1. 基本用法
2. 正則表達式
3. 使用正則表達式的stringr函數

1. 基本用法

• 字符串長度str_length()

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

• 字符串組合str_c()

# 將字符串組合為字符
> str_c("x", "y")
[1] "xy"
# 缺失值可傳染
> x <- c("abc", NA)
> str_c("|-", x, "-|")
[1] "|-abc-|" NA       
# 使用 str_replace_na() 函數顯示缺失值
> str_c("|-", str_replace_na(x), "-|")
[1] "|-abc-|" "|-NA-|" 
# 可自動循環短向量
> str_c("prefix-", c("a", "b", "c"), "-suffix")
[1] "prefix-a-suffix" "prefix-b-suffix" "prefix-c-suffix"
# 使用 collapse() 函數將字符向量合并為字符串
> str_c(c("x", "y", "z"), collapse = ",")
[1] "x,y,z"

• 字符串取子集 str_sub()

# start 和 end 參數給出了提取字符串的位置
> x <- c("apple", "banana", "pear")
> str_sub(x, 1, 3)
[1] "app" "ban" "pea"
#  負數表示從后往前數
> str_sub(x, -3, -1)
[1] "ple" "ana" "ear"
# 通過賦值形式修改字符串
> str_sub(x, 1, 1) <- str_to_upper(str_sub(x, 1, 1))
> x
[1] "Apple"  "Banana" "Pear"  

• 字符串排序str_sort() 或 str_order()

> x <- c("apple", "eggplant", "banana")
> str_sort(x, locale = "en")  # 英語
[1] "apple"    "banana"   "eggplant"

2. 正則表達式

2.1 特殊匹配

\在正則表示式中作為轉義字符，并且在字符串中也需要用\進行轉義。所以正則表達式字符串中需使用\\匹配轉義字符。
\\\\ 對應 \
\\. 對應 .
\\d 對應 \d

2.2 字符類與字符選項

特殊模式可以匹配多個字符
. 匹配除換行符外所有字符
\d 匹配任意數字
\s 匹配任意 空白 字符（如空格、制表符、換行符）
[abc] 匹配a或b或c
[^abc] 匹配除a\b\c外任何字符
[A-Za-z] 匹配所有大小寫英文字符

• 需要在字符串中對\進行轉義，如輸入\\d創建包含\d的正則表達式

2.3 錨點

^ 從字符串開頭進行匹配
$ 從字符串末尾進行匹配
\b 匹配單詞的邊界

2.4 重復

? 0次或1次
+ 1次或多次
* 0次或多次
{n} 匹配n次
{n, m} 匹配n次到m次

• 默認將匹配盡量長的字符串， 即貪婪的。在表達式后添加?將匹配盡量短的字符串

2.5 分組與回溯引用

\\1 重復匹配第一個()內的表達式
\\2 重復匹配第二個()內的表達式

3. 使用正則表達式的stringr函數

3.1 匹配檢測

• str_detect() 檢測字符向量是否匹配某種模式，返回長度相同的邏輯向量

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

邏輯向量中FALSE值為0，TRUE值為1，使得sum() mean()函數可用作統計

> mean(str_detect(words, pattern = "[aeiou]$"))
[1] 0.2765306

當匹配的邏輯條件復雜時，可通過單個正則表達式或邏輯運算符將多個str_detect()調用組合

# 如匹配非元音字母開頭的單詞
> no_vowels_1 <- !str_detect(words, "[aeiou]")
> no_vowels_2 <- str_detect(words, "^[^aeiou]+$")
> identical(no_vowels_1, no_vowels_2) 
[1] TRUE

• str_subset() 選取出所匹配的子集

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

結合filter()操作，選出某一列中符合匹配模式的字符串

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

• str_count 返回字符串中匹配的數量

> str_count(x, "a")
[1] 1 3 1

str_count() 與 mutate()配合使用

# 計算每個單詞中所包含的原音和輔音字母
> df %>%
+   mutate(
+     vowels = str_count(words, "[aeiou]"),
+     consonants = str_count(words, "[^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
 7 achieve      7      4          3
 8 across       8      2          4
 9 act          9      1          2
10 active      10      3          3
# … with 970 more rows

• exercise

# words表格中哪個單詞包含最大比例的元音字母
> df %>%
+   mutate(
+     vowels = str_count(word, "[aeiou]"),
+     length = str_length(word),
+     rate = vowels / length
+   ) %>%
+   arrange(desc(rate))
# A tibble: 980 x 5
   word       i vowels length  rate
   <chr>  <int>  <int>  <int> <dbl>
 1 a          1      1      1 1    
 2 area      49      3      4 0.75 
 3 idea     412      3      4 0.75 
 4 age       22      2      3 0.667
 5 ago       24      2      3 0.667
 6 air       26      2      3 0.667
 7 die      228      2      3 0.667
 8 due      250      2      3 0.667
 9 eat      256      2      3 0.667
10 europe   278      4      6 0.667
# … with 970 more rows

