Nothing
capture_first_glob <- structure(function
### Glob files, then use capture_first_vec to get meta-data from each
### file name, and combine with contents of each file.
(glob,
### string: glob specifying files to read.
...,
### pattern passed to capture_first_vec, used to get meta-data from
### file names.
READ=fread
### function of one argument (file name) which returns a data table,
### default data.table::fread.
){
file.vec <- Sys.glob(glob)
meta.dt <- capture_first_vec(file.vec, ...)
result.list <- list()
for(file.i in seq_along(file.vec)){
meta.i <- meta.dt[file.i]
result.i <- READ(file.vec[[file.i]])
result.list[[file.i]] <- data.table(meta.i, result.i)
}
rbindlist(result.list)
### Data table with columns of meta-data specified by pattern, plus
### contents of all files specified by glob.
}, ex=function(){
data.table::setDTthreads(1)
## Example 0: iris data, one file per species.
library(data.table)
dir.create(iris.dir <- tempfile())
icsv <- function(sp)file.path(iris.dir, paste0(sp, ".csv"))
data.table(iris)[, fwrite(.SD, icsv(Species)), by=Species]
dir(iris.dir)
data.table::fread(file.path(iris.dir,"setosa.csv"), nrows=2)
(iglob <- file.path(iris.dir,"*.csv"))
nc::capture_first_glob(iglob, Species="[^/]+", "[.]csv")
## Example 1: four files, two capture groups, custom read function.
db <- system.file("extdata/chip-seq-chunk-db", package="nc", mustWork=TRUE)
suffix <- if(interactive())"gz" else "head"
(glob <- paste0(db, "/*/*/counts/*", suffix))
Sys.glob(glob)
read.bedGraph <- function(f)data.table::fread(
f, skip=1, col.names = c("chrom","start", "end", "count"))
data.chunk.pattern <- list(
data="H.*?",
"/",
chunk="[0-9]+", as.integer)
(data.chunk.dt <- nc::capture_first_glob(glob, data.chunk.pattern, READ=read.bedGraph))
## Write same data set in Hive partition, then re-read.
if(requireNamespace("arrow") && arrow::arrow_with_dataset()){
path <- tempfile()
max_rows_per_file <- if(interactive())3 else 1000
arrow::write_dataset(
dataset=data.chunk.dt,
path=path,
format="csv",
partitioning=c("data","chunk"),
max_rows_per_file=max_rows_per_file)
hive.glob <- file.path(path, "*", "*", "*.csv")
hive.pattern <- list(
nc::field("data","=",".*?"),
"/",
nc::field("chunk","=",".*?", as.integer),
"/",
nc::field("part","-","[0-9]+", as.integer))
hive.dt <- nc::capture_first_glob(hive.glob, hive.pattern)
hive.dt[, .(rows=.N), by=.(data,chunk,part)]
}
## Example 2: more complex pattern.
count.dt <- nc::capture_first_glob(
glob,
data.chunk.pattern,
"/counts/",
name=list("McGill", id="[0-9]+", as.integer),
READ=read.bedGraph)
count.dt[, .(count=.N), by=.(data, chunk, name, chrom)]
if(require(ggplot2)){
ggplot()+
facet_wrap(~data+chunk+name+chrom, labeller=label_both, scales="free")+
geom_step(aes(
start, count),
data=count.dt)
}
## Example 3: parsing non-CSV data.
vignettes <- system.file("extdata/vignettes", package="nc", mustWork=TRUE)
non.greedy.lines <- list(
list(".*\n"), "*?")
optional.name <- list(
list(" ", chunk_name="[^,}]+"), "?")
chunk.pattern <- list(
before=non.greedy.lines,
"```\\{r",
optional.name,
parameters=".*",
"\\}\n",
code=non.greedy.lines,
"```")
chunk.dt <- nc::capture_first_glob(
paste0(vignettes, "/*.Rmd"),
"/v",
vignette_number="[0-9]", as.integer,
"-",
vignette_name=".*?",
".Rmd",
READ=function(f)nc::capture_all_str(f, chunk.pattern))
chunk.dt[, chunk_number := seq_along(chunk_name), by=vignette_number]
chunk.dt[, .(
vignette_number, vignette_name, chunk_number, chunk_name,
lines=nchar(code))]
cat(chunk.dt$code[2])
})
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