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Preface

Aim

This guide serves as a cheat sheet for wrangling tasks that I find myself often doing but don’t always remember the exact way to execute the code to achieve the result I want. pivot_longer is a good example. I use it fairly frequently, and it’s straightforward when you only want to convert a number of columns into a single “name” and “value” pair of new columns. As soon as the names_pattern argument is used, especially when regex is involved, things get complicated quickly. This guide contains worked examples which demonstrate different ways these common wrangling tasks might be performed, and provides ready-to-use code via a simple copy-paste.

R Libraries

The R code in this document relies on two main R libraries.

library(dplyr)
library(tidyr)

1 Renaming

1.1 Rename multiple columns

rename can be used to rename one or several columns by passing a named vector containing the new and old variable names. But that is often too tedious when you need to rename many columns. If the new column names have a common structure, e.g. they are all being converted to uppercase, or they are all being prepended with new_, then rename_with is the function to use.

E.g. converting column names to lowercase, and replacing periods with underscores from names (all columns by default)

head(iris, 2)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
iris %>%
    rename_with(~ tolower(gsub("\\.", "_", .x)), .cols = everything())
##   sepal_length sepal_width petal_length petal_width species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa

E.g. Add prefix to all but first column

iris %>%
    rename_with(~ paste0("var_", .x), .cols = -1)
##   Sepal.Length var_Sepal.Width var_Petal.Length var_Petal.Width var_Species
## 1          5.1             3.5              1.4             0.2      setosa
## 2          4.9             3.0              1.4             0.2      setosa

Note: .cols can take column names (quoted or unquoted, negated or not), indices (negated or not), and select helper functions like starts_with, contains, etc.

1.2 Rename column using string as column name

E.g.

var <- "new_name"
dat %>%
    rename("{new_name}" := old_name)

Alternatively:

dat %>%
    rename(!!sym(new_name) := old_name)

See also mutating and other uses using string as a column name

2 Mutating Values

2.1 Replace NAs, NaN, or Inf

E.g. Replace NAs in a specific column with zero

dat %>% 
    mutate(var = replace_na(var, 0))           # tidyr solution
    mutate(var = replace(var, is.na(var), 0))  # more general base R solution

Note: replace_na() will not work if the variable is a factor, and the replacement is not already a level for your factor (see here)

The replace_na() function is very specific and limited. For multiple variables, and replacing other special values, use across():

dat %>% 
    mutate(across(c(var1, var2), ~ replace(., is.na(.), 0))) %>%
    mutate(across(c(var3, var4), ~ replace(., is.nan(.), 0))) %>%
    mutate(across(var5,          ~ replace(., is.infinite(.), 999)))

2.2 Insert values for missing combinations

Example data set:

cancer <- tibble(
    sex = c("Male", "Female", "Female"),
    cancer = c("Prostate", "Ovarian", "Ovarian"),
    agegrp = c("40-50", "40-50", "51-60"),
    count = c(20, 16, 23)
)
cancer
## # A tibble: 3 × 4
##   sex    cancer   agegrp count
##   <chr>  <chr>    <chr>  <dbl>
## 1 Male   Prostate 40-50     20
## 2 Female Ovarian  40-50     16
## 3 Female Ovarian  51-60     23

E.g. Add missing agegrp for each sex-cancer combo

cancer %>% 
    complete(nesting(sex, cancer), agegrp)
## # A tibble: 4 × 4
##   sex    cancer   agegrp count
##   <chr>  <chr>    <chr>  <dbl>
## 1 Female Ovarian  40-50     16
## 2 Female Ovarian  51-60     23
## 3 Male   Prostate 40-50     20
## 4 Male   Prostate 51-60     NA

Add missing agegrp for each sex-cancer combo, imputing 0 for missing counts

cancer %>% 
    complete(nesting(sex, cancer), agegrp, fill = list(count = 0))
## # A tibble: 4 × 4
##   sex    cancer   agegrp count
##   <chr>  <chr>    <chr>  <dbl>
## 1 Female Ovarian  40-50     16
## 2 Female Ovarian  51-60     23
## 3 Male   Prostate 40-50     20
## 4 Male   Prostate 51-60      0

