R dplyr: rename variables using string functions

Question

(Somewhat related question: Enter new column names as string in dplyr's rename function)

In the middle of a dplyr chain (%>%), I would like to replace multiple column names with functions of their old names (using tolower or gsub, etc.)

library(tidyr); library(dplyr)
data(iris)
# This is what I want to do, but I'd like to use dplyr syntax
names(iris) <- tolower( gsub("\\.", "_", names(iris) ) )
glimpse(iris, 60)
# Observations: 150
# Variables:
#   $ sepal_length (dbl) 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6,...
#   $ sepal_width  (dbl) 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4,...
#   $ petal_length (dbl) 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4,...
#   $ petal_width  (dbl) 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3,...
#   $ species      (fctr) setosa, setosa, setosa, setosa, s...

# the rest of the chain:
iris %>% gather(measurement, value, -species) %>%
  group_by(species,measurement) %>%
  summarise(avg_value = mean(value))

~~I see ?rename takes the argument replace as a named character vector, with new names as values, and old names as names.~~

So I tried:

iris %>% rename(replace=c(names(iris)=tolower( gsub("\\.", "_", names(iris) ) )  ))

but this (a) returns Error: unexpected '=' in iris %>% ... and (b) requires referencing by name the data frame from the previous operation in the chain, which in my real use case I couldn't do.

iris %>% 
  rename(replace=c(    )) %>% # ideally the fix would go here
  gather(measurement, value, -species) %>%
  group_by(species,measurement) %>%
  summarise(avg_value = mean(value)) # I realize I could mutate down here 
                                     #  instead, once the column names turn into values, 
                                     #  but that's not the point
# ---- Desired output looks like: -------
# Source: local data frame [12 x 3]
# Groups: species
# 
#       species  measurement avg_value
# 1      setosa sepal_length     5.006
# 2      setosa  sepal_width     3.428
# 3      setosa petal_length     1.462
# 4      setosa  petal_width     0.246
# 5  versicolor sepal_length     5.936
# 6  versicolor  sepal_width     2.770
# ... etc ....

The elegant approach is: iris %>% `names<-`(.,tolower( gsub("\\.", "_", names(.) ) )) (I'm only joking.) — Frank
– Frank, Commented May 21, 2015 at 19:51
Some functions used in the answers below have been deprecated. rename_with is the latest dplyr verb to programmatically rename variables with a function. See answer below. — Paul Rougieux
– Paul Rougieux, Commented Mar 17, 2021 at 8:45

Guilherme Marthe · Accepted Answer · 2017-05-03 13:55:46Z

58

This is a very late answer, on May 2017

As of dplyr 0.5.0.9004, soon to be 0.6.0, many new ways of renaming columns, compliant with the maggritr pipe operator %>%, have been added to the package.

Those functions are:

rename_all
rename_if
rename_at

There are many different ways of using those functions, but the one relevant to your problem, using the stringr package is the following:

df <- df %>%
  rename_all(
      funs(
        stringr::str_to_lower(.) %>%
        stringr::str_replace_all(., '\\.', '_')
      )
  )

And so, carry on with the plumbing :) (no pun intended).

answered May 3, 2017 at 13:55

Guilherme Marthe

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2 Comments

C8H10N4O2 Over a year ago

Good to know, thanks. Also worth noting, you can do df %<>% foo() as shorthand for df <- df %>% foo()

MokeEire Over a year ago

Due to the new dplyr update where they changed how funs() works (really wish they hadn't), you need to substitute list for funs and place a tilde ~ before the function e.g. list(~str_replace(., to_replace, replacement))

Matthew Plourde · Accepted Answer · 2015-05-21 20:23:09Z

39

I think you're looking at the documentation for plyr::rename, not dplyr::rename. You would do something like this with dplyr::rename:

iris %>% rename_(.dots=setNames(names(.), tolower(gsub("\\.", "_", names(.)))))

edited May 21, 2015 at 20:23

answered May 21, 2015 at 19:47

Matthew Plourde

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6 Comments

Frank Over a year ago

You can put . in place of iris in its latter appearances.

