Às 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
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