I am trying to calculate multiple means and 95% ci by group for 90+ columns:
sample data:
Group| A_pre | A_post | B_pre | B_post
0 20 21 20 23
1 30 10 19 11
2 10 53 30 34
1 22 32 25 20
2 34 40 32 30
0 30 50 NA 40
0 39 40 19 20
1 40 NA 20 20
2 50 10 20 10
0 34 23 30 10
library(dplyr)
library(gmodels)
df <- df %>%
group_by(group) %>%
dplyr::summarize_all(list(~mean(., trim = 0), ~ci(.,)), na.rm=TRUE)
I get the error Error in UseMethod("ci") : no applicable method for 'ci' applied to an object of class "c('grouped_df', 'tbl_df', 'tbl', 'data.frame')"
I can get individual column ci using but that's time consuming for 90 columns:
library(rcompanion)
groupwiseMean(x ~ group,
data = df,
conf = 0.95,
digits = 3, na.rm = T)
Is there any way around the gmodels error or another way forward?
When you're using the %>% pipe, . refers to the thing being piped in, in this case your data frame. With the purrr-style lambdas, you should use .x for the argument.
All that said, summarize_all() is deprecated. You might try with the replacement across():
summarize(across(everything(),
.fns = list(
mean = ~mean(.x, na.rm = TRUE, trim = 0),
ci = ~ ci(.x, na.rm = TRUE)
)
))
It also looks like you are passing na.rm = TRUE to summarize_all() to the ... argument, but that will not be passed through when you use lambdas with ~ notation, so I've tried to correct that.
This is untested with no sample data provided, but I think it should work assume your data columns are all numeric.