Konrad Over a year ago

This is very useful, why you had to use rename_ instead of rename?

Matthew Plourde Over a year ago

Habit, since I mostly use dplyr programmatically

Matthew Plourde Over a year ago

@Konrad Actually, I don't have the doc in front of me, but I think the nonsafe version doesn't have the .dots argument

r2evans Over a year ago

FYI: rename_ is slowly being deprecated. I haven't found an obvious replacement, though @Frank's use of setNames seems the most direct (if not provided by dplyr).

|

Frank · Accepted Answer · 2015-05-21 20:27:28Z

31

Here's a way around the somewhat awkward rename syntax:

myris <- iris %>% setNames(tolower(gsub("\\.","_",names(.))))

edited May 21, 2015 at 20:27

answered May 21, 2015 at 19:59

Frank

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9 Comments

Anton Over a year ago

Another dependency for a workaround? This is getting more esoteric.

Matthew Plourde Over a year ago

You can replace setnames with setNames and drop the call to data.table.

Frank Over a year ago

@MatthewPlourde Do you know of a reason to prefer the longer rename over the simpler route? Your answer looks like rename_(.dots=this_answer), right? The help page for rename does not advertise modification by reference as setnames from data.table does.

Frank Over a year ago

@Anton A fair point, but that's the nature of workarounds. (Thanks to Mathhew's comment, the dependency is gone again.) I feel like the dplyr syntax should be extended to support the OP's expectations (based on plyr), like rename(replace_all=...). Seems deficient if constructing a named list and knowing to pass it to weird argument .dots is required here.

C8H10N4O2 Over a year ago

@Frank I wound up using your answer (+1) because it is a simpler way to do what I wanted -- and taught me about setNames-- but @MatthewPlourde more literally answered the question as written (i.e. using rename). Thanks for your time!

|

loki · Accepted Answer · 2021-09-08 07:55:27Z

29

As of 2020, rename_if, rename_at and rename_all are marked superseded. The up-to-date way to tackle this the dplyr way would be rename_with():

iris %>% rename_with(tolower)

or a more complex version:

iris %>% 
  rename_with(stringr::str_replace, 
              pattern = "Length", replacement = "len", 
              matches("Length"))

(edit 2021-09-08)
As mentioned in a comment by @a_leemo, this notation is not mentioned in the manual verbatim. Rather, one would deduce the following from the manual:

iris %>% 
  rename_with(~ stringr::str_replace(.x, 
                                     pattern = "Length", 
                                     replacement = "len"), 
              matches("Length"))

Both do the same thing, yet, I find the first solution a bit more readable. In the first example pattern = ... and replacement = ... are forwarded to the function as part of the ... dots implementation. For more details see ?rename_with and ?dots.

edited Sep 8, 2021 at 7:55

answered Nov 13, 2020 at 9:20

loki

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9 Comments

Erik Ruzek Over a year ago

Thank you! I was struggling to figure out how to code this using rename_with and this did the trick.

TobiO Over a year ago

how would one do this for a custom function @loki ? If I write the function in the rename_with statement it works to hand over the names automagically, if I define the function elsewhere, it doesn't argument is not an atomic vector

TobiO Over a year ago

just found out: simply do not give any argument to the function but specify it as a function mydataframe %>% rename_with(myawesomefunction)

a_leemo Over a year ago

This solved a problem I was having, thanks! But why are the arguments inside the str_replace() function pulled outside of it? I couldn't figure this syntax out from the help documentation.

loki Over a year ago

@LarissaCury you probably want to use mutate or rename with !!. Have a look at the examples with ?rlang::`topic-inject`.

|

alistaire · Accepted Answer · 2016-10-24 21:11:49Z

For this particular [but fairly common] case, the function has already been written in the janitor package:

library(janitor)

iris %>% clean_names()

