rollmean with grouped data.table returns a logical

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I am trying to use rollmean from the package zoo in a data.table while grouping data.

It works fine when all groups have enough data:

library(data.table)
dt = data.table(x=rep(c("a","b"),10),y=rnorm(20))

dt[,.(ma=rollmean(y, k = 7, fill=NA,align="right")), by = .(x)]

But when one of the groups has too little data, it returns an error 


dt2 = data.table(x=rep(c("c"),1),y=rnorm(1))

dt3=rbind(dt,dt2)
dt3[,.(ma=rollmean(y, k = 7, fill=NA,align="right")), by = .(x)]

Here's the error message:

Column 1 of result for group 3 is type 'logical' but expecting type 'double'. Column types must be consistent for each group.

It seems to happen because rollmean returns a logical (a mix of TRUE and NA) when it doesn't have enough data Given that my data is always positive I use the following trick to make my code run anyway

dt4=dt3[,.(ma=rollmean(y, k = 7, fill=-1,align="right")), by = .(x)]
dt4[ma==-1,ma:=NA]
dt4

Is there a proper/better way to do it?

data.table mean r
1 Answers

We can use the NA_real_ instead of NA as by default it would be NA_logical_

dt3[x == 'c', class(rollmean(y, k = 7, fill = NA, align = 'right'))] 
#[1] "logical"

With NA_real_ in fill, it would work fine

dt3[,.(ma=rollmean(y, k = 7, fill=NA_real_,align="right")), by = .(x)]
#    x          ma
# 1: a          NA
# 2: a          NA
# 3: a          NA
# 4: a          NA
# 5: a          NA
# 6: a          NA
# 7: a  0.19653855
# 8: a -0.05506344
# 9: a -0.17022022
#10: a -0.28731762
#11: b          NA
#12: b          NA
#13: b          NA
#14: b          NA
#15: b          NA
#16: b          NA
#17: b  0.02117906
#18: b -0.07079598
#19: b -0.05393943
#20: b  0.04511924
#21: c          NA
    x          ma

In other groups, it is also creating NA, but the difference is that it gets coerced to numeric NA when there are non-NA elements

user_37322710
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