R正则表达式常见函数
R正则表达式常见函数
这一篇文章主要记录R语言中一些正则表达函数。
1.模式匹配查询函数
查询成果的函数主要有grep、grepl、regexpr、gregexpr、regexec，这些函数的主要区别在于其输出功效名目差异，配合点是都包括正则表达式pattern和文本text这两个参数。
1.1grep
grep函数是R正则表达中最根基的函数，其参数pattern是待匹配的模式，参数text凡是是向量名目标文本。grep返回功效是一个数值向量，向量内容是pattern在text向量中的索引位置区间。
text = c("one word", "a sentence", "you and me", "three two one")
pat = "one"
grep(pat, text)
## [1] 1 4
当我们想要修改返回功效时，可以思量增加value参数，value=TRUE可以输出text向量中切合匹配模式的值。
grep(pat, text, value = TRUE)
## [1] "one word" "three two one"
invert参数可以获得除匹配模式以外的text值
grep(pat, text, invert = TRUE)
## [1] 2 3
grep(pat, text, invert = TRUE, value = TRUE)
## [1] "a sentence" "you and me"
1.2grepl
grepl函数返回的功效是逻辑变量值TRUE/FALSE.
grepl(pat, text)
## [1] TRUE FALSE FALSE TRUE
1.3regexpr
regexpr函数可以获得给定字符串中匹配模式呈现的较准确位置。较grep函数，regexpr不只可以判定出文本向量中哪个元素包括匹配模式，还可以识别匹配模式呈此刻文本向量每个元素的位置及模式长度。假如该向量元素没有匹配模式，则返回-1。
regexpr(pat, text)
## [1] 1 -1 -1 11
## attr(,"match.length")
## [1] 3 -1 -1 3
## attr(,"useBytes")
## [1] TRUE
1.4gregexpr
gregexpr与regexpr函数成果雷同，其区别在于其输出功效为列表(list)名目。
gregexpr(pat, text)
## [[1]]
## [1] 1
## attr(,"match.length")
## [1] 3
## attr(,"useBytes")
## [1] TRUE
##
## [[2]]
## [1] -1
## attr(,"match.length")
## [1] -1
## attr(,"useBytes")
## [1] TRUE
##
## [[3]]
## [1] -1
## attr(,"match.length")
## [1] -1
## attr(,"useBytes")
## [1] TRUE
##
## [[4]]
## [1] 11
## attr(,"match.length")
## [1] 3
## attr(,"useBytes")
## [1] TRUE
1.5regexec
regexec函数与gregexpr很是靠近，返回功效都有“.match.length”。
(x = regexec(pat, text))
## [[1]]
## [1] 1
## attr(,"match.length")
## [1] 3
##
## [[2]]
## [1] -1
## attr(,"match.length")
## [1] -1
##
## [[3]]
## [1] -1
## attr(,"match.length")
## [1] -1
##
## [[4]]
## [1] 11
## attr(,"match.length")
## [1] 3
2模式替换函数
模式替换函数主要有sub和gsub，二者的区别在于sub函数只替换文本中第一个匹配的元素，gsub则针对text中所有匹配元素。两个函数语法一直，行如：sub(pattern, replacement, text)。
Rstring = c("The R Foundation", "for Statistical Computing", "R is FREE software",
"R is a collaborative project")
# 用'RR'替换'R'
sub("R", "RR", Rstring)
## [1] "The RR Foundation" "for Statistical Computing"
## [3] "RR is FREE software" "RR is a collaborative project"
gsub("R", "RR", Rstring)
## [1] "The RR Foundation" "for Statistical Computing"
## [3] "RR is FRREE software" "RR is a collaborative project"
3.模式拆分函数
按照指定特征对文本举办拆分长短经常见的事情，strsplit函数是实现这一成果的函数。
sentence = c("R is a collaborative project with many contributors")
strsplit(sentence, " ")
## [[1]]
## [1] "R" "is" "a" "collaborative"
## [5] "project" "with" "many" "contributors"
tels = c("510-548-2238", "707-231-2440", "650-752-1300")
strsplit(tels, "-")
## [[1]]
## [1] "510" "548" "2238"
##
## [[2]]
## [1] "707" "231" "2440"
##
## [[3]]
## [1] "650" "752" "1300"
4.stringr包中实现正则表达的函数
stringr包的所有函数都有配合特征，其布局行如：str_function(string, pattern)。下面别离先容一些常用函数：
library(stringr)
4.1str_detect
str_detect函数用于检测字符向量中是否存在匹配模式,返回逻辑值TRUE/FALSE
some_objs = c("pen", "pencil", "marker", "spray")
str_detect(some_objs, "pen")
## [1] TRUE TRUE FALSE FALSE
some_objs[str_detect(some_objs, "pen")]
## [1] "pen" "pencil"
# 匹配日期名目标例子
strings = c("12 Jun 2002", " 8 September 2004 ", "22-July-2009 ", "01 01 2001",
"date", "02.06.2000", "xxx-yyy-zzzz", "$2,600")
# 界说日期名目
dates = "([0-9]{1,2})[- .]([a-zA-Z]+)[- .]([0-9]{4})"
str_detect(strings, dates)
## [1] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE
4.2str_extract和str_extract_all
str_extract函数的成果是剥离文本向量中每个元素中哦哦谁人第一个匹配的值,无匹配的元素返回NA，功效是一个和文本向量同等维度的向量。
paris_tweets = c("#Paris is chock-full of cultural and culinary attractions",
