How to use R dplyr's summarize to count the number of rows that match a criteria?

Question

I have a dataset that I want to summarize. First, I want the sum of the home and away games, which I can do. However, I also want to know how many outliers (defined as more than 300 points) are within each subcategory (home, away).

If I wasn't using summarize, I know dplyr has the count() function, but I'd like this solution to appear in my summarize() call. Here's what I have and what I've tried, which fails to perform:

#Test data
library(dplyr)

test <- tibble(score = c(100, 150, 200, 301, 150, 345, 102, 131),
                  location = c("home", "away", "home", "away", "home", "away", "home", "away"),
                  more_than_300 = c(FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE))


#attempt 1, count rows that match a criteria
test %>%
  group_by(location) %>%
  summarize(total_score = sum(score),
            n_outliers = nrow(.[more_than_300 == FALSE]))

Answer 1

You can use sum on logical vectors - it will automatically convert them into numeric values ( TRUE being equal to 1 and FALSE being equal to 0), so you need only do:

test %>%
  group_by(location) %>%
  summarize(total_score = sum(score),
            n_outliers  = sum(more_than_300))
#> # A tibble: 2 x 3
#>   location total_score n_outliers
#>   <chr>          <dbl>      <int>
#> 1 away             927          2
#> 2 home             552          0

Or, if these are your only 3 columns, an equivalent would be:

test %>%
  group_by(location) %>%
  summarize(across(everything(), sum))

In fact, you don't need to make the more_than_300 column - it would suffice to do:

test %>%
  group_by(location) %>%
  summarize(total_score = sum(score),
            n_outliers  = sum(score > 300))

Answer 2

In base R, we can try aggregate like this

> aggregate(.~location,test,sum)
  location score more_than_300
1     away   927             2
2     home   552             0

Answer 3

In base xtabs could be used to sum up per group.

xtabs(cbind(score, more_than_300) ~ ., test)
#location score more_than_300
#    away   927             2
#    home   552             0

Or by calculating the outliers on the fly and giving desired column names.

xtabs(cbind(total_score = score, n_outliers = score > 300) ~ location, test)
#location total_score n_outliers
#    away         927          2
#    home         552          0

Another option, also in base, will be rowsum .

with(test, rowsum(cbind(total_score = score, n_outliers = score > 300), location))
#     total_score n_outliers
#away         927          2
#home         552          0

xtabs and rowsum are specialized in calculating sums per group and might be performant in this task.