├── .gitignore
├── data
└── identifiers.rda
├── .Rbuildignore
├── NAMESPACE
├── R
├── data.R
└── detect.R
├── DESCRIPTION
├── data-raw
└── example_data.R
├── README.Rmd
├── README.md
└── LICENSE.md
/.gitignore:
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1 | .Rproj.user
2 | .Rhistory
3 | .RData
4 | man
5 | regexmagic.Rproj
6 |
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/data/identifiers.rda:
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https://raw.githubusercontent.com/jonocarroll/regexmagic/HEAD/data/identifiers.rda
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/.Rbuildignore:
--------------------------------------------------------------------------------
1 | ^data-raw$
2 | ^regexmagic\.Rproj$
3 | ^\.Rproj\.user$
4 | ^LICENSE\.md$
5 | ^README\.Rmd$
6 |
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/NAMESPACE:
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1 | # Generated by roxygen2: do not edit by hand
2 |
3 | export(categorise_regex)
4 | export(detect_pattern)
5 | export(escape_regex)
6 | export(find_common_substrings)
7 | export(known_patterns)
8 | export(split_by_length)
9 |
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/R/data.R:
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1 | #' Example data to be used for testing.
2 | #'
3 | #' This is reproducibly constructed, but should
4 | #' appear somewhat random, with at least some
5 | #' strings not matching the given patterns.
6 | #'
7 | #' In the inital version of this, the lengths of the strings
8 | #' will determine an initial split, but this should be
9 | #' relaxed in further iterations.
10 | #'
11 | #' @format A character vector of length 65.
12 | "identifiers"
13 |
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/DESCRIPTION:
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1 | Package: regexmagic
2 | Version: 0.0.0.9000
3 | Title: Automated Detection of Regular Expression Patterns
4 | Description: Given a vector of strings, detects patterns which regex match to
5 | as large a proportion of these as possible
6 | Authors@R: c(
7 | person("Jonathan", "Carroll", email = "rpkg@jcarroll.com.au", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-1404-5264")),
8 | person("Vincent", "Rouilly")
9 | )
10 | License: GPL-3
11 | Encoding: UTF-8
12 | LazyData: true
13 | ByteCompile: true
14 | URL: https://github.com/jonocarroll/regexmagic
15 | BugReports: https://github.com/jonocarroll/regexmagic/issues
16 | Imports:
17 | dplyr,
18 | purrr,
19 | stringr
20 | RoxygenNote: 6.0.1.9000
21 |
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/data-raw/example_data.R:
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1 | ## Create the example data to be used for testing
2 | ## This is reproducibly constructed, but should
3 | ## appear somewhat random, with at least some
4 | ## strings not matching to the given patterns.
5 | ##
6 | ## In the inital version of this, the lengths of the strings
7 | ## will determine an initial split, but this should be
8 | ## relaxed in further iterations.
9 | set.seed(1)
10 |
11 | make_string_1 <- function() {
12 | paste0("XY-", paste0(sample(0:9, 5, replace = TRUE), collapse = ""))
13 | }
14 |
15 | make_string_2 <- function() {
16 | paste0("500", paste0(sample(0:9, 8, replace = TRUE), collapse = ""), "R")
17 | }
18 |
19 | make_string_3 <- function() {
20 | paste0("AB.", paste0(sample(0:9, 3, replace = TRUE), collapse = ""), ".Z0_", sample(0:1, 1))
21 | }
22 |
23 | identifiers <- c(
24 | replicate(20, make_string_1()),
25 | replicate(15, make_string_2()),
26 | replicate(27, make_string_3()),
27 | "XX-00000",
28 | "50000000000X",
29 | "AX.000.Z0_0"
30 | )
31 |
32 | ## shuffle the data
33 | identifiers <- sample(identifiers)
34 |
35 |
--------------------------------------------------------------------------------
/R/detect.R:
--------------------------------------------------------------------------------
1 | missing_char <- "#"
2 |
3 | #' @export
