capture_melt_single {nc}

Capture and melt into a single column

Description

Match a regex to column names of a wide data frame (many columns/few rows), then melt/reshape the matching columns into a single result column in a taller/longer data table (fewer columns/more rows). It is for the common case of melting several columns of the same type in a "wide" input data table which has several distinct pieces of information encoded in each column name. For melting into several result columns of possibly different types, see capture_melt_multiple.

Usage

capture_melt_single(..., 
    value.name = "value", 
    na.rm = TRUE, verbose = getOption("datatable.verbose"))

Arguments

...

First argument must be a data frame to melt/reshape; column names of this data frame will be used as the subjects for regex matching. Other arguments (regex/conversion/engine) are passed to capture_first_vec along with nomatch.error=FALSE.

value.name

Name of the column in output which has values taken from melted/reshaped column values of input (passed to melt.data.table).

na.rm

remove missing values from melted data? (passed to melt.data.table)

verbose

Print verbose output messages? (passed to melt.data.table)

Value

Data table of reshaped/melted/tall/long data, with a new column for each named argument in the pattern, and additionally variable/value columns.

Author(s)

Toby Hocking <[e-mail]> [aut, cre]

Examples

data.table::setDTthreads(1)

## Example 1: melt iris data and barplot for each numeric variable.
(iris.tall <- nc::capture_melt_single(
  iris,
  part=".*",
  "[.]",
  dim=".*",
  value.name="cm"))
## Histogram of cm for each variable.
if(require("ggplot2")){
  ggplot()+
    theme_bw()+
    theme(panel.spacing=grid::unit(0, "lines"))+
    facet_grid(part ~ dim)+
    geom_bar(aes(cm), data=iris.tall)
}

## Example 2: melt who data and use type conversion functions for
## year limits (e.g. for censored regression).
if(requireNamespace("tidyr")){
  data(who, package="tidyr", envir=environment())
  ##2.1 just extract diagnosis and gender to chr columns.
  new.diag.gender <- list(#save pattern as list for re-use later.
    "new_?",
    diagnosis=".*",
    "_",
    gender=".")
  who.tall.chr <- nc::capture_melt_single(who, new.diag.gender, na.rm=TRUE)
  print(head(who.tall.chr))
  str(who.tall.chr)
  ##2.2 also extract ages and convert to numeric output columns.
  who.tall.num <- nc::capture_melt_single(
    who,
    new.diag.gender,#previous pattern for matching diagnosis and gender.
    ages=list(#new pattern for matching age range.
      min.years="0|[0-9]{2}", as.numeric,#in-line type conversion functions.
      max.years="[0-9]{0,2}", function(x)ifelse(x=="", Inf, as.numeric(x))),
    value.name="count",
    na.rm=TRUE)
  print(head(who.tall.num))
  str(who.tall.num)
  ##2.3 compute total count for each age range then display the
  ##subset with max.years lower than a threshold.
  who.age.counts <- who.tall.num[, .(
    total=sum(count)
  ), by=.(min.years, max.years)]
  print(who.age.counts[max.years < 50])
}

## Example 3: pepseq data.
if(requireNamespace("R.utils")){#for reading gz files with data.table
  pepseq.dt <- data.table::fread(
    system.file("extdata", "pepseq.txt", package="nc", mustWork=TRUE))
  u.pepseq <- pepseq.dt[, unique(names(pepseq.dt)), with=FALSE]
  nc::capture_melt_single(
    u.pepseq,
    "^",
    prefix=".*?",
    nc::field("D", "", ".*?"),
    "[.]",
    middle=".*?",
    "[.]",
    "[0-9]+",
    suffix=".*",
    "$")
}