CODEWITHSUNDEEP
r
pyne
pyne

Reputation: 507

r Remove parts of column name after certain characters

I have a large data set with thousands of columns. The column names include various unwanted characters as follows:

col1_3x_xxx
col2_3y_xyz
col3_3z_zyx

I would like to remove all character strings starting with "_3" from all column names to be left with clean:

col1
col2
col3

What is the most efficient way to do this for 5000+ columns?

Upvotes: 25

Views: 82525

Answers (5)

user123456
user123456

Reputation: 530

For someone looking for a pipeable solution (like I was), use dplyr::rename_with and stringr::str_remove:

  tibble(
    col1_3x_xxx = rnorm(3),
    col2_3y_xyz = rnorm(3),
    col3_3z_zyx = rnorm(3)
  ) %>% 
  rename_with(~ str_remove(., "_3.*"), everything())

# A tibble: 3 × 3
   col1  col2    col3
  <dbl> <dbl>   <dbl>
1 0.819 0.674  2.06  
2 0.597 0.554 -0.0586
3 0.490 0.878  0.708

Obs: for those who thought of rename_all(), it has been superseded by rename_with() (as well as rename_if(), rename_at()).

Upvotes: 3

user2110417
user2110417

Reputation:

You can use

names(df) = gsub(pattern = "_3.*", replacement = "", x = names(df))

Upvotes: 3

Rene Chan
Rene Chan

Reputation: 985

certainly late for this answer, but just in case someone is looking for a solution

colnames(df1)[col] <-  sub("_3.*", "", colnames(df1)[col])

And if you have multiple columns : 

for ( col in 1:ncol(df1)){
    colnames(df1)[col] <-  sub("_3.*", "", colnames(df1)[col])
}

Upvotes: 21

akrun
akrun

Reputation: 886938

We can use sub

sub("_3.*", "", df1[,1])
#[1] "col1" "col2" "col3"

Upvotes: 18

akuiper
akuiper

Reputation: 214927

We can try the str_extract with regular expression pattern "^[^_]+(?=_)":

stringr::str_extract(c("col1_3x_xxx", "col2_3y_xyz", "col3_3z_zyx"), "^[^_]+(?=_)")
[1] "col1" "col2" "col3"

where in the pattern:

The first ^ matches the beginning of the string; [^_]+ matches one or more non _ character, ^_ means any character but _. (?=...) stands for lookahead, so we are looking for pattern ahead of _.

Upvotes: 5

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