4 | known_patterns <- c(
5 | "[0-9]",
6 | "[A-Z]",
7 | "[[:punct:]]"
8 | )
9 |
10 | #' Split a vector by character count
11 | #'
12 | #' @md
13 | #' @param s vector of strings
14 | #'
15 | #' @return `list` of strings grouped by length
16 | #' @export
17 | split_by_length <- function(s) {
18 | split(s, nchar(s))
19 | }
20 |
21 | #' @export
22 | find_common_substrings <- function(s, tolerance = 0.95) {
23 | ## split by character
24 | ## assumes already split by character length
25 | chardf <- t(as.data.frame(purrr::map(s, ~strsplit(.x, NULL)[[1]])))
26 | most_matching <- apply(chardf, 2, function(x) names(which.max(table(x))))
27 | prop_matching <- sapply(seq_len(ncol(chardf)), function(x) sum(chardf[,x] == most_matching[x])/nrow(chardf))
28 | exact_matches <- prop_matching >= tolerance
29 | common_pattern <- paste0(ifelse(exact_matches, most_matching, missing_char), collapse = "")
30 | return(common_pattern)
31 | }
32 |
33 | #' @export
34 | detect_pattern <- function(s, ...) {
35 |
36 | charvec <- strsplit(find_common_substrings(s, ...), NULL)[[1]]
37 | unknown_symbols <- which(charvec == missing_char)
38 | best_pat <- unknown_symbols[NA]
39 | for (symbol in unknown_symbols) {
40 | s_char <- purrr::map_chr(strsplit(s, NULL), symbol)
41 | pat <- known_patterns[NA]
42 | pat <- sapply(known_patterns, function(kp) sum(!is.na(purrr::map_chr(s_char, ~stringr::str_match(.x, kp)))))
43 | best_pat[symbol] <- known_patterns[which.max(pat)]
44 | }
45 |
46 | detected_pattern <- escape_regex(paste0(ifelse(charvec == missing_char, best_pat, charvec), collapse = ""))
47 | return(detected_pattern)
48 |
49 | }
50 |
51 | #' @export
52 | categorise_regex <- function(strings, tolerance = 0.95) {
53 | string_list <- split_by_length(strings)
54 | guess <- purrr::map(string_list, detect_pattern, tolerance = tolerance)
55 | matches <- purrr::map2(string_list, guess, ~stringr::str_match(.x, .y))
56 | ## remove non-matches
57 | matches <- purrr::map(matches, ~.x[!is.na(.x)])
58 | nonmatches <- purrr::map2(string_list, matches, ~.x[! .x %in% .y])
59 |
60 | result <- purrr::pmap(list(guess, matches, nonmatches),
61 | ~list(regex = ..1, matches = ..2, nonmatches = ..3))
62 |
63 | message(" ** CATEGORISATION SUMMARY **")
64 |
65 |
66 |
67 | message(" ** Detected ", length(result), " categories and matched\n ",
68 | length(unlist(purrr::map(result, "matches"))) ," / ", (
69 | length(unlist(purrr::map(result, "nonmatches"))) + length(unlist(purrr::map(result, "matches")))),
70 | " ( ",
71 | format(length(unlist(purrr::map(result, "matches"))) / (
72 | length(unlist(purrr::map(result, "nonmatches"))) + length(unlist(purrr::map(result, "matches")))), digits = 3),
73 | "% ) strings **\n")
74 | purrr::walk2(result, names(result), ~{
75 | n_match <- length(.x$matches)
76 | n_nonmatch <- length(.x$nonmatch)
77 | n_results <- n_match + n_nonmatch
78 | message(" nchar: ", .y,
79 | "\nexample: ", .x$matches[[1]],
80 | "\n regex: ", .x$regex,
81 | "\n match: ", n_match, " / ", n_results,
82 | " ( ", format(100 * n_match / n_results, digits = 3), "% )\n")
83 | })
84 |
85 | return(invisible(result))
86 | }
87 |
88 | #' @export
89 | escape_regex <- function(s) {
90 | gsub(".", "\\.", s, fixed = TRUE)
91 | }
92 |
93 |
94 |
--------------------------------------------------------------------------------
/README.Rmd:
--------------------------------------------------------------------------------
1 | ---
2 | output: github_document
3 | ---
4 |
5 |
6 |
7 | ```{r setup, include = FALSE}
8 | knitr::opts_chunk$set(
9 | collapse = TRUE,
10 | comment = "#>",
11 | fig.path = "man/figures/README-",
12 | out.width = "100%"
13 | )
14 | library(regexmagic)
15 | ```
16 |
17 | # regexmagic
18 |
19 | The goal of regexmagic is to provide an automated method for classifying a vector
20 | of strings into groupings based on regex matches. This differs from finding matches to
21 | a known regex within a vector, rather this helps determine commonalities between
22 | strings.
