Nested if else statements over a number of columns

Question

I have a large data.frame where the first three columns contain information about a marker. The remaining columns are of numeric type for that

Answer 1

Edit: Updated solution using the fast melt/dcast methods implemented in data.table versions >= 1.9.0. Go here for more info.

require(data.table)
require(reshape2)
dt <- as.data.table(df)

# melt data.table
dt.m <- melt(dt, id=c("marker", "alleleA", "alleleB"), 
                 variable.name="id", value.name="val")
dt.m[, id := gsub("\\.[0-9]+$", "", id)] # replace `.[0-9]` with nothing
# aggregation
dt.m <- dt.m[, list(alleleA = alleleA[1], 
         alleleB = alleleB[1], val = max(val)), 
        keyby=list(marker, id)][val <= 0.8, val := NA]
# casting back
dt.c <- dcast.data.table(dt.m, marker + alleleA + alleleB ~ id)
#                        marker alleleA alleleB X345   X346   X818
# 1: chr3_21902130_21902131_A_T       A       T   NA 0.8626 0.8626
# 2: chr3_21902134_21902135_T_C       T       C   NA     NA     NA
# 3:   kgp5209280_chr3_21902067       T       A    1 1.0000 1.0000

---

Solution 1: Probably not the best way, but this is what I could think of at the moment:

mm <- t(apply(df[-(1:3)], 1, function(x) tapply(x, gl(3,3), max)))
mode(mm) <- "numeric"
mm[mm < 0.8] <- NA 
# you can set the column names of mm here if necessary
out <- cbind(df[, 1:3], mm)

#                       marker alleleA alleleB      1  2      3
# 1   kgp5209280_chr3_21902067       T       A 1.0000  1 1.0000
# 2 chr3_21902130_21902131_A_T       A       T 0.8626 NA 0.8626
# 3 chr3_21902134_21902135_T_C       T       C     NA NA     NA

gl(3,3) gives a factor with values 1,1,1,2,2,2,3,3,3 with levels 1,2,3. That is, tapply will take the values x 3 at a time and get their max (first 3, next 3 and the last 3). And apply sends each row one by one.

---

Solution 2: A data.table solution with melt and cast within data.table without using reshape or reshape2:

require(data.table)
dt <- data.table(df)
# melt your data.table to long format
dt.melt <- dt[, list(id = names(.SD), val = unlist(.SD)), 
                  by=list(marker, alleleA, alleleB)]
# replace `.[0-9]` with nothing
dt.melt[, id := gsub("\\.[0-9]+$", "", id)]
# get max value grouping by marker and id
dt.melt <- dt.melt[, list(alleleA = alleleA[1], 
                      alleleB = alleleB[1], 
                      val = max(val)), 
        keyby=list(marker, id)][val <= 0.8, val := NA]
# edit mnel (use setattr(,'names') to avoid copy by `names<-` within `setNames`
dt.cast <- dt.melt[, as.list(setattr(val,'names', id)), 
                   by=list(marker, alleleA, alleleB)]

#                        marker alleleA alleleB X345   X346   X818
# 1: chr3_21902130_21902131_A_T       A       T   NA 0.8626 0.8626
# 2: chr3_21902134_21902135_T_C       T       C   NA     NA     NA
# 3:   kgp5209280_chr3_21902067       T       A    1 1.0000 1.0000