Tidying Data with tidyr
Transform messy data into a tidy format to facilitate analysis using tidyr functions.
Tidying Data with tidyr is a free R Academy lesson on CoddyKit — lesson 3 of 3. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the R Academy learning path, one of 3 lessons in the course, and your progress syncs across the web and the CoddyKit app.
1
Introduction to Tidying Data with tidyr
The tidyr package helps in organizing messy data into a structured format.
2
Pivoting Data: Wide to Long
Use pivot_longer() to convert wide format data to long format.
library(tidyr)
data <- data.frame(Name = c('Alice', 'Bob'), Jan = c(10, 15), Feb = c(20, 25))
data_long <- pivot_longer(data, cols = c('Jan', 'Feb'), names_to = 'Month', values_to = 'Score')3
Pivoting Data: Long to Wide
Use pivot_wider() to convert long format data to wide format.
data_wide <- pivot_wider(data_long, names_from = 'Month', values_from = 'Score')4
Separating Columns
The separate() function splits one column into multiple columns.
data <- data.frame(Name = c('Alice_Smith', 'Bob_Jones'))
data_separated <- separate(data, Name, into = c('FirstName', 'LastName'), sep = '_')5
Unite Columns
The unite() function combines multiple columns into one.
data_united <- unite(data_separated, FullName, FirstName, LastName, sep = ' ')6
Handling Missing Values
Use drop_na() to remove missing values from a dataset.
library(dplyr)
data_clean <- data %>% drop_na()7
Replacing Missing Values
Use replace_na() to fill missing values with a specific value.
data_filled <- data %>% mutate(Score = replace_na(Score, 0))8
9
Combining Multiple tidyr Functions
You can use pivot_longer(), separate(), and unite() together for complete data tidying.
data_tidy <- data %>% pivot_longer(cols = c('Jan', 'Feb'), names_to = 'Month', values_to = 'Score') %>% separate(Name, into = c('FirstName', 'LastName'), sep = '_') %>% unite(FullName, FirstName, LastName, sep = ' ')10
Summary
In this lesson, you learned:
- How to reshape data using
pivot_longer()andpivot_wider(). - How to separate and unite columns for structured data.
- How to handle missing values using
drop_na()andreplace_na().
Frequently asked questions
Is the “Tidying Data with tidyr” lesson free?
Yes — the full text of “Tidying Data with tidyr” is free to read here on the web, and the R Academy course includes 3 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the R Academy course, upgrade to CoddyKit PRO.
What will I learn in “Tidying Data with tidyr”?
Transform messy data into a tidy format to facilitate analysis using tidyr functions. You practise R Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start R Academy?
No prior experience is required. R Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 3, so you can start here or from the beginning and move at your own pace.
How long does the “Tidying Data with tidyr” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this R Academy lesson?
Yes. Every R Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Introduction to Tidyverse
- Data Wrangling with dplyr
- Tidying Data with tidyr