How To Move A Column to the Front with dplyr

In this post we will learn how to change column order or move a column in R with dplyr. More specifically, we will learn how to move a single column of interest to first in the dataframe, before and after a specific column in the dataframe. We will use relocate() function available in dplyr version 1.0.0 to change the column position. And we will also see an example of moving a column to the front when working with dplyr version earlier than 1.0.0.

Let us load tidyverse first.

library("tidyverse")

As in other tidyverse 101 examples, we will use the fantastic Penguins dataset to illustrate the three ways to see data in a dataframe. Let us load the data from cmdlinetips.com’ github page.

path2data <- "https://raw.githubusercontent.com/cmdlinetips/data/master/palmer_penguins.csv"
penguins<- readr::read_csv(path2data)

Note that the last column in the data frame is sex column.

## Parsed with column specification:
## cols(
##   species = col_character(),
##   island = col_character(),
##   bill_length_mm = col_double(),
##   bill_depth_mm = col_double(),
##   flipper_length_mm = col_double(),
##   body_mass_g = col_double(),
##   sex = col_character()
## )

First, we will see how to move a column to first in the dataframe. To move a column to first in the dataframe, we use relocate() with the column name we want to move.

penguins %>% 
  relocate(sex)

This will move the column of interest to the first column.

## # A tibble: 344 x 7
##    sex   species island bill_length_mm bill_depth_mm flipper_length_…
##    <chr> <chr>   <chr>           <dbl>         <dbl>            <dbl>
##  1 male  Adelie  Torge…           39.1          18.7              181
##  2 fema… Adelie  Torge…           39.5          17.4              186
##  3 fema… Adelie  Torge…           40.3          18                195
##  4 <NA>  Adelie  Torge…           NA            NA                 NA
##  5 fema… Adelie  Torge…           36.7          19.3              193
##  6 male  Adelie  Torge…           39.3          20.6              190
##  7 fema… Adelie  Torge…           38.9          17.8              181
##  8 male  Adelie  Torge…           39.2          19.6              195
##  9 <NA>  Adelie  Torge…           34.1          18.1              193
## 10 <NA>  Adelie  Torge…           42            20.2              190
## # … with 334 more rows, and 1 more variable: body_mass_g <dbl>

We can also move the column of interest to a location after another column in the dataframe. In this example, we move the column “sex” to position after “species” column.

penguins %>% 
  relocate(sex, .after=species)

Notice that now the sex column is second column after the species.

## # A tibble: 344 x 7
##    species sex   island bill_length_mm bill_depth_mm flipper_length_…
##    <chr>   <chr> <chr>           <dbl>         <dbl>            <dbl>
##  1 Adelie  male  Torge…           39.1          18.7              181
##  2 Adelie  fema… Torge…           39.5          17.4              186
##  3 Adelie  fema… Torge…           40.3          18                195
##  4 Adelie  <NA>  Torge…           NA            NA                 NA
##  5 Adelie  fema… Torge…           36.7          19.3              193
##  6 Adelie  male  Torge…           39.3          20.6              190
##  7 Adelie  fema… Torge…           38.9          17.8              181
##  8 Adelie  male  Torge…           39.2          19.6              195
##  9 Adelie  <NA>  Torge…           34.1          18.1              193
## 10 Adelie  <NA>  Torge…           42            20.2              190
## # … with 334 more rows, and 1 more variable: body_mass_g <dbl>

Similarly we can also specify the location to be after another column present in the dataframe. In this example, we move sex column to be relocated after “bill_length_mm”.

penguins %>% 
  relocate(sex, .before=bill_length_mm)

## # A tibble: 344 x 7
##    species island sex   bill_length_mm bill_depth_mm flipper_length_…
##    <chr>   <chr>  <chr>          <dbl>         <dbl>            <dbl>
##  1 Adelie  Torge… male            39.1          18.7              181
##  2 Adelie  Torge… fema…           39.5          17.4              186
##  3 Adelie  Torge… fema…           40.3          18                195
##  4 Adelie  Torge… <NA>            NA            NA                 NA
##  5 Adelie  Torge… fema…           36.7          19.3              193
##  6 Adelie  Torge… male            39.3          20.6              190
##  7 Adelie  Torge… fema…           38.9          17.8              181
##  8 Adelie  Torge… male            39.2          19.6              195
##  9 Adelie  Torge… <NA>            34.1          18.1              193
## 10 Adelie  Torge… <NA>            42            20.2              190
## # … with 334 more rows, and 1 more variable: body_mass_g <dbl>

In this post, we saw how to move a single column to first and before or after another column. dplyr’s relocate() is versatile and can conditions as input to move multiple columns at the same time. Check out soon for more examples of using dplyr’s relocate().

How to Move a Column to the Front with dplyr version earlier to dplyr 1.0?

Note that the relocate() function is new in dplyr 1.0.0. If you are working with dplyr version earlier than 1.0.0, we can use select() and everything() function as follows to move a specific column to the front.

penguins %>% 
  select(sex, everything())

This approach also work with moving selected to column(s) to the last column with the negative sign. For example to to move a column to the last column in the dataframe, we use

penguins %>% 
  select(-sex, everything())

Related posts:

How To Remove Rows with Missing values using dplyr dplyr filter(): Filter/Select Rows based on conditions dplyr groupby() and summarize(): Group By One or More Variables dplyr mutate(): Create New Variables with mutate