dplyr mutate rowSums calculations or custom functions

Question

I'm trying to mutate a new variable from sort of row calculation, say rowSums as below

iris %>% 
  mutate_(sumVar = 
            iris %>% 
            select(Sepal.Length:Petal.Width) %>%
            rowSums)

the result is that "sumVar" is truncated to its first value(10.2):

Source: local data frame [150 x 6]
Groups: <by row>

   Sepal.Length Sepal.Width Petal.Length Petal.Width Species sumVar
1           5.1         3.5          1.4         0.2  setosa   10.2
2           4.9         3.0          1.4         0.2  setosa   10.2
3           4.7         3.2          1.3         0.2  setosa   10.2
4           4.6         3.1          1.5         0.2  setosa   10.2
5           5.0         3.6          1.4         0.2  setosa   10.2
6           5.4         3.9          1.7         0.4  setosa   10.2
..
Warning message:
Truncating vector to length 1 

Should it be rowwise applied? Or what's the right verb to use in these kind of calculations.

Edit:

More specifically, is there any way to realize the inline custom function with dplyr?

I'm wondering if it is possible do something like:

iris %>% 
  mutate(sumVar = colsum_function(Sepal.Length:Petal.Width))

Answer 1

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

Answer 2

A more complicated way would be:

 iris %>% select(Sepal.Length:Petal.Width) %>%
mutate(sumVar = rowSums(.)) %>% left_join(iris)

Answer 3

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.

Answer 4

Adding @docendodiscimus's comment as an answer. +1 to him!

iris %>% mutate(sumVar = rowSums(select(., contains("Sepal"))))

Answer 5

I am using this simple solution, which is a more robust modification of the answer by Davide Passaretti:

iris %>% select(Sepal.Length:Petal.Width) %>%
  transmute(sumVar = rowSums(.)) %>% bind_cols(iris, .)

(But it requires a defined row order, which should be fine, unless you work with remote datasets perhaps..)

Answer 6

You can also use a grep in place of contains or matches, just in case you need to get fancy with the regular expressions (matches doesn't seem to much like negative lookaheads and the like in my experience).

iris %>% mutate(sumVar = rowSums(select(., grep("Sepal", names(.)))))