Use dynamic variable names in `dplyr`

Question

Since you are dynamically building a variable name as a character value, it makes more sense to do assignment using standard data.frame indexing which allows for character values for column names. For example:

multipetal <- function(df, n) {
    varname <- paste("petal", n , sep=".")
    df[[varname]] <- with(df, Petal.Width * n)
    df
}

The mutate function makes it very easy to name new columns via named parameters. But that assumes you know the name when you type the command. If you want to dynamically specify the column name, then you need to also build the named argument.

---

dplyr version >= 1.0

With the latest dplyr version you can use the syntax from the glue package when naming parameters when using :=. So here the {} in the name grab the value by evaluating the expression inside.

multipetal <- function(df, n) {
  mutate(df, "petal.{n}" := Petal.Width * n)
}

If you are passing a column name to your function, you can use {{}} in the string as well as for the column name

meanofcol <- function(df, col) {
  mutate(df, "Mean of {{col}}" := mean({{col}}))
}
meanofcol(iris, Petal.Width)

---

dplyr version >= 0.7

dplyr starting with version 0.7 allows you to use := to dynamically assign parameter names. You can write your function as:

# --- dplyr version 0.7+---
multipetal <- function(df, n) {
    varname <- paste("petal", n , sep=".")
    mutate(df, !!varname := Petal.Width * n)
}

For more information, see the documentation available form vignette("programming", "dplyr").

---

dplyr (>=0.3 & <0.7)

Slightly earlier version of dplyr (>=0.3 <0.7), encouraged the use of "standard evaluation" alternatives to many of the functions. See the Non-standard evaluation vignette for more information (vignette("nse")).

So here, the answer is to use mutate_() rather than mutate() and do:

# --- dplyr version 0.3-0.5---
multipetal <- function(df, n) {
    varname <- paste("petal", n , sep=".")
    varval <- lazyeval::interp(~Petal.Width * n, n=n)
    mutate_(df, .dots= setNames(list(varval), varname))
}

---

dplyr < 0.3

Note this is also possible in older versions of dplyr that existed when the question was originally posed. It requires careful use of quote and setName:

# --- dplyr versions < 0.3 ---
multipetal <- function(df, n) {
    varname <- paste("petal", n , sep=".")
    pp <- c(quote(df), setNames(list(quote(Petal.Width * n)), varname))
    do.call("mutate", pp)
}

In the new release of dplyr (0.6.0 awaiting in April 2017), we can also do an assignment (:=) and pass variables as column names by unquoting (!!) to not evaluate it

 library(dplyr)
 multipetalN <- function(df, n){
      varname <- paste0("petal.", n)
      df %>%
         mutate(!!varname := Petal.Width * n)
 }

 data(iris)
 iris1 <- tbl_df(iris)
 iris2 <- tbl_df(iris)
 for(i in 2:5) {
     iris2 <- multipetalN(df=iris2, n=i)
 }   

Checking the output based on @MrFlick's multipetal applied on 'iris1'

identical(iris1, iris2)
#[1] TRUE

After a lot of trial and error, I found the pattern UQ(rlang::sym("some string here"))) really useful for working with strings and dplyr verbs. It seems to work in a lot of surprising situations.

Here's an example with mutate. We want to create a function that adds together two columns, where you pass the function both column names as strings. We can use this pattern, together with the assignment operator :=, to do this.

