User-friendly functions for extracting a data table (row for each match, column for each group) from non-tabular text data using regular expressions, and for melting/reshaping columns that match a regular expression. Please read and cite my related R Journal papers, if you use this code!
fruit.vec <- c("granny smith apple", "blood orange and yellow banana")
fruit.pattern <- list(type=".*?", " ", fruit="orange|apple|banana")
nc::capture_first_vec(fruit.vec, fruit.pattern)
#> type fruit
#> 1: granny smith apple
#> 2: blood orange
nc::capture_all_str(fruit.vec, fruit.pattern)
#> type fruit
#> 1: granny smith apple
#> 2: blood orange
#> 3: and yellow banana Quick demo of reshaping functions
(one.iris <- 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 #> 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, part=".*", "[.]", column=".*") #> Species part Length Width #> 1: setosa Petal 1.4 0.2 #> 2: setosa Sepal 5.1 3.5 nc::capture_melt_multiple(one.iris, column=".*", "[.]", dim=".*") #> Species dim Petal Sepal #> 1: setosa Length 1.4 5.1 #> 2: setosa Width 0.2 3.5
install.packages("nc")
## or:
if(!require(devtools))install.packages("devtools")
devtools::install_github("tdhock/nc")
Watch the screencast tutorial videos!
The main functions provided in nc are:
Subject nc function Similar to And Single stringcapture_all_str stringr::str_match_all rex::re_matches Character vector capture_first_vec stringr::str_match rex::re_matches Data frame chr cols capture_first_df tidyr::extract/separate_wider_regex data.table::tstrsplit Data frame col names capture_melt_single tidyr::pivot_longer data.table::melt Data frame col names capture_melt_multiple tidyr::pivot_longer data.table::melt File paths capture_first_glob arrow::open_dataset
capture_first_vec and capture_first_df, which capture the first match in each of several subjects (character vector, data frame character columns).capture_all_str which captures all matches in a single string, or a single multi-line text file. The vignette also shows how to use capture_all_str on several different strings/files, using data.table by syntax.capture_melt_single and capture_melt_multiple which match a regex to the column names of a wide data frame, then melt/reshape the matching columns. These functions are especially useful when more than one separate piece of information can be captured from each column name, e.g. the iris column names Petal.Width, Sepal.Width, etc each have two pieces of information (flower part and measurement dimension).nc::capture_first_glob.By default, nc uses PCRE. Other options include ICU and RE2.
To tell nc that you would like to use a certain engine,
Every function also has an engine argument, e.g.
nc::capture_first_vec(
"foo a\U0001F60E# bar",
before=".*?",
emoji="\\p{EMOJI_Presentation}",
after=".*",
engine="ICU")
#> before emoji after
#> 1 foo a 😎 # bar
For an detailed comparison of regex C libraries in R (ICU, PCRE, TRE, RE2), see my R journal (2019) paper about namedCapture.
The nc reshaping functions provide functionality similar to packages tidyr, stats, data.table, reshape, reshape2, cdata, utils, etc. The main difference is that nc::capture_melt_* support named capture regular expressions with type conversion, which (1) makes it easier to create/maintain a complex regex, and (2) results in less repetition in user code. For a detailed comparison, see my R Journal (2021) paper about nc.
Below I list the main differences between the functions in nc and other analogous R functions:
nc functions all have the capture_ prefix for easy auto-completion.nc is always a data.table (other packages output either a list, character matrix, or data frame).nc::capture_first_df modifies the input if it is a data table, whereas tidyr functions always copy the input table.nc::capture_first_vec stops with an error if any subjects do not match, whereas other functions return NA/missing rows.nc::capture_all_str only supports capturing multiple matches in a single subject (returning a data table), whereas other functions support multiple subjects (and return list of character matrices). For handling multiple subjects using nc, use DT[, nc::capture_all_str(subject), by] (see vignette 2 for more info).nc::capture_melt_single and nc::capture_melt_multiple use regex for wide-to-tall data reshaping, see Vignette 3 and my R Journal (2021) paper for more info. Whereas in nc these are two separate functions, other packages typically provide a single function which does both kinds of reshaping, for example measure in data.table.nc::capture_first_glob is for reading any kind of regularly named files into R using regex, whereas arrow::open_dataset requires a particular naming scheme (does not support regex).nc::measure can be used to create the measure.vars argument of data.table::melt, and nc::capture_longer_spec can be used to create the spec argument of tidyr::pivot_longer. This can be useful if you want to use nc to define the regex, but you want to use the other package functions to do the reshape.nc::field is provided for defining patterns that match subjects like variable=value, and create a column/group named variable (useful to avoid repeating variable names in regex code). See vignette 2 for more info.nc::alternatives_with_shared_groups is provided for defining a pattern containing alternatives with shared groups. See vignette 5 for more info.RetroSearch is an open source project built by @garambo | Open a GitHub Issue
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