一、安裝加載R包

if(!require("tidyverse"))install.packages("tidyverse")
if(!require("stringr"))install.packages("stringr")
library(tidyverse)
library(stringr)

二、 字符串基礎(chǔ)

2.1 字符串長(zhǎng)度

str_length()

e.g.

str_length(c("a", "R for data science", NA))
#> [1]  1 18 NA

2.2 字符串組合

str_c(..., sep = "", collapse = FALSE)

2.2.1 組合2個(gè)及以上字符串

e.g.1

str_c("x", "y")
#> [1] "xy"
str_c("x", "y", "z")
#> [1] "xyz"

e.g.2

str_c("x", "y", sep = ", ")
#> [1] "x, y"

e.g.3 注意str_c是向量化的，所以有循環(huán)補(bǔ)齊功能

str_c("prefix-", c("a", "b", "c"), "-suffix")
#> [1] "prefix-a-suffix" "prefix-b-suffix" "prefix-c-suffix"

2.2.2 將字符向量合并為字符串

str_c(c("x", "y", "z"), collapse = ". ")
#> [1] "x. y. z"

2.3 字符串取子集

str_sub(string, start = 1L, end = -1L)

1、提取字符串的一部分

x <- c("Apple", "Banana", "Pear")
str_sub(x, 1, 3)
#> [1] "App" "Ban" "Pea"
# 負(fù)數(shù)代表從后往前數(shù)
str_sub(x, -3, -1)
#> [1] "ple" "ana" "ear"

2、提取后賦值

str_sub(x, 1, 1) <- str_to_lower(str_sub(x, 1, 1))
x
#> [1] "apple"  "banana" "pear"

2.4 練習(xí)

str_trim(string, side = c("both", "left", "right"))

作用：修剪字符串中的空格

str_pad(string, width, side = c("left", "right", "both"), pad = " ")

作用：增加字符串中的空格

三、正則表達(dá)式

3.1 基礎(chǔ)匹配

重點(diǎn)：

1. .可以代表任意字符
   

2. 若想匹配字符.則需要添加轉(zhuǎn)義符\ 所以字符.用正則表達(dá)式表示為 \. 然后再寫成字符串的形式為"\\."
3. 字符\ 用正則表達(dá)式為\\再寫成字符串的形式為"\\\\"

e.g.

str_view(c("abc", "a.c", "bef"), "a\\.c")

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3.2 錨點(diǎn)

重點(diǎn)：

1. ^從字符串開頭進(jìn)行匹配
2. $從字符串末尾進(jìn)行匹配
3. 可以使用\b來匹配單詞的邊界

3.3 練習(xí)

如何匹配字符串""

思路：

1. 你需要一個(gè)轉(zhuǎn)移符號(hào)告訴正則表達(dá)式你要的是所代表的特殊含義，所以其正則表達(dá)式為 \$\^\$
2. 使用字符串表示正則表達(dá)式；而\在字符串中也表示轉(zhuǎn)義，所以要再加一個(gè)轉(zhuǎn)義符："\\$\\^\\$"
3. 最后加上錨點(diǎn)："^\\$\\^\\$$"

3.4 字符類與字符選項(xiàng)

重點(diǎn)：

• \d可以匹配任意數(shù)字
• \s可以匹配任意空白字符（如空格、制表符和換行符）
• [abc]可以匹配a、b或c
• [^abc]可以匹配出a、b、c外的任意字符

牢記！因?yàn)閈在字符串中也表示轉(zhuǎn)義，所以創(chuàng)建\d的正則表達(dá)式需要在字符串中對(duì)\進(jìn)行轉(zhuǎn)義，因此需輸入\\d

還可以使用字符選項(xiàng)創(chuàng)建多個(gè)可選的模式

注意：|的優(yōu)先級(jí)很低?。。?！

abc|xyz匹配的是abc或xyz，而不是abcyz或abxyz

3.5 重復(fù)

正則表達(dá)式一個(gè)強(qiáng)大的功能是可以控制一個(gè)模式能夠匹配多少次

重點(diǎn)1：

• ?：0次或1次
• +：1次或多次
• *：0次或多次

注意：

1、只重復(fù)其前方的一個(gè)字符?。。?/p>

2、這些字符優(yōu)先級(jí)非常高

重點(diǎn)2：

• {n}：匹配n次
• {n, }：匹配n次或更多次
• { ,m}：最多匹配m次
• {n, m}：匹配n到m次

重點(diǎn)3：

默認(rèn)的匹配方式是“貪婪的”：正則表達(dá)式會(huì)匹配盡量長(zhǎng)的字符串。

通過在正則表達(dá)式后添加一個(gè)?，可以將匹配方式改為“懶惰的”，即匹配盡量短的字符串。

x <- "1888 is the longest year in Roman numerals: MDCCCLXXXVIII"
str_view(x, 'C{2,3}?')

