前言
昨天我们介绍 R 数据处理的时候，对字符串的操作都是用自带的函数。
虽然 R 的字符串并不是它的强项，看起来也不是那么的优雅，但是字符串在数据处理和清洗过程中还是扮演者较为重要的角色。
所以，今天我就讲下 R 字符串处理的第三方包 —— stringr
stringr 包提供了一组内聚函数，尽可能使字符串的操作简单化。
stringr 中主要包括四个函数族：
- 1.字符操作：这些函数允许操作字符串向量中每个字符串的字符
- 2.提供添加、删除和操作空白字符的工具
- 3.提供对语境敏感的操作
- 4.模式匹配函数。总共包含 4 中模式，其中最常用的就是正则表达式
安装
# install
install.packages("stringr")
# 从 GitHub 中安装最新的开发版本:
# 需要用到 devtools 包，如果没有这个包，需要安装一下
if (!require("devtools"))
install.packages("devtools")
devtools::install_github("tidyverse/stringr")
使用
导入
library(stringr)
stringr 中的所有函数都以 str_ 开头，并接受一个字符串向量作为第一个参数
1.单字符串操作
-
str_length: 字符串长度
str_length(string)
参数：
-
string: 字符串或字符串向量
示例：
> s <- c("why", "video", "cross", "extra", "deal", "authority")
> str_length(s)
# [1] 3 5 5 5 4 9
> str_length("love")
# [1] 4
> str_length(c("love", NA))
# [1] 4 NA
-
str_sub: 提取和修改字符串
str_sub(string, start = 1L, end = -1L)
参数：
-
string: 字符串或字符串向量 -
start: 开始位置 -
end: 结束位置
示例：
x <- c("abcdef", "ghifjk")
# The 3rd letter
> str_sub(x, 3, 3)
# [1] "c" "i"
# The 2nd to 2nd-to-last character
> str_sub(x, 2, -2)
# [1] "bcde" "hifj"
修改字符串
> str_sub(x, 3, 3) <- "X"
> x
# [1] "abXdef" "ghXfjk"
-
str_dup: 复制字符串
str_dup(string, times)
参数：
-
string: 字符串或字符串向量 -
times: 复制数量
示例：
> str_dup(x, c(2, 3))
# [1] "abcdefabcdef" "ghifjkghifjkghifjk"
> str_dup(x, c(2))
# [1] "abcdefabcdef" "ghifjkghifjk"
> str_dup(x, 2)
# [1] "abcdefabcdef" "ghifjkghifjk"
-
str_c: 字符串拼接
str_c(..., sep = "", collapse = NULL)
参数：
-
...: 多参数的输入 -
sep: 把多个字符串参数拼接为一个字符串的连接符号。 -
collapse: 把多个向量参数拼接为一个字符串的连接符号。
> x <- c("why", "video", "cross", "extra", "deal", "authority")
> str_c(x, collapse = ", ")
# [1] "why, video, cross, extra, deal, authority"
> str_c("why", "not", "my", sep = "-")
[1] "why-not-my"
2.空白字符
1.str_pad: 补充字符串的长度
str_pad(string, width, side = c("left", "right", "both"), pad = " ")
参数：
-
string: -
width: 字符串填充后的长度 -
side: 填充方向-
left: 左边填充 -
right: 右边填充 -
both: 两边都填充
-
-
pad: 用于填充的字符
> x <- c("abc", "defghi")
> str_pad(x, 10) # 默认在左边
# [1] " abc" " defghi"
> str_pad(x, 10, "both")
# [1] " abc " " defghi "
> str_pad(x, 10, pad = "=")
# [1] "====abcdef" "====ghifjk"
如果你设置的字符串填充长度小于其本身，那么不会产生任何作用
str_pad(x, 4)
#> [1] " abc" "defghi"
2.str_trunc: 省略过长字符
str_trunc(string, width, side = c("right", "left", "center"), ellipsis = "...")
参数：
-
string: 字符串或字符串向量 -
width: 最大字符串长度 -
side： 省略方式-
center: 中间省略 -
left: 左边省略 -
right: 右边省略
-
示例
> x <- "This string is moderately long"
> rbind(
+ str_trunc(x, 20, "right"),
+ str_trunc(x, 20, "left"),
+ str_trunc(x, 20, "center")
+ )
