Capture first match"
In nc: Named Capture to Data Tables

Capture first match

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Capture first match in each character vector element

Consider the following vector which contains genome position strings,

pos.vec <- c(
  "chr10:213,054,000-213,055,000",
  "chrM:111,000",
  "chr1:110-111 chr2:220-222") # two possible matches.

To capture the first genome position in each string, we use the following syntax. The first argument is the subject character vector, and the other arguments are pasted together to make a capturing regular expression. Each named argument generates a capture group; the R argument name is used for the column name of the result.

(chr.dt <- nc::capture_first_vec(
  pos.vec, 
  chrom="chr.*?",
  ":",
  chromStart="[0-9,]+"))
str(chr.dt)

We can add type conversion functions on the same line as each named argument:

keep.digits <- function(x)as.integer(gsub("[^0-9]", "", x))
(int.dt <- nc::capture_first_vec(
  pos.vec, 
  chrom="chr.*?",
  ":",
  chromStart="[0-9,]+", keep.digits))
str(int.dt)

Below we use list variables to create patterns which are re-usable, and we use an un-named list to generate a non-capturing optional group:

pos.pattern <- list("[0-9,]+", keep.digits)
range.pattern <- list(
  chrom="chr.*?",
  ":",
  chromStart=pos.pattern,
  list(
    "-",
    chromEnd=pos.pattern
  ), "?")
nc::capture_first_vec(pos.vec, range.pattern)

In summary, nc::capture_first_vec takes a variable number of arguments:

The first argument is the subject character vector.
The other arguments specify the pattern, via character strings, functions, and/or lists.
If a pattern (character/list) is named, a capture group is generated in the regex.
Each function is used to convert the text extracted by the previous named pattern argument.
Lists may be used to avoid repetition in the definition of the pattern and type conversion functions.
Each list generates a group in the regex (named list => named capture group, un-named list => non-capturing group).
All patterns are pasted together in the order that they appear in the argument list.

View generated regex

To see the generated regular expression pattern string, call nc::var_args_list with the variable number of arguments that specify the pattern:

nc::var_args_list(range.pattern)

The generated regex is the pattern element of the resulting list above. The other element fun.list indicates the names and type conversion functions to use with the capture groups.

Error/NA if any subjects do not match

The default is to stop with an error if any subject does not match:

bad.vec <- c(bad="does not match", pos.vec)
nc::capture_first_vec(bad.vec, range.pattern)

Sometimes you want to instead report a row of NA when a subject does not match. In that case, use nomatch.error=FALSE:

nc::capture_first_vec(bad.vec, range.pattern, nomatch.error=FALSE)

Other regex engines

By default nc uses the PCRE regex engine. Other choices include ICU and RE2. Each engine has different features, which are discussed in my R journal paper.

The engine is configurable via the engine argument or the nc.engine option:

u.subject <- "a\U0001F60E#"
u.pattern <- list(emoji="\\p{EMOJI_Presentation}")
old.opt <- options(nc.engine="ICU")
nc::capture_first_vec(u.subject, u.pattern)
nc::capture_first_vec(u.subject, u.pattern, engine="PCRE") 
nc::capture_first_vec(u.subject, u.pattern, engine="RE2")
options(old.opt)

For more details see the "engines" vignette,

vignette("v6-engines", package="nc")

Capture first match from one or more character columns in a table

We also provide nc::capture_first_df which extracts text from several columns of a data.frame, using a different regular expression for each column.

It requires a data.frame (or data.table) as the first argument.
It takes a variable number of other arguments, all of which must be named. For each other argument we call nc::capture_first_vec on one column of the input table.
Each argument name specifies a column of the input table which will be used as the subject in nc::capture_first_vec.
Each argument value specifies a pattern to be used with nc::capture_first_vec, in list/character/function format as explained in the previous section.
The return value is a data.table with the same number of rows as the input, but with an additional column for each named capture group. New columns have the same names as the capture groups, so make sure they are unique.
This function inputs data.frame and outputs data.table so can be used in a pipe.
For memory efficiency when the input is a data.table, it is modified to avoid copying the entire table.

