R: aggregate data while adding new count column using base R [duplicate] - r

Question

This question already has answers here:
Apply several summary functions (sum, mean, etc.) on several variables by group in one call
(7 answers)
Closed 1 year ago.
I would like to aggregate a data frame while also adding in a new column (N) that counts the number of rows per value of the grouping variable, in base R.
This is trivial in dplyr:
library(dplyr)
data(iris)
combined_summary <- iris %>% group_by(Species) %>% group_by(N=n(), add=TRUE) %>% summarize_all(mean)
> combined_summary
# A tibble: 3 x 6
# Groups: Species [3]
Species N Sepal.Length Sepal.Width Petal.Length Petal.Width
<fct> <int> <dbl> <dbl> <dbl> <dbl>
1 setosa 50 5.01 3.43 1.46 0.246
2 versicolor 50 5.94 2.77 4.26 1.33
3 virginica 50 6.59 2.97 5.55 2.03
I am however in the unfortunate position of having to write this code in an environment that doesn't allow for packages to be used (don't ask; it's not my decision). So I need a way to do this in base R.
I can do it in base R in a long-winded way as follows:
# First create the aggregated tables separately
summary_means <- aggregate(. ~ Species, data=iris, FUN=mean)
summary_count <- aggregate(Sepal.Length ~ Species, data=iris[, c("Species", "Sepal.Length")], FUN=length)
> summary_means
Species Sepal.Length Sepal.Width Petal.Length Petal.Width
1 setosa 5.006 3.428 1.462 0.246
2 versicolor 5.936 2.770 4.260 1.326
3 virginica 6.588 2.974 5.552 2.026
> summary_count
Species Sepal.Length
1 setosa 50
2 versicolor 50
3 virginica 50
# Then rename the count column
colnames(summary_count)[2] <- "N"
> summary_count
Species N
1 setosa 50
2 versicolor 50
3 virginica 50
# Finally merge the two dataframes
combined_summary_baseR <- merge(x=summary_count, y=summary_means, by="Species", all.x=TRUE)
> combined_summary_baseR
Species N Sepal.Length Sepal.Width Petal.Length Petal.Width
1 setosa 50 5.006 3.428 1.462 0.246
2 versicolor 50 5.936 2.770 4.260 1.326
3 virginica 50 6.588 2.974 5.552 2.026
Is there any way to do this in a more efficient way in base R?

Answer 1

Here is a base R option using a single by call (to aggregate)
do.call(rbind, by(
iris[-ncol(iris)], iris[ncol(iris)], function(x) c(N = nrow(x), colMeans(x))))
# N Sepal.Length Sepal.Width Petal.Length Petal.Width
#setosa 50 5.006 3.428 1.462 0.246
#versicolor 50 5.936 2.770 4.260 1.326
#virginica 50 6.588 2.974 5.552 2.026
Using colMeans ensures that the column names are carried through which avoids an additional setNames call.
Update
In response to your comment, to have row names as a separate column requires an extra step.
d <- do.call(rbind, by(
iris[-ncol(iris)], iris[ncol(iris)], function(x) c(N = nrow(x), colMeans(x))))
cbind(Species = rownames(d), as.data.frame(d))
Not as concise as the initial by call. I think we're having a clash of philosophies here. In dplyr (and the tidyverse) row names are generally avoided, to be consistent with the principles of "tidy data". In base R row names are common and are (more or less) consistently carried through data operations. So in a way you're asking for a mix of dplyr (tidy) and base R data structure concepts which may not be the best/robust approach.