I had written a previous (similar) post here where I was trying to create a wide table as opposed to a long table. I realized that its best to have my table in the long format so I am posting it as a different question. I am also posting what I have tried.
I am using R
to rbind about ~11000 files using:
# get list of ~11000 files
lfiles <- list.files(pattern = "*.tsv", full.names = TRUE)
# row-bind the files
# use rbindlist to rbind and fread to read files
# use mclapply I am assigning 32 cores to it
# add the file basename as the id to identify rows
dat <- rbindlist(mclapply(lfiles, function(X) {
data.frame(id = basename(tools::file_path_sans_ext(X)),
fread(X))},mc.cores = 32))
I am using R because my downstream processing like creating plots etc is in R. I have two questions:
1. Is there a way I can make my code more efficient/faster? I know the number of rows expected at the end so will it help if I preallocate the dataframe?
2. How should I save (in what format) this huge data - as .RData or as a database or something else?
As an additional info: I have three types of files for which I want this done. They look like this:
[centos@ip data]$ head C021_0011_001786_tumor_RNASeq.abundance.tsv
target_id length eff_length est_counts tpm
ENST00000619216.1 68 26.6432 10.9074 5.69241
ENST00000473358.1 712 525.473 0 0
ENST00000469289.1 535 348.721 0 0
ENST00000607096.1 138 15.8599 0 0
ENST00000417324.1 1187 1000.44 0.0673096 0.000935515
ENST00000461467.1 590 403.565 3.22654 0.11117
ENST00000335137.3 918 731.448 0 0
ENST00000466430.5 2748 2561.44 162.535 0.882322
ENST00000495576.1 1319 1132.44 0 0
[centos@ip data]$ head C021_0011_001786_tumor_RNASeq.rsem.genes.norm_counts.hugo.tab
gene_id C021_0011_001786_tumor_RNASeq
TSPAN6 1979.7185
TNMD 1.321
DPM1 1878.8831
SCYL3 452.0372
C1orf112 203.6125
FGR 494.049
CFH 509.8964
FUCA2 1821.6096
GCLC 1557.4431
[centos@ip data]$ head CPBT_0009_1_tumor_RNASeq.rsem.genes.norm_counts.tab
gene_id CPBT_0009_1_tumor_RNASeq
ENSG00000000003.14 2005.0934
ENSG00000000005.5 5.0934
ENSG00000000419.12 1100.1698
ENSG00000000457.13 2376.9100
ENSG00000000460.16 1536.5025
ENSG00000000938.12 443.1239
ENSG00000000971.15 1186.5365
ENSG00000001036.13 1091.6808
ENSG00000001084.10 1602.7165
Any help would be much appreciated!
Thanks!
As many before me have documented, I also find that rbindlist() is the fastest method and rbind() is the slowest. bind_rows() is half as fast as rbindlist() .
rbind() in R The rbind() function can be used to bind or combine several vectors, matrices, or data frames by rows.
The binding or combining of the rows is very easy with the rbind() function in R. rbind() stands for row binding. In simpler terms joining of multiple rows to form a single batch. It may include joining two data frames, vectors, and more.
You can't do this faster than using fread
and rbindlist
in R. But, you should not use data.frame
and copy the data. Instead assign by reference:
DF <- fread(X)
DF[, id := basename(tools::file_path_sans_ext(X))]
return(DF)
However, you should consider using a database.
PS: The correct regex is ".+\\.tsv$"
. This matches any file name with one or more characters followed by a dot and the string "tsv" followed by the end of the file name.
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