Math function using multiple matching criteria - r

Question

I'm new to this but I'm pretty sure this question hasn't been answered, or I'm just not good at searching....
I would like to subtract the values in multiple rows from a particular row based on matching columns and values. My actual data will be a large matrix with >5000 columns, eaching needing to be subtracted by a blank value that matches the a value in a factor column.
Here is an example data table:
c1 c2 c3 c4 c5
r1 A 1 2 3 aa
r2 B 2 3 4 bb
r3 C 3 4 5 aa
r4 D 4 1 6 bb
r5 Blank 2 3 4 aa
r6 Blank 3 4 5 bb
I would like to subtract the c2,c3,and c4 values of c1 ="Blank" row from A,B,and C using the c5 factor to define which Blank values are used (aa or bb). I would like the "Blank" values to be subtracted from all rows sharing c5 info.
(i know this is confusing to describe)
So the results would look like this:
c1 c2 c3 c4 c5
r1 A -1 -1 -1 aa
r2 B -1 -1 -1 bb
r3 C 1 1 1 aa
r4 D 1 -3 1 bb
I've seen the ddply function work for doing something like this with a single column, but I wasn't able to expand that to perform this task for multiple columns. I'm a noob though...
Thank you for your help!

Answer 1

This is not tested for all possible cases, but should give you an idea:
df <- read.table(text =
"c1 c2 c3 c4 c5
r1 A 1 2 3 aa
r2 B 2 3 4 bb
r3 C 3 4 5 aa
r4 D 4 1 6 bb
r5 Blank 2 3 4 aa
r6 Blank 3 4 5 bb", header = T)
library(data.table)
# separate dataset into two
dt <- data.table(df, key = "c5")
dt.blank <- dt[c1 == "Blank"]
dt <- dt[c1 != "Blank"]
# merge into resulting dataset
dt.res <- dt[dt.blank]
# update each column
columns.count <- ncol(dt)
for(i in 2:(columns.count-1)) {
dt.res[[i]] <- dt.res[[i]] - dt.res[[i + columns.count]]
}
# > dt.res
# c1 c2 c3 c4 c5 i.c1 i.c2 i.c3 i.c4
# 1: A -1 -1 -1 aa Blank 2 3 4
# 2: C 1 1 1 aa Blank 2 3 4
# 3: B -1 -1 -1 bb Blank 3 4 5
# 4: D 1 -3 1 bb Blank 3 4 5

Answer 2

First split your data, since there's no reason you have them in a single data structure. Then apply the function:
# recreate your data
df <- data.frame(rbind(c(1:3, "aa"), c(2:4, "bb"), c(3:5, "aa"), c(4,1,6, "bb"), c(2:4, "aa"), c(3:5, "bb")))
df[,1:3] <- apply(df[,1:3], 2, as.integer)
# split it
blank1 <- df[5,]
blank2 <- df[6,]
df <- df[1:4,]
for (i in 1:nrow(df)) {
if (df[i,4] == "aa") {df[i,1:3] <- df[i,1:3] - blank1[1:3]}
else {df[i,1:3] <- df[i,1:3] - blank2[1:3]}
}
There are a few different was to run the loop, including vectorizing. But this suffices. I'd also argue that there's no reason to keep the labels "aa" v "bb" in the initial data structure either, which would make this simpler; but it's your choice.