Sorry for posting a non understandable code. In my screen the dataset
looked correctly.
I recreated my dataset, folllowing your example:
test<-data.frame(matrix(c( 8, 8, 5 , 5 ,NA ,NA , 1, 15, 20, 5, NA, 17,
2 , 5 , 5, 2 , 5 ,NA, 5 ,10, 10, 5 ,12, NA),
c( 18, 5, 5, 5, NA, 9, 2, 2, 10, 7 , 5, 19,
NA, 10, NA, 4, NA, 8, NA, 5, 10, 3, 17, NA),
c( 4, 3, 3, 2, 2, 4, 3, 3, 2, 4, 4 ,3, 4, 4, 4, 2,
2, 3, 2, 3, 3, 2, 2 ,4),
c(3, 8, 1, 2, 4, 2, 7, 6, 3, 5, 1, 3, 8, 4, 7, 5,
8, 5, 1, 2, 4, 7, 6, 6)))
colnames(test)
<-c("cp1","cp2","role","groupid")
What I have done so far is the following, that works:
test %>%
group_by(groupid) %>%
mutate(across(starts_with("cp"), list(mean = mean)))
But the problem is with NA: everytime the mean encounters a NA, it creates
NA for all group members.
I need the software to calculate the mean ignoring NA. So when the group is
made of three people, mean of the three.
If the group is two values and an NA, calculate the mean of two.
My code works , creates a mean at each position for three subjects,
replacing instead of the value of the single, the group mean.
But when NA appears, all the group gets NA.
Perhaps there is a different way to obtain the same result.
On Mon, 16 Sept 2024 at 11:35, Rui Barradas <ruipbarradas at sapo.pt>
wrote:
> ?s 08:28 de 16/09/2024, Francesca escreveu:
> > Dear Contributors,
> > I hope someone has found a similar issue.
> >
> > I have this data set,
> >
> >
> >
> > cp1
> > cp2
> > role
> > groupid
> > 1
> > 10
> > 13
> > 4
> > 5
> > 2
> > 5
> > 10
> > 3
> > 1
> > 3
> > 7
> > 7
> > 4
> > 6
> > 4
> > 10
> > 4
> > 2
> > 7
> > 5
> > 5
> > 8
> > 3
> > 2
> > 6
> > 8
> > 7
> > 4
> > 4
> > 7
> > 8
> > 8
> > 4
> > 7
> > 8
> > 10
> > 15
> > 3
> > 3
> > 9
> > 15
> > 10
> > 2
> > 2
> > 10
> > 5
> > 5
> > 2
> > 4
> > 11
> > 20
> > 20
> > 2
> > 5
> > 12
> > 9
> > 11
> > 3
> > 6
> > 13
> > 10
> > 13
> > 4
> > 3
> > 14
> > 12
> > 6
> > 4
> > 2
> > 15
> > 7
> > 4
> > 4
> > 1
> > 16
> > 10
> > 0
> > 3
> > 7
> > 17
> > 20
> > 15
> > 3
> > 8
> > 18
> > 10
> > 7
> > 3
> > 4
> > 19
> > 8
> > 13
> > 3
> > 5
> > 20
> > 10
> > 9
> > 2
> > 6
> >
> >
> >
> > I need to to average of groups, using the values of column groupid,
and
> > create a twin dataset in which the mean of the group is replaced
instead
> of
> > individual values.
> > So for example, groupid 3, I calculate the mean (12+18)/2 and then I
> > replace in the new dataframe, but in the same positions, instead of 12
> and
> > 18, the values of the corresponding mean.
> > I found this solution, where db10_means is the output dataset, db10 is
my
> > initial data.
> >
> > db10_means<-db10 %>%
> > group_by(groupid) %>%
> > mutate(across(starts_with("cp"), list(mean = mean)))
> >
> > It works perfectly, except that for NA values, where it replaces to
all
> > group members the NA, while in some cases, the group is made of some
NA
> and
> > some values.
> > So, when I have a group of two values and one NA, I would like that
for
> > those with a value, the mean is replaced, for those with NA, the NA is
> > replaced.
> > Here the mean function has not the na.rm=T option associated, but it
> > appears that this solution cannot be implemented in this case. I am
not
> > even sure that this would be enough to solve my problem.
> > Thanks for any help provided.
> >
> Hello,
>
> Your data is a mess, please don't post html, this is plain text only
> list. Anyway, I managed to create a data frame by copying the data to a
> file named "rhelp.txt" and then running
>
>
>
> db10 <- scan(file = "rhelp.txt", what = character())
> header <- db10[1:4]
> db10 <- db10[-(1:4)] |> as.numeric()
> db10 <- matrix(db10, ncol = 4L, byrow = TRUE) |>
> as.data.frame() |>
> setNames(header)
>
> str(db10)
> #> 'data.frame': 25 obs. of 4 variables:
> #> $ cp1 : num 1 5 3 7 10 5 2 4 8 10 ...
> #> $ cp2 : num 10 2 1 4 4 5 6 4 4 15 ...
> #> $ role : num 13 5 3 6 2 8 8 7 7 3 ...
> #> $ groupid: num 4 10 7 4 7 3 7 8 8 3 ...
>
>
> And here is the data in dput format.
>
>
>
> db10 <-
> structure(list(
> cp1 = c(1, 5, 3, 7, 10, 5, 2, 4, 8, 10, 9, 2,
> 2, 20, 9, 13, 3, 4, 4, 10, 17, 8, 3, 13, 10),
> cp2 = c(10, 2, 1, 4, 4, 5, 6, 4, 4, 15, 15, 10,
> 4, 2, 11, 10, 14, 2, 4, 0, 20, 18, 4, 3, 9),
> role = c(13, 5, 3, 6, 2, 8, 8, 7, 7, 3, 10, 5,
> 11, 5, 3, 13, 12, 15, 1, 3, 15, 10, 19, 5, 2),
> groupid = c(4, 10, 7, 4, 7, 3, 7, 8, 8, 3, 2, 5,
> 20, 12, 6, 4, 6, 7, 16, 7, 3, 7, 8, 20, 6)),
> class = "data.frame", row.names = c(NA, -25L))
>
>
>
> As for the problem, I am not sure if you want summarise instead of
> mutate but here is a summarise solution.
>
>
>
> library(dplyr)
>
> db10 %>%
> group_by(groupid) %>%
> summarise(across(starts_with("cp"), ~ mean(.x, na.rm = TRUE)))
>
> # same result, summarise's new argument .by avoids the need to group_by
> db10 %>%
> summarise(across(starts_with("cp"), ~ mean(.x, na.rm = TRUE)),
.by > groupid)
>
>
>
> Can you post the expected output too?
>
> Hope this helps,
>
> Rui Barradas
>
>
> --
> Este e-mail foi analisado pelo software antiv?rus AVG para verificar a
> presen?a de v?rus.
> www.avg.com
>
--
Francesca
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