Add missing cancers for each sex-agegrp combo, imputing 0 for missing counts

cancer %>% 
    complete(nesting(sex, agegrp), cancer, fill = list(count = 0))
## # A tibble: 6 × 4
##   sex    agegrp cancer   count
##   <chr>  <chr>  <chr>    <dbl>
## 1 Female 40-50  Ovarian     16
## 2 Female 40-50  Prostate     0
## 3 Female 51-60  Ovarian     23
## 4 Female 51-60  Prostate     0
## 5 Male   40-50  Ovarian      0
## 6 Male   40-50  Prostate    20

Add all missing combinations

cancer %>% 
    complete(sex, agegrp, cancer, fill = list(count = 0))
## # A tibble: 8 × 4
##   sex    agegrp cancer   count
##   <chr>  <chr>  <chr>    <dbl>
## 1 Female 40-50  Ovarian     16
## 2 Female 40-50  Prostate     0
## 3 Female 51-60  Ovarian     23
## 4 Female 51-60  Prostate     0
## 5 Male   40-50  Ovarian      0
## 6 Male   40-50  Prostate    20
## 7 Male   51-60  Ovarian      0
## 8 Male   51-60  Prostate     0

2.3 Mutate across multiple columns as new columns

dat %>%
    mutate(across(starts_with("var"), ~ .x * 2, .names = "new_{col}"))

E.g.

iris %>%
    mutate(across(starts_with("Sepal"), ~ .x * 2, .names = "new_{col}")) %>%
    head(2)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species new_Sepal.Length
## 1          5.1         3.5          1.4         0.2  setosa             10.2
## 2          4.9         3.0          1.4         0.2  setosa              9.8
##   new_Sepal.Width
## 1               7
## 2               6

2.4 Mutate across referring to column name

cur_column gives the name of the current column. E.g.

dat <- tibble(
    condition = c(rep("asthma", 2), rep("obesity", 2)),
    asthma = c(TRUE, TRUE, FALSE, TRUE),
    obesity = c(TRUE, FALSE, TRUE, TRUE)
)

dat %>%
    mutate(across(c("asthma", "obesity"), ~  ifelse(condition == cur_column(), NA, .x)))
## # A tibble: 4 × 3
##   condition asthma obesity
##   <chr>     <lgl>  <lgl>  
## 1 asthma    NA     TRUE   
## 2 asthma    NA     FALSE  
## 3 obesity   FALSE  NA     
## 4 obesity   TRUE   NA

2.5 Convert all of one data type to another data type

E.g. convert all character-valued columns to factors, and all numeric columns to integers

dat %>%
    mutate(across(where(is.character), as.factor)) %>%
    mutate(across(where(is.double), as.integer))

2.6 Mutate column using string as column name

To create a new column:

var <- "new_col"

dat %>%
    mutate("{var}" := old_col * 100)
# or
dat %>%
    mutate(!!sym(var) := old_col * 100)

To apply the mutation to the same column:

dat %>%
    mutate("{var}" := get(var) * 100)0)
# or
dat %>%
    mutate(!!sym(var) := !!sym(var) * 100)

Note: var can be a vector. Alternatively, if var is a list, replace !!sym() with !!!syms().

See also renaming and other uses using string as a column name

3 Reshaping data frames

3.1 Pivot longer with multiple variables in 1 column name

E.g. Pivot on these columns using the word before the first underscore as “name” and the word after as the values (new columns)

dat %>%
    pivot_longer(
        c(A_long, B_long, A_lat, B_lat, A_location, B_location),
        names_to = c("name", ".value"),
        names_pattern = "(.*)_(.*)"  # or names_sep = "_"
    )

E.g. Pivot on these columns using the word before the first underscore as “name” and the remainder as the values (new columns)

dat %>%
    pivot_longer(
        c(A_long, B_long, A_lat, B_lat, A_location_name, B_location_name),
        names_to = c("name", ".value"),
        names_pattern = "([^_])_(.*)"   # Everything before first underscore + remainder
    )

E.g. Pivot all columns except id.1 and id.2 using last character as “name” and the word before the period as the values (new columns)

dat %>%
    pivot_longer(
        -c(id.1, id.2),
        names_to = c(".value", "name"),
        names_pattern = "(.*).(.$)"   # or names_sep = "\\."
    )

