Example 1 Barplots with geom_bar() from ggplot2:

Barplots are a common and effective way to visualize categorical data. We can use the geom_bar() function from the ggplot2 package to create barplots in R. The geom_bar() function accepts a variable for the x-axis and plots the number of times each value of the variable appears in the dataset[3].

library(ggplot2)

# Create a barplot using geom_bar()
ggplot(data = iris, aes(x = Species, fill = factor(Species))) +
  geom_bar() +
  theme_minimal() +
  labs(
    title = "Bar Chart of Species Count",
    ylab = "Count",
    fill = "Species"
  )

Explanation: – Load the ggplot2 package using library(ggplot2). – The iris dataset is already available in R, so we can directly use it. – The aes() function specifies the aesthetic mappings, where x represents the variable on the x-axis. – The geom_bar() function creates the barplot.

Try creating a barplot with the Species variable from the iris dataset using the provided code. Experiment with different variables and datasets to explore the patterns and distributions within your data.

Example 2 Grouped Boxplot with base R and ggplot2

A grouped boxplot is a useful chart for comparing the distribution of a continuous variable across different categories of a categorical variable. We can create a grouped boxplot using both base R and ggplot2.

# Create a grouped boxplot using  base R
boxplot(Sepal.Length ~ Species, data = iris)

# Create a grouped boxplot using ggplot2
ggplot(data = iris, aes(x = Species, y = Sepal.Length,
                        fill = factor(Species))) +
  geom_boxplot() +
  theme_minimal() +
  labs(fill = "Species")

Example 3 Mosaic Plot

A mosaic plot is a graphical representation of the relationship between two or more categorical variables. It displays the proportions of each category within the variables and allows for visual comparison.

mosaicplot(table(iris$Species, iris$Petal.Width))

Explanation: – The table() function creates a contingency table of the two variables, Species and Petal.Width, from the iris dataset. – The mosaicplot() function creates the mosaic plot.

Create a mosaic plot with the Species and Petal.Width variables from the iris dataset using the provided code. Explore the relationships and proportions between the variables. Experiment with different combinations of variables to gain insights from the mosaic plot.