由于您没有包含数据或预期输出的可重现示例，因此我不能确定，但我认为您要查找的是str_extract_all的simplify = TRUE参数。
?str_extract_all上的示例：

shopping_list <- c("apples x4", "bag of flour", "bag of sugar", "milk x2")

# without simplify = TRUE
str_extract_all(shopping_list, "\\b[a-z]+\\b")
[[1]]
[1] "apples"

[[2]]
[1] "bag"   "of"    "flour"

[[3]]
[1] "bag"   "of"    "sugar"

[[4]]
[1] "milk"

# with simplify = TRUE
str_extract_all(shopping_list, "\\b[a-z]+\\b", simplify = TRUE)
     [,1]     [,2] [,3]   
[1,] "apples" ""   ""     
[2,] "bag"    "of" "flour"
[3,] "bag"    "of" "sugar"
[4,] "milk"   ""   ""

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使用您添加的示例：

dat <- "Count #1 as Filed: In Violation of; 21 O.S. 645; Count #2 as Filed: In Violation of; 21 O.S. 1541.1;Docket 1"

str_extract_all(dat, "(?<=Violation of;)(\\D|\\d){4,20}(?=;Count |;Docket)",
                simplify = TRUE)

     [,1]             
[1,] " 21 O.S. 1541.1"

型

这不是最有效的解决方案，但与其他人相比，我可以理解：

df = tribble(
  ~foo,
  "1,2",
  "3,4"
)

df %>% mutate(
  col1 = str_extract_all(foo, "\\d+", simplify = TRUE)[,1],
  col2 = str_extract_all(foo, "\\d+", simplify = TRUE)[,2],
)

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退货：

# A tibble: 2 x 3
  foo   col1  col2 
  <chr> <chr> <chr>
1 1,2   1     2    
2 3,4   3     4

型

您可以使用tidyverse包来实现这一点。您的示例中的正则表达式模式不适用于提供的某些示例文本，因为它总是需要尾随分号。下面使用的模式应该更简单，但可能需要根据实际文本进行一些调整。

library(tidyverse)

df %>% 
  mutate(charges = str_extract_all(charge, "(?<=Violation of;\\s).+?(?=(;|$))")) %>% # extracts the different charges
  select(-charge) %>%  # dropping the raw text can be skipped
  unnest(charges) %>%  # seperates the different charges for each name
  group_by(name) %>%   # in this sample there is only a name, but hopefully the real data has some sort of unique id - there could be lots of Jane Doe's in this data
  mutate(statute = paste0('statute', row_number())) %>% # adds a statute number to each charge
  spread(statute, charges) # shift the data from long to wide

# A tibble: 3 x 3
# Groups:   name [3]
  name       statute1        statute2             
  <chr>      <chr>           <chr>                
1 BLACK,JOHN 21 O.S. 645  21 O.S. 1541.1    
2 DOE, JANE  21 O.S. 1435 21 O.S. 1760(A)(1)
3 ROSS, BOB  21 O.S. 1592 NA

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样本数据：

df <- data_frame(name = c('BLACK,JOHN', 'DOE, JANE', 'ROSS, BOB'), 
                 charge = c('Count #1 as Filed: In Violation of; 21 O.S. 645; Count #2 as Filed: In Violation of; 21 O.S. 1541.1;Docket 1',
                            'Count #3 as Filed: In Violation of; 21 O.S. 1435; Count #4 as Filed: In Violation of; 21 O.S. 1760(A)(1)',
                            'Count #2 as Filed: In Violation of; 21 O.S. 1592'))

型

可以使用separate_wider_regex函数：

data <- data.frame(
    charge = c("Count #1 as Filed: In Violation of; 21 O.S. 645; Count #2 as Filed: In Violation of; 21 O.S. 1541.1;Docket 1"))

library(tidyr)

separate_wider_regex(data, charge, patterns = c("Count #1 as Filed: In Violation of; ", statute1 = "[^;]+", "; Count #2 as Filed: In Violation of; ", statute2 = "[^;]+","; Count #3 as Filed: In Violation of; ", statute3 = "[^;]+"), too_few = "align_start")

# Output
# A tibble: 1 × 3
  statute1    statute2       statute3
  <chr>       <chr>          <chr>   
1 21 O.S. 645 21 O.S. 1541.1 NA

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