E.g. splitting one column into three using first two delimiters:

dat <- tibble(
    id = 1:3,
    male.age.5 = 4,
    male.age.10 = 5.5,
    `male.age.15.and.over` = 6,
    female.age.5 = 3.5,
    female.age.10 = 9, 
    `female.age.15.and.over` = 7
)

Solution 1:

dat %>%
    pivot_longer(
        -id,
        names_to = c("sex", "age", ".value"),
        names_pattern = "([^\\.]*).(age).(.*)"  # or names_pattern = "([^\\.]*).([^\\.]*).(.*)"
    )
## # A tibble: 6 × 6
##      id sex    age     `5`  `10` `15.and.over`
##   <int> <chr>  <chr> <dbl> <dbl>         <dbl>
## 1     1 male   age     4     5.5             6
## 2     1 female age     3.5   9               7
## 3     2 male   age     4     5.5             6
## 4     2 female age     3.5   9               7
## 5     3 male   age     4     5.5             6
## 6     3 female age     3.5   9               7

Solution 2:

dat %>%
    rename_with(~gsub(".and.over", "+", .x)) %>%
    pivot_longer(
        -id,
        names_to = c("sex", "age", ".value"),
        names_sep = "\\."   # or either of the above two names_pattern solutions
    )
## # A tibble: 6 × 6
##      id sex    age     `5`  `10` `15+`
##   <int> <chr>  <chr> <dbl> <dbl> <dbl>
## 1     1 male   age     4     5.5     6
## 2     1 female age     3.5   9       7
## 3     2 male   age     4     5.5     6
## 4     2 female age     3.5   9       7
## 5     3 male   age     4     5.5     6
## 6     3 female age     3.5   9       7

3.2 Split the values in one column into multiple rows (strsplit)

E.g. splitting values in a column based on a delimiter

tibble(col_1 = LETTERS[1:3]) %>%
    mutate(year = paste(c(2021:2025), collapse = ", ")) %>%
    mutate(year = strsplit(year, ", ")) %>%
    unnest(year)
## # A tibble: 15 × 2
##    col_1 year 
##    <chr> <chr>
##  1 A     2021 
##  2 A     2022 
##  3 A     2023 
##  4 A     2024 
##  5 A     2025 
##  6 B     2021 
##  7 B     2022 
##  8 B     2023 
##  9 B     2024 
## 10 B     2025 
## 11 C     2021 
## 12 C     2022 
## 13 C     2023 
## 14 C     2024 
## 15 C     2025

3.3 Split one column into two columns (separate)

E.g. splitting a column based on the first space only

tibble(car = rownames(mtcars)) %>%
    slice(1:5) %>%
    separate(col = car, into = c("manufacturer", "make"), sep = "^\\S*\\K\\s+")
## # A tibble: 5 × 2
##   manufacturer make      
##   <chr>        <chr>     
## 1 Mazda        RX4       
## 2 Mazda        RX4 Wag   
## 3 Datsun       710       
## 4 Hornet       4 Drive   
## 5 Hornet       Sportabout

3.4 Convert named vector to tibble and vice versa

to: named vector one-row df two-column df
named vector 
tibble(!!!dat)
 
dat %>%
    stack() %>%
    tibble() %>%
    rename(names = 2)     
    # To match reverse code"
one-row df 
dat %>% 
    unlist()
 
dat %>% 
    unlist() %>%
    stack() %>%
    tibble() %>%
    rename(names = 2)
two-column df 
dat %>%
    pull(values, names)
 
dat %>%
    pull(values, names) %>%
    tibble(!!!.)

