从字符变量中提取/分类定量信息

Question

我有以下类型的

data.frame

id	文字信息
1	从10%增加到60%
2	购买100%
3	从5%增加到45%
4	购买99%

我想处理 text_information (

character

) 变量，以便获得以下输出：

id	分享	分享_差异	类型
1	0.6	0.5	增加
2	1	不适用	购买
3	0.45	0.4	增加
4	0.99	不适用	购买

建议如何使用

R

来完成此操作？

Answer 1

使用正则表达式：

library(dplyr)
library(stringr)

data.frame(
  id = 1:4,
  text_information = c(
    "Increase from 10% to 60%", 
    "Purchase 100%", 
    "Increase from 5% to 45%", 
    "Purchase 99%"
  )
) %>% 
  mutate(
    share_1 = as.numeric(str_extract(text_information, "(\\d+)%", 1)),
    share_2 = as.numeric(str_extract(text_information, "(\\d+)% to (\\d+)%$", 2)),
    share = if_else(is.na(share_2), share_1, share_2) / 100,
    share_difference = (share_2 - share_1) / 100,
    type = tolower(str_extract(text_information, "(Increase|Purchase)"))
  ) %>% 
  select(id, share, share_difference, type)
#>   id share share_difference     type
#> 1  1  0.60              0.5 increase
#> 2  2  1.00               NA purchase
#> 3  3  0.45              0.4 increase
#> 4  4  0.99               NA purchase

^{创建于 2024-04-08，使用 reprex v2.1.0}

Answer 2

使用 dplyr 和 tidyr

library(dplyr)
library(tidyr)

#Create dataframe
id <- c(1,2,3,4)
text_information <- c("increase from 10% to 60%", "Purchase 100%","Increase 
from 5% to 45%","Purchase 99%")
df <- data.frame(id,text_information)

# Processing text
df %>% 
  mutate(text_num = trimws(gsub("[^0-9 ]", "", text_information))) %>%
  separate(text_num, into=c("v1","v2","v3"), sep = " ", fill = "left") %>% 
  mutate(share=as.numeric(pmax(v1,v3,na.rm=TRUE))/100,
     share_difference = (as.numeric(v3)-as.numeric(v1))/100,
     type = if_else(is.na(share_difference),'purchase','increase')) %>% 
  select(text_information,share,share_difference,type)