Overview of nc functionality

2024-02-21

Overview of nc functionality

Here is an index of topics which are explained in the different vignettes, along with an overview of functionality using simple examples.

Capture first match in several subjects

Capture first is for the situation when your input is a character vector (each element is a different subject), you want find the first match of a regex to each subject, and your desired output is a data table (one row per subject, one column per capture group in the regex).

subject.vec <- c(
  "chr10:213054000-213,055,000",
  "chrM:111000",
  "chr1:110-111 chr2:220-222")
nc::capture_first_vec(
  subject.vec, chrom="chr.*?", ":", chromStart="[0-9,]+", as.integer)
#>     chrom chromStart
#>    <char>      <int>
#> 1:  chr10  213054000
#> 2:   chrM     111000
#> 3:   chr1        110

A variant is doing the same thing, but with input subjects coming from a data table/frame with character columns.

library(data.table)
subject.dt <- data.table(
  JobID = c("13937810_25", "14022192_1"),
  Elapsed = c("07:04:42", "07:04:49"))
int.pat <- list("[0-9]+", as.integer)
nc::capture_first_df(
  subject.dt,
  JobID=list(job=int.pat, "_", task=int.pat),
  Elapsed=list(hours=int.pat, ":", minutes=int.pat, ":", seconds=int.pat))
#>          JobID  Elapsed      job  task hours minutes seconds
#>         <char>   <char>    <int> <int> <int>   <int>   <int>
#> 1: 13937810_25 07:04:42 13937810    25     7       4      42
#> 2:  14022192_1 07:04:49 14022192     1     7       4      49

Capture all matches in a single subject

Capture all is for the situation when your input is a single character string or text file subject, you want to find all matches of a regex to that subject, and your desired output is a data table (one row per match, one column per capture group in the regex).

nc::capture_all_str(
  subject.vec, chrom="chr.*?", ":", chromStart="[0-9,]+", as.integer)
#>     chrom chromStart
#>    <char>      <int>
#> 1:  chr10  213054000
#> 2:   chrM     111000
#> 3:   chr1        110
#> 4:   chr2        220

Reshape a data table with regularly named columns

Capture melt is for the situation when your input is a data table/frame that has regularly named columns, and your desired output is a data table with those columns reshaped into a taller/longer form. In that case you can use a regex to identify the columns to reshape.

(one.iris <- data.frame(iris[1,]))
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1          5.1         3.5          1.4         0.2  setosa
nc::capture_melt_single  (one.iris, part  =".*", "[.]", dim   =".*")
#>    Species   part    dim value
#>     <fctr> <char> <char> <num>
#> 1:  setosa  Sepal Length   5.1
#> 2:  setosa  Sepal  Width   3.5
#> 3:  setosa  Petal Length   1.4
#> 4:  setosa  Petal  Width   0.2
nc::capture_melt_multiple(one.iris, column=".*", "[.]", dim   =".*")
#>    Species    dim Petal Sepal
#>     <fctr> <char> <num> <num>
#> 1:  setosa Length   1.4   5.1
#> 2:  setosa  Width   0.2   3.5
nc::capture_melt_multiple(one.iris, part  =".*", "[.]", column=".*")
#>    Species   part Length Width
#>     <fctr> <char>  <num> <num>
#> 1:  setosa  Petal    1.4   0.2
#> 2:  setosa  Sepal    5.1   3.5

Reading regularly named data files

Capture glob is for the situation when you have several data files on disk, with regular names that you can match with a glob/regex. In the example below we first write one CSV file for each iris Species,

dir.create(iris.dir <- tempfile())
icsv <- function(sp)file.path(iris.dir, paste0(sp, ".csv"))
data.table(iris)[, fwrite(.SD, icsv(Species)), by=Species]
#> Empty data.table (0 rows and 1 cols): Species
dir(iris.dir)
#> [1] "setosa.csv"     "versicolor.csv" "virginica.csv"

We then use a glob and a regex to read those files in the code below:

nc::capture_first_glob(file.path(iris.dir,"*.csv"), Species="[^/]+", "[.]csv")
#>        Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#>         <char>        <num>       <num>        <num>       <num>
#>   1:    setosa          5.1         3.5          1.4         0.2
#>   2:    setosa          4.9         3.0          1.4         0.2
#>   3:    setosa          4.7         3.2          1.3         0.2
#>   4:    setosa          4.6         3.1          1.5         0.2
#>   5:    setosa          5.0         3.6          1.4         0.2
#>  ---                                                            
#> 146: virginica          6.7         3.0          5.2         2.3
#> 147: virginica          6.3         2.5          5.0         1.9
#> 148: virginica          6.5         3.0          5.2         2.0
#> 149: virginica          6.2         3.4          5.4         2.3
#> 150: virginica          5.9         3.0          5.1         1.8

Helper functions for defining complex pattterns

Helpers describes various functions that simplify the definition of complex regex patterns. For example nc::field helps avoid repetition below,

subject.vec <- c("sex_child1", "age_child1", "sex_child2")
pattern <- list(
  variable="age|sex", "_",
  nc::field("child", "", "[12]", as.integer))
nc::capture_first_vec(subject.vec, pattern)
#>    variable child
#>      <char> <int>
#> 1:      sex     1
#> 2:      age     1
#> 3:      sex     2

It also explains how to define common sub-patterns which are used in several different alternatives.

subject.vec <- c("mar 17, 1983", "26 sep 2017", "17 mar 1984")
pattern <- nc::alternatives_with_shared_groups(
  month="[a-z]{3}", day="[0-9]{2}", year="[0-9]{4}",
  list(month, " ", day, ", ", year),
  list(day, " ", month, " ", year))
nc::capture_first_vec(subject.vec, pattern)
#>     month    day   year
#>    <char> <char> <char>
#> 1:    mar     17   1983
#> 2:    sep     26   2017
#> 3:    mar     17   1984