Rename Multiple Columns Given Character Vectors of Column Names and Replacement

Rename multiple columns given character vectors of column names and replacement

I don't know if this is the right way to approach it, but

library(dplyr)
df %>% rename_at(vars(col.from), function(x) col.to) %>% head(2)
#               mpg cyl disp bar drat   foo  qsec baz am gear carb
# Mazda RX4      21   6  160 110  3.9 2.620 16.46   0  1    4    4
# Mazda RX4 Wag  21   6  160 110  3.9 2.875 17.02   0  1    4    4

Also note that I live in the future:

# packageVersion("dplyr")
# # [1] ‘0.7.0’

Rename multiple columns by names

setnames from the data.tablepackage will work on data.frames or data.tables

library(data.table)
d <- data.frame(a=1:2,b=2:3,d=4:5)
setnames(d, old = c('a','d'), new = c('anew','dnew'))
d


 #   anew b dnew
 # 1    1 2    4
 # 2    2 3    5

Note that changes are made by reference, so no copying (even for data.frames!)

Renaming multiple columns from vector in dplyr chain

An option with rename_at would be

df %>% 
  rename_at(vars(starts_with('old_name')), ~ new_names)
# A tibble: 1 x 6
#    RID Var1  Var2  new_name1 new_name2 new_name3
#   <dbl> <chr> <chr>     <dbl>     <dbl>     <dbl>
#1  1.00 A     B          4.00      8.00      20.0

But, it is possible to make a function that works with rename_if by creating a logical index on the column names

df %>%
    rename_if(grepl("^old_name", names(.)), ~ new_names)
# A tibble: 1 x 6
#    RID Var1  Var2  new_name1 new_name2 new_name3
#  <dbl> <chr> <chr>     <dbl>     <dbl>     <dbl>
#1  1.00 A     B          4.00      8.00      20.0

The rename_if in general is checking at the values of the columns instead of the column names i.e.

new_names2 <- c('var1', 'var2')
df %>%
     rename_if(is.character, ~ new_names2)
# A tibble: 1 x 6
#    RID var1  var2  old_name1 old_name2 old_name3
#   <dbl> <chr> <chr>     <dbl>     <dbl>     <dbl>
#1  1.00 A     B          4.00      8.00      20.0

Rename selected columns with a vector of strings

I managed to solve the problem thanks to @mt1022 comment. According to How to rename multiple columns given character vectors of column names and replacement in dplyr 0.6.0?:

First a vector with the new names have to be created.

names_boston =  c(paste0("BOS_", sprintf("%02d", 1:31), "_time"))

Then the columns can be selected using grep, and the new names can be feed to rename_at.

df %>%
rename_at(vars(grep("Time", names(.))), ~names_boston)

And to avoid creating new vectors you can actually feed the vector to the previous line of code:

df %>%
    rename_at(vars(grep("Time", names(.))), ~c(paste0("BOS_", sprintf("%02d", 1:31), "_time")))

Replace column names in dataframe based on a character vector that is ordered differently

We can use cols to reorder the columns in our data frame:

df[,cols]

This basically uses the order of cols to reorder our data frame. This can be seen as follows:

df[,c("B","A")]
df[,c("A","B")] 

If we had a vector named cols and set it as either c("A","B") or c("B","A"), we could then use this "trick" to help us reorder the columns.

Rename multiple columns with a vector

Instead of starts_with, we could use matches as starts_with expects a single input, while matches can have multiple values if we create a single string concatened with | (OR)

library(dplyr)
library(stringr)
df %>%
   rename_at(vars(matches(str_c(ensembl, collapse="|"))),
          ~genes) 

-output

# A tibble: 1 x 3
#  sample    ACTB GAPDH
#  <chr>    <dbl> <dbl>
1 sample_A    12    20

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Or another option is to remove the . followed by the characters in the column names that starts with 'Ensembl' and use a named vector in rename

df %>% 
    rename_at(vars(starts_with("Ensembl")), ~ str_remove(., "\\..*")) %>%
    rename(!!! setNames(ensembl, genes))
# A tibble: 1 x 3
#  sample    ACTB GAPDH
#  <chr>    <dbl> <dbl>
#1 sample_A    12    20

How to rename columns by substituting suffix in R?

library(tidyverse)

eat10.18 %>%
  rename_with(~str_replace(.,'_10p$', '_p_10'))

    id eat_10 eat_p_10 run_p_10
1 1000      2        1        1
2 1001      4        2        1
3 1002      1        3        2

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