23 |
24 | ## Installation
25 |
26 | You can install the development version from [GitHub](https://github.com/) with:
27 |
28 | ```{r, eval = FALSE}
29 | # install.packages("devtools")
30 | devtools::install_github("jonocarroll/regexmagic")
31 | ```
32 |
33 | ## Example
34 |
35 | Given the vector of strings in the (provided) example data, this package
36 | will determine the groupings by regex
37 |
38 | ```{r example data}
39 | data(identifiers)
40 | print(identifiers)
41 | ```
42 |
43 | Within this example data there are 3 distinct patterns, along with 3 identifers which
44 | do not match exactly these patterns (confounders). The goal is to produce a package
45 | which can detect the common patterns and sort the identifiers into the correct
46 | groups.
47 |
48 | ## Methodology
49 |
50 | First, common substrings are identified. These are allowed some tolerance
51 | by which the identifiers may deviate. By default this is 95% of samples should
52 | match the pattern.
53 |
54 | With the example data, we can detect the common substrings
55 |
56 | ```{r}
57 | purrr::map(split_by_length(identifiers), find_common_substrings)
58 | ```
59 |
60 | This has become confused by the confounder in each group which destroys the
61 | perfect relationship. We can improve this by lowering the tolerance
62 |
63 | ```{r}
64 | (guess <- purrr::map(split_by_length(identifiers), find_common_substrings, tolerance = 0.9))
65 | ```
66 |
67 | This successfully identifies the common substrings, but leaves the pattern determining
68 | the missing parts unknown. Next we need to determine common patterns for these. We can
69 | search some given patterns to see if each character matches this enought times. The
70 | pre-defined patterns are
71 |
72 | ```{r}
73 | known_patterns
74 | ```
75 |
76 | Appying these, one character at a time, and seeing which pattern matches the most number
77 | of characters at a position, we can determine which pattern best fits at that position
78 |
79 | ```{r}
80 | (guess <- purrr::map(split_by_length(identifiers), detect_pattern, tolerance = 0.9))
81 | ```
82 |
83 | How many identifiers match these patterns?
84 |
85 | ```{r}
86 | matches <- purrr::map2(split_by_length(identifiers), guess, ~stringr::str_match(.x, .y))
87 | names(matches) <- guess
88 | matches
89 | ```
90 |
91 | ## Working Prototype
92 |
93 | Now that the pieces seem to work, we can apply the categorisations in a function,
94 | returning (invisibly) a list of matches and non-matches, and printing a summary
95 | to the screen
96 |
97 | ```{r}
98 | results <- categorise_regex(identifiers, tolerance = 0.9)
99 | ```
100 |
101 | where we see the single confounder in each case is not matched.
102 |
103 | The actual categorisations are also available
104 |
105 | ```{r}
106 | results
107 | ```
108 |
109 |
110 | ## Yet To Do
111 |
112 | - reduce 'runs' of patterns, e.g. `[0-9][0-9]` to `[0-9]{2}`
113 | - find shortest regex which matches, e.g. `[AB]` vs `[A-Z]`
114 | - variable-length identifiers
115 | - multiple identifiers with a given length
116 | - most testing
117 | - documentation
118 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 |
2 |
3 |
4 | # regexmagic
5 |
6 | The goal of regexmagic is to provide an automated method for classifying
7 | a vector of strings into groupings based on regex matches. This differs
8 | from finding matches to a known regex within a vector, rather this helps
9 | determine commonalities between strings.
