[![Travis build status](https://travis-ci.org/moodymudskipper/unglue.svg?branch=master)](https://travis-ci.org/moodymudskipper/unglue) [![Codecov test coverage](https://codecov.io/gh/moodymudskipper/unglue/branch/master/graph/badge.svg)](https://codecov.io/gh/moodymudskipper/unglue?branch=master) # unglue

The package *unglue* features functions such as `unglue()`, `unglue_data()` and `unglue_unnest()` which provide in many cases a more readable alternative to base regex functions. Simple cases indeed don’t require regex knowledge at all. It uses a syntax inspired from the functions of Jim Hester’s *glue* package to extract matched substrings using a pattern, but is not endorsed by the authors of *glue* nor *tidyverse* packages. It is completely dependency free, though formula notation of functions is supported if *rlang* is installed. ## Installation: ``` r remotes::install_github("moodymudskipper/unglue") ``` ### using an example from `?glue::glue` backwards ``` r library(unglue) library(glue) library(magrittr) library(utils) glued_data <- head(mtcars) %>% glue_data("{rownames(.)} has {hp} hp") glued_data #> Mazda RX4 has 110 hp #> Mazda RX4 Wag has 110 hp #> Datsun 710 has 93 hp #> Hornet 4 Drive has 110 hp #> Hornet Sportabout has 175 hp #> Valiant has 105 hp unglue_data(glued_data, "{rownames(.)} has {hp} hp") #> 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 ``` ### use several patterns, the first that matches will be used ``` r 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 ``` Note that the second pattern uses some regex, regex needs to be typed after an `=` sign, if its has no left hand side then the expression won’t be attributed to a variable. in fact the pattern `"{foo}"` is a shorthand for `"{foo=.*?}"`. ### escaping characters Special characters outside of the curly braces should not be escaped. ``` r sentences <- c("666 is [a number]", "foo is [a word]", "42 is [the answer]", "Area 51 is [unmatched]") patterns2 <- c("{number=\\d+} is [{what}]", "{word=\\D+} is [{what}]") unglue_data(sentences, patterns2) #> number what word #> 1 666 a number #> 2 a word foo #> 3 42 the answer #> 4 ``` ### type conversion In order to convert types automatically we can set `convert = TRUE`, in the example above the column `number` will be converted to numeric. ``` r unglue_data(sentences, patterns2, convert = TRUE) #> number what word #> 1 666 a number #> 2 NA a word foo #> 3 42 the answer #> 4 NA ``` `convert = TRUE` triggers the use of `utils::type.convert` with parameter `as.is = TRUE`. We can also set `convert` to another conversion function such as `readr::type_convert`, or to a formula is *rlang* is installed. ### `unglue_unnest()` `unglue_unnest()` is named as a tribute to `tidyr::unnest()` as it’s equivalent to using successively `unglue()` and `unnest()` on a data frame column. It is similar to `tidyr::extract()` in its syntax and efforts were made to make it as consistent as possible. ``` r unglue_unnest(facts_df, facts, patterns) #> id place adjective place_type bigger_place #> 1 1 Antarctica largest desert the world #> 2 2 Russia largest country Europe #> 3 3 Vatican smallest country Europe #> 4 4 Disneyland most visited place Europe #> 5 5 Green Land largest island the world 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! #> 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 ``` ### `unglue_vec()` While `unglue()` returns a list of data frames, `unglue_vec()` returns a character vector (unless `convert = TRUE`), if several matches are found in a string the extracted match will be chosen by name or by position. ``` r unglue_vec(sentences, patterns2, "number") #> [1] "666" NA "42" NA unglue_vec(sentences, patterns2, 1) #> [1] "666" "foo" "42" NA ``` ### `unglue_detect()` `unglue_detect()` returns a logical vector, it’s convenient to check that the input was matched by a pattern, or to subset the input to take a look at unmatched elements. ``` r unglue_detect(sentences, patterns2) #> [1] TRUE TRUE TRUE FALSE subset(sentences, !unglue_detect(sentences, patterns2)) #> [1] "Area 51 is [unmatched]" ``` ### `unglue_regex()` `unglue_regex()` returns a character vector of regex patterns, all over functions are wrapped around it and it can be used to leverage the *unglue* in other functions. ``` r unglue_regex(patterns) #> The {adjective} {place_type} in {bigger_place} is {place}! #> "^The (.*?) (.*?) in (.*?) is (.*?)!$" #> {place} is the {adjective} {place_type=[^ ]+} in {bigger_place}!{=.*} #> "^(.*?) is the (.*?) ([^ ]+) in (.*?)!.*$" unglue_regex(patterns, named_capture = TRUE) #> The {adjective} {place_type} in {bigger_place} is {place}! #> "^The (?.*?) (?.*?) in (?.*?) is (?.*?)!$" #> {place} is the {adjective} {place_type=[^ ]+} in {bigger_place}!{=.*} #> "^(?.*?) is the (?.*?) (?[^ ]+) in (?.*?)!.*$" unglue_regex(patterns, attributes = TRUE) #> The {adjective} {place_type} in {bigger_place} is {place}! #> "^The (.*?) (.*?) in (.*?) is (.*?)!$" #> {place} is the {adjective} {place_type=[^ ]+} in {bigger_place}!{=.*} #> "^(.*?) is the (.*?) ([^ ]+) in (.*?)!.*$" #> attr(,"groups") #> attr(,"groups")$`The {adjective} {place_type} in {bigger_place} is {place}!` #> adjective place_type bigger_place place #> 1 2 3 4 #> #> attr(,"groups")$`{place} is the {adjective} {place_type=[^ ]+} in {bigger_place}!{=.*}` #> place adjective place_type bigger_place #> 1 2 3 4 ``` ### `unglue_sub()` `unglue_sub()` substitute substrings using strings or replacement functions ``` r unglue_sub( c("a and b", "foo or BAR"), c("{x} and {y}", "{x} or {z}"), list(x= "XXX", y = toupper, z = ~tolower(.))) #> [1] "XXX and B" "XXX or bar" ``` ### duplicated labels We can ensure that a pattern is repeated by repeating its label ``` r unglue_data(c("black is black","black is dark"), "{color} is {color}") #> color #> 1 black #> 2 ``` We can change this behavior by feeding a function to the `multiple` parameter, in that case this function will be applied on the matches. ``` r unglue_data(c("System: Windows, Version: 10","System: Ubuntu, Version: 18"), "System: {OS}, Version: {OS}", multiple = paste) #> OS #> 1 Windows 10 #> 2 Ubuntu 18 ```

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