Group_by / summarize by two variables within a function - r

Question

I would like to write a function that summarize the provided data by some specified criteria, in this case by age
The example data is a table of users' age and their stats.
df <- data.frame('Age'=rep(18:25,2), 'X1'=10:17, 'X2'=28:35,'X4'=22:29)
Next I define the output columns that are relevant for the analysis
output_columns <- c('Age', 'X1', 'X2', 'X3')
This function computes the basic the sum of X1. X2 and X3 grouped by age.
aggr <- function(data, criteria, output_columns){
k <- data %>% .[, colnames(.) %in% output_columns] %>%
group_by_(.dots = criteria) %>%
#summarise_each(funs(count), age) %>%
summarize_if(is.numeric, sum)
return (k)
}
When I call it like this
> e <- aggr(df, "Age", output_columns)
> e
# A tibble: 8 x 3
Age X1 X2
<int> <int> <int>
1 18 20 56
2 19 22 58
3 20 24 60
4 21 26 62
5 22 28 64
6 23 30 66
7 24 32 68
8 25 34 70
I want to have another column called count which shows the number of observations in each age group. Desired output is
> desired
Age X1 X2 count
1 18 20 56 2
2 19 22 58 2
3 20 24 60 2
4 21 26 62 2
5 22 28 64 2
6 23 30 66 2
7 24 32 68 2
8 25 34 70 2
I have tried different ways to do that, e.g. tally(), summarize_each
etc. They all deliver wrong results.
I believe their should be an easy and simple way to do that.
Any help is appreciated.

Answer 1

Since you're already summing all variables, you can just add a column of all 1s before the summary function
aggr <- function(data, criteria, output_columns){
data %>%
.[, colnames(.) %in% output_columns] %>%
group_by_(.dots = criteria) %>%
mutate(n = 1L) %>%
summarize_if(is.numeric, sum)
}
# A tibble: 8 x 4
Age X1 X2 n
<int> <int> <int> <int>
1 18 20 56 2
2 19 22 58 2
3 20 24 60 2
4 21 26 62 2
5 22 28 64 2
6 23 30 66 2
7 24 32 68 2
8 25 34 70 2

Answer 2

We could create the 'count' column before summarise_if
aggr<- function(data, criteria, output_columns){
data %>%
select(intersect(names(.), output_columns))%>%
group_by_at(criteria)%>%
group_by(count = n(), add= TRUE) %>%
summarize_if(is.numeric,sum) %>%
select(setdiff(names(.), 'count'), count)
}
aggr(df,"Age",output_columns)
# A tibble: 8 x 4
# Groups: Age [8]
# Age X1 X2 count
# <int> <int> <int> <int>
#1 18 20 56 2
#2 19 22 58 2
#3 20 24 60 2
#4 21 26 62 2
#5 22 28 64 2
#6 23 30 66 2
#7 24 32 68 2
#8 25 34 70 2

Answer 3

In base R you could do
aggr <- function(data, criteria, output_columns){
ds <- data[, colnames(data) %in% output_columns]
d <- aggregate(ds, by=list(criteria), function(x) c(sum(x), length(x)))
"names<-"(do.call(data.frame, d)[, -c(2:3, 5)], c(names(ds), "n"))
}
> with(df, aggr(df, Age, output_columns))
Age X1 X2 n
1 18 20 56 2
2 19 22 58 2
3 20 24 60 2
4 21 26 62 2
5 22 28 64 2
6 23 30 66 2
7 24 32 68 2
8 25 34 70 2