10 |
11 | ## Installation
12 |
13 | You can install the development version from
14 | [GitHub](https://github.com/) with:
15 |
16 | ``` r
17 | # install.packages("devtools")
18 | devtools::install_github("jonocarroll/regexmagic")
19 | ```
20 |
21 | ## Example
22 |
23 | Given the vector of strings in the (provided) example data, this package
24 | will determine the groupings by regex
25 |
26 | ``` r
27 | data(identifiers)
28 | print(identifiers)
29 | #> [1] "XY-27121" "AB.312.Z0_0" "XX-00000" "XY-20687"
30 | #> [5] "50006955595R" "50000000000X" "XY-92612" "50095973410R"
31 | #> [9] "50066227417R" "XY-86755" "50018372252R" "AB.122.Z0_0"
32 | #> [13] "AB.935.Z0_1" "XY-70476" "50050222847R" "XY-74486"
33 | #> [17] "50015512791R" "XY-92436" "50071469441R" "XY-67174"
34 | #> [21] "XY-47337" "50095731925R" "50063296214R" "XY-21637"
35 | #> [25] "AB.010.Z0_1" "AB.243.Z0_1" "AB.363.Z0_1" "XY-48420"
36 | #> [29] "AB.464.Z0_0" "AB.424.Z0_0" "AB.952.Z0_0" "AB.654.Z0_0"
37 | #> [33] "XY-47937" "AB.483.Z0_0" "AB.391.Z0_1" "AB.604.Z0_0"
38 | #> [37] "AX.000.Z0_0" "50074522550R" "XY-89660" "AB.898.Z0_1"
39 | #> [41] "50084037368R" "XY-03564" "50079836993R" "AB.610.Z0_0"
40 | #> [45] "AB.214.Z0_1" "AB.872.Z0_0" "AB.497.Z0_1" "AB.532.Z0_1"
41 | #> [49] "XY-30383" "XY-24708" "AB.213.Z0_1" "XY-45418"
42 | #> [53] "AB.039.Z0_1" "XY-88379" "AB.634.Z0_1" "AB.013.Z0_0"
43 | #> [57] "XY-38334" "50018653451R" "AB.041.Z0_0" "50021858177R"
44 | #> [61] "XY-23592" "AB.359.Z0_0" "AB.058.Z0_0" "50083386769R"
45 | #> [65] "AB.710.Z0_1"
46 | ```
47 |
48 | Within this example data there are 3 distinct patterns, along with 3
49 | identifers which do not match exactly these patterns (confounders). The
50 | goal is to produce a package which can detect the common patterns and
51 | sort the identifiers into the correct groups.
52 |
53 | ## Methodology
54 |
55 | First, common substrings are identified. These are allowed some
56 | tolerance by which the identifiers may deviate. By default this is 95%
57 | of samples should match the pattern.
58 |
59 | With the example data, we can detect the common substrings
60 |
61 | ``` r
62 | purrr::map(split_by_length(identifiers), find_common_substrings)
63 | #> $`8`
64 | #> [1] "XY-#####"
65 | #>
66 | #> $`11`
67 | #> [1] "AB.###.Z0_#"
68 | #>
69 | #> $`12`
70 | #> [1] "500#########"
71 | ```
72 |
73 | This has become confused by the confounder in each group which destroys
74 | the perfect relationship. We can improve this by lowering the
75 | tolerance
76 |
77 | ``` r
78 | (guess <- purrr::map(split_by_length(identifiers), find_common_substrings, tolerance = 0.9))
79 | #> $`8`
80 | #> [1] "XY-#####"
81 | #>
82 | #> $`11`
83 | #> [1] "AB.###.Z0_#"
84 | #>
85 | #> $`12`
86 | #> [1] "500########R"
87 | ```
88 |
89 | This successfully identifies the common substrings, but leaves the
90 | pattern determining the missing parts unknown. Next we need to determine
91 | common patterns for these. We can search some given patterns to see if
92 | each character matches this enough times. The pre-defined patterns are
93 |
94 | ``` r
95 | known_patterns
96 | #> [1] "[0-9]" "[A-Z]" "[[:punct:]]"
97 | ```
98 |
99 | Appying these, one character at a time, and seeing which pattern matches
100 | the most number of characters at a position, we can determine which
101 | pattern best fits at that
102 | position
103 |
104 | ``` r
105 | (guess <- purrr::map(split_by_length(identifiers), detect_pattern, tolerance = 0.9))
106 | #> $`8`
107 | #> [1] "XY-[0-9][0-9][0-9][0-9][0-9]"
108 | #>
109 | #> $`11`
110 | #> [1] "AB\\.[0-9][0-9][0-9]\\.Z0_[0-9]"
111 | #>
112 | #> $`12`
113 | #> [1] "500[0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9]R"
114 | ```
115 |
116 | How many identifiers match these
117 | patterns?
