df_match_variable: First match from every row, variable argument syntax

In tdhock/namedCapture: Named Capture Regular Expressions

First match from every row, variable argument syntax

Description

Extract text from several columns of a data.frame, using a different named capture regular expression for each column. Uses str_match_variable on each column/pattern indicated in ... – argument names are interpreted as column names of subject; argument values are passed as the pattern to str_match_variable.

Usage

df_match_variable(...)

Arguments

...

subject.df, colName1=list(groupName1=pattern1, fun1, etc), colName2=list(etc), etc. First (un-named) argument should be a data.frame with character columns of subjects for matching. The other arguments need to be named (and the names e.g. colName1 and colName2 need to be column names of the subject data.frame). The other argument values specify the regular expression, and must be character/function/list. All patterns must be character vectors of length 1. If the pattern is a named argument in R, we will add a named capture group (?<groupName1>pattern1) in the regex. All patterns are pasted together to obtain the final pattern used for matching. Each named pattern may be followed by at most one function (e.g. fun1) which is used to convert the previous named pattern. Lists are parsed recursively for convenience.

Value

data.frame with same number of rows as subject, with an additional column for each named capture group specified in ... (actually the value is created via cbind so if subject is something else like a data.table then the value is too).

Author(s)

Toby Dylan Hocking

Examples

## The JobID column can be match with a complicated regular
## expression, that we will build up from small sub-pattern list
## variables that are easy to understand independently.
(sacct.df <- data.frame(
  JobID = c(
    "13937810_25", "13937810_25.batch",
    "13937810_25.extern", "14022192_[1-3]", "14022204_[4]"),
  Elapsed = c(
    "07:04:42", "07:04:42", "07:04:49",
    "00:00:00", "00:00:00"),
  stringsAsFactors=FALSE))

## Just match the end of the range.
int.pattern <- list("[0-9]+", as.integer)
end.pattern <- list(
  "-",
  task_end=int.pattern)
namedCapture::df_match_variable(sacct.df, JobID=end.pattern)

## Match the whole range inside square brackets.
range.pattern <- list(
  "[[]",
  task_start=int.pattern,
  end.pattern, "?", #end is optional.
  "[]]")
namedCapture::df_match_variable(sacct.df, JobID=range.pattern)

## Match either a single task ID or a range, after an underscore.
task.pattern <- list(
  "_",
  list(
    task_id=int.pattern,
    "|",#either one task(above) or range(below)
    range.pattern))
namedCapture::df_match_variable(sacct.df, JobID=task.pattern)

## Match type suffix alone.
type.pattern <- list(
  "[.]",
  type=".*")
namedCapture::df_match_variable(sacct.df, JobID=type.pattern)

## Match task and optional type suffix.
task.type.pattern <- list(
  task.pattern,
  type.pattern, "?")
namedCapture::df_match_variable(sacct.df, JobID=task.type.pattern)

## Match full JobID and Elapsed columns.
(task.df <- namedCapture::df_match_variable(
  sacct.df,
  JobID=list(
    job=int.pattern,
    task.type.pattern),
  Elapsed=list(
    hours=int.pattern,
    ":",
    minutes=int.pattern,
    ":",
    seconds=int.pattern)))
str(task.df)

tdhock/namedCapture documentation built on Jan. 27, 2024, 9:02 p.m.