Bar plots (geom_bar, geom_col) in R Programming - Mini Project: Build & Apply

Bar plots with geom_bar and geom_col in R

📖 Scenario: You are working with a small store's sales data. You want to visualize how many items were sold for each product category using bar plots.

🎯 Goal: Create a bar plot using geom_bar and another bar plot using geom_col in R with ggplot2 to show the number of items sold per product category.

📋 What You'll Learn

Create a data frame called sales_data with columns category and items_sold with exact values

Create a variable called bar_plot_geom_bar that uses ggplot with geom_bar(stat = "count") to plot counts of categories

Create a variable called bar_plot_geom_col that uses ggplot with geom_col() to plot items_sold per category

Print both plots using print()

💡 Why This Matters

🌍 Real World

Bar plots are commonly used in business to quickly see how different categories compare in counts or values, such as sales per product type.

💼 Career

Data analysts and data scientists use bar plots to communicate insights clearly to teams and stakeholders.

1

Create the sales data frame

Create a data frame called sales_data with two columns: category containing the values 'Fruits', 'Vegetables', 'Dairy', 'Bakery', and items_sold containing the values 50, 30, 20, 40 respectively.

R Programming

# Create the sales_data data frame with category and items_sold columns
# Your code here

Need a hint?

Use data.frame() with category = c(...) and items_sold = c(...) to create the data frame.

2

Create bar plot using geom_bar

Create a variable called bar_plot_geom_bar that uses ggplot with sales_data as data, mapping category to the x-axis, and add geom_bar(stat = "count") to plot the count of each category.

R Programming

sales_data <- data.frame(
  category = c('Fruits', 'Vegetables', 'Dairy', 'Bakery'),
  items_sold = c(50, 30, 20, 40)
)
# Create bar_plot_geom_bar using ggplot with geom_bar(stat = "count")
# Your code here

Need a hint?

Use ggplot(sales_data, aes(x = category)) + geom_bar(stat = "count") to create the bar plot.

3

Create bar plot using geom_col

Create a variable called bar_plot_geom_col that uses ggplot with sales_data as data, mapping category to the x-axis and items_sold to the y-axis, and add geom_col() to plot the exact items sold per category.

R Programming

library(ggplot2)
sales_data <- data.frame(
  category = c('Fruits', 'Vegetables', 'Dairy', 'Bakery'),
  items_sold = c(50, 30, 20, 40)
)
bar_plot_geom_bar <- ggplot(sales_data, aes(x = category)) + geom_bar(stat = "count")
# Create bar_plot_geom_col using ggplot with geom_col()
# Your code here

Need a hint?

Use ggplot(sales_data, aes(x = category, y = items_sold)) + geom_col() to create the bar plot with exact values.

4

Print both bar plots

Use print() to display both bar_plot_geom_bar and bar_plot_geom_col plots.

R Programming

library(ggplot2)
sales_data <- data.frame(
  category = c('Fruits', 'Vegetables', 'Dairy', 'Bakery'),
  items_sold = c(50, 30, 20, 40)
)
bar_plot_geom_bar <- ggplot(sales_data, aes(x = category)) + geom_bar(stat = "count")
bar_plot_geom_col <- ggplot(sales_data, aes(x = category, y = items_sold)) + geom_col()
# Print both bar_plot_geom_bar and bar_plot_geom_col
# Your code here

Need a hint?

Use print(bar_plot_geom_bar) and print(bar_plot_geom_col) to show the plots.