承接上次继续学习stringr这个包，今天学习这几个函数str_detect， str_subset，str_extract，str_replace，str_split
1.str_detect
该函数适用于模式匹配，即变量里面到底有没有我们需要的匹配的字符,返回值为逻辑值T or F，同时可以结合使用sum，mean等基础函数
x <- c("apple", "banana", "pear")
str_detect(x, "e")
#> [1] TRUE FALSE TRUE
#很明显第二个不含字母e，返回的逻辑值为false
# 统计多少字母a开头的的单词
sum(str_detect(x, "^a"))
#> [1] 1
mean(str_detect(x, "^a"))
#> [1] 0.333
str_detect()的一个常见用法是选择与模式匹配的元素。您可以通过逻辑子设置或者使用更方便
str_subset来实现这一点
x[str_detect(x, "^a")]
[1] "apple"
或者
str_subset(x,"^a")
[1] "apple"
however, your strings will be one column of a data frame, and you’ll want to use filter instead
如果是数据框，我们就需要使用filter函数了
df <- tibble(
word = words,
i = seq_along(word)
)
> 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
2.str_subset
是提取可以匹配到的原始数据内容，即提取的是第一个参数中含有可以匹配到的变量
colours <- c("red", "orange", "yellow", "green", "blue", "purple")
colour_match <- str_c(colours, collapse = "|")
has_colour <- str_subset(sentences, colour_match)
has_colour
[1] "Glue the sheet to the dark blue background." "Two blue fish swam in the tank."
[3] "The colt reared and threw the tall rider." "The wide road shimmered in the hot sun."
[5] "See the cat glaring at the scared mouse." "A wisp of cloud hung in the blue air."
[7] "Leaves turn brown and yellow in the fall." "He ordered peach pie with ice cream."
ok ，我们可以看到匹配到的是sentence的变量内容
3.str_extract
要提取匹配的实际文本
matches <- str_extract(has_colour, colour_match)
head(matches)
#> [1] "blue" "blue" "red" "red" "red" "blue"
# 注意，str_extract()只提取第一个匹配，就是只提取句子中第一个可以匹配的值。我们可以很容易地看到，首先选择所有匹配大于1的句子
more <- sentences[str_count(sentences, colour_match) > 1]
str_extract(more, colour_match)
#> [1] "blue" "green" "orange"
4.str_replace
str_replace()和str_replace_all()允许用新字符串替换匹配项。最简单的用法是用固定的字符串替换模式
就是字符串替换
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-"
很明显默认情况下，每一项只匹配第一个匹配到的字母
使用str_replace_all()可以通过提供一个命名向量来执行多个替换
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"
5.str_split
使用str_split()将字符串拆分为多个部分。例如，我们可以把句子分成几个单词，但是返回的是一个列表
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."
"a|b|c|d" %>%
str_split("\\|") %>%
.[[1]]
#> [1] "a" "b" "c" "d"
否则，与返回列表的其他stringr函数一样，您可以使用simplify = TRUE来返回一个矩阵:
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,] ""
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