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.