How to deal with missings in rowMeans? How to keep the output of RowMeans with mutate?

[LS_Dufner]

Dear Community,

I want to calculate the mean across a specific amount of variables and tried using mutate with rowMeans.
So far, there are two difficulties about it:

a) I get an error that 'x' must be numeric - is that because of NA for missings? How to deal with it?

b) In other versions with mutate the new var is shown in the tibble, but not added/saved to the existing dataset? How can I really add and keep it?

I used this code:

ssco_str <- c("s1abc", "s1cde", "s1efg")
fhprofs %>% mutate(ssco_meanall = rowMeans(fhprofs, na.rm=TRUE),
              ssco_strmean = rowMeans(select(fhprofs, ssco_str, na.rm=TRUE)))

The tibble I get from another code looked like this (mean in output of the tibble but not saved in dataframe):
fhprofs %>%
select (s1abc, s1cde, s1efg ) %>%
mutate(
ssco_strmean=(s1abc+ s1cde,+s1efg)/3
)

A tibble: 60 x 4

                s1strgarbm                     s1strgarbo                  s1strgbkol                                ssco_strmean
                 <dbl+lbl>                      <dbl+lbl>                   <dbl+lbl>                                       <dbl>

1 5 [trifft voll und ganz zu] 5 [trifft voll und ganz zu] 5 [trifft voll und ganz zu] 5
2 5 [trifft voll und ganz zu] 5 [trifft voll und ganz zu] 5 [trifft voll und ganz zu] 5
3 4 [trifft voll und ganz zu] 5 [trifft voll und ganz zu] 5 [trifft voll und ganz zu] 4.67
4 5 [trifft voll und ganz zu] 1 [trifft voll und ganz zu] 5 [trifft voll und ganz zu] 3.67

... with 50 more rows

[LS_Dufner]

By the way: How can I add a tibble here that actually looks like a tibble?

[pieterjanvc]

Hi,

I think it will indeed be easier if you provide a sample dataset we can work with. Take a look at the post below on how to build a reprex and share code and data efficiently. A reprex consists of the minimal code and data needed to recreate the issue/question you're having. You can find instructions how to build and share one here:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

Good luck

[LS_Dufner]

Okay, so this is the code for an example dataframe and the mean calculation, that did not work. Only problem now, I also get additional error messages about the type of data. What could be wrong about the data.frame now?

subset_ssco <- data.frame(
                 s1abc = c(1, 2, 3, 4, 5, 6, 7),
                 s1cde = c(3, 2, NA, 1, 1, NA, 4),
                 s1efg = c(10, 5, 2, NA, 3, 5, 3)
)
subset_ssco %>% mutate(
  ssco_meanall = rowMeans(
    select(
      s1abc, s1cde, s1efg, 
      na.rm=TRUE)))

Error in mutate():
! Problem while computing ssco_meanall = rowMeans(select(s1abc, s1cde, s1efg, na.rm = TRUE)).
Caused by error in UseMethod():
! no applicable method for 'select' applied to an object of class "c('double', 'numeric')"
Backtrace:

1. subset_ssco %>% ...
2. dplyr::select(s1abc, s1cde, s1efg, na.rm = TRUE)
   Error in mutate(., ssco_meanall = rowMeans(select(s1abc, s1cde, s1efg, :

Caused by error in UseMethod():
! no applicable method for 'select' applied to an object of class "c('double', 'numeric')"

[nirgrahamuk]

You can do this sort of thing :

subset_ssco %>% mutate(
  ssco_meanall = rowMeans(across(c(s1abc,s1cde,s1efg)),na.rm = TRUE))

subset_ssco %>% mutate(
  ssco_meanall = rowMeans(across(where(is.numeric)),na.rm = TRUE))

i.e. use across rather than select.