How much space does one save by reformatting the coverage .txt files? For SGW

reformatCoverage.R

library(reshape2)
hasDT <- library(data.table, logical.return=TRUE) # load package, if not installed return FALSE

# ==================
# = Make Fake Data =
# ==================
# ---- Options affecting size of 'big' ----
nScaff <- 50 # number of scaffolds (or 'pages')
nIndex <- 100 # number of loci or indices (or 'words')

# ---- Scaffold is combination of letters and numbers; a character ----
sL <- function() sample(LETTERS, 3, replace=TRUE) # 3 letters per scaffold name
sN <- function() sample(1:9, 5, replace=TRUE) # 5 numbers per scaffold name
scaffs <- replicate(nScaff, paste0(c(sL(),sN()),collapse="")) # make scaffold name

# ---- index is the "word" and is an integer ----
index <- 1:nIndex # generate 'indices'

# ---- value is the coverage or some measured value of interest; is an integer ----
value <- sample(1:4, length(scaffs)*length(index), replace=TRUE) # coverage is either 1, 2, 3, or 4

# ---- combine these elements into a data.table / data.frame ----
# note that data.table is a lot faster, though not noticable for small stuff
if(hasDT){ # if you have data.table installed, use it
	big <- data.table::CJ(scaffold=scaffs,index=index)[,value:=value] # CJ is cross join
	# note: don't need the data.table::CJ(), can just use CJ(). Namespace explicitly stated for clarity.
}else{ # otherwise just use data.frame
	big <- cbind(expand.grid(scaffold=scaffs, index=index), value=value)
	# note: expand.grid() is analagous to CJ(); cbind() is slow compared to data.table's :=
}

# ===============================
# = 'Cast' into a matrix/ array =
# ===============================
# change object dimensions
# SPACE-SAVING STEP
smaller <- reshape2::acast(data=big, formula=index~scaffold) # syntax is rows ~ columns
# note: if there was another dimension, you could dow row~column~z~4thD etc.
# also, can specify value.var=value if there are other columns that aren't dimensions/ fill values


# ====================================
# = Compare object sizes in R memory =
# ====================================
bigMem <- print(object.size(big), units='Kb')
smallerMem <- print(object.size(smaller), units='Kb')


# ============================================
# = Compare object sizes saved as text files =
# ============================================
# ---- names ----
bTxt <- "~/Desktop/big.txt"
sTxt <- "~/Desktop/smaller.txt"

# ---- do big ----
write.table(big, file=bTxt, sep="\t") # write
bigTxt <- file.size(bTxt) # size
file.remove(bTxt) # remove

# ---- do small ----
write.table(smaller, file=sTxt, sep="\t") # write
smallerTxt <- file.size(sTxt) # size
file.remove(sTxt) # remove

# ==============================================
# = Compare object sizes saved as .RData files =
# ==============================================
# ---- names ----
bRD <- "~/Desktop/big.RData"
sRD <- "~/Desktop/smaller.RData"

# ---- do big ----
save(big, file=bRD) # write
bigRD <- file.size(bRD) # size
file.remove(bRD) # remove

# ---- do small ----
save(smaller, file=sRD) # write
smallerRD <- file.size(sRD) # size
file.remove(sRD) # remove

# ====================
# = Comparison Table =
# ====================
sizes <- c(bigMem, smallerMem, bigTxt, smallerTxt, bigRD, smallerRD) # object sizes in bytes
rowN <- c("big (original)","small (matrix)") # row names
colN <- c("in R memory","as .txt file","as .RData file") # column names
sumMat <- matrix(sizes, nrow=2, ncol=3, dimnames=list(rowN,colN))/1E3 # in KB
smallPercent <- apply(sumMat, 2, function(x)x[2]/x[1]) # size of smaller as a percent of big
(sumMat2 <- rbind(sumMat, smallPercent=smallPercent)) # assign and print

Also, if files are currently being saved as non-reshaped (big) .txt files, if you reshaped them AND compressed them as .RData files, that'd be 2% of the original file size.