## Take column `name1`, add it to column `name2`, and call the result `new_name`
mutate_values <- function(new_name, name1, name2){
  mtcars %>% 
    mutate(UQ(rlang::sym(new_name)) :=  UQ(rlang::sym(name1)) +  UQ(rlang::sym(name2)))
}
mutate_values('test', 'mpg', 'cyl')

The pattern works with other dplyr functions as well. Here's filter:

## filter a column by a value 
filter_values <- function(name, value){
  mtcars %>% 
    filter(UQ(rlang::sym(name)) != value)
}
filter_values('gear', 4)

Or arrange:

## transform a variable and then sort by it 
arrange_values <- function(name, transform){
  mtcars %>% 
    arrange(UQ(rlang::sym(name)) %>%  UQ(rlang::sym(transform)))
}
arrange_values('mpg', 'sin')

For select, you don't need to use the pattern. Instead you can use !!:

## select a column 
select_name <- function(name){
  mtcars %>% 
    select(!!name)
}
select_name('mpg')

With rlang 0.4.0 we have curly-curly operators ({{}}) which makes this very easy. When a dynamic column name shows up on the left-hand side of an assignment, use :=.

library(dplyr)
library(rlang)

iris1 <- tbl_df(iris)

multipetal <- function(df, n) {
   varname <- paste("petal", n , sep=".")
   mutate(df, {{varname}} := Petal.Width * n)
}

multipetal(iris1, 4)

# A tibble: 150 x 6
#   Sepal.Length Sepal.Width Petal.Length Petal.Width Species petal.4
#          <dbl>       <dbl>        <dbl>       <dbl> <fct>     <dbl>
# 1          5.1         3.5          1.4         0.2 setosa      0.8
# 2          4.9         3            1.4         0.2 setosa      0.8
# 3          4.7         3.2          1.3         0.2 setosa      0.8
# 4          4.6         3.1          1.5         0.2 setosa      0.8
# 5          5           3.6          1.4         0.2 setosa      0.8
# 6          5.4         3.9          1.7         0.4 setosa      1.6
# 7          4.6         3.4          1.4         0.3 setosa      1.2
# 8          5           3.4          1.5         0.2 setosa      0.8
# 9          4.4         2.9          1.4         0.2 setosa      0.8
#10          4.9         3.1          1.5         0.1 setosa      0.4
# … with 140 more rows

We can also pass quoted/unquoted variable names to be assigned as column names.

multipetal <- function(df, name, n) {
   mutate(df, {{name}} := Petal.Width * n)
}

multipetal(iris1, temp, 3)

# A tibble: 150 x 6
#   Sepal.Length Sepal.Width Petal.Length Petal.Width Species  temp
#          <dbl>       <dbl>        <dbl>       <dbl> <fct>   <dbl>
# 1          5.1         3.5          1.4         0.2 setosa  0.6  
# 2          4.9         3            1.4         0.2 setosa  0.6  
# 3          4.7         3.2          1.3         0.2 setosa  0.6  
# 4          4.6         3.1          1.5         0.2 setosa  0.6  
# 5          5           3.6          1.4         0.2 setosa  0.6  
# 6          5.4         3.9          1.7         0.4 setosa  1.2  
# 7          4.6         3.4          1.4         0.3 setosa  0.900
# 8          5           3.4          1.5         0.2 setosa  0.6  
# 9          4.4         2.9          1.4         0.2 setosa  0.6  
#10          4.9         3.1          1.5         0.1 setosa  0.3  
# … with 140 more rows

It works the same with

multipetal(iris1, "temp", 3)

Here's another version, and it's arguably a bit simpler.

multipetal <- function(df, n) {
    varname <- paste("petal", n, sep=".")
    df<-mutate_(df, .dots=setNames(paste0("Petal.Width*",n), varname))
    df
}

for(i in 2:5) {
    iris <- multipetal(df=iris, n=i)
}

> head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species petal.2 petal.3 petal.4 petal.5
1          5.1         3.5          1.4         0.2  setosa     0.4     0.6     0.8       1
2          4.9         3.0          1.4         0.2  setosa     0.4     0.6     0.8       1
3          4.7         3.2          1.3         0.2  setosa     0.4     0.6     0.8       1
4          4.6         3.1          1.5         0.2  setosa     0.4     0.6     0.8       1
5          5.0         3.6          1.4         0.2  setosa     0.4     0.6     0.8       1
6          5.4         3.9          1.7         0.4  setosa     0.8     1.2     1.6       2