Mutating Column in 'Dplyr' Using 'Rowsums'

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.

R mutate() with rowSums()

The difference in result might be due to the fact that part_langs is a grouped dataframe, as can be seen from the output of strshown in your post:

grouped_df [7 x 15] (S3: grouped_df/tbl_df/tbl/data.frame). 

If this is the reason, then ungroup first and rerun your code:

library(dplyr)
part_langs <- part_langs %>% ungroup

dplyr mutate rowSums calculations or custom functions

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.

Why does my R mutate across with rowSums not work (Error: Problem with `mutate()` input `..2`. x 'x' must be numeric ℹ Input `..2` is `rowSums(.)`.)?

We may use adorn_totals

library(dplyr)
library(janitor)
test %>%
     adorn_totals("col", name = "total")

-output

  resource_name project sep_2021 oct_2021 total
        Justin      P1        1        5     6
         Corey      P2        2        2     4
        Justin      P3       NA        1     1

---

With rowSums and across, the syntax would be

test %>% 
   mutate(total = rowSums(across(contains("_20")), na.rm = TRUE))

-output

# A tibble: 3 x 5
  resource_name project sep_2021 oct_2021 total
  <chr>         <chr>      <dbl>    <dbl> <dbl>
1 Justin        P1             1        5     6
2 Corey         P2             2        2     4
3 Justin        P3            NA        1     1

---

In the OP's code, the across selects the columns, but the rowSums is done on the entire data (.) instead of the one that is selected

Using mutate, if_else, and rowSums to create a new var based on condition

You can sum the Data value only where Code = 100 for each month.

library(dplyr)

df %>%
  group_by(month) %>%
  mutate(newvar = sum(Data[Code == 100], na.rm = TRUE)) %>%
  ungroup

Conditional mutating with regex in dplyr using rowSum

You could do:

test %>% 
mutate(net_correct = select(.,setdiff(contains("total_score"), contains("partner"))) %>%  replace(., . == -1, 0) %>%  rowSums())

#  total_score_1 total_score_2 partner_total_score_1 total_score_3 total_score_4 letter net_correct
#1             1            -1                     1             1            -1      B           2
#2             1             1                     1            -1             1      C           3
#3            -1            -1                    -1            -1             1      A           1

R Dplyr mutate new column by calculating from other columns with conditionally replaced values

In base R, we can subtract 1 from the data, use pmin to restrict the value greater than 3 to 3 and get the rowSums

df$x3 <- rowSums(pmin(as.matrix(df-1), 3))

-output

> df
  x1 x2 x3
1  1  5  3
2  2  4  4
3  3  3  4
4  4  2  4
5  5  1  3

mutate and rowSums exclude columns

I'm only just learning dplyr, so perhaps it is because of version upgrades, but this does now work:

d %>% mutate(Total=rowSums(select(d,-Epsilon, -Alpha)))

These days, I usually see folks use the dot notation:

d %>% mutate(Total=rowSums(select(.,-Epsilon, -Alpha)))

A slightly more manageable example:

df2 = data.frame(A=sample(0:20,10), B=sample(0:20, 10), C=sample(0:20,10), D=LETTERS[1:10])
df2
    A  B  C D
1  19  0  9 A
2   6 10 14 B
3  13 20  6 C
4  20  4 15 D
5   9 14  8 E
6  11  1 18 F
7   4 15 13 G
8  17  5  0 H
9  16  3 16 I
10  2  6  1 J
df2 %>% mutate(total=rowSums(select(.,-D)))
    A  B  C D total
1  19  0  9 A    28
2   6 10 14 B    30
3  13 20  6 C    39
4  20  4 15 D    39
5   9 14  8 E    31
6  11  1 18 F    30
7   4 15 13 G    32
8  17  5  0 H    22
9  16  3 16 I    35
10  2  6  1 J     9

NOTE:
The question you linked to has an updated answer that shows yet another new method that demonstrates some new dplyr features:

df2 %>% mutate(total=rowSums(select_if(., is.numeric)))
    A  B  C D total
1  19  0  9 A    28
2   6 10 14 B    30
3  13 20  6 C    39
4  20  4 15 D    39
5   9 14  8 E    31
6  11  1 18 F    30
7   4 15 13 G    32
8  17  5  0 H    22
9  16  3 16 I    35
10  2  6  1 J     9

Use dplyr's mutate with columns that may or may not exist

One possibility:

library(dplyr)

df %>%
  mutate(total = rowSums(across(matches("^\\w$")))) 

This sums up every row that is named with a single letter.

If you want to sum up every row, replace matches("^\\w$") by everything().

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