Data Reshaping with tidyr
Intermediate
8 hrs
3 Concepts
Your Learning Map
📌 You already know
You can wrangle a data frame with dplyr verbs.
🎯 You'll learn here
Reshaping data between wide and long form with
pivot_longer / pivot_wider, and handling NA.🌍 Where it's used
Real data arrives in the wrong shape — survey columns per year, sensor readings per device; tidying it is half the job.
🔗 Unlocks next
Tidy (long) data is exactly what ggplot2 expects.
M1
Wide and Long Format
Concept 1
pivot_longer — Wide to Long
Tidy data: one observation per row, one variable per column. ggplot2 and most models require long format.
pivot_longer() converts multiple columns into two: a 'names' column and a 'values' column.
R
library(tidyr)
wide <- tibble(student=c('A','B'), Math=c(92,88), Science=c(85,91))
long <- wide |> pivot_longer(cols=-student, names_to='subject', values_to='score')
# Result: 4 rows (2 students x 2 subjects)
Solved Examples
Example 1
Apply the concept of pivot_longer — Wide to Long to a sample dataset. Show at least two approaches.
# See the code example above and adapt it to your data. # Always check your output with str() and head().
Self-Assessment (2 questions)
Q1. pivot_longer() transforms data from:
pivot_longer() gathers many columns into key-value pairs, making wide data long (tidy).
Q2. In tidy data:
Tidy data has one column per variable and one row per observation.
Concept 2
pivot_wider — Long to Wide
pivot_wider() is the reverse — convert a key-value long format back to wide format.
R
long |> pivot_wider(names_from=subject, values_from=score)
# Back to original wide format
R — A wide summary table
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Output (verified)
gear cyl 3 4 5 4 1 8 2 6 2 4 1 8 12 0 2
Solved Examples
Example 1
Apply the concept of pivot_wider — Long to Wide to a sample dataset. Show at least two approaches.
# See the code example above and adapt it to your data. # Always check your output with str() and head().
Self-Assessment (2 questions)
Q1. pivot_wider() is the inverse of:
pivot_wider() spreads key-value pairs back into multiple columns - the inverse of pivot_longer().
Q2. A good use of pivot_wider() is to:
pivot_wider() turns the values of a column into separate columns - handy for summary tables.
M2
Missing Data
Concept 1
Handling NA Values
R uses NA for missing values. Always check for NAs before analysis.
Key functions: is.na(), na.omit(), replace_na(), fill(), drop_na().
R
df |> drop_na() # remove rows with any NA
df |> replace_na(list(x=0)) # replace specific column NAs
df |> fill(x, .direction='down') # forward fill
df |> mutate(x=if_else(is.na(x), mean(x,na.rm=TRUE), x)) # mean impute
Solved Examples
Example 1
Apply the concept of Handling NA Values to a sample dataset. Show at least two approaches.
# See the code example above and adapt it to your data. # Always check your output with str() and head().
Self-Assessment (2 questions)
Q1. In R, a missing value is represented by:
NA marks a missing value; NULL is the absence of an object, and 0 or "" are real values.
Q2. To drop rows that contain any missing value, you can use:
tidyr::drop_na() removes rows containing NA; fill()/replace_na() instead fill them in.