dplyr rowwise sum and other functions like max
If I wanted to sum over some variables in a data-frame using dplyr, I could do:
> head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
> select(iris, starts_with('Petal')) %>% rowSums()
[1] 1.6 1.6 1.5 1.7 1.6 2.1 1.7 1.7 1.6 1.6 1.7 1.8 1.5 1.2 1.4 1.9 1.7 1.7 2.0 1.8 1.9 1.9 1.2 2.2 2.1 1.8 2.0 1.7 1.6 1.8 1.8 1.9 1.6 1.6 1.7 1.4
[37] 1.5 1.5 1.5 1.7 1.6 1.6 1.5 2.2 2.3 1.7 1.8 1.6 1.7 1.6 6.1 6.0 6.4 5.3 6.1 5.8 6.3 4.3 5.9 5.3 4.5 5.7 5.0 6.1 4.9 5.8 6.0 5.1 6.0 5.0 6.6 5.3
[73] 6.4 5.9 5.6 5.8 6.2 6.7 6.0 4.5 4.9 4.7 5.1 6.7 6.0 6.1 6.2 5.7 5.4 5.3 5.6 6.0 5.2 4.3 5.5 5.4 5.5 5.6 4.1 5.4 8.5 7.0 8.0 7.4 8.0 8.7 6.2 8.1
[109] 7.6 8.6 7.1 7.2 7.6 7.0 7.5 7.6 7.3 8.9 9.2 6.5 8.0 6.9 8.7 6.7 7.8 7.8 6.6 6.7 7.7 7.4 8.0 8.4 7.8 6.6 7.0 8.4 8.0 7.3 6.6 7.5 8.0 7.4 7.0 8.2
[145] 8.2 7.5 6.9 7.2 7.7 6.9
That's fine, but I would have thought rowwise accomplishes the same thing, but it doesn't,
> select(iris, starts_with('Petal')) %>% rowwise() %>% sum()
[1] 743.6
What I particularly want to do is select a set of columns, and create a new variable each value of which is the maximum value of each row of the selected columns. For example, if I selected the "Petal" columns, by maximum values would be 1.4, 1.4, 1.3 and so on.
I could do it like this:
> select(iris, starts_with('Petal')) %>% apply(1, max)
and that's fine. But I'm just curious as to why the rowwise approach doesn't work. I realize I am using rowwise incorrectly, I'm just not sure why it is wrong.
Answer
The problem is that the entire data frame is passed as dot despite the rowwise. To handle this use do which will interpret dot as meaning just the current row. One further problem is that the dot within do will represent the row as a list so convert it appropriately.
library(dplyr)
iris %>%
slice(1:6) %>%
select(starts_with('Petal')) %>%
rowwise() %>%
do( (.) %>% as.data.frame %>% mutate(sum = sum(.)) ) %>%
ungroup
giving:
# A tibble: 6 x 3
Petal.Length Petal.Width sum
* <dbl> <dbl> <dbl>
1 1.40 0.200 1.60
2 1.40 0.200 1.60
3 1.30 0.200 1.50
4 1.50 0.200 1.70
5 1.40 0.200 1.60
6 1.70 0.400 2.10
dplyr 1.0 - added later
Since this was asked dplyr 1.0 was released and it has cur_data() which can be used to simplify the above eliminating the need for do. cur_data() within a rowwise block refers only to the current row.
iris %>%
slice(1:6) %>%
select(starts_with('Petal')) %>%
rowwise() %>%
mutate(sum = sum(cur_data())) %>%
ungroup