This function can greatly simplify the code required to create numeric data columns from character data columns. For example consider the following data which was output from the sacct program.

(sacct.df <- data.frame(
  Elapsed = c(
    "07:04:42", "07:04:42", "07:04:49",
    "00:00:00", "00:00:00"),
  JobID=c(
    "13937810_25",
    "13937810_25.batch",
    "13937810_25.extern",
    "14022192_[1-3]",
    "14022204_[4]"),
  stringsAsFactors=FALSE))

Say we want to filter by the total Elapsed time (which is reported as hours:minutes:seconds), and base job id (which is the number before the underscore in the JobID column). We could start by converting those character columns to integers via:

int.pattern <- list("[0-9]+", as.integer)
range.pattern <- list(
  "\\[",
  task1=int.pattern,
  list(
    "-",#begin optional end of range.
    taskN=int.pattern
  ), "?", #end is optional.
  "\\]")
nc::capture_first_df(sacct.df, JobID=range.pattern, nomatch.error=FALSE)

The result shown above is another data frame with an additional column for each capture group. Next, we define another pattern that matches either one task ID or the previously defined range pattern:

task.pattern <- list(
  "_",
  list(
    task=int.pattern,
    "|",#either one task(above) or range(below)
    range.pattern))
nc::capture_first_df(sacct.df, JobID=task.pattern)

Below we match the complete JobID column:

job.pattern <- list(
  job=int.pattern,
  task.pattern,
  list(
    "[.]",
    type=".*"
  ), "?")
nc::capture_first_df(sacct.df, JobID=job.pattern)

Below we match the Elapsed column with a different regex:

elapsed.pattern <- list(
  hours=int.pattern,
  ":",
  minutes=int.pattern,
  ":",
  seconds=int.pattern)
nc::capture_first_df(sacct.df, JobID=job.pattern, Elapsed=elapsed.pattern)

Overall the result is another data table with an additional column for each capture group. Note in the code below that the input sacct.df was not modified, but if the input is data.table then it is modified:

nc::capture_first_df(sacct.df, JobID=job.pattern)
sacct.df
(sacct.DT <- data.table::as.data.table(sacct.df))
nc::capture_first_df(sacct.DT, JobID=job.pattern)
sacct.DT

Complex example: fsa file names

In this section we explain how to use various features to parse the fsa file names in PROVEDIt. Here are a few representative examples:

fsa.vec <- c(#control samples:
  "A01-Ladder-PP16-001.10sec.fsa",
  "D07_Ladder-IP_004.5sec.fsa",
  "A12_RB121514ADG_001.10sec.fsa", 
  "A10_RB102191515LEA-IP_001.10sec.fsa",
  ##single-source samples:
  "A02-RD12-0002-35-0.5PP16-001.10sec.fsa", 
  "G01_RD14-0003-35d3S30-0.01563P-Q10.0_003.10sec.fsa",
  "A06_RD14-0003-24d3a-0.0625IP-Q0.8_001.10sec.fsa", 
  "A08-RD12-0002-01d-0.125PP16-001.10sec.fsa",
  "A10-RD12-0002-04d1-0.0625PP16-001.10sec.fsa", 
  "C02_RD14-0003-15d2b-0.25IP-Q0.5_003.5sec.fsa",
  ##mixture samples:
  "A02-RD12-0002-1_2-1;9-0.125PP16-001.10sec.fsa", 
  "H07_RD14-0003-35_36_37_38_39-1;4;4;4;1-M2I35-0.75IP-QLAND_004.5sec.fsa")

The goal is to build a regex that can convert this character vector to a data table with different columns for the different variables. The structure of the file names is explained in the supplementary materials PDF. We will build a complex regex in terms of simpler sub-patterns. First let's just match the start of each file name, which has the row/column of the 96-well plate in which the sample was tested:

well.pattern <- list(
  "^",
  well.letter="[A-H]",
  well.number="[0-9]+", as.integer)
nc::capture_first_vec(fsa.vec, well.pattern)

