations

  1. Rows and Columns:
    • Rows represent the categories of one variable.
    • Columns represent the categories of another variable.
  2. Cells:
    • Each cell displays the frequency or count of data points that belong to the intersection of a row and column category.
  3. Margins:
    • Row and column totals provide summaries for each variable.
    • The grand total shows the overall sample size.

Types of Proportions in Cross-Tabulations

  1. Row Proportions: Show the percentage of each row total represented by a cell.
  2. Column Proportions: Show the percentage of each column total represented by a cell.
  3. Table Proportions: Show the percentage of the overall total represented by a cell.

Table Proportions

Table proportions in cross-tabulations refer to the relative frequency or percentage of counts within the entire table, calculated by dividing each cell’s count by the total sum of all counts in the table. These proportions allow you to examine the contribution of each cell to the overall data set.

Table Proportions

Code 
cat_stats(coffee$caffeine, coffee$taste, prop = "table")

Row Proportions

Row proportions refer to the relative frequency or percentage of counts within each row of a contingency table. In a cross-tabulation, row proportions allow you to compare how the distribution of one variable varies within each category of another variable, within a row.

Row Proportions

Code 
cat_stats(coffee$caffeine, coffee$taste, prop = "row")

Column Proportions

Column proportions refer to the relative frequency or percentage of counts within each column of a contingency table. These proportions allow you to compare how the distribution of one variable varies across different categories of another variable, within a column.

Column Proportions

Code 
cat_stats(coffee$caffeine, coffee$taste, prop = "col")

Stacked Bar Plot in R

Code 
ggplot(DATA, aes(x = VAR1, y = after_stat(count), fill = VAR2)) +
  geom_bar()

Stacked Bar Plot in R

Code 
ggplot(coffee, aes(x = caffeine, y = after_stat(count), fill = taste)) +
  geom_bar()

Stacked Bar Plot in R

Code 
ggplot(coffee, aes(y = caffeine, x = after_stat(count), fill = taste)) +
  geom_bar()

Pie Charts

  • The Great American Coffee Taste Test

  • Categorical Data

  • Continguency Tables

  • Bar Plots

  • Cross-Tabulation

  • Pie Charts

  • Theming

Pie Charts

A pie chart is a circular statistical graphic divided into slices, where each slice represents a proportion or percentage of the whole. The size of each slice is proportional to the relative frequency or magnitude of the category it represents.

Pie Charts

Key Features of Pie Charts

  1. Circular Format:
    • The chart is shaped like a circle, symbolizing a whole (100% or 1).
  2. Slices:
    • Each slice corresponds to a category and its size represents the contribution of that category to the total.
  3. Labels:
    • Slices are often labeled with the category name and the percentage or value they represent.

Pie Chart in R

Code 
df_pie <- cat_stats(coffee$caffeine, tbl_df = TRUE)$table
ggplot(df_pie, aes(cat = Category, val = n, fill = Category)) +
  geom_pie()

Pie Chart in R

Code 
coffee_pie <- cat_stats(coffee$caffeine, tbl_df = TRUE)$table
ggplot(coffee_pie, aes(cat = Category, val = n, fill = Category)) +
  geom_pie()

Theming

  • The Great American Coffee Taste Test

  • Categorical Data

  • Continguency Tables

  • Bar Plots

  • Cross-Tabulation

  • Pie Charts

  • Theming

Themes

The R packages ThemePark and ggthemes allows you to change the overall look of a plot.

All you need to do is add the theme to the plot.