118 |
119 | ``` r
120 | matches <- purrr::map2(split_by_length(identifiers), guess, ~stringr::str_match(.x, .y))
121 | names(matches) <- guess
122 | matches
123 | #> $`XY-[0-9][0-9][0-9][0-9][0-9]`
124 | #> [,1]
125 | #> [1,] "XY-27121"
126 | #> [2,] NA
127 | #> [3,] "XY-20687"
128 | #> [4,] "XY-92612"
129 | #> [5,] "XY-86755"
130 | #> [6,] "XY-70476"
131 | #> [7,] "XY-74486"
132 | #> [8,] "XY-92436"
133 | #> [9,] "XY-67174"
134 | #> [10,] "XY-47337"
135 | #> [11,] "XY-21637"
136 | #> [12,] "XY-48420"
137 | #> [13,] "XY-47937"
138 | #> [14,] "XY-89660"
139 | #> [15,] "XY-03564"
140 | #> [16,] "XY-30383"
141 | #> [17,] "XY-24708"
142 | #> [18,] "XY-45418"
143 | #> [19,] "XY-88379"
144 | #> [20,] "XY-38334"
145 | #> [21,] "XY-23592"
146 | #>
147 | #> $`AB\\.[0-9][0-9][0-9]\\.Z0_[0-9]`
148 | #> [,1]
149 | #> [1,] "AB.312.Z0_0"
150 | #> [2,] "AB.122.Z0_0"
151 | #> [3,] "AB.935.Z0_1"
152 | #> [4,] "AB.010.Z0_1"
153 | #> [5,] "AB.243.Z0_1"
154 | #> [6,] "AB.363.Z0_1"
155 | #> [7,] "AB.464.Z0_0"
156 | #> [8,] "AB.424.Z0_0"
157 | #> [9,] "AB.952.Z0_0"
158 | #> [10,] "AB.654.Z0_0"
159 | #> [11,] "AB.483.Z0_0"
160 | #> [12,] "AB.391.Z0_1"
161 | #> [13,] "AB.604.Z0_0"
162 | #> [14,] NA
163 | #> [15,] "AB.898.Z0_1"
164 | #> [16,] "AB.610.Z0_0"
165 | #> [17,] "AB.214.Z0_1"
166 | #> [18,] "AB.872.Z0_0"
167 | #> [19,] "AB.497.Z0_1"
168 | #> [20,] "AB.532.Z0_1"
169 | #> [21,] "AB.213.Z0_1"
170 | #> [22,] "AB.039.Z0_1"
171 | #> [23,] "AB.634.Z0_1"
172 | #> [24,] "AB.013.Z0_0"
173 | #> [25,] "AB.041.Z0_0"
174 | #> [26,] "AB.359.Z0_0"
175 | #> [27,] "AB.058.Z0_0"
176 | #> [28,] "AB.710.Z0_1"
177 | #>
178 | #> $`500[0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9]R`
179 | #> [,1]
180 | #> [1,] "50006955595R"
181 | #> [2,] NA
182 | #> [3,] "50095973410R"
183 | #> [4,] "50066227417R"
184 | #> [5,] "50018372252R"
185 | #> [6,] "50050222847R"
186 | #> [7,] "50015512791R"
187 | #> [8,] "50071469441R"
188 | #> [9,] "50095731925R"
189 | #> [10,] "50063296214R"
190 | #> [11,] "50074522550R"
191 | #> [12,] "50084037368R"
192 | #> [13,] "50079836993R"
193 | #> [14,] "50018653451R"
194 | #> [15,] "50021858177R"
195 | #> [16,] "50083386769R"
196 | ```
197 |
198 | ## Working Prototype
199 |
200 | Now that the pieces seem to work, we can apply the categorisations in a
201 | function, returning (invisibly) a list of matches and non-matches, and
202 | printing a summary to the screen
203 |
204 | ``` r
205 | results <- categorise_regex(identifiers, tolerance = 0.9)
206 | #> ** CATEGORISATION SUMMARY **
207 | #> ** Detected 3 categories and matched
208 | #> 62 / 65 ( 0.954% ) strings **
209 | #> nchar: 8
210 | #> example: XY-27121
211 | #> regex: XY-[0-9][0-9][0-9][0-9][0-9]
212 | #> match: 20 / 21 ( 95.2% )
213 | #> nchar: 11
214 | #> example: AB.312.Z0_0
215 | #> regex: AB\.[0-9][0-9][0-9]\.Z0_[0-9]
216 | #> match: 27 / 28 ( 96.4% )
217 | #> nchar: 12
218 | #> example: 50006955595R
219 | #> regex: 500[0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9]R
220 | #> match: 15 / 16 ( 93.8% )
221 | ```
222 |
223 | where we see the single confounder in each case is not matched.
