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

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

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

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

Hint

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

Practice

1. What is the main difference between geom_bar() and geom_col() in R's ggplot2 package?

easy

A. geom_bar() counts data automatically; geom_col() uses provided values for bar heights.

B. geom_bar() creates line plots; geom_col() creates bar plots.

C. geom_bar() requires numeric x-axis; geom_col() requires categorical x-axis.

D. geom_bar() is for pie charts; geom_col() is for bar charts.

Solution

1. Step 1: Understand geom_bar() behavior

   geom_bar() automatically counts the number of occurrences for each category on the x-axis and plots bars based on these counts.

2. Step 2: Understand geom_col() behavior

   geom_col() uses the y-values provided in the data directly as the height of the bars, without counting.

3. Final Answer:

   geom_bar() counts data automatically; geom_col() uses provided values for bar heights. -> Option A

4. Quick Check:

   Counting vs direct values = A [OK]

Hint: Remember: bar counts vs bar heights from data [OK]

Common Mistakes:

• Thinking both functions count data automatically
• Confusing bar plots with line or pie charts
• Assuming geom_col() counts data

2. Which of the following is the correct syntax to create a bar plot using geom_col() with ggplot2 in R?

easy

A. ggplot(data, aes(x=category)) + geom_col()

B. ggplot(data, aes(x=category, y=value)) + geom_col()

C. ggplot(data, aes(y=value)) + geom_col()

D. ggplot(data, aes(x=value, y=category)) + geom_col()

Solution

1. Step 1: Identify required aesthetics for geom_col()

   geom_col() needs both x (categorical) and y (numeric) aesthetics to set bar positions and heights.

2. Step 2: Check each option's aesthetics

   ggplot(data, aes(x=category, y=value)) + geom_col() correctly maps x to category and y to value. Options A and C miss one aesthetic, and D swaps x and y incorrectly.

3. Final Answer:

   ggplot(data, aes(x=category, y=value)) + geom_col() -> Option B

4. Quick Check:

   Both x and y needed for geom_col() = B [OK]

Hint: geom_col() needs both x and y aesthetics [OK]

Common Mistakes:

• Omitting y aesthetic for geom_col()
• Swapping x and y aesthetics
• Using geom_col() without specifying y

3. Given the following R code, what will be the height of the bars in the plot?

library(ggplot2)
data <- data.frame(fruit = c('apple', 'banana', 'apple', 'orange', 'banana', 'banana'))
ggplot(data, aes(x=fruit)) + geom_bar()

medium

A. Bars show counts: apple=2, banana=3, orange=1

B. Bars show values from a y column (not provided)

C. Bars will be empty because y is missing

D. Bars show sum of fruit names alphabetically

Solution

1. Step 1: Understand geom_bar() default behavior

   Without a y aesthetic, geom_bar() counts the number of rows for each x category.

2. Step 2: Count occurrences in data

   apple appears 2 times, banana 3 times, orange 1 time.

3. Final Answer:

   Bars show counts: apple=2, banana=3, orange=1 -> Option A

4. Quick Check:

   geom_bar() counts rows = A [OK]

Hint: geom_bar() counts rows if y not given [OK]

Common Mistakes:

• Expecting geom_bar() to use y values without specifying
• Thinking bars will be empty without y
• Confusing counts with sums or other calculations

4. Consider this R code snippet:

library(ggplot2)
data <- data.frame(category = c('A', 'B', 'C'), value = c(5, 3, 8))
ggplot(data, aes(x=category)) + geom_bar(stat='identity')

What is the problem with this code?

medium

A. The geom_bar() function does not accept stat argument.

B. The data frame has wrong column names.

C. The x aesthetic should be numeric, not categorical.

D. Using stat='identity' with geom_bar() requires y aesthetic but it's missing.

Solution

1. Step 1: Understand stat='identity' in geom_bar()

   When using stat='identity', geom_bar() expects a y aesthetic to set bar heights directly.

2. Step 2: Check the code's aesthetics

   The code only maps x=category but does not map y=value, so bar heights are undefined.

3. Final Answer:

   Using stat='identity' with geom_bar() requires y aesthetic but it's missing. -> Option D

4. Quick Check:

   stat='identity' needs y aesthetic = C [OK]

Hint: Use y aesthetic with stat='identity' in geom_bar() [OK]

Common Mistakes:

• Forgetting to add y aesthetic with stat='identity'
• Assuming geom_bar() always counts data
• Thinking stat argument is invalid

5. You have a data frame with categories and their sales numbers:

sales_data <- data.frame(
  product = c('Pen', 'Pencil', 'Eraser', 'Pen', 'Pencil'),
  sales = c(10, 5, 8, 7, 3)
)

You want to create a bar plot showing total sales per product. Which code correctly creates this plot?

hard

A. ggplot(sales_data, aes(x=product, y=sales)) + geom_bar()

B. ggplot(sales_data, aes(x=product)) + geom_col()

C. ggplot(sales_data, aes(x=product, y=sales)) + geom_col()

D. ggplot(sales_data, aes(x=product)) + geom_bar()

Solution

1. Step 1: Understand data and goal

   The data has multiple rows per product. Goal is bars with heights = sum(sales) per product (Pen=17, Pencil=8, Eraser=8).

2. Step 2: Choose correct plotting method

   geom_col(aes(x=product, y=sales)) uses stat='identity' and sums y values per x category for bar heights.

3. Step 3: Check options

   A: correct, sums sales. B: no y, invalid. C: geom_bar() defaults to stat='count', ignores y, counts rows (2,2,1). D: counts rows (2,2,1).

4. Final Answer:

   ggplot(sales_data, aes(x=product, y=sales)) + geom_col() -> Option C

5. Quick Check:

   geom_col() sums y per x = A [OK]

Hint: geom_col(aes(x,y)) sums y per category [OK]

Common Mistakes:

• Using geom_bar() (default count) when needing sum of y
• Omitting y aesthetic with geom_col()
• Thinking manual aggregation is always required