merge_tsv: merge tsv files

In drmjc/mjcbase: General helper functions for dealing with \R objects/data types

Description

Given a vector of file paths to tab delimited files, import each in turn, and grow the result. Each subsequent file can add new columns or rows not previously imported, or overwrite data from previously imported data. To do this, row.names and col.names must be present in each of the imported files.

Usage

merge_tsv(files, ...)

Arguments

files	a character vector of file paths
...	arguments passed to read.delim

Value

a data.frame of merged data

use cases

In a typical use case, you import a bunch of chronologically ordered files, where newer files may have added new columns of data, thrown away no longer needed columns, may update the characteristics of data represented in older files, and may add new records. For example, importing clinical data from TCGA BRCA, where an initial data file of 863x101, with 2 follow up files containing 113x63 and 286x44 data, respectively. The resulting merged file contains 863 rows and 118 columns, so clearly no new records were added, but new columns specific to patient follow-up were added.

Author(s)

Mark Cowley, 2012-09-04

Examples

## Not run: 
# import 3 files from TCGA Breast cancer data
	files <- c(
  "clinical_patient_public_brca.txt",
  "clinical_follow_up_v1.5_public_brca_mod.txt",
  "clinical_follow_up_v2.1_public_brca_mod.txt"
 )

lapply(files, function(f) dim(read.delim(f, check=F, row.names=1)))
# [[1]]
# [1] 863 101
# [[2]]
# [1] 113  63
# [[3]]
# [1] 286  44

a <- merge_tsv(files, row.names=1, check.names=F, stringsAsFactors=F)
# dim(a)
# [1] 863 118
## so subsequent follow up files didn't add any new rows, but did add 17 columns
## of follow up data, and updated upto 113+286 rows of data.

## End(Not run)

drmjc/mjcbase documentation built on May 15, 2019, 2:27 p.m.