"Some time in #Paris along Canal St.-Martin famous by #Amelie", "While you're in #Paris, stop at cafe: http://goo.gl/yaCbW",
"Paris, the city of light")
# 界说模式,剥离#Paris
hash = "#[a-zA-Z]{1,}"
str_extract(paris_tweets, hash)
## [1] "#Paris" "#Paris" "#Paris" NA
str_extract_all成果雷同str_extract，可是它会剥离文本向量中每个元素中所有切合匹配模式的值，无匹配时返回character(0)，功效名目为list名目。
str_extract_all(paris_tweets, hash)
## [[1]]
## [1] "#Paris"
##
## [[2]]
## [1] "#Paris" "#Amelie"
##
## [[3]]
## [1] "#Paris"
##
## [[4]]
## character(0)
4.3str_match和str_match_all
较str_extract和str_extract_all，str_match和str_match_all会给出具体的匹配信息。
str_match(strings, dates)
## [,1] [,2] [,3] [,4]
## [1,] "12 Jun 2002" "12" "Jun" "2002"
## [2,] "8 September 2004" "8" "September" "2004"
## [3,] "22-July-2009" "22" "July" "2009"
## [4,] NA NA NA NA
## [5,] NA NA NA NA
## [6,] NA NA NA NA
## [7,] NA NA NA NA
## [8,] NA NA NA NA
str_match_all(paris_tweets, hash)
## [[1]]
## [,1]
## [1,] "#Paris"
##
## [[2]]
## [,1]
## [1,] "#Paris"
## [2,] "#Amelie"
##
## [[3]]
## [,1]
## [1,] "#Paris"
##
## [[4]]
## character(0)
4.4str_locate和str_locate_all
这两个函数是用于定位匹配模式在文本向量每个元素中的位置，同上前者只定位第一个呈现的匹配模式，后者针对所有匹配的模式;str_locate返回的是两列的矩阵，str_locate_all返回的是list。
str_locate(paris_tweets, hash)
## start end
## [1,] 1 6
## [2,] 14 19
## [3,] 17 22
## [4,] NA NA
str_locate_all(paris_tweets, hash)
## [[1]]
## start end
## [1,] 1 6
##
## [[2]]
## start end
## [1,] 14 19
## [2,] 54 60
##
## [[3]]
## start end
## [1,] 17 22
##
## [[4]]
## start end
4.5str_replace和str_replace_all
这两个函数用于替换，用指定模式替换匹配模式。
cities = c("San Francisco", "Barcelona", "Naples", "Paris")
str_replace(cities, "[aeiou]", ";")
## [1] "S;n Francisco" "B;rcelona" "N;ples" "P;ris"
str_replace(cities, "[^aeiou]", ";")
## [1] ";an Francisco" ";arcelona" ";aples" ";aris"
str_replace_all(cities, pattern = "[aeiou]", ";")
## [1] "S;n Fr;nc;sc;" "B;rc;l;n;" "N;pl;s" "P;r;s"
str_replace_all(cities, pattern = "[^aeiou]", ";")
## [1] ";a;;;;a;;i;;o" ";a;;e;o;a" ";a;;e;" ";a;i;"
4.6str_split和str_split_fixed
str_split语法形如： str_split(string, pattern, n=Inf),个中n是返回的支解数目。
sentence = c("R is a collaborative project with many contributors")
str_split(sentence, " ")
## [[1]]
## [1] "R" "is" "a" "collaborative"
## [5] "project" "with" "many" "contributors"
# 支解电话号码
tels = c("510-548-2238", "707-231-2440", "650-752-1300")
str_split(tels, "-")
## [[1]]
## [1] "510" "548" "2238"
##
## [[2]]
## [1] "707" "231" "2440"
##
## [[3]]
## [1] "650" "752" "1300"
# 说明参数n的用法
flavors = c("chocolate", "vanilla", "cinnamon", "mint", "lemon")
str_split(flavors, "[aeiou]")
## [[1]]
## [1] "ch" "c" "l" "t" ""
##
## [[2]]
## [1] "v" "n" "ll" ""
##
## [[3]]
## [1] "c" "nn" "m" "n"
##
## [[4]]
## [1] "m" "nt"
##
## [[5]]
## [1] "l" "m" "n"
str_split(flavors, "[aeiou]", n = 2)
## [[1]]
## [1] "ch" "colate"
##
## [[2]]
## [1] "v" "nilla"
##
## [[3]]
## [1] "c" "nnamon"
##
## [[4]]
## [1] "m" "nt"
##
## [[5]]
## [1] "l" "mon"
str_split_fixed函数同样用于支解，其语法名目为：str_split_fixed(string, pattern, n)，这里n没有默认值，必需指定。
# 以n为界支解为两部门
str_split_fixed(flavors, "n", 2)
## [,1] [,2]
## [1,] "chocolate" ""
## [2,] "va" "illa"
## [3,] "ci" "namon"
## [4,] "mi" "t"
## [5,] "lemo" ""
# 以n为界支解三个部门
str_split_fixed(flavors, "n", 3)
## [,1] [,2] [,3] ## [1,] "chocolate" "" "" ## [2,] "va" "illa" "" ## [3,] "ci" "" "amon" ## [4,] "mi" "t" "" ## [5,] "lemo" "" ""文章转自：http://blog.163.com/[email protected]/blog/static/692285082014325113834380/
