R: Calculating matrix summary statistics by factor dplyr

Question

What is the best way to calculate summary statistics for a matrix with factors (using R)?

Sample data:

df <- read.table(text="
  Gender  Obs1   Obs2    Obs3 
1   F     0.0   -1.25   -1.23 
2   M    -0.5    0.83   -0.25 
3   UNK   1.0    1.22    0.24 
4   F     1.0    0.25    0.76 
5   F     0.5    1.02    0.0  
6   M     2.0   -0.05    0.66 
7   M     1.0    5.80    NA   
8   M     1.0    NA      1.0
9   UNK   0.0    NA      2.2", header=T)

df <- as.data.frame(df)

Expected output:

Gender | Obs1_count | Obs2_count | Obs3_count | Obs1_mean | Obs2_mean | Obs3_mean
---------------------------------------------------------------------------------

  F           3          3            3          0.5        0.00667      -0.157
  M           4          3            3          0.875      2.0433        0.47
  UNK         2          1            2          0.5        1.22          1.22

What I've tried:

library(dplyr)
summaryStats <- df %>% group_by(Gender) %>% na.omit() %>%
                      summarise_all(.funs = funs(
                                            count = length(.),
                                            mean = mean(.)))

Answer 1

We can use sum on a logical vector to get the 'count' of non-NA elements, the mean also have an argument na.rm = TRUE to remove those NA elements. So, we don't need na.omit() . Though, it does remove NA elements, it also remove a complete row whenever there is only a single NA on that row. In effect, the 'count' gets reduced for some columns and 'mean' also changes based on the removal of rows with non-NA values

df %>% 
    group_by(Gender) %>% 
    summarise_all(.funs = funs(count = sum(!is.na(.)), 
                               mean = mean(., na.rm = TRUE)))
# A tibble: 3 x 7
#  Gender Obs1_count Obs2_count Obs3_count Obs1_mean Obs2_mean Obs3_mean
#  <fct>       <int>      <int>      <int>     <dbl>     <dbl>     <dbl>
#1 F               3          3          3     0.5     0.00667    -0.157
#2 M               4          3          3     0.875   2.19        0.47 
#3 UNK             2          1          2     0.5     1.22        1.22