224 |
225 | The actual categorisations are also available
226 |
227 | ``` r
228 | results
229 | #> $`8`
230 | #> $`8`$regex
231 | #> [1] "XY-[0-9][0-9][0-9][0-9][0-9]"
232 | #>
233 | #> $`8`$matches
234 | #> [1] "XY-27121" "XY-20687" "XY-92612" "XY-86755" "XY-70476" "XY-74486"
235 | #> [7] "XY-92436" "XY-67174" "XY-47337" "XY-21637" "XY-48420" "XY-47937"
236 | #> [13] "XY-89660" "XY-03564" "XY-30383" "XY-24708" "XY-45418" "XY-88379"
237 | #> [19] "XY-38334" "XY-23592"
238 | #>
239 | #> $`8`$nonmatches
240 | #> [1] "XX-00000"
241 | #>
242 | #>
243 | #> $`11`
244 | #> $`11`$regex
245 | #> [1] "AB\\.[0-9][0-9][0-9]\\.Z0_[0-9]"
246 | #>
247 | #> $`11`$matches
248 | #> [1] "AB.312.Z0_0" "AB.122.Z0_0" "AB.935.Z0_1" "AB.010.Z0_1" "AB.243.Z0_1"
249 | #> [6] "AB.363.Z0_1" "AB.464.Z0_0" "AB.424.Z0_0" "AB.952.Z0_0" "AB.654.Z0_0"
250 | #> [11] "AB.483.Z0_0" "AB.391.Z0_1" "AB.604.Z0_0" "AB.898.Z0_1" "AB.610.Z0_0"
251 | #> [16] "AB.214.Z0_1" "AB.872.Z0_0" "AB.497.Z0_1" "AB.532.Z0_1" "AB.213.Z0_1"
252 | #> [21] "AB.039.Z0_1" "AB.634.Z0_1" "AB.013.Z0_0" "AB.041.Z0_0" "AB.359.Z0_0"
253 | #> [26] "AB.058.Z0_0" "AB.710.Z0_1"
254 | #>
255 | #> $`11`$nonmatches
256 | #> [1] "AX.000.Z0_0"
257 | #>
258 | #>
259 | #> $`12`
260 | #> $`12`$regex
261 | #> [1] "500[0-9][0-9][0-9][0-9][0-9][0-9][0-9][0-9]R"
262 | #>
263 | #> $`12`$matches
264 | #> [1] "50006955595R" "50095973410R" "50066227417R" "50018372252R"
265 | #> [5] "50050222847R" "50015512791R" "50071469441R" "50095731925R"
266 | #> [9] "50063296214R" "50074522550R" "50084037368R" "50079836993R"
267 | #> [13] "50018653451R" "50021858177R" "50083386769R"
268 | #>
269 | #> $`12`$nonmatches
270 | #> [1] "50000000000X"
271 | ```
272 |
273 | ## Yet To Do
274 |
275 | - reduce ‘runs’ of patterns, e.g. `[0-9][0-9]` to `[0-9]{2}`
276 | - find shortest regex which matches, e.g. `[AB]` vs `[A-Z]`
277 | - variable-length identifiers
278 | - multiple identifiers with a given length
279 | - most testing
280 | - documentation
281 |
--------------------------------------------------------------------------------
/LICENSE.md:
--------------------------------------------------------------------------------
1 | GNU General Public License
2 | ==========================
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499 | version” applies to it, you have the option of following the terms and
500 | conditions either of that numbered version or of any later version published by the
501 | Free Software Foundation. If the Program does not specify a version number of the GNU
502 | General Public License, you may choose any version ever published by the Free
503 | Software Foundation.