# [,1]
# [1,] "This string is mo..."
# [2,] "...s moderately long"
# [3,] "This stri...ely long"
3. str_trim: 去掉字符串的空格和TAB
str_trim(string, side = c("both", "left", "right"))
参数：
-
string: 字符串或字符串向量 -
side： 过滤方式-
both: 两边都过滤 -
left: 左边过滤 -
right: 右边过滤
-
示例
x <- c("Short", "This is a long string")
x <- c(" a ", "b ", " c")
str_trim(x)
#> [1] "a" "b" "c"
str_trim(x, "left")
#> [1] "a " "b " "c"
4. str_wrap: 控制字符串输出格式
str_wrap(string, width = 80, indent = 0, exdent = 0)
参数：
-
string: 字符串或字符串向量。 -
width: 设置一行所占的宽度。 -
indent: 段落首行的缩进值 -
exdent: 段落非首行的缩进值
示例
jabberwocky <- str_c(
"`Twas brillig, and the slithy toves ",
"did gyre and gimble in the wabe: ",
"All mimsy were the borogoves, ",
"and the mome raths outgrabe. "
)
cat(str_wrap(jabberwocky, width = 40))
# `Twas brillig, and the slithy toves did
# gyre and gimble in the wabe: All mimsy
# were the borogoves, and the mome raths
# outgrabe.
3.语境
1.大小写转换
str_to_upper(string, locale = "")
str_to_lower(string, locale = "")
str_to_title(string, locale = "")
参数：
-
string: 字符串。 -
locale: 按哪种语言习惯
示例
x <- "I like horses."
str_to_upper(x)
#> [1] "I LIKE HORSES."
str_to_title(x)
#> [1] "I Like Horses."
str_to_lower(x)
#> [1] "i like horses."
# Turkish has two sorts of i: with and without the dot
str_to_lower(x, "tr")
#> [1] "ı like horses."
2. 排序
# 对值排序
str_sort(x, decreasing = FALSE, na_last = TRUE, locale = "", ...)
str_order(x, decreasing = FALSE, na_last = TRUE, locale = "", ...)
参数：
-
x: 字符串或字符串向量。 -
decreasing: 排序方向。 -
na_last:NA放置的位置：TRUE放到最后，FALSE放到最前，NA过滤处理 -
locale: 按哪种语言习惯排序
> x <- c("y", "i", "k")
> str_order(x)
# [1] 2 3 1
> str_sort(x)
# [1] "i" "k" "y"
# In Lithuanian, y comes between i and k
> str_sort(x, locale = "lt")
# [1] "i" "y" "k"
3. 编码方式
str_conv(string, encoding)
参数：
-
string: 字符串或字符串向量。 -
encoding: 编码名。
示例：
# 把中文字符字节化
> x <- charToRaw('你好')
> x
[1] c4 e3 ba c3
# 默认 win 系统字符集为 GBK，GB2312 为 GBK 字集，转码正常
> str_conv(x, "GBK")
[1] "你好"
> str_conv(x, "GB2312")
[1] "你好"
# 在 mac 系统下
> x <- charToRaw('你好')
> str_conv(x, "GBK")
[1] "浣犲ソ"
> str_conv(x, "GB2312")
# [1] "浣\032濂\032"
# Warning messages:
# 1: In stri_conv(string, encoding, "UTF-8") :
# mac
> str_conv(x, "UTF-8")
# [1] "你好"
4.模式匹配
每个模式匹配函数的前两个参数都相同
-
string： 字符串或字符串向量 -
pattern： 匹配模式
字符串和匹配模式
strings <- c(
"apple",
"219 733 8965",
"329-293-8753",
"Work: 579-499-7527; Home: 543.355.3679"
)
phone <- "([2-9][0-9]{2})[- .]([0-9]{3})[- .]([0-9]{4})"
1.匹配字符串的字符
str_detect(string, pattern)
示例：
# 匹配字符串的字符
> str_detect(strings, phone)
# [1] FALSE TRUE TRUE TRUE
2. 模式在字符串中的位置
str_locate(string, pattern)
str_locate_all(string, pattern)
示例：
> (loc <- str_locate(strings, phone))
#> start end
#> [1,] NA NA
#> [2,] 1 12
#> [3,] 1 12
#> [4,] 7 18
> str_locate_all(strings, phone)
#> [[1]]
#> start end
#>
#> [[2]]
#> start end
#> [1,] 1 12
#>
#> [[3]]
#> start end
#> [1,] 1 12
#>
#> [[4]]
#> start end
#> [1,] 7 18
#> [2,] 27 38
3. 从字符串中提取匹配模式
str_extract(string, pattern)
str_extract_all(string, pattern, simplify = FALSE)
示例：
> val <- c("a1", 467, "ab2")
# 返回匹配的数字
> str_extract(val, "\\d")