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3.6 分組與溯源引用

括號(hào)的作用：

1、消除復(fù)雜表達(dá)式中的歧義，闡明優(yōu)先級(jí)

2、定義“分組”信息，同時(shí)可以通過回溯引用如（\1，\2等）來引用這些分組。

注意：

1個(gè)括號(hào)為1組，\1代表回溯引用第1組即第1個(gè)括號(hào)里的內(nèi)容，\2代表回溯引用第2組即第2個(gè)括號(hào)里的內(nèi)容，\3代表回溯引用第3組即第3個(gè)括號(hào)里的內(nèi)容。

str_view(fruit, "(..)\\1", match = TRUE)

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四、工具

學(xué)習(xí)stringr的多個(gè)函數(shù)，應(yīng)用正則表達(dá)式：

• 確定與某種模式相匹配的字符串
• 找出匹配的位置str_detect
• 提取出匹配的內(nèi)容str_extract
• 使用新值替換匹配內(nèi)容str_replace
• 基于匹配拆分字符串str_split

4.1 匹配檢測(cè)

想要確定一個(gè)字符串向量能否匹配一種模式，使用str_detect函數(shù)，它返回一個(gè)與輸入向量具有相同長(zhǎng)度的邏輯向量。

x <- c("apple", "banana", "pear")
str_detect(x, "e")
#> [1]  TRUE FALSE  TRUE

str_detect配合邏輯值取子集和str_subset函數(shù)可起到一樣的效果

words[str_detect(words, "x$")]
#> [1] "box" "sex" "six" "tax"

str_subset()函數(shù)

str_subset(words, "x$")
#> [1] "box" "sex" "six" "tax"

重點(diǎn)：

然而，字符串通常式數(shù)據(jù)框的一列，此時(shí)我們可以用filter操作

df <- tibble(
  word = words, 
  i = seq_along(word)
)
df %>% 
  filter(str_detect(word, "x$"))
#> # A tibble: 4 x 2
#>   word      i
#>   <chr> <int>
#> 1 box     108
#> 2 sex     747
#> 3 six     772
#> 4 tax     841

str_detect()函數(shù)的一種變體是str_count()，后者不是簡(jiǎn)單返回是或否，而是返回字符串中匹配的數(shù)量

str_count()

x <- c("apple", "banana", "pear")
str_count(x, "a")
#> [1] 1 3 1

str_count可以同mutate()函數(shù)一同使用：

計(jì)算word數(shù)據(jù)中元音字母和輔音字母的數(shù)量

df %>% 
  mutate(
    vowels = str_count(word, "[aeiou]"),
    consonants = str_count(word, "[^aeiou]")
  )
#> # A tibble: 980 x 4
#>   word         i vowels consonants
#>   <chr>    <int>  <int>      <int>
#> 1 a            1      1          0
#> 2 able         2      2          2
#> 3 about        3      3          2
#> 4 absolute     4      4          4
#> 5 accept       5      2          4
#> 6 account      6      3          4
#> # … with 974 more rows

4.2 提取匹配內(nèi)容

利用stringr的內(nèi)置數(shù)據(jù)集sentences做練習(xí)

length(sentences)
#> [1] 720
head(sentences)
#> [1] "The birch canoe slid on the smooth planks." 
#> [2] "Glue the sheet to the dark blue background."
#> [3] "It's easy to tell the depth of a well."     
#> [4] "These days a chicken leg is a rare dish."   
#> [5] "Rice is often served in round bowls."       
#> [6] "The juice of lemons makes fine punch."

1、創(chuàng)建一個(gè)顏色向量

colours <- c("red", "orange", "yellow", "green", "blue", "purple")
colour_match <- str_c(colours, collapse = "|")
colour_match
#> [1] "red|orange|yellow|green|blue|purple"

2、選取包含這個(gè)顏色的句子

has_colour <- str_subset(sentences, colour_match)

3、提取這些句子里所包含的顏色，就知道句子里有哪些顏色了

matches <- str_extract(has_colour, colour_match)
head(matches)
#> [1] "blue" "blue" "red"  "red"  "red"  "blue"

注意：

str_extract()只提取了每個(gè)句子的第一個(gè)匹配?。。ㄒ粋€(gè)句子里可能有2個(gè)或以上的顏色單詞）

這是stringr函數(shù)的一種通用模式，因?yàn)閱蝹€(gè)匹配可以使用更簡(jiǎn)單的數(shù)據(jù)結(jié)構(gòu)。要想得到所有匹配，可以使用str_extract_all()函數(shù)，他會(huì)返回一個(gè)列表

str_extract_all(has_colour, colour_match) %>%
    .[1:3]
#[[1]]
#[1] "blue"
#
#[[2]]
#[1] "blue"
#
#[[3]]
#[1] "red"

