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

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this R code using tidy data principles?

Consider this R code that uses a tidy data frame. What will be the output of the summary call?

R Programming

library(dplyr)
data <- tibble(
  id = c(1, 1, 2, 2),
  variable = c("height", "weight", "height", "weight"),
  value = c(170, 65, 180, 75)
)
data_wide <- data %>%
  pivot_wider(names_from = variable, values_from = value)
summary(data_wide)

A Error in pivot_wider(names_from = variable, values_from = value) : could not find function "pivot_wider"

B

       id        variable         value     
 Min.   :1   Length:4           Min.   :65.0  
 1st Qu.:1   Class :character   1st Qu.:66.25 
 Median :1.5 Mode  :character   Median :72.5  
 Mean   :1.5                    Mean   :97.5  
 3rd Qu.:2                    3rd Qu.:87.5  
 Max.   :2                    Max.   :180.0 

C

       id        height         weight     
 Min.   :1   Min.   :170.0   Min.   :65.0  
 1st Qu.:1   1st Qu.:172.5   1st Qu.:67.5  
 Median :1.5 Median :175.0   Median :70.0  
 Mean   :1.5 Mean   :175.0   Mean   :70.0  
 3rd Qu.:2   3rd Qu.:177.5   3rd Qu.:72.5  
 Max.   :2   Max.   :180.0   Max.   :75.0  

D

       id        height         weight     
 Min.   :1   Min.   :65.0    Min.   :170.0 
 1st Qu.:1   1st Qu.:67.5    1st Qu.:172.5 
 Median :1.5 Median :70.0    Median :175.0 
 Mean   :1.5 Mean   :70.0    Mean   :175.0 
 3rd Qu.:2   3rd Qu.:72.5    3rd Qu.:177.5 
 Max.   :2   Max.   :75.0    Max.   :180.0 

💡 Hint

Think about how pivot_wider reshapes data from long to wide format, making columns for each variable.

The pivot_wider function converts the tidy long data into a wide format where each variable becomes a column. The summary then shows statistics for id, height, and weight. Option C correctly reflects this output.

🧠 Conceptual

intermediate

Why is tidy data important for analysis?

Which of the following best explains why tidy data makes analysis easier?

A Tidy data stores all variables in columns and observations in rows, making it easier to apply functions and visualize data.

B Tidy data compresses the dataset to use less memory, speeding up analysis.

C Tidy data stores data in a single string, simplifying text processing.

D Tidy data automatically removes missing values, so analysis is always accurate.

💡 Hint

Think about how data organized in rows and columns helps tools work better.

Tidy data means each variable is a column and each observation is a row. This structure fits well with most analysis tools and makes it easy to apply functions and create plots. The other options describe incorrect or unrelated ideas.

❓ Predict Output

advanced

What is the output of this R code reshaping untidy data?

Given this untidy data frame, what will be the output of the pivot_longer call?

R Programming

library(tidyr)
data <- data.frame(
  id = 1:2,
  height = c(170, 180),
  weight = c(65, 75)
)
data_long <- pivot_longer(data, cols = c(height, weight), names_to = "variable", values_to = "value")
print(data_long)

A Error in pivot_longer(data, cols = c(height, weight), names_to = "variable", values_to = "value") : could not find function "pivot_longer"

B

  id variable value
1  1   height   170
2  1   weight    65
3  2   height   180
4  2   weight    75

C

  id height weight
1  1    170     65
2  2    180     75

D

  id variable value
1  1   height   65
2  1   weight   170
3  2   height   75
4  2   weight   180

💡 Hint

Remember that pivot_longer turns columns into rows.

The pivot_longer function converts the wide format data into a long format where 'height' and 'weight' become values in a 'variable' column, with their corresponding values in 'value'. Option B shows this correctly.

🔧 Debug

advanced

What error does this R code produce and why?

What error will this code produce and why?

R Programming

library(tidyr)
data <- data.frame(
  id = 1:2,
  height = c(170, 180),
  weight = c(65, 75)
)
data_long <- pivot_longer(data, cols = c(height, weight), names_to = "variable")

A Error: unused argument (names_to = "variable")

B No error, code runs successfully and returns long format data

C Error: object 'pivot_longer' not found

D Error: argument "values_to" is missing, with no default

💡 Hint

Check the required arguments for pivot_longer.

The pivot_longer function requires the values_to argument to specify the name of the column for values. Omitting it causes an error about a missing argument. Option D correctly describes this error.

🚀 Application

expert

How many rows will the tidy data have after reshaping?

Given this untidy data frame, how many rows will the tidy data have after applying pivot_longer to columns height and weight?

R Programming

library(tidyr)
data <- data.frame(
  id = 1:3,
  height = c(170, 180, 175),
  weight = c(65, 75, 70)
)
data_long <- pivot_longer(data, cols = c(height, weight), names_to = "variable", values_to = "value")

A 6

B 3

C 2

D 9

💡 Hint

Each original row will produce multiple rows after pivoting columns into rows.

There are 3 rows and 2 columns being pivoted. Each row produces 2 rows in the long format, so total rows = 3 * 2 = 6. Option A is correct.

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