Why tidy data enables analysis in R Programming - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a tidy data frame with columns 'name' and 'score'.

R Programming

data <- data.frame(name = c("Alice", "Bob"), score = c(85, [1]))

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

A95

B80

C75

D90

Attempts:

3 left

💡 Hint

Common Mistakes

Using a score lower than Alice's by mistake.

The score for Bob should be 90 to complete the data frame correctly.

2fill in blank

medium

Complete the code to select only the 'score' column from the tidy data frame.

R Programming

library(dplyr)
scores <- data %>% select([1])

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

Agender

Bscore

Cage

Dname

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting the 'name' column instead of 'score'.

We select the 'score' column to focus on the scores only.

3fill in blank

hard

Fix the error in the code to filter scores greater than 80.

R Programming

filtered <- data %>% filter(score [1] 80)

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

A>

B<

C==

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' which filters lower scores.

To get scores greater than 80, use the '>' operator.

4fill in blank

hard

Fill both blanks to create a summary of average scores by name.

R Programming

summary <- data %>% group_by([1]) %>% summarise(avg_score = mean([2]))

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

Aname

Bscore

Cage

Dgender

Attempts:

3 left

💡 Hint

Common Mistakes

Grouping by wrong column or averaging wrong column.

Group by 'name' and calculate mean of 'score' to summarize average scores.

5fill in blank

hard

Fill all three blanks to filter scores above 80, select 'name' and 'score', and arrange by score descending.

R Programming

result <- data %>% filter(score [1] 80) %>% select([2], [3]) %>% arrange(desc(score))

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

A>

Bname

Cscore

Dage

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong filter operator or selecting wrong columns.

Filter scores greater than 80, select 'name' and 'score' columns, then arrange by descending score.

Practice

1. What is the main reason tidy data makes analysis easier in R?

easy

A. Data is stored in a single file format

B. Data is always numeric

C. Each variable is a column and each observation is a row

D. Data is sorted alphabetically

Solution

1. Step 1: Understand tidy data structure

   Tidy data means each variable is stored in its own column and each observation in its own row.

2. Step 2: Connect tidy data to analysis ease

   This structure makes it easier for R functions and packages to process and analyze data efficiently.

3. Final Answer:

   Each variable is a column and each observation is a row -> Option C

4. Quick Check:

   Tidy data = variable as column, observation as row [OK]

Hint: Remember: tidy means variables in columns, observations in rows [OK]

Common Mistakes:

• Thinking tidy data means data is sorted
• Assuming tidy data must be numeric only
• Confusing file format with data structure

2. Which of the following is the correct way to represent tidy data in R?

easy

A. Each column is a variable, each row is an observation

B. Data stored as a list of vectors without names

C. Each row is a variable, each column is an observation

D. Data stored as a single vector with mixed types

Solution

1. Step 1: Recall tidy data format

   Tidy data requires each column to represent a variable and each row to represent an observation.

2. Step 2: Match options to tidy data

   Each column is a variable, each row is an observation correctly states this format, while others do not follow tidy data principles.

3. Final Answer:

   Each column is a variable, each row is an observation -> Option A

4. Quick Check:

   Columns = variables, rows = observations [OK]

Hint: Variables go in columns, observations go in rows [OK]

Common Mistakes:

• Swapping rows and columns roles
• Using unnamed lists instead of data frames
• Mixing data types in a single vector

3. Given this R data frame:

df <- data.frame(
  id = c(1, 2),
  age = c(30, 25),
  height = c(170, 160)
)

Which statement about this data is true?

medium

A. It is tidy because each variable is a column and each observation is a row

B. It is not tidy because variables are mixed in rows

C. It is not tidy because data types differ in columns

D. It is tidy because all data is numeric

Solution

1. Step 1: Examine data frame structure

   The data frame has columns named id, age, and height, each representing a variable.

2. Step 2: Check observations in rows

   Each row represents one observation with values for all variables.

3. Final Answer:

   It is tidy because each variable is a column and each observation is a row -> Option A

4. Quick Check:

   Variables in columns and observations in rows = tidy [OK]

Hint: Check if columns are variables and rows are observations [OK]

Common Mistakes:

• Confusing data type differences as untidiness
• Thinking numeric-only data is required
• Assuming mixing variables in rows is okay

4. Consider this R code snippet:

df <- data.frame(
  name = c("Alice", "Bob"),
  score = c(85, 90),
  score = c(88, 92)
)

What is the main problem with this data frame?

medium

A. Rows are not observations

B. Data frame has missing rows

C. Data types are inconsistent in columns

D. Duplicate column names cause data loss

Solution

1. Step 1: Identify duplicate column names

   The data frame defines two columns both named 'score', which causes the first to be overwritten.

2. Step 2: Understand impact on tidy data

   Duplicate names cause loss of data and break tidy data principles where each variable must have a unique column.

3. Final Answer:

   Duplicate column names cause data loss -> Option D

4. Quick Check:

   Duplicate columns overwrite data causing loss [OK]

Hint: Check for unique column names to avoid data loss [OK]

Common Mistakes:

• Ignoring duplicate column names
• Assuming missing rows cause the issue
• Confusing data type inconsistency with column duplication

5. You have a messy data frame where multiple variables are combined in one column like this:

df <- data.frame(
  id = c(1, 2),
  info = c("age:30;height:170", "age:25;height:160")
)

Which approach best tidies this data for analysis in R?

hard

A. Keep 'info' as one column and analyze text directly

B. Split the 'info' column into separate 'age' and 'height' columns

C. Convert 'info' into a list column without splitting

D. Remove the 'info' column to simplify data

Solution

1. Step 1: Identify problem with combined variables

   The 'info' column mixes two variables (age and height) in one column, which is untidy.

2. Step 2: Apply tidying principle

   Splitting 'info' into separate columns for age and height makes each variable its own column, following tidy data rules.

3. Final Answer:

   Split the 'info' column into separate 'age' and 'height' columns -> Option B

4. Quick Check:

   Separate variables into columns for tidy data [OK]

Hint: Split combined columns into separate variable columns [OK]

Common Mistakes:

• Analyzing combined text without splitting
• Removing useful data instead of tidying
• Using list columns that complicate analysis