How to calculate rolling mean for each column - r

Question

I want to calculate mean of every five rows for each column by group, and I tried:
name<-colnames(df[,4:10])
df1<-for (i in name){
df%>%
group_by(A)%>%
summarise(!!paste(i,"mean"):=rollapplyr(get(i),5,mean,fill = NA,by.column=T))
}
result df1 is NULL
then I tried:
for (i in name){
df%>%
group_by(A)%>%
mutate(!!paste(i,"mean"):=rollapplyr(get(i),5,mean,fill = NA,by.column=T))
}
This could run, but nothing happen, df remains the same. And if I assign above code to df1, df1 is still NULL.
I also tried rollmean
df1<- for (i in name){
+ df%>%
+ group_by(CONM)%>%
+ mutate(!!paste(i,"mean"):=rollmean(get(i),5,fill = NA,align = "right"))
+ }
But still get NULL.
My data is like this:
CONM A B C
a 1 2 3
a 2 3 4
a 3 4 5
a 4 5 6
a 5 6 7
a 6 7 8
And I want to get this result for each CONM:
CONM A B C A_mean B_mean C_mean
a 1 2 3 NA NA NA
a 2 3 4 NA NA NA
a 3 4 5 NA NA NA
a 4 5 6 NA NA NA
a 5 6 7 3 4 5
a 6 7 8 4 5 6
b 1 2 3 NA NA NA
Could someone help me with this? Should I use other packages? Thanks

Answer 1

We can use mutate with across to loop over the columns A to C, specify a lambda function (function(.) or tidyverse shortform ~) to apply the function rollmean on the column
library(dplyr)
library(zoo)
df %>%
group_by(CONM) %>%
mutate(across(A:C, ~ rollmean(., 5, fill = NA, align = 'right'),
.names = '{col}_mean')) %>%
ungroup
-output
# A tibble: 7 x 7
# CONM A B C A_mean B_mean C_mean
# <chr> <int> <int> <int> <dbl> <dbl> <dbl>
#1 a 1 2 3 NA NA NA
#2 a 2 3 4 NA NA NA
#3 a 3 4 5 NA NA NA
#4 a 4 5 6 NA NA NA
#5 a 5 6 7 3 4 5
#6 a 6 7 8 4 5 6
#7 b 1 2 3 NA NA NA
Or as #G. Grothendieck mentioned, the rollmeanr would do the right alignment
df %>%
group_by(CONM) %>%
mutate(across(A:C, ~ rollmeanr(., 5, fill = NA), .names = '{col}_mean'))
data
df <- structure(list(CONM = c("a", "a", "a", "a", "a", "a", "b"), A = c(1L,
2L, 3L, 4L, 5L, 6L, 1L), B = c(2L, 3L, 4L, 5L, 6L, 7L, 2L), C = c(3L,
4L, 5L, 6L, 7L, 8L, 3L)), class = "data.frame", row.names = c(NA,
-7L))