How to perform the equivalent of Excel sumifs in dplyr where there are multiple conditions?-CodePudding

I get the correct output shown below, with code beneath, in the SumIfs_1 column which calculates the sum of all Code2's in the array for the single condition where all Code1's in the array are < current row Code1:

  Name Group Code1 Code2 SumIfs_1
1    B     1     0     1        1
2    R     1     1     0        2
3    R     1     1     2        2
4    R     2     3     0        4
5    R     2     3     1        4
6    B     3     4     2        5
7    A     3    -1     1        0
8    A     4     0     0        1
9    A     1     0     0        1

Code:

library(dplyr)

myData <- 
  data.frame(
    Name = c("B","R","R","R","R","B","A","A","A"),
    Group = c(1,1,1,2,2,3,3,4,1),
    Code1 = c(0,1,1,3,3,4,-1,0,0),
    Code2 = c(1,0,2,0,1,2,1,0,0)
  )

myData %>% mutate(SumIfs_1 = sapply(1:n(), function(x) sum(Code2[1:n()][Code1[1:n()] < Code1[x]])))

I'd like to expand the code to add another condition to the above sumifs() equivalent, creating a sumifs() with multiple conditions, where we add only those Code 2's for Groups in the array that are < than the current row Group, as further explained in this image (orange shows what already works in the Excel equivalent of the above code for SumIfs_1, yellow shows the sumifs() with more conditions that I am trying to add (SumIfs_2)):

Any recommendations for how to do this?

I'd like to stick with sapply() if possible, and more importantly I'd like to stick with dplyr or base R as I'm trying to prevent package bloat.

For what it's worth, here's my humble attempt to generate the SumIfs_2 column (which does not work):

myData %>% mutate(SumIfs_2 = sapply(1:n(), function(x) sum(Code2[1:n()][Code1[1:n()] < Code1[x]][Group[1:n()] < Group[x]])))

CodePudding user response：

You're doing the same thing pretty much, you just need to add another & condition where you are subsetting.

Also you don't need to call Code1[1:n()], when you call Code1 it already takes all of the values in the Code1 column.

I believe you are looking for

myData %>% mutate(SumIfs_2 = sapply(1:n(), function(x) sum(Code2[(Code1 < Code1[x]) & (Group < Group[x])])))

  Name Group Code1 Code2 SumIfs_2
1    B     1     0     1        0
2    R     1     1     0        0
3    R     1     1     2        0
4    R     2     3     0        3
5    R     2     3     1        3
6    B     3     4     2        4
7    A     3    -1     1        0
8    A     4     0     0        1
9    A     1     0     0        0