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I am writing a package with several functions that accept a dataframe object as well as the the dataframe's column names as arguments.

Here is a simplified example:

func = function(df,vars){
    head(df[,vars])
}

#column args as strings
func(mtcars,c("mpg","cyl"))

Instead of supplying the column names as strings, I would like the function to accept (and suggest/auto-complete) the column names like in dplyr functions.

#dplyr-style args
func(mtcars, mpg, cyl)

#which doesnt work because mpg and cyl don't exist as objects

I considered using the ... as function arguments but this would still involve using strings.

Any help would be appreciated.

Claudio Paladini
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    You cannot get autocomplete as that is done by the IDE. You nee to use `deparse` and `substitute` for the names to be used in the way you wish. – NelsonGon Jul 28 '22 at 17:31
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    Note that dplyr does not have auto-complete/suggest. This is within magrittr package. ie once you use the `%>%` then you have the variables available. Right now, even though your function does not work, having `mtcars %>%func()` then press `tab` key will give you list of all the variables available – Onyambu Jul 28 '22 at 18:26

2 Answers2

2

A possible solution, using dplyr:

library(dplyr)

func = function(df,...){
  df %>% 
    select(...) %>% 
    head
}


func(mtcars, mpg, cyl)
#>                    mpg cyl
#> Mazda RX4         21.0   6
#> Mazda RX4 Wag     21.0   6
#> Datsun 710        22.8   4
#> Hornet 4 Drive    21.4   6
#> Hornet Sportabout 18.7   8
#> Valiant           18.1   6

func(mtcars, mpg)

#>                    mpg
#> Mazda RX4         21.0
#> Mazda RX4 Wag     21.0
#> Datsun 710        22.8
#> Hornet 4 Drive    21.4
#> Hornet Sportabout 18.7
#> Valiant           18.1

Or in base R:

func = function(df,...){
  head(df[, sapply(substitute(...()), deparse)])
}

func(mtcars, mpg, cyl)
#>                    mpg cyl
#> Mazda RX4         21.0   6
#> Mazda RX4 Wag     21.0   6
#> Datsun 710        22.8   4
#> Hornet 4 Drive    21.4   6
#> Hornet Sportabout 18.7   8
#> Valiant           18.1   6

func(mtcars, mpg)

#> [1] 21.0 21.0 22.8 21.4 18.7 18.1
PaulS
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2

You can use

subset(df, select = item)

You should check out Advanced R by Hadley Wickham which is extremely interesting, if somewhat, well, advanced. In particular:

20.4 Data masks

In this section, you’ll learn about the data mask, a data frame where the evaluated code will look first for variable definitions. The data mask is the key idea that powers base functions like with(), subset() and transform(), and is used throughout the tidyverse in packages like dplyr and ggplot2.

gaut
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