This is an alternative approach to Arun's rather elegant answer using data.table. I decided to post it because it contains two additional aspects that are important considerations in your problem:
Floating point comparison: comparison to see if a floating point value is in an interval requires consideration of the round-off error in computing the interval. This is the general problem of comparing floating point representations of real numbers. See this and this in the context of R. The following implements this comparison in the function in.interval.
Multiple matches: your interval match criterion can result in multiple matches if the intervals overlap. The following assumes that you only want the first match (with respect to increasing rows of each txt.import.matrix matrix). This is implemented in the function match.interval and explained in the notes to follow. Other logic is needed if you want to get something like the average of the areas that match your criterion.
To find the matching row(s) in a matrix from txt.import.matrix for each row in the matrix reduced.list.pre.filtering, the following code vectorizes the application of the comparison function over the space of all enumerated pairs of rows between reduced.list.pre.filtering and the matrix from txt.import.matrix. Functionally for this application, this is the same as Arun's solution using data.table's non-equi joins; however, the non-equi join feature is more general and the data.table implementation is most likely better optimized for both memory usage and speed for even this application.
in.interval <- function(x, center, deviation, tol = .Machine$double.eps^0.5) {
return (abs(x-center) <= (deviation + tol))
}
match.interval <- function(r, t) {
r.rt <- rep(r[,1], each=nrow(t))
t.rt <- rep(t[,2], times=nrow(r))
r.mz <- rep(r[,2], each=nrow(t))
t.mz <- rep(t[,4], times=nrow(r)) ## 1.
ind <- which(in.interval(r.rt, t.rt, 0.02) &
in.interval(r.mz, t.mz, 0.0002))
r.ind <- floor((ind - 1)/nrow(t)) + 1 ## 2.
dup <- duplicated(r.ind)
r.ind <- r.ind[!dup]
t.ind <- ind[!dup] - (r.ind - 1)*nrow(t) ## 3.
return(cbind(r.ind,t.ind))
}
get.area.matched <- function(r, t) {
match.ind <- match.interval(r, t)
area <- rep(NA,nrow(r))
area[match.ind[,1]] <- t[match.ind[,2], 3] ## 4.
return(area)
}
res <- cbind(reduced.list.pre.filtering,
do.call(cbind,lapply(txt.import.matrix,
get.area.matched,
r=reduced.list.pre.filtering))) ## 5.
colnames(res) <- c(colnames(reduced.list.pre.filtering),
sapply(seq_len(length(txt.import.matrix)),
function(i) {return(paste0("Area.[",i,"]"))})) ## 6.
print(res)
## RT. m.z. Area.[1] Area.[2]
##[1,] 1.01 358.9777 2820.1 7820.1
##[2,] 1.07 368.4238 NA 8271.8
##[3,] 2.05 284.3295 6674.0 12674.0
##[4,] 2.03 922.0092 5856.3 NA
##[5,] 3.03 261.1299 27814.6 NA
##[6,] 3.56 869.4558 NA NA
Notes:
This part constructs the data to enable the vectorization of the application of the comparison function over the space of all enumerated pairs of rows between reduced.list.pre.filtering and the matrix from txt.import.matrix. The data to be constructed are four arrays that are the replications (or expansions) of the two columns, used in the comparison criterion, of reduced.list.pre.filtering in the row dimension of each matrix from txt.import.matrix and the replications of the two columns, used in the comparison criterion, of each matrix from txt.import.matrix in the row dimension of reduced.list.pre.filtering. Here, the term array refers to either a 2-D matrix or a 1-D vector. The resulting four arrays are:
r.rt is the replication of the RT. column of reduced.list.pre.filtering (i.e., r[,1]) in the row dimension of tt.rt is the replication of the RT. column of the matrix from txt.import.matrix (i.e., t[,2]) in the row dimension of rr.mz is the replication of the m.z. column of reduced.list.pre.filtering (i.e. r[,2]) in the row dimension of tt.mz is the replication of the m.z. column of the matrix from txt.import.matrix (i.e. t[,4]) in the row dimension of r
What is important is that the indices for each of these arrays enumerate all pairs of rows in r and t in the same manner. Specifically, viewing these arrays as 2-D matrices of size M by N where M=nrow(t) and N=nrow(r), the row indices correspond to the rows of t and the column indices correspond to the rows of r. Consequently, the array values (over all four arrays) at the i-th row and the j-th column (of each of the four arrays) are the values used in the comparison criterion between the j-th row of r and the i-th row of t. Implementation of this replication process uses the R function rep. For example, in computing r.rt, rep with each=M is used, which has the effect of treating its array input r[,1] as a row vector and replicating that row M times to form M rows. The result is such that each column, which corresponds to a row in r, has the RT. value from the corresponding row of r and that value is the same for all rows (of that column) of r.rt, each of which corresponds to a row in t. This means that in comparing that row in r to any row in t, the value of RT. from that row in r is used. Conversely, in computing t.rt, rep with times=N is used, which has the effect of treating its array input as a column vector and replicating that column N times to form a N columns. The result is such that each row in t.rt, which corresponds to a row in t, has the RT. value from the corresponding row of t and that value is the same for all columns (of that row) of t.rt, each of which corresponds to a row in r. This means that in comparing that row in t to any row in r, the value of RT. from that row in t is used. Similarly, the computations of r.mz and t.mz follow using the m.z. column from r and t, respectively.
