Use quoted variable in group_by() %>% mutate() fun

Question

Reproducible example

cats <-
  data.frame(
    name = c(letters[1:10]),
    weight = c(rnorm(5, 10, 1), rnorm(5, 20, 3)),
    type = c(rep("not_fat", 5), rep("fat", 5))
  )

get_means <- function(df, metric, group) {
  df %>%
    group_by(.[[group]]) %>%
    mutate(mean_stat = mean(.[[metric]])) %>%
    pull(mean_stat) %>%
    unique()
}

get_means(cats, metric = "weight", group = "type")

What I tried

I expect to get two values back, instead I get one value. It appears that the groupby is failing.

I tried everything including using quo(), eval() and substitute(), UQ(), !!, and a whole host of other things to try and make the stuff inside the group_by() work.

This seems awfully simple but I can't figure it out.

Reasoning for code

The decision for variables to be in quotes is because I am using them in ggplot aes_string() calls. I excluded ggplot code inside the function to simplify the code, otherwise it'd be easy because we could use standard evaluation.

Answer 1

using *_at functions :

library(dplyr)
get_means <- function(df, metric, group) {
  df %>%
    group_by_at(group) %>%
    mutate_at(metric,list(mean_stat = mean)) %>%
    pull(mean_stat) %>%
    unique()
}

get_means(cats, metric = "weight", group = "type")
# [1] 10.12927 20.40541

data

set.seed(1)
cats <-
  data.frame(
    name = c(letters[1:10]),
    weight = c(rnorm(5, 10, 1), rnorm(5, 20, 3)),
    type = c(rep("not_fat", 5), rep("fat", 5))
  )

Answer 2

I think the "intended" way to do this in the tidyeval framework is to enter the arguments as names (rather than strings) and then quote the arguments using enquo(). ggplot2 understands tidy evaluation operators so this works for ggplot2 as well.

First, let's adapt the dplyr summary function in your example:

library(tidyverse)
library(rlang)

get_means <- function(df, metric, group) {

  metric = enquo(metric)
  group = enquo(group)

  df %>%
    group_by(!!group) %>%
    summarise(!!paste0("mean_", as_label(metric)) := mean(!!metric))
}

get_means(cats, weight, type)

  type    mean_weight
1 fat            20.0
2 not_fat        10.2

get_means(iris, Petal.Width, Species)

  Species    mean_Petal.Width
1 setosa                0.246
2 versicolor            1.33 
3 virginica             2.03

Now add in ggplot:

get_means <- function(df, metric, group) {

  metric = enquo(metric)
  group = enquo(group)

  df %>%
    group_by(!!group) %>%
    summarise(mean_stat = mean(!!metric)) %>% 
    ggplot(aes(!!group, mean_stat)) + 
      geom_point()
}

get_means(cats, weight, type)

I'm not sure what type of plot you have in mind, but you can plot the data and summary values using tidy evaluation. For example:

plot_func = function(data, metric, group) {

  metric = enquo(metric)
  group = enquo(group)

  data %>% 
    ggplot(aes(!!group, !!metric)) + 
      geom_point() +
      geom_point(data=. %>% 
                   group_by(!!group) %>%
                   summarise(!!metric := mean(!!metric)),
                 shape="_", colour="red", size=8) + 
      expand_limits(y=0) +
      scale_y_continuous(expand=expand_scale(mult=c(0,0.02)))
}

plot_func(cats, weight, type)

FYI, you can allow the function to take any number of grouping variables (including none) using the ... argument and enquos instead of enquo (which also requires the use of !!! (unquote-splice) instead of !! (unquote)).

get_means <- function(df, metric, ...) {

  metric = enquo(metric)
  groups = enquos(...)

  df %>%
    group_by(!!!groups) %>%
    summarise(!!paste0("mean_", quo_text(metric)) := mean(!!metric))
}

get_means(mtcars, mpg, cyl, vs)

    cyl    vs mean_mpg
1     4     0     26  
2     4     1     26.7
3     6     0     20.6
4     6     1     19.1
5     8     0     15.1

get_means(mtcars, mpg)

  mean_mpg
1     20.1

Answer 3

I would go with slight modification (if I understand correctly what you would like to achive):

 get_means <- function(df, metric, group) {
      df %>%
        group_by(!!sym(group)) %>%
        summarise(mean_stat = mean(!!sym(metric)))%>% pull(mean_stat)
    }
    get_means(cats, "weight", "type")

[1] 20.671772  9.305811

gives exactly same output as :

cats %>% group_by(type) %>% summarise(mean_stat=mean(weight)) %>%
  pull(mean_stat)

[1] 20.671772  9.305811

Answer 4

The magrittr pronoun . represents the whole data, so you've taken the mean of all observations. Instead, use the tidy eval pronoun .data which represents the slice of data frame for the current group:

get_means <- function(df, metric, group) {
  df %>%
    group_by(.data[[group]]) %>%
    mutate(mean_stat = mean(.data[[metric]])) %>%
    pull(mean_stat) %>%
    unique()
}

Answer 5

If you want to use strings for the names, as in your example, the correct way to do this is to convert the string to a symbol with sym and unquote with !!:

get_means <- function(df, metric, group) {
    df %>%
      group_by(!!sym(group)) %>%
      mutate(mean_stat = mean(!!sym(metric))) %>%
      pull(mean_stat) %>%
      unique()
}

get_means(cats, metric = "weight", group = "type")
[1] 10.06063 17.45906

If you want to use bare names in your function, then use enquo with !!:

get_means <- function(df, metric, group) {
    group <- enquo(group)
    metric <- enquo(metric)
    df %>%
      group_by(!!group) %>%
      mutate(mean_stat = mean(!!metric)) %>%
      pull(mean_stat) %>%
      unique()
}

get_means(cats, metric = weight, group = type)
[1] 10.06063 17.45906

---

What is happening in your example?

Interestingly .[[group]], does work for grouping, but not the way you think. This subsets the stated column of the dataframe as a vector, then makes that a new variable that it groups on:

cats %>%
    group_by(.[['type']])

# A tibble: 10 x 4
# Groups:   .[["type"]] [2]
   name  weight type    `.[["type"]]`
   <fct>  <dbl> <fct>   <fct>        
 1 a       9.60 not_fat not_fat      
 2 b       8.71 not_fat not_fat      
 3 c      12.0  not_fat not_fat      
 4 d       8.48 not_fat not_fat      
 5 e      11.5  not_fat not_fat      
 6 f      17.0  fat     fat          
 7 g      20.3  fat     fat          
 8 h      17.3  fat     fat          
 9 i      15.3  fat     fat          
10 j      17.4  fat     fat  

Your problem comes with the mutate statement. Instead of selecting the, mutate(mean_stat = mean(.[['weight']])) simply extracts the weight column as a vector, computes the mean, and then assigns that single value to the new column

cats %>%
    group_by(.[['type']]) %>%
      mutate(mean_stat = mean(.[['weight']]))
# A tibble: 10 x 5
# Groups:   .[["type"]] [2]
   name  weight type    `.[["type"]]` mean_stat
   <fct>  <dbl> <fct>   <fct>             <dbl>
 1 a       9.60 not_fat not_fat            13.8
 2 b       8.71 not_fat not_fat            13.8
 3 c      12.0  not_fat not_fat            13.8
 4 d       8.48 not_fat not_fat            13.8
 5 e      11.5  not_fat not_fat            13.8
 6 f      17.0  fat     fat                13.8
 7 g      20.3  fat     fat                13.8
 8 h      17.3  fat     fat                13.8
 9 i      15.3  fat     fat                13.8
10 j      17.4  fat     fat                13.8