Error when using dplyr {{ }} with aggregate inside a function-CodePudding

I am trying to use aggregate inside a function by using dplyrs {{ }} notation to select the column to aggregate on.

filter <- function(df, level) {
 df <- aggregate(.~ {{level}}, data=df, FUN=sum) 
 return(df)
}

however I get the error

Error in model.frame.default(formula = cbind(phylum, '12K1B.txt', '12K2B.txt',  :  
variable lengths differ (found for '{{ level }}')

I have double checked my data and there are no missing or NA values and everything works as expected when I run it outside of the function so I am not sure what is causing the error.

CodePudding user response：

{{ }} is tidyverse syntax, and should only work inside tidyverse verbs.

If we want to achieve something like this

aggregate(. ~ Species, data = iris, sum)
     Species Sepal.Length Sepal.Width Petal.Length Petal.Width
1     setosa        250.3       171.4         73.1        12.3
2 versicolor        296.8       138.5        213.0        66.3
3  virginica        329.4       148.7        277.6       101.3

We can make a formula on the fly, manipulating as text like so

aggregate_var <- function(df, level) {
  level <- deparse(substitute(level))
  aggregate(formula(paste(". ~", level)), data=df, FUN=sum) 
}

aggregate_var(iris, Species)
     Species Sepal.Length Sepal.Width Petal.Length Petal.Width
1     setosa        250.3       171.4         73.1        12.3
2 versicolor        296.8       138.5        213.0        66.3
3  virginica        329.4       148.7        277.6       101.3

As an aside - filter is a popular function name, perhaps a more detailed description is useful. Also note that an explicit return statement and the assignment to df are not needed here.

CodePudding user response：

You may use get to achieve this.

funn <- function(df, level){
  df <- aggregate(.~ get(level), data=df, FUN=sum) 
  return(df)
}
funn(iris, "Species")

  get(level) Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1     setosa        250.3       171.4         73.1        12.3      50
2 versicolor        296.8       138.5        213.0        66.3     100
3  virginica        329.4       148.7        277.6       101.3     150

CodePudding user response：

To complement the other answers already provided, if you wanted to use {{, the dplyr way is:

my_fun <- function(df, level) {
  df |>
    group_by({{ level }}) |>
    summarize(across(everything(), sum))
}

my_fun(iris, Species)
# A tibble: 3 x 5
  Species    Sepal.Length Sepal.Width Petal.Length Petal.Width
  <fct>             <dbl>       <dbl>        <dbl>       <dbl>
1 setosa             250.        171.         73.1        12.3
2 versicolor         297.        138.        213          66.3
3 virginica          329.        149.        278.        101.