dplyr conditional summarise function - r

Question

I have this situation where I need a different summary function based on a condition.
For example, using iris, say for some reason I wanted the sum of the petal width if the species was setosa, otherwise I wanted the mean of the petal width.
Naively, I wrote this using case_when, which does not work:
iris <- tibble::as_tibble(iris)
iris %>%
group_by(Species) %>%
summarise(pwz = case_when(
Species == "setosa" ~ sum(Petal.Width, na.rm = TRUE),
TRUE ~ mean(Petal.Width, na.rm = TRUE)))
Error in summarise_impl(.data, dots) :
Column pwz must be length 1 (a summary value), not 50
I eventually found something like this, summarizing using each method, and then in a mutate picking which one I actually wanted:
iris %>%
group_by(Species) %>%
summarise(pws = sum(Petal.Width, na.rm = TRUE),
pwm = mean(Petal.Width, na.rm = TRUE)) %>%
mutate(pwz = case_when(
Species == "setosa" ~ pws,
TRUE ~ pwm)) %>%
select(-pws, -pwm)
But that seems more than a bit awkward with creating all these summarized values and only picking one at the end, especially when my real case_when is a lot more complicated. Can I not use case_when inside of summarise? Do I have my syntax wrong? Any help is appreciated!
Edit: I suppose I should have pointed out that I have multiple conditions/functions (just assume I've got, depending on the variable, some that need mean, sum, max, min, or other summary).

Answer 1

This is pretty easy with data.table
library(data.table)
iris2 <- as.data.table(iris)
iris2[, if(Species == 'setosa') sum(Petal.Width)
else mean(Petal.Width)
, by = Species]
More concisely, but maybe not as clear
iris2[, ifelse(Species == 'setosa', sum, mean)(Petal.Width)
, by = Species]
With dplyr you can do
iris %>%
group_by(Species) %>%
summarise(pwz = if_else(first(Species == "setosa")
, sum(Petal.Width)
, mean(Petal.Width)))
Note:
I'm thinking it probably makes more sense to "spread" your data with tidyr::spread so that each day has a column for temperature, rainfall, etc. Then you can use summarise in the usual way.

Answer 2

Why not calculate at the row level first, then summarize?
iris %>% group_by(Species) %>% mutate(pwz = case_when(
Species == "setosa" ~ sum(Petal.Width, na.rm = TRUE),
TRUE ~ mean(Petal.Width, na.rm = TRUE))) %>%
summarize(pwz= first(pwz))
# A tibble: 3 x 2
Species pwz
<fctr> <dbl>
1 setosa 12.300
2 versicolor 1.326
3 virginica 2.026

Answer 3

data(iris)
library(dplyr)
sum_species <- c('setosa')
iris %>%
group_by(Species) %>%
summarise(pwz_sum = sum(Petal.Width, na.rm=T),
pwz_mean= mean(Petal.Width, na.rm=T)) %>%
ungroup() %>%
mutate(pwz = if_else(Species %in% sum_species, pwz_sum, pwz_mean))

Answer 4

You could always do something like this if you want to put everything in the summarise function. But it's no less complicated than your original workaround:
iris %>%
group_by(Species) %>%
summarise(pwz =
sum(Petal.Width, na.rm = TRUE)*
(1/n()*mean(Species != "setosa") +
mean(Species == "setosa")))

Answer 5

You could split your data.frame and then use map2_dfr to apply a different function on each part and stitch the results back together:
library(tidyverse) # purrr & dplyr
iris %>%
arrange(Species=="setosa") %>%
split(.,.$Species=="setosa") %>%
map2_dfr(c(mean,sum),~.x %>% group_by(Species) %>% summarize_at("Petal.Width",.y))
# # A tibble: 3 x 2
# Species Petal.Width
# <fctr> <dbl>
# 1 versicolor 1.326
# 2 virginica 2.026
# 3 setosa 12.300