Bar plots (geom_bar, geom_col) in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a basic bar plot using ggplot2 with geom_bar.

R Programming

library(ggplot2)
data <- data.frame(category = c('A', 'B', 'C'), count = c(10, 20, 15))
ggplot(data, aes(x = category)) + geom_bar(stat = [1])

Drag options to blanks, or click blank then click option'

A"identity"

B"mean"

C"sum"

D"count"

Attempts:

3 left

💡 Hint

Common Mistakes

Using stat = "identity" when data does not have y values.

geom_bar by default uses stat = "count" to count the number of cases for each category.

2fill in blank

medium

Complete the code to create a bar plot using geom_col with the height of bars from the count column.

R Programming

library(ggplot2)
data <- data.frame(category = c('A', 'B', 'C'), count = c(10, 20, 15))
ggplot(data, aes(x = category, y = [1])) + geom_col()

Drag options to blanks, or click blank then click option'

Acount

Bcategory

Cx

Dy

Attempts:

3 left

💡 Hint

Common Mistakes

Mapping x to y aesthetic instead of count.

geom_col uses the y aesthetic to set the height of bars, so we map y to the count column.

3fill in blank

hard

Fix the error in the code to correctly plot counts of categories using geom_bar.

R Programming

library(ggplot2)
data <- data.frame(category = c('A', 'B', 'A', 'C', 'B', 'B'))
ggplot(data, aes(x = [1])) + geom_bar()

Drag options to blanks, or click blank then click option'

Acount

Bcategory

Cvalue

Dx

Attempts:

3 left

💡 Hint

Common Mistakes

Using stat = "identity" without y aesthetic.

When using geom_bar with stat = "identity", you must provide y values. Here, we want to count categories, so remove stat = "identity" or map x to category and use default stat.

4fill in blank

hard

Fill both blanks to create a bar plot with bars colored by category and width set to 0.5.

R Programming

library(ggplot2)
data <- data.frame(category = c('A', 'B', 'C'), count = c(5, 10, 7))
ggplot(data, aes(x = category, y = count, fill = [1])) + geom_col(width = [2])

Drag options to blanks, or click blank then click option'

Acategory

Bcount

C0.5

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using count for fill aesthetic.

Setting width to 1 when asked for 0.5.

We fill bars by category and set width to 0.5 for narrower bars.

5fill in blank

hard

Fill all three blanks to create a stacked bar plot showing counts by category and subgroup.

R Programming

library(ggplot2)
data <- data.frame(category = c('A', 'A', 'B', 'B'), subgroup = c('X', 'Y', 'X', 'Y'), count = c(3, 2, 5, 4))
ggplot(data, aes(x = [1], y = [2], fill = [3])) + geom_col()

Drag options to blanks, or click blank then click option'

Acategory

Bcount

Csubgroup

Dgroup

Attempts:

3 left

💡 Hint

Common Mistakes

Using group instead of subgroup for fill.

Mapping y to subgroup instead of count.

To create a stacked bar plot, map x to category, y to count, and fill to subgroup.

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