3.2 提取匹配內容

str_extract() 提取匹配的實際文本

# 以 stringr::sentences 為例
> 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." 
# 構建正則表達式 color_match
> colors <- c("red", "orange", "yellow", "green", "blue", "purple")
> color_match <- str_c(colors, collapse = "|")
> color_match
[1] "red|orange|yellow|green|blue|purple"
# 挑選出匹配的句子
> has_color <- str_subset(sentences, color_match)
> length(has_color)
# 提取匹配句子中包含的顏色
> matches <- str_extract(has_color, color_match)
> matches 
 [1] "blue"   "blue"   "red"    "red"    "red"    "blue"  
 [7] "yellow" "red"    "red"    "green"  "red"    "red"   
[13] "blue"   "red"    "red"    "red"    "red"    "blue"  
[19] "red"    "blue"   "red"    "green"  "red"    "red"   
[25] "red"    "red"    "red"    "red"    "green"  "red"   
[31] "green"  "red"    "purple" "green"  "red"    "red"   
[37] "red"    "red"    "red"    "blue"   "red"    "blue"  
[43] "red"    "red"    "red"    "red"    "green"  "green" 
[49] "green"  "red"    "red"    "yellow" "red"    "orange"
[55] "red"    "red"    "red"   

• str_extract() 只提取第一個匹配，可使用str_extract_all() 得到所有匹配

• exercise

# 提取以ing結尾的所有單詞
pattern1 <- "\\b[A-Za-z]+ing\\b"
str_extract(sentences, pattern1)

# 比較：
pattern2 <- "ing\\b"
str_extract(sentences, pattern2)

pattern1 與 pattern2 在 str_subset() 函數中都可以提取出包含ing結尾單詞的句子， 但只有pattern1 可在 str_extract() 中提取出結尾為ing的單詞

3.3 分組匹配

str_match() 可在提取出完整匹配后給出每個獨立分組，結果返回矩陣

e.g. 提取跟在 a 或 the 后面的單詞

# 構建正則表達式，定義為 至少有1個非空格字符的字符序列
> noun <- "(a|the)([^ ]+)"
# 提取匹配到的字符串
> has_noun <- sentences %>%
+   str_subset(noun) %>%
+   head(10)
# 提取所匹配的具體內容
> has_noun %>% str_extract(noun)
 [1] "the smooth" "the sheet"  "the depth"  "a chicken" 
 [5] "the parked" "the sun"    "the huge"   "the ball"  
 [9] "the woman"  "a helps"   
# 提取完整內容并顯示分組的匹配結果
> 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"  

• str_extract_all() 提取所有匹配

3.4 替換匹配的內容

str_replace(string, pattern, replacement) 使用固定字符串替換匹配內容

> x <- c("1 apple", "2 pear", "3 banana")
> str_replace(x, pattern = "[aeiou]", replacement = "-")
[1] "1 -pple"  "2 p-ar"   "3 b-nana"
> str_replace_all(x, "[aeiou]", "-")
[1] "1 -ppl-"  "2 p--r"   "3 b-n-n-"
# 可同時執行多個替換
> str_replace_all(x, c("1" = "one", "2" = "two", "3" = "three"))
[1] "one apple"    "two pear"     "three banana"

可利用回溯引用來插入匹配中的分組

> head(sentences)[1:5]
[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."     

> 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."  

注意正則表達式中的空格！

3.5 拆分

str_split(string, pattern, n = Inf, simplify = FLASE)
默認結果返回列表形式

> sentences %>%
+   head(5) %>%
+   str_split(" ")
[[1]]
[1] "The"     "birch"   "canoe"   "slid"    "on"      "the"    
[7] "smooth"  "planks."

[[2]]
[1] "Glue"        "the"         "sheet"       "to"         
[5] "the"         "dark"        "blue"        "background."

[[3]]
[1] "It's"  "easy"  "to"    "tell"  "the"   "depth" "of"   
[8] "a"     "well."

[[4]]
[1] "These"   "days"    "a"       "chicken" "leg"     "is"     
[7] "a"       "rare"    "dish."  

[[5]]
[1] "Rice"   "is"     "often"  "served" "in"     "round" 
[7] "bowls."

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

可通過 boundary() 函數指定字母、行或句子和單詞邊界進行拆分

> x <- "This is a sentence. This is another sentence"
> str_split(x, boundary("word"))[[1]]
[1] "This"     "is"       "a"        "sentence" "This"    
[6] "is"       "another"  "sentence"

3.6 定位匹配內容

str_locate(string, pattern) 可以給出每個匹配位置的開始和結束， 結合 str_sub() 函數提取或修改匹配的內容。