Now, let's match the end of each file name:

end.pattern <- list(
  "[-_]",
  capillary="[0-9]+", as.integer,
  "[.]",
  seconds="[0-9]+", as.integer,
  "sec[.]fsa$")
nc::capture_first_vec(fsa.vec, end.pattern)

Now, let's take a look at what's in between:

between.dt <- nc::capture_first_vec(
  fsa.vec, well.pattern, between=".*?", end.pattern)
between.dt$between

Notice that PP16/P/IP are the kit types. There may optionally be a DNA template mass before, and optionally a Q score after. Let's match those:

mass.pattern <- list(
  template.nanograms="[0-9.]*", as.numeric)
kit.pattern <- nc::quantifier(
  kit=nc::alternatives("PP16", "IP", "P"),
  "?")
q.pattern <- nc::quantifier(
  "-Q",
  Q.chr=nc::alternatives("LAND", "[0-9.]+"),
  "?")
old.opt <- options(width=100)
(before.dt <- nc::capture_first_vec(
  between.dt$between,
  "^",
  before=".*?",
  mass.pattern, kit.pattern, q.pattern, "$"))
options(old.opt)

Now to match the before column, we need some alternatives. The first four rows are controls, and the other rows are samples which are indicated by a project ID (prefix RD) and then a sample ID. The mixture samples at the bottom contain semicolon-delimited mixture proportions.

project.pattern <- list(project="RD[0-9]+-[0-9]+", "-")
single.pattern <- list(id="[0-9]+", as.integer)
mixture.pattern <- list(
  ids.chr="[0-9_]+",
  "-",
  parts.chr="[0-9;]+",
  "-")
single.or.mixture <- list(
  project.pattern,
  nc::alternatives(mixture.pattern, single.pattern))
control.pattern <- list(control="[^-_]+")
single.mixture.control <- list("[-_]", nc::alternatives(
  single.or.mixture, control.pattern))
(rest.dt <- nc::capture_first_vec(
  before.dt$before, single.mixture.control, rest=".*"))

The rest may contain some information related to the dilution and treatment, which we can parse as follows:

dilution.pattern <- nc::quantifier(
  "[dM]",
  dilution.number="[0-9]?", as.integer,
  "?")
treatment.pattern <- nc::quantifier(
  treatment.letter="[a-zA-Z]",
  nc::quantifier(treatment.number="[0-9]+", as.integer, "?"),
  "?")
dilution.treatment <- list(
  dilution.pattern,
  treatment.pattern,
  "[_-]?")
nc::capture_first_vec(rest.dt$rest, "^", dilution.treatment, "$")

Now we just have to put all of those patterns together to get a full match:

fsa.pattern <- list(
  well.pattern,
  single.mixture.control,
  dilution.treatment,
  mass.pattern, kit.pattern, q.pattern,  
  end.pattern)
(match.dt <- nc::capture_first_vec(fsa.vec, fsa.pattern))

And to verify we parsed everything correctly we print the subjects next to each row:

disp.dt <- data.table::data.table(match.dt)
names(disp.dt) <- paste(1:ncol(disp.dt))
old.opt <- options("datatable.print.colnames"="none")
split(disp.dt, fsa.vec)
options(old.opt)

Incidentally, if you print the regex as a string it looks like something that would be essentially impossible to write/maintain by hand (without nc helper functions):

nc::var_args_list(fsa.pattern)$pattern

In conclusion, we have shown how nc can help build complex regex patterns from simple, understandable sub-patterns in R code. The general workflow we have followed in this section can be generalized to other projects. First, identify a small set of subjects to use for testing (fsa.vec in the code above). Second, create some sub-patterns for the start/end of the subjects, and create a sub-pattern that will capture everything else in between. Third, create some more sub-patterns to match the "everything else" in the previous step (e.g. before.dt$before, between.dt$between, rest.dt$rest above). Repeat the process until there is nothing left to match, and then concatenate the sub-patterns to match the whole subject.