Bar plots (geom_bar, geom_col) in R Programming - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of geom_bar with default stat

What is the output of this R code using ggplot2?

library(ggplot2)
data <- data.frame(category = c('A', 'B', 'A', 'C', 'B', 'A'))
ggplot(data, aes(x = category)) + geom_bar()

R Programming

library(ggplot2)
data <- data.frame(category = c('A', 'B', 'A', 'C', 'B', 'A'))
ggplot(data, aes(x = category)) + geom_bar()

A A bar plot with bars showing counts: A=3, B=2, C=1

B A bar plot with bars showing sum of category values

C A scatter plot with points at categories A, B, C

D An error because y aesthetic is missing

💡 Hint

geom_bar by default counts the number of occurrences of each x value.

geom_bar() without specifying y uses stat='count' which counts how many times each x value appears. Here, 'A' appears 3 times, 'B' 2 times, and 'C' once.

❓ Predict Output

intermediate

Difference between geom_bar and geom_col

Given this data and code, what does the plot show?

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

R Programming

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

A A bar plot with bars heights exactly 5, 3, and 8 for A, B, and C

B A bar plot counting occurrences of each category ignoring value

C A line plot connecting points (A,5), (B,3), (C,8)

D An error because geom_col requires stat='count'

💡 Hint

geom_col uses the y aesthetic as the bar height directly.

geom_col() uses the y values directly for bar heights, unlike geom_bar() which counts occurrences by default.

🔧 Debug

advanced

Error in geom_bar with y aesthetic

What error does this code produce and why?

library(ggplot2)
data <- data.frame(category = c('A', 'B', 'C'), value = c(2, 4, 6))
ggplot(data, aes(x = category, y = value)) + geom_bar()

R Programming

library(ggplot2)
data <- data.frame(category = c('A', 'B', 'C'), value = c(2, 4, 6))
ggplot(data, aes(x = category, y = value)) + geom_bar()

A Plot shows bars with heights 2, 4, and 6.

B Error: stat_count() must not be used with a y aesthetic.

C Error: object 'value' not found.

D No error; bars show counts ignoring y values.

💡 Hint

geom_bar by default uses stat='count' which does not accept y values.

geom_bar() with default stat='count' cannot use y aesthetic. To use y, you must use geom_col() or specify stat='identity'.

🧠 Conceptual

advanced

When to use geom_col instead of geom_bar

Which situation best describes when geom_col() should be used instead of geom_bar()?

A When you want to count the number of occurrences of each category.

B When you want to create a scatter plot with bars.

C When you want to plot pre-summarized values as bar heights.

D When you want to plot a line graph connecting categories.

💡 Hint

Think about whether your data already has the values to plot or if you want to count.

geom_col() uses the y values directly as bar heights, so it is used when data already contains summarized values. geom_bar() counts occurrences by default.

❓ Predict Output

expert

Bar plot with grouped data and position dodge

What does this code produce?

library(ggplot2)
data <- data.frame(
  group = rep(c('G1', 'G2'), each = 3),
  category = rep(c('A', 'B', 'C'), 2),
  value = c(2, 5, 3, 4, 1, 6)
)
ggplot(data, aes(x = category, y = value, fill = group)) + geom_col(position = 'dodge')

R Programming

library(ggplot2)
data <- data.frame(
  group = rep(c('G1', 'G2'), each = 3),
  category = rep(c('A', 'B', 'C'), 2),
  value = c(2, 5, 3, 4, 1, 6)
)
ggplot(data, aes(x = category, y = value, fill = group)) + geom_col(position = 'dodge')

A A line plot connecting values for each group across categories

B A stacked bar plot with bars for G1 and G2 stacked in each category

C An error because position='dodge' is invalid

D A grouped bar plot with side-by-side bars for G1 and G2 in each category with heights from value

💡 Hint

position='dodge' places bars side by side instead of stacking.

geom_col() with position='dodge' creates grouped bars side by side for each fill group within each x category.

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