R Academy · Lesson

separate() and unite() for String Columns

Split and merge column values containing multiple pieces of information.

Lesson 3 of 413 steps

separate() and unite() for String Columns is a free R Academy lesson on CoddyKit. This is lesson 3 of 4. Any 3 lessons of this learning path are free to read in full — after that, CoddyKit PRO unlocks every lesson, plus hands-on practice with a built-in code editor and a 24/7 AI tutor. It is part of the R Academy learning path, and your progress syncs across the web and the CoddyKit app. The R Academy course includes 4 lessons in total.

Splitting and Combining String Columns

Raw data often has multiple pieces of information crammed into a single column (e.g., '2024-03-15' contains year, month, and day). tidyr's separate() and unite() functions split one column into many, or combine many into one.

library(tidyr)

# Date stored as a single string column
df <- data.frame(
  id = 1:4,
  date = c('2024-01-15','2024-03-22','2023-11-08','2024-06-30'),
  value = c(100, 200, 150, 175)
)

print(df)

separate() — Split One Column into Many

separate(df, col, into, sep) splits a character column into multiple new columns using a separator. The into argument names the new columns; sep is the delimiter (default: any non-alphanumeric character).

library(tidyr)

df <- data.frame(
  id = 1:4,
  date = c('2024-01-15','2024-03-22','2023-11-08','2024-06-30')
)

# Split 'date' into year, month, day
separate(
  df,
  col = date,
  into = c('year','month','day'),
  sep = '-'
)

separate() with convert Argument

By default, separate() creates character columns. Set convert = TRUE to automatically convert the new columns to the most appropriate type (integer, numeric, logical). This is especially useful for year and month columns.

library(tidyr)

df <- data.frame(
  event = c('2024-1','2024-2','2023-12'),
  score = c(85, 90, 78)
)

result <- separate(
  df,
  col = event,
  into = c('year','month'),
  sep = '-',
  convert = TRUE
)

print(result)
cat('year type:', class(result$year), '\n')
cat('month type:', class(result$month))

separate() with remove = FALSE

By default separate() removes the original column. Set remove = FALSE to keep it alongside the new split columns. This is useful when you want to verify the split or keep the original for reference.

library(tidyr)

df <- data.frame(
  full_code = c('US-CA-001','US-TX-002','UK-LDN-003')
)

separate(
  df,
  col = full_code,
  into = c('country','state','code'),
  sep = '-',
  remove = FALSE   # Keep original column
)

separate() on Fixed Positions

Instead of a delimiter, you can split at fixed character positions by passing an integer vector to sep. Positive integers count from the left; negative integers count from the right.

library(tidyr)

# Product code: first 3 chars = category, next 4 = id
df <- data.frame(
  code = c('ABC1234','XYZ5678','DEF9012'),
  qty = c(10, 20, 15)
)

separate(
  df,
  col = code,
  into = c('category','product_id'),
  sep = 3    # Split after position 3
)

unite() — Combine Columns into One

unite(df, col, ..., sep) combines multiple columns into a single character column. The first argument after df is the new column name, followed by the columns to combine, then the separator string.

library(tidyr)

df <- data.frame(
  first = c('Alice','Bob','Carol'),
  last = c('Smith','Jones','White'),
  score = c(85, 90, 78)
)

unite(
  df,
  col = 'full_name',
  first, last,
  sep = ' '
)

unite() with remove = FALSE

Like separate(), unite() removes the source columns by default. Set remove = FALSE to retain the original columns alongside the new combined column. Useful when you need both forms.

library(tidyr)

df <- data.frame(
  year = c(2024, 2024, 2023),
  month = c(1, 3, 12),
  day = c(15, 22, 8)
)

unite(
  df,
  col = 'date_str',
  year, month, day,
  sep = '-',
  remove = FALSE
)

unite() to Build Keys

A common use of unite() is creating composite keys by combining multiple identifier columns. This is useful before joining tables that need a shared key built from multiple fields.

library(tidyr)
library(dplyr)

orders <- data.frame(
  year = c(2024, 2024, 2023),
  region = c('East','West','East'),
  seq_num = c(1, 1, 2)
)

orders_keyed <- orders %>%
  unite(col = 'order_key', year, region, seq_num, sep = '_')

print(orders_keyed)

separate_rows() — Split to Multiple Rows

separate_rows(df, col, sep) splits a cell containing multiple values into separate rows. This is different from separate() which splits to columns. It's useful when a cell contains a comma-separated list of items.

library(tidyr)

# Tags stored as comma-separated values in one cell
df <- data.frame(
  id = 1:3,
  title = c('Intro to R','Data Viz','ML Basics'),
  tags = c('r,beginner','r,ggplot,visualization','r,ml,modeling')
)

separate_rows(df, tags, sep = ',')

Chaining separate() and unite()

You can chain separate() and unite() in a pipeline to reformat date strings, fix inconsistent formats, or create new composite identifiers from existing columns.

library(tidyr)
library(dplyr)

# Reformat date from YYYY-MM-DD to DD/MM/YYYY
df <- data.frame(
  id = 1:3,
  date = c('2024-01-15','2024-03-22','2023-11-08')
)

df %>%
  separate(date, into=c('year','month','day'), sep='-') %>%
  unite(col='date_eu', day, month, year, sep='/')

Handling Extra or Missing Pieces

separate() has an extra argument for when there are more pieces than into columns: 'warn' (default), 'drop' (discard extras), or 'merge' (keep last piece unsplit). Similarly, fill handles fewer pieces: 'warn', 'right', or 'left'.

library(tidyr)

# Inconsistent data: some rows have 2 parts, some have 3
df <- data.frame(
  code = c('A-1','B-2-extra','C-3'),
  value = c(10, 20, 30)
)

# 'merge' keeps the last piece unsplit
separate(df, code, into=c('prefix','suffix'), sep='-',
         extra='merge')

Quick Check

Which function would you use to split rows where a column contains comma-separated values like 'tag1,tag2,tag3' into one row per tag?

Answer it in the interactive lesson

Recap: separate() and unite()

Key takeaways for separate() and unite():

separate(col, into, sep) — split one column into multiple columns by delimiter or position
convert = TRUE — automatically type-convert the split results
remove = FALSE — keep the original column in both functions
unite(col, ..., sep) — combine multiple columns into one string column
separate_rows(col, sep) — split delimited values into multiple rows (not columns)
extra and fill arguments handle inconsistent split results

library(tidyr)
library(dplyr)

df <- data.frame(
  id = 1:3,
  datetime = c('2024-01-15 09:30','2024-03-22 14:15','2023-11-08 11:45')
)

df %>%
  separate(datetime, into=c('date','time'), sep=' ') %>%
  separate(date, into=c('year','month','day'), sep='-', convert=TRUE) %>%
  separate(time, into=c('hour','minute'), sep=':', convert=TRUE)

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Yes — any 3 lessons of the R Academy learning path, including “separate() and unite() for String Columns”, are free to read in full here on the web. After that, CoddyKit PRO unlocks every lesson, plus interactive practice with a built-in code editor and a 24/7 AI tutor. The R Academy course includes 4 lessons in total.

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Split and merge column values containing multiple pieces of information. 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.

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All lessons in this course

Wide to Long with pivot_longer()
Long to Wide with pivot_wider()
separate() and unite() for String Columns
Nesting and Unnesting Data Frames

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