CODEWITHSUNDEEP
rdplyrmultiple-conditions
MaVe
MaVe

Reputation: 219

Creating new variable with dplyr::mutate based on multiple conditions and corresponding variable names passed by string vector (or tidyselect)

I'm pretty sure this was discussed before but I'm struggling verbalizing the problem: For example, I'm looking for this data frame...

iris %>%
    mutate(has_petal_1.4 = Petal.Length == 1.4 | Petal.Width == 1.4,
           width_greater_1 = Sepal.Width > 1 & Petal.Width > 1)

...without having to name the variables in the conditions explicitly. Is there a way to pass the variable names using a string vector? Unfortunately, this doesn't seem to work:

varsel <- c('Petal.Length', 'Petal.Width')
iris %>%
  mutate(has_petal_1.4 = 1.4 %in% c(!!! syms(varsel)))

Moreover, I wonder whether there is a solution using tidyselect within the mutate() function. So far, I used the new and handy across() function in order to mutate multiple variables. Is it possible to use it for conditions as well? Here another example that doesn't work:

iris %>%
  mutate(has_petal_1.4 = across(c(starts_with('Petal')), function(x) {1.4 %in% x}))

Any help is highly appreciated.

Upvotes: 1

Views: 657

Answers (1)

akrun
akrun

Reputation: 887048

There are multiple ways, one option is c_across

library(dplyr) # >= 1.0.0
iris %>% 
    rowwise %>% 
    mutate(has_petal_1.4 = any(c_across(varsel) == 1.4),
           width_greater_1 = all(c_across(ends_with('Width')) > 1)) %>%
    ungroup
# A tibble: 150 x 7
#   Sepal.Length Sepal.Width Petal.Length Petal.Width Species has_petal_1.4 width_greater_1
#          <dbl>       <dbl>        <dbl>       <dbl> <fct>   <lgl>         <lgl>          
# 1          5.1         3.5          1.4         0.2 setosa  TRUE          FALSE          
# 2          4.9         3            1.4         0.2 setosa  TRUE          FALSE          
# 3          4.7         3.2          1.3         0.2 setosa  FALSE         FALSE          
# 4          4.6         3.1          1.5         0.2 setosa  FALSE         FALSE          
# 5          5           3.6          1.4         0.2 setosa  TRUE          FALSE          
# 6          5.4         3.9          1.7         0.4 setosa  FALSE         FALSE          
# 7          4.6         3.4          1.4         0.3 setosa  TRUE          FALSE          
# 8          5           3.4          1.5         0.2 setosa  FALSE         FALSE          
# 9          4.4         2.9          1.4         0.2 setosa  TRUE          FALSE          
#10          4.9         3.1          1.5         0.1 setosa  FALSE         FALSE          
# … with 140 more rows

Or a faster option with rowSums

iris %>%     
    mutate(has_petal_1.4 =  rowSums(select(., varsel) == 1.4) > 0,
           width_greater_1 = rowSums(select(., ends_with('Width')) > 1) == 2)

Upvotes: 1

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