Mutating column in `dplyr` using `rowSums`

The examples do not work because you are nesting select in mutate and using bare variable names. In this case, select is trying to do something like

> -df$ids
Error in -df$ids : invalid argument to unary operator

which fails because you can't negate a character string (i.e. -"i1" or -"i2" makes no sense). Either of the formulations below works:

df %>% mutate(blubb = rowSums(select_(., "X1", "X2")))
df %>% mutate(blubb = rowSums(select(., -3)))

or

df %>% mutate(blubb = rowSums(select_(., "-ids")))

as suggested by @Haboryme.

select_ is deprecated. You can use:

library(dplyr)
df <- data.frame(matrix(rnorm(20), 10, 2),
                 ids = paste("i", 1:20, sep = ""),
                 stringsAsFactors = FALSE)
df %>% 
  mutate(blubb = rowSums(select(., .dots = c("X1", "X2"))))

# Or more generally:
desired_columns <- c("X1", "X2")
df %>% 
  mutate(blubb = rowSums(select(., .dots = all_of(desired_columns))))

Adding to this old thread because I searched on this question then realized I was asking the wrong question. Also, I detect some yearning in this and related questions for the proper pipe steps way to do this.

The answers here are somewhat non-intuitive because they are trying to use the dplyr vernacular with non-"tidy" data. IF you want to do it the dplyr way, make the data tidy first, using gather(), and then use summarise()

library(tidyverse)

df <- data.frame(matrix(rnorm(20), 10, 2),
                 ids = paste("i", 1:20, sep = ""),
                 stringsAsFactors = FALSE)

df %>% gather(key=Xn,value="value",-ids) %>% 
  group_by(ids) %>% 
  summarise(rowsum=sum(value))

#> # A tibble: 20 x 2
#>    ids   rowsum
#>    <chr>       <dbl>
#>  1 i1          0.942
#>  2 i10        -0.330
#>  3 i11         0.942
#>  4 i12        -0.721
#>  5 i13         2.50 
#>  6 i14        -0.611
#>  7 i15        -0.799
#>  8 i16         1.84 
#>  9 i17        -0.629
#> 10 i18        -1.39 
#> 11 i19         1.44 
#> 12 i2         -0.721
#> 13 i20        -0.330
#> 14 i3          2.50 
#> 15 i4         -0.611
#> 16 i5         -0.799
#> 17 i6          1.84 
#> 18 i7         -0.629
#> 19 i8         -1.39 
#> 20 i9          1.44

If you care about the order of the ids when they are not sortable using arrange(), make that column a factor first.

  df %>% 
  mutate(ids=as_factor(ids)) %>% 
  gather(key=Xn,value="value",-ids) %>% 
  group_by(ids) %>% 
  summarise(rowsum=sum(value))