##   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
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa
## .          ...         ...          ...         ...     ...

so all together,

iris %>% 
    clean_names() %>%
    gather(measurement, value, -species) %>%
    group_by(species,measurement) %>%
    summarise(avg_value = mean(value))

## Source: local data frame [12 x 3]
## Groups: species [?]
## 
##       species  measurement avg_value
##        <fctr>        <chr>     <dbl>
## 1      setosa petal_length     1.462
## 2      setosa  petal_width     0.246
## 3      setosa sepal_length     5.006
## 4      setosa  sepal_width     3.428
## 5  versicolor petal_length     4.260
## 6  versicolor  petal_width     1.326
## 7  versicolor sepal_length     5.936
## 8  versicolor  sepal_width     2.770
## 9   virginica petal_length     5.552
## 10  virginica  petal_width     2.026
## 11  virginica sepal_length     6.588
## 12  virginica  sepal_width     2.974

mtelesha · Accepted Answer · 2018-09-19 13:29:03Z

9

My eloquent attempt using base, stringr and dplyr:

EDIT: library(tidyverse) now includes all three libraries.

library(tidyverse)
library(maggritr) # Though in tidyverse to use %>% pipe you need to call it 
# library(dplyr)
# library(stringr)
# library(maggritr)

names(iris) %<>% # pipes so that changes are apply the changes back
    tolower() %>%
    str_replace_all(".", "_")

I do this for building functions with piping.

my_read_fun <- function(x) {
    df <- read.csv(x) %>%
    names(df) %<>%
        tolower() %>%
        str_replace_all("_", ".")
    tempdf %<>%
        select(a, b, c, g)
}

edited Sep 19, 2018 at 13:29

answered Nov 11, 2015 at 13:40

mtelesha

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2 Comments

Frank Over a year ago

str_replace_all is not in either of those packages. Fyi, no need to include "edit" notations in the text of your answer; just make it the best answer possible. Folks can see the edit history if they want by clicking a link below the answer.

sbha Over a year ago

The period in the first str_replace_all function should be escaped \\. - otherwise everything is replaced with an underscore

sbha · Accepted Answer · 2018-03-09 22:30:03Z

2

Both select() and select_all() can be used to rename columns.

If you wanted to rename only specific columns you can use select:

iris %>% 
  select(sepal_length = Sepal.Length, sepal_width = Sepal.Width, everything()) %>% 
  head(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

rename does the same thing, just without having to include everything():

iris %>% 
  rename(sepal_length = Sepal.Length, sepal_width = Sepal.Width) %>% 
  head(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

select_all() works on all columns and can take a function as an argument:

iris %>% 
  select_all(tolower)

iris %>% 
  select_all(~gsub("\\.", "_", .))

or combining the two:

iris %>% 
  select_all(~gsub("\\.", "_", tolower(.))) %>% 
  head(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

answered Mar 9, 2018 at 22:30

sbha

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1 Comment

dre Over a year ago

this worked better and is much more straightforward than anything in the rename family... it's strange that it's easier to use a select_all with ~gsub than rename_at or rename_if with some kind of predicate of variable declaration... it seems like that's what rename_* is for

Taz · Accepted Answer · 2019-08-02 12:18:07Z

2

In case you don't want to write the regular expressions yourself, you could use

the snakecase-pkg which is very flexible,
janitor::make_clean_names() which has some nice defaults or
janitor::clean_names() which does the same as make_clean_names(), but works directly on data frames.

Invoking them inside of a pipeline should be straightforward.

library(magrittr)
library(snakecase)

iris %>% setNames(to_snake_case(names(.)))
iris %>% tibble::as_tibble(.name_repair = to_snake_case)
iris %>% purrr::set_names(to_snake_case)
iris %>% dplyr::rename_all(to_snake_case)
iris %>% janitor::clean_names()

answered Aug 2, 2019 at 12:18

Taz

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