unglue.RdThe functions unglue_data(), unglue(), unglue_vec() and unglue_unnest()
extract matched substrings using a syntax inspired from glue::glue().
Simple cases don't require regex knowledge at all.
unglue(x, patterns, open = "{", close = "}", convert = FALSE, multiple = NULL)
unglue_data(
x,
patterns,
open = "{",
close = "}",
convert = FALSE,
multiple = NULL,
na = NA_character_
)
unglue_vec(
x,
patterns,
var = 1,
open = "{",
close = "}",
convert = FALSE,
multiple = NULL,
na = NA_character_
)
unglue_unnest(
data,
col,
patterns,
open = "{",
close = "}",
remove = TRUE,
convert = FALSE,
multiple = NULL,
na = NA_character_
)a character vector to unglue.
a character vector or a list of character vectors, if a list,
items will be pasted using an empty separator ("").
The opening delimiter.
The closing delimiter.
If TRUE, will convert columns of output using
utils::type.convert() with parameter as.is = TRUE, alternatively, can
be a converting function, such as readr::type_convert. Formula notation
is supported if the package rlang is installed, so things like
convert = ~type_convert(., numerals = "warn.loss") are possible.
The aggregation function to use if several subpatterns are
named the same, by default no function is used and subpatterns named the
same will match the same value. If a function is provided it will be fed
the conflicting values as separate arguments. Formula notation
is supported if the package rlang is installed.
string to use when there is no match
for unglue_vec(), the numeric index or the name of the subpattern to extract from
a data frame.
column containing the character vector to extract values from.
whether to remove the column col once extraction is performed
For unglue()a list of one row data frames, for unglue_data a
data frame, for unglue_unnest the data frame input with additional columns
built from extracted values, for unglue_vec an atomic vector.
Depending on the task you might want:
unglue_data() to return a data frame from a character vector,
just as glue::glue_data() does in reverse
unglue() to return a list of data frames containing the matches
unglue_vec() to extract one value by element of x, chosen by index or by
name.
unglue_unnest() to extract value from a column of a data frame to new columns
To build the relevant regex pattern special characters will be escaped in the
input pattern and the subpatterns will be replaced with (.*?) if in standard
"{foo}" form. An alternate regular expression can be provided after = so that
"{foo=\\d}" will be translated into "(\\d)".
Sometimes we might want to use regex to match a part of the text that won't
be extracted, in these cases we just need to omit the name as in "{=\\d}".
unglue_unnest()'s name is a tribute to tidyr::unnest() because
unglue_unnest(data, col, patterns) returns a similar output as
dplyr::mutate(data, unglued = unglue(col, patterns)) %>% tidyr::unnest()
(without requiring any extra package).
It is also very close to tidyr::extract() and efforts were made to make
the syntax consistent with the latter.
# using an awample from ?glue::glue
if(require(magrittr) && require(glue)) {
glued_data <- mtcars %>% glue_data("{rownames(.)} has {hp} hp")
unglue_data(glued_data, "{rownames(.)} has {hp} hp")
}
#> Loading required package: magrittr
#> Loading required package: glue
#> rownames... hp
#> 1 Mazda RX4 110
#> 2 Mazda RX4 Wag 110
#> 3 Datsun 710 93
#> 4 Hornet 4 Drive 110
#> 5 Hornet Sportabout 175
#> 6 Valiant 105
#> 7 Duster 360 245
#> 8 Merc 240D 62
#> 9 Merc 230 95
#> 10 Merc 280 123
#> 11 Merc 280C 123
#> 12 Merc 450SE 180
#> 13 Merc 450SL 180
#> 14 Merc 450SLC 180
#> 15 Cadillac Fleetwood 205
#> 16 Lincoln Continental 215
#> 17 Chrysler Imperial 230
#> 18 Fiat 128 66
#> 19 Honda Civic 52
#> 20 Toyota Corolla 65
#> 21 Toyota Corona 97
#> 22 Dodge Challenger 150
#> 23 AMC Javelin 150
#> 24 Camaro Z28 245
#> 25 Pontiac Firebird 175
#> 26 Fiat X1-9 66
#> 27 Porsche 914-2 91
#> 28 Lotus Europa 113
#> 29 Ford Pantera L 264
#> 30 Ferrari Dino 175
#> 31 Maserati Bora 335
#> 32 Volvo 142E 109
facts <- c("Antarctica is the largest desert in the world!",
"The largest country in Europe is Russia!",
"The smallest country in Europe is Vatican!",
"Disneyland is the most visited place in Europe! Disneyland is in Paris!",
"The largest island in the world is Green Land!")
facts_df <- data.frame(id = 1:5, facts)
patterns <- c("The {adjective} {place_type} in {bigger_place} is {place}!",
"{place} is the {adjective} {place_type=[^ ]+} in {bigger_place}!{=.*}")
unglue_data(facts, patterns)
#> place adjective place_type bigger_place
#> 1 Antarctica largest desert the world
#> 2 Russia largest country Europe
#> 3 Vatican smallest country Europe
#> 4 Disneyland most visited place Europe
#> 5 Green Land largest island the world
sentences <- c("666 is [a number]", "foo is [a word]",
"42 is [the answer]", "Area 51 is [unmatched]")
patterns <- c("{number=\\d+} is [{what}]", "{word=\\D+} is [{what}]")
unglue_data(sentences, patterns)
#> number what word
#> 1 666 a number <NA>
#> 2 <NA> a word foo
#> 3 42 the answer <NA>
#> 4 <NA> <NA> <NA>
unglue_unnest(facts_df, facts, patterns)
#> id
#> 1 1
#> 2 2
#> 3 3
#> 4 4
#> 5 5
unglue_unnest(facts_df, facts, patterns, remove = FALSE)
#> id facts
#> 1 1 Antarctica is the largest desert in the world!
#> 2 2 The largest country in Europe is Russia!
#> 3 3 The smallest country in Europe is Vatican!
#> 4 4 Disneyland is the most visited place in Europe! Disneyland is in Paris!
#> 5 5 The largest island in the world is Green Land!