如果設(shè)置參數(shù)simplify = TRUE，那么返回一個(gè)矩陣，其中較短的匹配循環(huán)補(bǔ)齊

x <- c("a", "a b", "a b c")
str_extract_all(x, "[a-z]", simplify = TRUE)
#>      [,1] [,2] [,3]
#> [1,] "a"  ""   ""  
#> [2,] "a"  "b"  ""  
#> [3,] "a"  "b"  "c"

4.3 分組匹配

同樣探索內(nèi)置數(shù)據(jù)集sentences，假設(shè)我們想從句子中提取出名詞，可以換個(gè)思路一般a/the后面跟的是名詞，所以a/the+ 空格+1個(gè)以上非空格字符即可

noun <- "(a|the) ([^ ]+)"

has_noun <- sentences %>%
  str_subset(noun) %>%
  head(10)
has_noun %>% 
  str_extract(noun)
#>  [1] "the smooth" "the sheet"  "the depth"  "a chicken"  "the parked"
#>  [6] "the sun"    "the huge"   "the ball"   "the woman"  "a helps"

str_extract()可以給出完整匹配，str_match()則可以給出每個(gè)獨(dú)立分組（即每個(gè)括號(hào)里的內(nèi)容），返回的不是向量而是矩陣，其中一列為完整匹配，后面的是每個(gè)分組的匹配

str_match()

has_noun %>% 
  str_match(noun)
#>       [,1]         [,2]  [,3]     
#>  [1,] "the smooth" "the" "smooth" 
#>  [2,] "the sheet"  "the" "sheet"  
#>  [3,] "the depth"  "the" "depth"  
#>  [4,] "a chicken"  "a"   "chicken"
#>  [5,] "the parked" "the" "parked" 
#>  [6,] "the sun"    "the" "sun"    
#>  [7,] "the huge"   "the" "huge"   
#>  [8,] "the ball"   "the" "ball"   
#>  [9,] "the woman"  "the" "woman"  
#> [10,] "a helps"    "a"   "helps"

與str_extract()函數(shù)一樣，如果想要找出每個(gè)字符串的所有匹配，需要使用str_match_all()

tidyr::extract( data, col, into, regex = "([[:alnum:]]+)", remove = TRUE, convert = FALSE, ...)

如果數(shù)據(jù)類型是tibble，使用tidyr::extract()更容易，其工作原理與str_match()相似，只是要求為每個(gè)分組提供一個(gè)名稱，以作為新列放在tibble中

tibble(sentence = sentences) %>%
  extract(sentence, 
  into = c("article", "noun"), 
  "(a|the) ([^ ]+)",
  remove = FALSE)
#> # A tibble: 720 x 3
#>   sentence                                    article noun   
#>   <chr>                                       <chr>   <chr>  
#> 1 The birch canoe slid on the smooth planks.  the     smooth 
#> 2 Glue the sheet to the dark blue background. the     sheet  
#> 3 It's easy to tell the depth of a well.      the     depth  
#> 4 These days a chicken leg is a rare dish.    a       chicken
#> 5 Rice is often served in round bowls.        <NA>    <NA>   
#> 6 The juice of lemons makes fine punch.       <NA>    <NA>   
#> # … with 714 more rows

4.4 替換匹配內(nèi)容

str_replace()和str_replace_all()函數(shù)可以使用新字符串替換匹配內(nèi)容。

str_replace(string, pattern, replacement)

1、使用固定字符串替換匹配內(nèi)容

x <- c("apple", "pear", "banana")
str_replace(x, "[aeiou]", "-")
#> [1] "-pple"  "p-ar"   "b-nana"
str_replace_all(x, "[aeiou]", "-")
#> [1] "-ppl-"  "p--r"   "b-n-n-"

2、通過提供一個(gè)命名向量，使用str_replace_all()函數(shù)可以同時(shí)執(zhí)行多個(gè)替換：

x <- c("1 house", "2 cars", "3 people")
str_replace_all(x, c("1" = "one", "2" = "two", "3" = "three"))
#> [1] "one house"    "two cars"     "three people"

3、使用回溯引用來插入匹配中的分組，下面代碼交換了第二個(gè)和第三個(gè)單詞的順序：

sentences %>% 
  str_replace("([^ ]+) ([^ ]+) ([^ ]+)", "\\1 \\3 \\2") %>% 
  head(5)
#> [1] "The canoe birch slid on the smooth planks." 
#> [2] "Glue sheet the to the dark blue background."
#> [3] "It's to easy tell the depth of a well."     
#> [4] "These a days chicken leg is a rare dish."   
#> [5] "Rice often is served in round bowls."

