dplyr mutate rowSums calculations or custom functions
This is more of a workaround but could be used
iris %>% mutate(sumVar = rowSums(.[1:4]))
As written in comments, you can also use a select
inside of mutate to get the columns you want to sum up, for example
iris %>%
mutate(sumVar = rowSums(select(., contains("Sepal")))) %>%
head
or
iris %>%
mutate(sumVar = select(., contains("Sepal")) %>% rowSums()) %>%
head
You can use rowwise()
function:
iris %>%
rowwise() %>%
mutate(sumVar = sum(c_across(Sepal.Length:Petal.Width)))
#> # A tibble: 150 x 6
#> # Rowwise:
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species sumVar
#> <dbl> <dbl> <dbl> <dbl> <fct> <dbl>
#> 1 5.1 3.5 1.4 0.2 setosa 10.2
#> 2 4.9 3 1.4 0.2 setosa 9.5
#> 3 4.7 3.2 1.3 0.2 setosa 9.4
#> 4 4.6 3.1 1.5 0.2 setosa 9.4
#> 5 5 3.6 1.4 0.2 setosa 10.2
#> 6 5.4 3.9 1.7 0.4 setosa 11.4
#> 7 4.6 3.4 1.4 0.3 setosa 9.7
#> 8 5 3.4 1.5 0.2 setosa 10.1
#> 9 4.4 2.9 1.4 0.2 setosa 8.9
#> 10 4.9 3.1 1.5 0.1 setosa 9.6
#> # ... with 140 more rows
"c_across()
uses tidy selection syntax so you can to succinctly select many variables"'
Finally, if you want, you can use %>% ungroup
at the end to exit from rowwise.