504 |
505 | If the Program specifies that a proxy can decide which future versions of the GNU
506 | General Public License can be used, that proxy's public statement of acceptance of a
507 | version permanently authorizes you to choose that version for the Program.
508 |
509 | Later license versions may give you additional or different permissions. However, no
510 | additional obligations are imposed on any author or copyright holder as a result of
511 | your choosing to follow a later version.
512 |
513 | ### 15. Disclaimer of Warranty
514 |
515 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW.
516 | EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
517 | PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER
518 | EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
519 | MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE
520 | QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE
521 | DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
522 |
523 | ### 16. Limitation of Liability
524 |
525 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY
526 | COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS
527 | PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL,
528 | INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE
529 | PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE
530 | OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE
531 | WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
532 | POSSIBILITY OF SUCH DAMAGES.
533 |
534 | ### 17. Interpretation of Sections 15 and 16
535 |
536 | If the disclaimer of warranty and limitation of liability provided above cannot be
537 | given local legal effect according to their terms, reviewing courts shall apply local
538 | law that most closely approximates an absolute waiver of all civil liability in
539 | connection with the Program, unless a warranty or assumption of liability accompanies
540 | a copy of the Program in return for a fee.
541 |
542 | _END OF TERMS AND CONDITIONS_
543 |
544 | ## How to Apply These Terms to Your New Programs
545 |
546 | If you develop a new program, and you want it to be of the greatest possible use to
547 | the public, the best way to achieve this is to make it free software which everyone
548 | can redistribute and change under these terms.
549 |
550 | To do so, attach the following notices to the program. It is safest to attach them
551 | to the start of each source file to most effectively state the exclusion of warranty;
552 | and each file should have at least the “copyright” line and a pointer to
553 | where the full notice is found.
554 |
555 |
556 | Copyright (C) 2018 Jonathan Carroll
557 |
558 | This program is free software: you can redistribute it and/or modify
559 | it under the terms of the GNU General Public License as published by
560 | the Free Software Foundation, either version 3 of the License, or
561 | (at your option) any later version.
562 |
563 | This program is distributed in the hope that it will be useful,
564 | but WITHOUT ANY WARRANTY; without even the implied warranty of
565 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
566 | GNU General Public License for more details.
567 |
568 | You should have received a copy of the GNU General Public License
569 | along with this program. If not, see .
570 |
571 | Also add information on how to contact you by electronic and paper mail.
572 |
573 | If the program does terminal interaction, make it output a short notice like this
574 | when it starts in an interactive mode:
575 |
576 | regexmagic Copyright (C) 2018 Jonathan Carroll
577 | This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.
578 | This is free software, and you are welcome to redistribute it
579 | under certain conditions; type 'show c' for details.
580 |
581 | The hypothetical commands `show w` and `show c` should show the appropriate parts of
582 | the General Public License. Of course, your program's commands might be different;
583 | for a GUI interface, you would use an “about box”.
584 |
585 | You should also get your employer (if you work as a programmer) or school, if any, to
586 | sign a “copyright disclaimer” for the program, if necessary. For more
587 | information on this, and how to apply and follow the GNU GPL, see
588 | <>.
589 |
590 | The GNU General Public License does not permit incorporating your program into
591 | proprietary programs. If your program is a subroutine library, you may consider it
592 | more useful to permit linking proprietary applications with the library. If this is
593 | what you want to do, use the GNU Lesser General Public License instead of this
594 | License. But first, please read
595 | <>.
596 |
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