Note: when converting a two-column tibble to named vector, pull(dat, values, names) is preferred over

library(janitor)          # For row_to_names()
dat %>%
    t() %>%
    row_to_names(row_number = 1) %>%     # Use first row as column names
    as_tibble()

4 Other wrangling

4.1 String as column name in dplyr functions

vars <- c("foo", "bar")

# The following are equivalent:
dat %>%
    group_by(across(all_of(vars)))            # or
    group_by(foo, bar)                        # or
    group_by(across(all_of(c("foo", var))))   # or
    group_by(foo, !!sym(var))                 # or
    group_by(!!!syms(vars))

This also applies to arrange(). For select() or relocate(), drop across(), e.g. select(all_of(vars)).

var = "foo" 

# The following are equivalent:
dat %>%
    rename(new_foo = !!sym(var))                           # or
    rename(!!sym(glue("new_{var}")) := !!sym(var))         # or
    rename_with(~ paste0("new_", .x), .cols = !!sym(var))

For use of !!, sym(), and !!sym(), see here. For use of !!!, see here

See also renaming and mutating using string as a column name

4.2 Grouping by negation

Put the negation inside across(), not before it. E.g. to group by all columns except “foo” and “bar”:

dat %>%
    group_by(across(-c(foo, bar)))

# or

vars <- c("foo", "bar")
dat %>%
    group_by(across(-vars))

A practical example is when you want to sum a column of counts by all groups:

dat %>%
    group_by(across(-n)) %>%
    summarise(n = sum(n)

4.3 Sum across multiple columns

Summing across multiple columns:

dat %>%
    group_by(var1) %>%
    mutate(sum = rowSums(across(starts_with("value_"))))

Compare to summing across multiple rows:

dat %>%
    group_by(var1) %>%
    summarise(across(starts_with("value_"), ~ sum(., na.rm = TRUE)))

If the columns to be summed have a naming convention, this can be done fast:

dat %>%
    mutate(
        # pop = pop.1 + pop.2 + pop.3 +  ... + pop.100          # Hard-coded, but fast
        # pop = apply(dat %>% select(starts_with("pop.")), 1, sum)       # VERY slow
        pop = select(., starts_with("pop.")) %>% rowSums(na.rm = TRUE)   # FAST
    )

4.4 Filenames and filepaths

List all files in a particular folder, with optional regex string match (as a vector)

files <- list.files(
    "/data/my_folder", 
    pattern = "a*z\\.xlsx",
    full.names = TRUE
)

Split vector of filepaths into directory and file name (with extension)

tibble(full_name = files) %>%
    mutate(
        filepath = dirname(full_name),
        file = basename(full_name)
    )

Note: dirname and basename are both base R functions.

Extract file extension from filename:

tools::file_ext(filename)

5 Shortcuts and speed hacks

5.1 Converting age to 5-year age groups

E.g.

dat <- tibble(age = 2:20)

dat %>% 
    mutate(
        agegrp = paste(floor(age /5) * 5L, (floor(age /5) + 1L) * 5L - 1L, sep = "-"),
        agegrp = ifelse(agegrp == "20-24", "20+", agegrp)
    )
## # A tibble: 19 × 2
##      age agegrp
##    <int> <chr> 
##  1     2 0-4   
##  2     3 0-4   
##  3     4 0-4   
##  4     5 5-9   
##  5     6 5-9   
##  6     7 5-9   
##  7     8 5-9   
##  8     9 5-9   
##  9    10 10-14 
## 10    11 10-14 
## 11    12 10-14 
## 12    13 10-14 
## 13    14 10-14 
## 14    15 15-19 
## 15    16 15-19 
## 16    17 15-19 
## 17    18 15-19 
## 18    19 15-19 
## 19    20 20+

5.2 Relabelling factor levels without converting to string

This requires the forcats library. E.g.

library(forcats)
tibble(
    age_band = c("0-4", "5-9", "10-14", "15-19", "20-24", "25+")
) %>%
    mutate(
        age_band2 = fct_collapse(     # Combine levels
            age_band,
            "0-9" = c("0-4", "5-9"),
            "10-19" = c("10-14", "15-19")
        ),
        # Relabel all levels with function
        age_band2 = fct_relabel(age_band2, ~ sub("-", ".", .x)),
        # Relabel specific levels manually
        age_band2 = fct_recode(age_band2, "0.09" = "0.9"),
    ) %>%
    select(age_band, age_band2) %>%
    distinct()
## # A tibble: 6 × 2
##   age_band age_band2
##   <chr>    <fct>    
## 1 0-4      0.09     
## 2 5-9      0.09     
## 3 10-14    10.19    
## 4 15-19    10.19    
## 5 20-24    20.24    
## 6 25+      25+

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