I am following the instructions laid out here to create a clean table of summary statistics.
In these instructions, the input to the summary_table() function is a list of lists, as shown here:
our_summary1 <-
list("Miles Per Gallon" =
list("min" = ~ min(.data$mpg),
"max" = ~ max(.data$mpg),
"mean (sd)" = ~ qwraps2::mean_sd(.data$mpg)),
"Displacement" =
list("min" = ~ min(.data$disp),
"median" = ~ median(.data$disp),
"max" = ~ max(.data$disp),
"mean (sd)" = ~ qwraps2::mean_sd(.data$disp)),
"Weight (1000 lbs)" =
list("min" = ~ min(.data$wt),
"max" = ~ max(.data$wt),
"mean (sd)" = ~ qwraps2::mean_sd(.data$wt)),
"Forward Gears" =
list("Three" = ~ qwraps2::n_perc0(.data$gear == 3),
"Four" = ~ qwraps2::n_perc0(.data$gear == 4),
"Five" = ~ qwraps2::n_perc0(.data$gear == 5))
)
I have 48 variables in my dataset, and each variable has its own column. Is there a cleaner way for me to cycle through all the columns in my dataframe to create an object like the one above without typing it out manually like this? I would ideally prefer a solution using the tidyverse.
One thing I was considering doing was changing my data to long format, then using group_by() to group by each of the columns from the original data, then using summarise(). However, my understanding is that this would yield a single list, not a list of lists like is necessary for summary_table().
If there is a completely different way of creating a summary table than what I am trying to do here, please let me know. This one looked the neatest of the options I was considering. For each variable, I'd like to be able to rename it and include the minimum value, maximum value, mean, and standard deviation.
As you noted, you could turn your data to a longer format and use summarize(). The trick is to create a list column within each summarize:
library(dplyr)
library(tidyr)
summarized <- mtcars %>%
pivot_longer(cols = c(mpg, wt, disp)) %>%
group_by(name) %>%
summarize(lst = list(list(mean = mean(value),
max = max(value),
min = min(value),
sd = sd(value))))
summarized
#> # A tibble: 3 x 2
#> name lst
#> * <chr> <list>
#> 1 disp <named list [4]>
#> 2 mpg <named list [4]>
#> 3 wt <named list [4]>
This can then be turned into a list of lists with deframe() from the tibble package.
library(tibble)
result <- deframe(summarized)
str(result)
#> List of 3
#> $ disp:List of 4
#> ..$ mean: num 231
#> ..$ max : num 472
#> ..$ min : num 71.1
#> ..$ sd : num 124
#> $ mpg :List of 4
#> ..$ mean: num 20.1
#> ..$ max : num 33.9
#> ..$ min : num 10.4
#> ..$ sd : num 6.03
#> $ wt :List of 4
#> ..$ mean: num 3.22
#> ..$ max : num 5.42
#> ..$ min : num 1.51
#> ..$ sd : num 0.978