I am trying to create a grouped summary that reports the number of records in each group and then also shows the means of a series of variables.
I can only work out how to do this as two separate summaries which I then join together. This works fine but I wonder if there is a more elegant way to do this?
dailyn<-daily %>% # this summarises n
group_by(type) %>%
summarise(n=n()) %>%
dailymeans <- daily %>% # this summarises the means
group_by(type) %>%
summarise_at(vars(starts_with("d.")),funs(mean(., na.rm = TRUE))) %>%
dailysummary<-inner_join(dailyn,dailymeans) #this joins the two parts together
The data I'm working with is a dataframe like this:
daily<-data.frame(type=c("A","A","B","C","C","C"),
d.happy=c(1,5,3,7,2,4),
d.sad=c(5,3,6,3,1,2))
You can do this in one call, by grouping, using mutate instead of summarize, and then use slice() to keep the first row of each type:
daily %>% group_by(type) %>%
mutate(n = n()) %>%
mutate_at(vars(starts_with("d.")),funs(mean(., na.rm = TRUE))) %>%
slice(1L)
Edit: It might be clearer how this works, in this modified example
daily_summary <- daily %>% group_by(type) %>%
mutate(n = n()) %>%
mutate_at(vars(starts_with("d.")),funs("mean" = mean(., na.rm = TRUE)))
daily_summary
# Source: local data frame [6 x 6]
# Groups: type [3]
#
# # A tibble: 6 x 6
# type d.happy d.sad n d.happy_mean d.sad_mean
# <fctr> <dbl> <dbl> <int> <dbl> <dbl>
#1 A 1 5 2 3.000000 4
#2 A 5 3 2 3.000000 4
#3 B 3 6 1 3.000000 6
#4 C 7 3 3 4.333333 2
#5 C 2 1 3 4.333333 2
#6 C 4 2 3 4.333333 2
daily_summary %>%
slice(1L)
# Source: local data frame [3 x 6]
# Groups: type [3]
#
# # A tibble: 3 x 6
# type d.happy d.sad n d.happy_mean d.sad_mean
# <fctr> <dbl> <dbl> <int> <dbl> <dbl>
#1 A 1 5 2 3.000000 4
#2 B 3 6 1 3.000000 6
#3 C 7 3 3 4.333333 2