Recode dataframe based on one column - in reverse - r

Question

I asked this question a while ago (Recode dataframe based on one column) and the answer worked perfectly. Now however, i almost want to do the reverse. Namely, I have a (700k * 2000) of 0/1/2 or NA. In a separate dataframe I have two columns (Ref and Obs). The 0 corresponds to two instances of Ref, 1 is one instance of Ref and one instance of Obs and 2 is two Obs. To clarify, data snippet:
Genotype File ---
Ref Obs
A G
T C
G C
Ref <- c("A", "T", "G")
Obs <- c("G", "C", "C")
Current Data---
Sample.1 Sample.2 .... Sample.2000
0 1 2
0 0 0
0 NA 1
mat <- matrix(nrow=3, ncol=3)
mat[,1] <- c(0,0,0)
mat[,2] <- c(1,0,NA)
mat[,3] <- c(2,0,1)
Desired Data format---
Sample.1 Sample.1 Sample.2 Sample.2 Sample.2000 Sample.2000
A A A G G G
T T T T T T
G G 0 0 G C
I think that's right. The desired data format has two columns (space separated) for each sample. 0 in this format (plink ped file for the bioinformaticians out there) is missing data.

Answer 1

MAJOR ASSUMPTION: your data is in 3 element frames, i.e. you want to apply your mapping to the first 3 rows, then the next 3, and so on, which I think makes sense given DNA frames. If you want a rolling 3 element window this will not work (but code can be modified to make it work). This will work for an arbitrary number of columns, and arbitrary number of 3 row groups:
# Make up a matrix with your properties (4 cols, 6 rows)
col <- 4L
frame <- 3L
mat <- matrix(sample(c(0:2, NA_integer_), 2 * frame * col, replace=T), ncol=col)
# Mapping data
Ref <- c("A", "T", "G")
Obs <- c("G", "C", "C")
map.base <- cbind(Ref, Obs)
num.to.let <- matrix(c(1, 1, 1, 2, 2, 2), byrow=T, ncol=2) # how many from each of ref obs
# Function to map 0,1,2,NA to Ref/Obs
re_map <- function(mat.small) { # 3 row matrices, with col columns
t(
mapply( # iterate through each row in matrix
function(vals, map, num.to.let) {
vals.2 <- unlist(lapply(vals, function(x) map[num.to.let[x + 1L, ]]))
ifelse(is.na(vals.2), 0, vals.2)
},
vals=split(mat.small, row(mat.small)), # a row
map=split(map.base, row(map.base)), # the mapping for that row
MoreArgs=list(num.to.let=num.to.let) # general conversion of number to Obs/Ref
) )
}
# Split input data frame into 3 row matrices (assumes frame size 3),
# and apply mapping function to each group
mat.split <- split.data.frame(mat, sort(rep(1:(nrow(mat) / frame), frame)))
mat.res <- do.call(rbind, lapply(mat.split, re_map))
colnames(mat.res) <- paste0("Sample.", rep(1:ncol(mat), each=2))
print(mat.res, quote=FALSE)
# Sample.1 Sample.1 Sample.2 Sample.2 Sample.3 Sample.3 Sample.4 Sample.4
# 1 G G A G G G G G
# 2 C C 0 0 T C T C
# 3 0 0 G C G G G G
# 1 A A A A A G A A
# 2 C C C C T C C C
# 3 C C G G 0 0 0 0

Answer 2

I am not sure but this could be what you need:
first same simple data
geno <- data.frame(Ref = c("A", "T", "G"), Obs = c("G", "C", "C"))
data <- data.frame(s1 = c(0,0,0),s2 = c(1, 0, NA))
then a couple of functions:
f <- function(i , x, geno){
x <- x[i]
if(!is.na(x)){
if (x == 0) {y <- geno[i , c(1,1)]}
if (x == 1) {y <- geno[i, c(1,2)]}
if (x == 2) {y <- geno[i, c(2,2)]}
}
else y <- c(0,0)
names(y) <- c("s1", "s2")
y
}
g <- function(x, geno){
Reduce(rbind, lapply(1:length(x), FUN = f , x = x, geno = geno))
}
The way f() is defined may not be the most elegant but it does the job
Then simply run it as a doble for loop in a lapply fashion
as.data.frame(Reduce(cbind, lapply(data , g , geno = geno )))
hope it helps

Answer 3

Here's one way based on the sample data in your answer:
# create index
idx <- lapply(data, function(x) cbind((x > 1) + 1, (x > 0) + 1))
# list of matrices
lst <- lapply(idx, function(x) {
tmp <- apply(x, 2, function(y) geno[cbind(seq_along(y), y)])
replace(tmp, is.na(tmp), 0)
})
# one data frame
as.data.frame(lst)
# s1.1 s1.2 s2.1 s2.2
# 1 A A A G
# 2 T T T T
# 3 G G 0 0