group data by tolerance via index list - r

Question

I dont know how to explain it shortly. I try my best:
I have the following example data:
Data<-data.frame(A=c(1,2,3,5,8,9,10),B=c(5.3,9.2,5,8,10,9.5,4),C=c(1:7))
and a index
Ind<-data.frame(I=c(5,6,2,4,1,3,7))
The value in Ind corresponds to the C column in the Data. Now I want to start with the first Ind value, and find the corresponding row in the Data data.frame (column C). From that row I want to go up and down and find values in column A that are in a tolerance range of 1. I want to write these values into a result dataframe add a group id column and delete it in the dataframe Data (where I found them). Then I start with the next entry in the Index dataframe Ind and so an until the data.frame Data is empty. I know how to match my Ind with column C of my Data and how to write and delete and the other stuff in a for loop, but I dont know the main point, which is my question here:
when I have found my row in the Data, how can I look up fitting values of column A in the tolerance range up and below that entry to get my Group id?
what I want to get is this result:
A B C Group
1 5.3 1 2
2 9.2 2 2
3 5 3 2
5 8 4 3
8 10 5 1
9 9.5 6 1
10 4 7 4
Maybe somebody could help me with the critical point in my question or even how to solve this issue in a fast way.
Many thanks!

Answer 1

Generally: avoid deleting or growing a data frame row by row inside a loop. R's memory management means that every time you add or delete a row, another copy of the data frame is made. Garbage collection will eventually discard the "old" copies of the data frame, but garbage can quickly accumulate and reduce performance. Instead, add a logical column to the Data data frame, and set "extracted" rows to TRUE. So like this:
Data$extracted <- rep(FALSE,nrow(Data))
As for your problem: I get a different set of grouping numbers, but the groups are identical.
There might be a more elegant way to do this, but this will get it done.
# store results in a separate list
res <- list()
group.counter <- 1
# loop until they're all done.
for(idx in Ind$I) {
# skip this iteration if idx is NA.
if(is.na(idx)) {
next
}
# dat.rows is a logical vector which shows the rows where
# "A" meets the tolerance requirement.
# specify the tolerance here.
mytol <- 1
# the next only works for integer compare.
# also not covered: what if multiple values of C
# match idx? do we loop over each corresponding value of A,
# i.e. loop over each value of 'target'?
target <- Data$A[Data$C == idx]
# use the magic of vectorized logical compare.
dat.rows <-
( (Data$A - target) >= -mytol) &
( (Data$A - target) <= mytol) &
( ! Data$extracted)
# if dat.rows is all false, then nothing met the criteria.
# skip the rest of the loop
if( ! any(dat.rows)) {
next
}
# copy the rows to the result list.
res[[length(res) + 1]] <- data.frame(
A=Data[dat.rows,"A"],
B=Data[dat.rows,"B"],
C=Data[dat.rows,"C"],
Group=group.counter # this value will be recycled to match length of A, B, C.
)
# flag the extraction.
Data$extracted[dat.rows] <- TRUE
# increment the group counter
group.counter <- group.counter + 1
}
# now make a data.frame from the results.
# this is the last step in how we avoid
#"growing" a data.frame inside a loop.
resData <- do.call(rbind, res)