I got a data frame with a lot of columns and want to summarise them with multiple functions.
test_df <- data.frame(Group = sample(c("A", "B", "C"), 10, T), var1 = sample(1:5, 10, T), var2 = sample(3:7, 10, T))
test_df %>%
group_by(Group) %>%
summarise_all(c(Mean = mean, Sum = sum))
# A tibble: 3 x 5
Group var1_Mean var2_Mean var1_Sum var2_Sum
<chr> <dbl> <dbl> <int> <int>
1 A 3.14 5.14 22 36
2 B 4.5 4.5 9 9
3 C 4 6 4 6
This results in a tibble with the first row Group and column names with a combination of the previous column name and the function name. The desired result is a table with the previous column names as first row and the groups and functions in the column names.
I can achive this with
test_longer <- test_df %>% pivot_longer(cols = starts_with("var"), names_to = "var", values_to = "val")
# Add row number because spread needs unique identifiers for rows
test_longer <- test_longer %>%
group_by(Group) %>%
mutate(grouped_id = row_number())
spread(test_longer, Group, val) %>%
select(-grouped_id) %>%
group_by(var) %>%
summarise_all(c(Mean = mean, Sum = sum), na.rm = T)
# A tibble: 2 x 7
var A_Mean B_Mean C_Mean A_Sum B_Sum C_Sum
<chr> <dbl> <dbl> <dbl> <int> <int> <int>
1 var1 3.14 4.5 4 22 9 4
2 var2 5.14 4.5 6 36 9 6
But this seems to be a rather long detour... There probably is a better way, but I could not find it. Any suggestions? Thank you
There's lots of ways to go about it, but I would simplify it by pivoting to a longer data frame initially, and then grouping by var and group. Then you can just pivot wider to get the final result you want. Note that I used summarize(across()) which replaces the deprecated summarize_all(), even though with a single column could've just manually specified Mean = ... and Sum = ....
set.seed(123)
test_df %>%
pivot_longer(
var1:var2,
names_to = "var"
) %>%
group_by(Group, var) %>%
summarize(
across(
everything(),
list(Mean = mean, Sum = sum),
.names = "{.fn}"
),
.groups = "drop"
) %>%
pivot_wider(
names_from = "Group",
values_from = c(Mean, Sum),
names_glue = "{Group}_{.value}"
)
#> # A tibble: 2 × 7
#> var A_Mean B_Mean C_Mean A_Sum B_Sum C_Sum
#> <chr> <dbl> <dbl> <dbl> <int> <int> <int>
#> 1 var1 1 2.5 3.2 1 10 16
#> 2 var2 5 4.5 4.4 5 18 22