4.5 拆分

str_split()函數(shù)可以將字符串拆分為多個(gè)片段。

str_split(string, pattern, n = Inf, simplify = FALSE)

舉例：

sentences %>%
  head(5) %>% 
  str_split(" ")
#> [[1]]
#> [1] "The"     "birch"   "canoe"   "slid"    "on"      "the"     "smooth" 
#> [8] "planks."
#> 
#> [[2]]
#> [1] "Glue"        "the"         "sheet"       "to"          "the"        
#> [6] "dark"        "blue"        "background."
#> 
#> [[3]]
#> [1] "It's"  "easy"  "to"    "tell"  "the"   "depth" "of"    "a"     "well."
#> 
#> [[4]]
#> [1] "These"   "days"    "a"       "chicken" "leg"     "is"      "a"      
#> [8] "rare"    "dish."  
#> 
#> [[5]]
#> [1] "Rice"   "is"     "often"  "served" "in"     "round"  "bowls."

因?yàn)樽址蛄康拿總€(gè)分量會(huì)包含不同數(shù)量的片段，所以str_split()返回一個(gè)列表，所以如果你拆分的是一個(gè)長(zhǎng)度為1的向量，那么只要簡(jiǎn)單地提取列表第一個(gè)元素即可：

"a|b|c|d" %>% 
  str_split("\\|") %>% 
  .[[1]]
#> [1] "a" "b" "c" "d"

或者將修改參數(shù)為simplify = TRUE，返回的是一個(gè)矩陣

sentences %>%
  head(5) %>% 
  str_split(" ", simplify = TRUE)
#>      [,1]    [,2]    [,3]    [,4]      [,5]  [,6]    [,7]     [,8]         
#> [1,] "The"   "birch" "canoe" "slid"    "on"  "the"   "smooth" "planks."    
#> [2,] "Glue"  "the"   "sheet" "to"      "the" "dark"  "blue"   "background."
#> [3,] "It's"  "easy"  "to"    "tell"    "the" "depth" "of"     "a"          
#> [4,] "These" "days"  "a"     "chicken" "leg" "is"    "a"      "rare"       
#> [5,] "Rice"  "is"    "often" "served"  "in"  "round" "bowls." ""           
#>      [,9]   
#> [1,] ""     
#> [2,] ""     
#> [3,] "well."
#> [4,] "dish."
#> [5,] ""

還可以設(shè)定拆分片段的最大數(shù)量：

下面代碼設(shè)定拆分的片段最多為2個(gè)片段

fields <- c("Name: Hadley", "Country: NZ", "Age: 35")
fields %>% str_split(": ", n = 2, simplify = TRUE)
#>      [,1]      [,2]    
#> [1,] "Name"    "Hadley"
#> [2,] "Country" "NZ"    
#> [3,] "Age"     "35"

boundary()函數(shù)，可以通過字母、行、句子和單詞邊界來拆分字符串。

boundary(type = c("character", "line_break", "sentence", "word"),  skip_word_none = NA, ...)

e.g.

x <- "This is a sentence.  This is another sentence."

str_split(x, " ")[[1]]
#> [1] "This"      "is"        "a"         "sentence." ""          "This"     
#> [7] "is"        "another"   "sentence."
str_split(x, boundary("word"))[[1]]
#> [1] "This"     "is"       "a"        "sentence" "This"     "is"       "another" 
#> [8] "sentence"

認(rèn)真觀察上面的結(jié)果，可以發(fā)現(xiàn)boundary()函數(shù)的拆分結(jié)果是要更好的。

五、正則表達(dá)式的其他應(yīng)用

R基礎(chǔ)包中兩個(gè)常用函數(shù)，他們也可以使用正則表達(dá)式

• apropos()函數(shù)可以在全局環(huán)境空間中搜索所有可用對(duì)象。當(dāng)我們沒法確切的想起函數(shù)名稱是，這個(gè)函數(shù)很好用，舉例：

apropos("replace")
#> [1] "%+replace%"       "replace"          "replace_na"       "setReplaceMethod"
#> [5] "str_replace"      "str_replace_all"  "str_replace_na"   "theme_replace"

• dir()函數(shù)可以列出一個(gè)目錄下的所有文件。

dir()的pattern參數(shù)可以是一個(gè)正則表達(dá)式，此時(shí)他只返回與這個(gè)模式相匹配的文件名。舉例：

dir(pattern = "\\.csv$")
[1] "clinical.csv"       "raw_ntnbr.csv"      "raw_tnbr.csv"      
[4] "total_clinical.csv"

這章內(nèi)容都很重要，可以參考英文版R數(shù)據(jù)科學(xué)：https://r4ds.had.co.nz/strings.html

及其課后習(xí)題答案：https://jrnold.github.io/r4ds-exercise-solutions/strings.html#splitting