Multiple patterns matching with a column by dplyr-CodePudding

I'd like to create a column that is derived from a column that is character typed. I have some set of patterns, which is possible to accept and the others shouldn't be accepted.

Here is what I tried:

library(dplyr)

set.seed(1)

index <- sample(1:nrow(iris),10)

iris2 <- iris[index,]

required_cols <- c('ersicol','inic')

iris2 %>% 
mutate(logical_column = case_when(any(sapply(required_cols,grepl,x = Species)) ~ 'WORKED',
                                  TRUE ~ 'NOT_WORKED'))

In this case, all logical_column is marked as 'WORKED' but only 'ersicol' or 'inic' pattern including observations should be marked as 'WORKED'.

The desired output should be like:

   Sepal.Length Sepal.Width Petal.Length Petal.Width Species    logical_column
          <dbl>       <dbl>        <dbl>       <dbl> <fct>      <chr>         
 1          5.8         2.7          4.1         1   versicolor WORKED        
 2          6.4         2.8          5.6         2.1 virginica  WORKED        
 3          4.4         3.2          1.3         0.2 setosa     NOT_WORKED        
 4          4.3         3            1.1         0.1 setosa     NOT_WORKED        
 5          7           3.2          4.7         1.4 versicolor WORKED        
 6          5.4         3            4.5         1.5 versicolor WORKED        
 7          5.4         3.4          1.7         0.2 setosa     NOT_WORKED        
 8          7.6         3            6.6         2.1 virginica  WORKED        
 9          6.1         2.8          4.7         1.2 versicolor WORKED        
10          4.6         3.4          1.4         0.3 setosa     NOT_WORKED

Thanks in advance.

CodePudding user response：

The any is the key here. It just takes from the full data, instead, use rowwise if we want to use the OP's code

library(dplyr)
iris2 %>%
    rowwise %>%
    mutate(logical_column = case_when(any(sapply(required_cols,
           grepl,x = Species)) 
         ~ 'WORKED',
    
                                  TRUE ~ 'NOT_WORKED')) %>%
    ungroup

-output

# A tibble: 10 × 6
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species    logical_column
          <dbl>       <dbl>        <dbl>       <dbl> <fct>      <chr>         
 1          5.8         2.7          4.1         1   versicolor WORKED        
 2          6.4         2.8          5.6         2.1 virginica  WORKED        
 3          4.4         3.2          1.3         0.2 setosa     NOT_WORKED    
 4          4.3         3            1.1         0.1 setosa     NOT_WORKED    
 5          7           3.2          4.7         1.4 versicolor WORKED        
 6          5.4         3            4.5         1.5 versicolor WORKED        
 7          5.4         3.4          1.7         0.2 setosa     NOT_WORKED    
 8          7.6         3            6.6         2.1 virginica  WORKED        
 9          6.1         2.8          4.7         1.2 versicolor WORKED        
10          4.6         3.4          1.4         0.3 setosa     NOT_WORKED

It may be more efficient, if we make use of vectorized options - paste (str_c) the 'required_cols' to a single string (collapse = "|"), use str_detect to check if the substring present, convert it to numeric index ( 1) and make use of the index for replacing a vector

library(stringr)
iris2 %>% 
   mutate(logical_column = c("NOT_WORKED", "WORKED")[
     1   str_detect(Species, str_c(required_cols, collapse = "|"))])

-output

  Sepal.Length Sepal.Width Petal.Length Petal.Width    Species logical_column
68           5.8         2.7          4.1         1.0 versicolor         WORKED
129          6.4         2.8          5.6         2.1  virginica         WORKED
43           4.4         3.2          1.3         0.2     setosa     NOT_WORKED
14           4.3         3.0          1.1         0.1     setosa     NOT_WORKED
51           7.0         3.2          4.7         1.4 versicolor         WORKED
85           5.4         3.0          4.5         1.5 versicolor         WORKED
21           5.4         3.4          1.7         0.2     setosa     NOT_WORKED
106          7.6         3.0          6.6         2.1  virginica         WORKED
74           6.1         2.8          4.7         1.2 versicolor         WORKED
7            4.6         3.4          1.4         0.3     setosa     NOT_WORKED