# [1] "1" "4" "2"
> str_extract_all(val, "\\d")
# [[1]]
# [1] "1"
# [[2]]
# [1] "4" "6" "7"
# [[3]]
# [1] "2"
# 返回匹配的字符
> str_extract(val, "[a-z]+")
# [1] "a" NA "ab"
> str_extract_all(val, "\\w+")
# [[1]]
# [1] "a1"
# [[2]]
# [1] "467"
# [[3]]
# [1] "ab2"
4. 从字符串中提取匹配组
str_match(string, pattern)
str_match_all(string, pattern)
示例
> str_match(strings, phone)
#> [,1] [,2] [,3] [,4]
#> [1,] NA NA NA NA
#> [2,] "219 733 8965" "219" "733" "8965"
#> [3,] "329-293-8753" "329" "293" "8753"
#> [4,] "579-499-7527" "579" "499" "7527"
> str_match_all(strings, phone)
#> [[1]]
#> [,1] [,2] [,3] [,4]
#>
#> [[2]]
#> [,1] [,2] [,3] [,4]
#> [1,] "219 733 8965" "219" "733" "8965"
#>
#> [[3]]
#> [,1] [,2] [,3] [,4]
#> [1,] "329-293-8753" "329" "293" "8753"
#>
#> [[4]]
#> [,1] [,2] [,3] [,4]
#> [1,] "579-499-7527" "579" "499" "7527"
#> [2,] "543.355.3679" "543" "355" "3679"
5. 替换匹配的字符串
str_replace(string, pattern, replacement)
str_replace_all(string, pattern, replacement)
str_replace_na(string, replacement = "NA")
replacement 为替字符串
示例：
# 替换第一个匹配
> str_replace(strings, phone, "XXX-XXX-XXXX")
#> [1] "apple"
#> [2] "XXX-XXX-XXXX"
#> [3] "XXX-XXX-XXXX"
#> [4] "Work: XXX-XXX-XXXX; Home: 543.355.3679"
# 替换所有匹配
> str_replace_all(strings, phone, "XXX-XXX-XXXX")
#> [1] "apple"
#> [2] "XXX-XXX-XXXX"
#> [3] "XXX-XXX-XXXX"
#> [4] "Work: XXX-XXX-XXXX; Home: XXX-XXX-XXXX"
> str_replace_na(c(NA, "abc", "def"))
# [1] "NA" "abc" "def"
6. 字符串分割
str_split(string, pattern, n = Inf) # 返回 list
str_split_fixed(string, pattern, n) # 返回 matrix
n 为分割次数
示例：
str_split("a-b-c", "-")
#> [[1]]
#> [1] "a" "b" "c"
str_split_fixed("a-b-c", "-", n = 2)
#> [,1] [,2]
#> [1,] "a" "b-c"
7. 字符串计数
str_count(string, pattern = "")
示例：
> str_count(strings, phone)
# [1] 0 1 1 2
8. 返回的匹配字符串
str_subset(string, pattern)
示例：
> str_subset(strings, phone)
# [1] "219 733 8965"
# [2] "329-293-8753"
# [3] "Work: 579-499-7527; Home: 543.355.3679"
9. 提取单词
word(string, start = 1L, end = start, sep = fixed(" "))
-
string: 字符串，字符串向量。 -
start: 开始位置。 -
end: 结束位置。 -
sep: 匹配字符。
示例
> val <- c("hello world", "this is my, girl")
# 默认以空格分割，取第一个位置的字符串
> word(val, 1)
# [1] "hello" "this"
> word(val, -1)
# [1] "world" "girl"
> word(val, 2, -1)
# [1] "world" "is my, girl"
# 以,分割，取第一个位置的字符串
> val <- '111,222,333,444'
> word(val, 1, sep = fixed(','))
[1] "111"
> word(val, 3, sep = fixed(','))
[1] "333"
四种模式
-
boundary: 匹配字符、行、句子或单词之间的边界
x <- "This is a sentence."
str_split(x, boundary("word"))
#> [[1]]
#> [1] "This" "is" "a" "sentence"
str_count(x, boundary("word"))
#> [1] 4
str_extract_all(x, boundary("word"))
#> [[1]]
#> [1] "This" "is" "a" "sentence"
-
coll: 定义字符串标准排序规则。
i <- c("I", "İ", "i", "ı")
i
#> [1] "I" "İ" "i" "ı"
str_subset(i, coll("i", ignore_case = TRUE))
#> [1] "I" "i"
str_subset(i, coll("i", ignore_case = TRUE, locale = "tr"))
#> [1] "İ" "i"
-
fixed: 定义用于匹配的字符，包括正则表达式中的转义符
> str_count(c("a.", ".", ".a.",NA), ".")
[1] 2 1 3 NA
# 用fixed匹配字符
> str_count(c("a.", ".", ".a.",NA), fixed("."))
[1] 1 1 2 NA
-
regex: 定义正则表达式，默认就是正则表达式
> val
[1] "a1" "467" "ab2"
> str_extract(val, regex("\\w+"))
[1] "a1" "467" "ab2"
