R:字符串的Cut equivalent

Question

我有一个不同类型的口罩清单，我想把它们分类为N95，外科，布料或其他。

     df<-data.frame(mask_type=
c("Surgical Mask (3M 1800)",                                                                      
     "N95 FFR (Wilson 1105N) (2x 3mm leaks)",                                                        
     "N95 FFR (San Huei United Company 1895N) (2x 3mm leaks)",                                       
     "Surgical Mask (Primed  PG4-1073) (2x 3mm leaks)",                                              
     "Surgical Mask (3M 1800) (2x 3mm leaks)",                                                       
     "N95 FFR (Wilson 1105N) (4x 3mm leaks)",                                                        
     "Cloth FFR (San Huei United Company 1895N) (4x 3mm leaks)",                                       
     "Cloth Mask (Primed  PG4-1073) (4x 3mm leaks)") 

这样做可以过滤掩码，但不会创建“其他”列。你觉得我离得远吗？

需要(Dplyr)需要(Tidyr)

df %>%
  mutate(TYPE=stringr::str_detect(mask_type,"N95 | surgical | cloth")) %>%
  filter(TYPE=TRUE) %>%
  select(mask_type)

Answer 1

如果字符串中存在任何模式'Surgical|N95|Cloth'，则使用str_extract进行提取。如果都不存在，它将返回可以用'Other'替换的NA。

library(dplyr)
library(stringr)

df %>%
  mutate(TYPE= str_extract(mask_type, regex('Surgical|N95|Cloth', ignore_case = TRUE)), 
         TYPE = replace(TYPE, is.na(TYPE), 'Other'))

Answer 2

我们可以使用base R

lst1 <- with(df, regmatches(mask_type, gregexpr('Surgical|N95|Cloth', mask_type)))
df$TYPE <- sapply(lst1, function(x) if(length(x) == 0) 'Other' else x)