Order descending based on a maximum value of a group, and then remove the maximum column in dplyr

user_3625022 0
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Suppose, in the iris data set, that I want to:

  • Order by Species based on a column containing the maximum Sepal.Length, in descending order.
  • Remove the maximum Sepal.Length column.
  • Within each Species, keeping the order from the first step above, order Sepal.Length in descending order.

The following code yields the desired output:

library(dplyr)

df <- iris %>%
  group_by(Species) %>%
  mutate(max.Sepal.length = max(Sepal.Length, na.rm = TRUE)) %>%
  as.data.frame() %>%
  arrange(desc(max.Sepal.length)) %>%
  select(-max.Sepal.length)

df[,"Species"] <- factor(df[,"Species"],
                         levels = unique(df[,"Species"]),
                         ordered = TRUE)

df <- df %>%
  arrange(Species, desc(Sepal.Length)) %>%
  as.data.frame()

However, suppose instead that I want to write this as a function:

df_order <- function(df, group_col, value_col) {
  df <- df %>%
    group_by({{ group_col }}) %>%
    mutate("max_{{value_col}}" := max({{value_col}}, na.rm = TRUE)) %>%
    as.data.frame() %>%
    arrange(desc("max_{{value_col}}")) %>%
    select(-"max_{{value_col}}")
  
  df[,"{{group_col}}"] <- factor(df[,"{{group_col}}"],
                           levels = unique(df[,"{{group_col}}"]),
                           ordered = TRUE)
  
  df <- df %>%
    arrange({{group_col}}, desc({{value_col}})) %>%
    as.data.frame()
  return(df)
}

df_order(iris, Species, Sepal.Length)

Alas, this doesn't work. Could someone point me to where my code is wrong? I am not extremely familiar with how dplyr has integrated with glue.

dplyr r
1 Answers

Here is one way to correct it - i.e. convert to string and use that string for wherever it needs

df_order <- function(df, group_col, value_col) {
   value_col_str <- rlang::as_string(rlang::ensym(value_col))
   group_col_str <- rlang::as_string(rlang::ensym(group_col))
   df <- df %>%
     group_by({{ group_col }}) %>%
     mutate("max_{{value_col}}" := max({{value_col}}, na.rm = TRUE)) %>%
     as.data.frame() %>%
     arrange(desc(!! rlang::sym(glue::glue("max_{value_col_str}")))) %>%
     select(-glue::glue("max_{value_col_str}"))
  
   df[,group_col_str] <- factor(df[,group_col_str],
                            levels = unique(df[,group_col_str]),
                            ordered = TRUE)
  
   df <- df %>%
     arrange({{group_col}}, desc({{value_col}})) %>%
     as.data.frame()
   return(df)
 }

-testing

out <- df_order(iris, Species, Sepal.Length)
 Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1            7.9         3.8          6.4         2.0  virginica
2            7.7         3.8          6.7         2.2  virginica
3            7.7         2.6          6.9         2.3  virginica
4            7.7         2.8          6.7         2.0  virginica
5            7.7         3.0          6.1         2.3  virginica
6            7.6         3.0          6.6         2.1  virginica
7            7.4         2.8          6.1         1.9  virginica
8            7.3         2.9          6.3         1.8  virginica
...

identical(out, df)
[1] TRUE
user_37322710
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