R data.table根据字符串值将一行拆分为多行[重复]答案

【问题标题】：R data.table split a row into multiple rows based on string values [duplicate]R data.table根据字符串值将一行拆分为多行[重复]
【发布时间】：2020-08-24 07:56:42
【问题描述】：

我有一个这样的 data.table：

dt <- data.table("title"=c("First Title", "Second Title", "Third Title", "Fourth Title"), 
                 "sha"=c("12345", "2345; 66543; 33423", "22222; 12345678;", "666662345; 444"))

我怎样才能得到这个：

      title                sha
1:  First Title           12345
2: Second Title           2345 
3: Second Title           66543
4: Second Title           33423
5:  Third Title           22222
6:  Third Title           12345678
7: Fourth Title           666662345
8: Fourth Title           444

提前致谢！

【问题讨论】：

标签： r data.table

【解决方案1】：

这是一种适合您的tstrsplit 方法：

library(data.table)
dt[, lapply(.SD, function(x) unlist(tstrsplit(x, "; ?"))),
   .SDcols = "sha",by = c("title","date")]
          title     date       sha
1:  First Title 1/1/2020     12345
2: Second Title 1/2/2020      2345
3: Second Title 1/2/2020     66543
4: Second Title 1/2/2020     33423
5:  Third Title 1/3/2020     22222
6:  Third Title 1/3/2020  12345678
7: Fourth Title 1/4/2020 666662345
8: Fourth Title 1/4/2020       444

数据

dt <- data.table("title"=c("First Title", "Second Title", "Third Title", "Fourth Title"), 
                  "sha"=c("12345", "2345; 66543; 33423", "22222; 12345678;", "666662345; 444"),
                  "date" = c("1/1/2020","1/2/2020","1/3/2020","1/4/2020"))

【讨论】：

是否可以在此操作之后保留其他列而不仅仅是标题和 sha，例如我还有一个日期列

【解决方案2】：

这是我的dplyr 解决方案：

dt %>% 
  group_by(title) %>% 
  separate_rows(sha, sep = ";") %>% 
  mutate(sha = as.numeric(sha)) %>% 
  filter(!is.na(sha))

应该给你这个：

# A tibble: 8 x 2
# Groups:   title [4]
  title              sha
  <chr>            <dbl>
1 First Title      12345
2 Second Title      2345
3 Second Title     66543
4 Second Title     33423
5 Third Title      22222
6 Third Title   12345678
7 Fourth Title 666662345
8 Fourth Title       444

【讨论】：

@Blind0ne 更简单的方法：dt %>% separate_rows(sha, sep = ";") %>% na_if("") %>% drop_na()
@Neel 谢谢！
您实际上并不需要drop_na()，但您确实需要过滤空向量。 separate_rows(dt,sha) %>% filter(sha != "").

【解决方案3】：

这是另一个使用data.table的解决方案：

dt[, .(sha = unlist(tstrsplit(sha, ";", type.convert = TRUE))), by = "title"]

#           title       sha
# 1:  First Title     12345
# 2: Second Title      2345
# 3: Second Title     66543
# 4: Second Title     33423
# 5:  Third Title     22222
# 6:  Third Title  12345678
# 7: Fourth Title 666662345
# 8: Fourth Title       444

【讨论】：