This performs the vectorized comparison resulting in a M by N logical matrix where the i-th row and the j-th column is TRUE if the j-th row of r matches the criterion with the i-th row of t, and FALSE otherwise. The output of which() is the array of array indices to this logical comparison result matrix where its element is TRUE. We want to convert these array indices to the row and column indices of the comparison result matrix to refer back to the rows of r and t. The next line extracts the column indices from the array indices. Note that the variable name is r.ind to denote that these correspond to the rows of r. We extract this first because it is important for detecting multiple matches for a row in r.
This part handles possible multiple matches in t for a given row in r. Multiple matches will show up as duplicate values in r.ind. As stated above, the logic here only keeps the first match in terms of increasing rows in t. The function duplicated returns all the indices of duplicate values in the array. Therefore removing these elements will do what we want. The code first removes them from r.ind, then it removes them from ind, and finally computes the column indices to the comparison result matrix, which corresponds to the rows of t, using the pruned ind and r.ind. What is returned by match.interval is a matrix whose rows are matched pair of row indices with its first column being row indices to r and its second column being row indices to t.
The get.area.matched function simply uses the result from match.ind to extract the Area from t for all matches. Note that the returned result is a (column) vector with length equaling to the number of rows in r and initialized to NA. In this way rows in r that has no match in t has a returned Area of NA.
This uses lapply to apply the function get.area.matched over the list txt.import.matrix and append the returned matched Area results to reduced.list.pre.filtering as column vectors. Similarly, the appropriate column names are also appended and set in the result res.
Edit: Alternative implementation using the foreach package
In hindsight, a better implementation uses the foreach package for vectorizing the comparison. In this implementation, the foreach and magrittr packages are required
require("magrittr") ## for %>%
require("foreach")
Then the code in match.interval for vectorizing the comparison
r.rt <- rep(r[,1], each=nrow(t))
t.rt <- rep(t[,2], times=nrow(r))
r.mz <- rep(r[,2], each=nrow(t))
t.mz <- rep(t[,4], times=nrow(r)) # 1.
ind <- which(in.interval(r.rt, t.rt, 0.02) &
in.interval(r.mz, t.mz, 0.0002))
can be replaced by
ind <- foreach(r.row = 1:nrow(r), .combine=cbind) %:%
foreach(t.row = 1:nrow(t)) %do%
match.criterion(r.row, t.row, r, t) %>%
as.logical(.) %>% which(.)
where the match.criterion is defined as
match.criterion <- function(r.row, t.row, r, t) {
return(in.interval(r[r.row,1], t[t.row,2], 0.02) &
in.interval(r[r.row,2], t[t.row,4], 0.0002))
}
This is easier to parse and reflects what is being performed. Note that what is returned by the nested foreach combined with cbind is again a logical matrix. Finally, the application of the get.area.matched function over the list txt.import.matrix can also be performed using foreach:
res <- foreach(i = 1:length(txt.import.matrix), .combine=cbind) %do%
get.area.matched(reduced.list.pre.filtering, txt.import.matrix[[i]]) %>%
cbind(reduced.list.pre.filtering,.)
The complete code using foreach is as follows:
require("magrittr")
require("foreach")
in.interval <- function(x, center, deviation, tol = .Machine$double.eps^0.5) {
return (abs(x-center) <= (deviation + tol))
}
match.criterion <- function(r.row, t.row, r, t) {
return(in.interval(r[r.row,1], t[t.row,2], 0.02) &
in.interval(r[r.row,2], t[t.row,4], 0.0002))
}
match.interval <- function(r, t) {
ind <- foreach(r.row = 1:nrow(r), .combine=cbind) %:%
foreach(t.row = 1:nrow(t)) %do%
match.criterion(r.row, t.row, r, t) %>%
as.logical(.) %>% which(.)
# which returns 1-D indices (row-major),
# convert these to 2-D indices in (row,col)
r.ind <- floor((ind - 1)/nrow(t)) + 1 ## 2.
# detect duplicates in r.ind and remove them from ind
dup <- duplicated(r.ind)
r.ind <- r.ind[!dup]
t.ind <- ind[!dup] - (r.ind - 1)*nrow(t) ## 3.
return(cbind(r.ind,t.ind))
}
get.area.matched <- function(r, t) {
match.ind <- match.interval(r, t)
area <- rep(NA,nrow(r))
area[match.ind[,1]] <- t[match.ind[,2], 3]
return(area)
}
res <- foreach(i = 1:length(txt.import.matrix), .combine=cbind) %do%
get.area.matched(reduced.list.pre.filtering, txt.import.matrix[[i]]) %>%
cbind(reduced.list.pre.filtering,.)
colnames(res) <- c(colnames(reduced.list.pre.filtering),
sapply(seq_len(length(txt.import.matrix)),
function(i) {return(paste0("Area.[",i,"]"))}))
Hope this helps.
