Calculate mean and sd for given variables in a dataframe-CodePudding

Given a vector of names of numeric variables in a dataframe, I need to calculate mean and sd for each variable. For example, given the mtcars dataset and the following vector of variable names:

vars_to_transform <- c("mpg", "disp")

I'd like to have the following as result:

The first solution that came into my mind is the following:

library(dplyr)
library(purrr)

data("mtcars")

vars_to_transform <- c("mpg", "disp")

vars_to_transform %>% 
  map_dfr( function(x) { c(variable = x, avg = mean(mtcars[[x]], na.rm = T), sd = sd(mtcars[[x]], na.rm = T)) } )

The result is the following:

As you can see, all the returned variables are characters, but I expected to have numbers for avg and sd.

Is there a way to fix this? Or is there any better solution than this?

P.S. I'm using purr 0.3.4

CodePudding user response：

The following works (instead of using c() in your code, use tibble):

vars_to_transform %>% 
  map_dfr(~ tibble(variable = .x, avg = mean(mtcars[[.x]], na.rm = T), 
          sd = sd(mtcars[[.x]], na.rm = T)))

Explanation: With c(), you are using a vector, whose elements must have the same type (character in your case, because variable is character). With tibble, one can have a different type per element.

@Gwang-Jin Kim suggests, in a comment bellow that I thank, one could also have used list instead of tibble.

Or try with adding type.convert:

library(dplyr)
library(purrr)

data("mtcars")

vars_to_transform <- c("mpg", "disp")

vars_to_transform %>% 
  map_dfr( function(x) { c(variable = x, avg = mean(mtcars[[x]], na.rm = T), sd = sd(mtcars[[x]], na.rm = T)) } ) %>% 
  type.convert(as.is=T)

#> # A tibble: 2 × 3
#>   variable   avg     sd
#>   <chr>    <dbl>  <dbl>
#> 1 mpg       20.1   6.03
#> 2 disp     231.  124.

CodePudding user response：

Seems like an overcomplicated way of doing select->pivot->group->summarise.

mtcars %>% 
    select(all_of(vars_to_transform)) %>%
    pivot_longer(everything()) %>% 
    group_by(name) %>% 
    summarise(
        mean = mean(value),
        sd = sd(value)
    )
# A tibble: 2 x 3
  name   mean     sd
  <chr> <dbl>  <dbl>
1 disp  231.  124.  
2 mpg    20.1   6.03

CodePudding user response：

Another option:

library(purrr)
library(dplyr)

vars_to_transform <- c("mpg", "disp")
funs <- lst(mean, sd)

mtcars %>%
  select(all_of(vars_to_transform)) %>%
  map_df(~ funs %>%
           map(exec, .x), .id = "var")

# A tibble: 2 x 3
  var    mean     sd
  <chr> <dbl>  <dbl>
1 mpg    20.1   6.03
2 disp  231.  124.

CodePudding user response：

m <- mtcars %>% select(vars_to_transform)
tibble(variable = names(m), avg = apply(m, 2, mean), sd = apply(m, 2, sd))

## A tibble: 2 × 3
#  variable   avg     sd
#  <chr>    <dbl>  <dbl>
#1 mpg       20.1   6.03
#2 disp     231.  124.