Count rows in the past year according to a condition using data.table-CodePudding

According to this previous post, I can add a column with a count of occurrences in the past year in the following way:

df[, boundary := date - 365]
df[, counts := df[df, .N,  on = .(id, date < date, date > boundary), by = .EACHI]$N]

This works fine for me. However, I want to do this by counting only the number of occurrences where another column has a specific value. For example, given a dataset like this

id  type  date
ny  0     2021-09-27   
ny  0     2021-09-09
ny  1     2021-08-01
ny  1     2021-07-07
ch  0     2020-04-01
ch  1     2020-03-01 
ch  0     2020-02-01

I want to count only the number of rows where type = 1. How can I amend the function above to do this? I tried something like this, but it doesn't work:

df[, counts := df[df, .N(type = 1),  on = .(id, date < date, date > boundary), by = .EACHI]$N]

EDIT: Expected output for the above dataset would be:

id  type  date         counts
ny  0     2021-09-27   2
ny  0     2021-09-09   2
ny  1     2021-08-01   1  
ny  1     2021-07-07   0  
ch  0     2020-04-01   1
ch  1     2020-03-01   0 
ch  0     2020-02-01   0

CodePudding user response：

You may calculate sum(type == 1) instead of .N.

setDT(df)
df[, boundary := date - 365]

df[, counts := df[df, sum(type == 1),  
      on = .(id, date < date, date > boundary), by = .EACHI]$V1]
df[is.na(counts), counts := 0]
df

#   id type       date   boundary counts
#1: ny    0 2021-09-27 2020-09-27      2
#2: ny    0 2021-09-09 2020-09-09      2
#3: ny    1 2021-08-01 2020-08-01      1
#4: ny    1 2021-07-07 2020-07-07      0
#5: ch    0 2020-04-01 2019-04-02      1
#6: ch    1 2020-03-01 2019-03-02      0
#7: ch    0 2020-02-01 2019-02-01      0