I have a dataset with multiple and I would like to extract the first two characters from the sr column. Lastly, these characters will be stored in a new column.
Basically, I want to have a new column permit_type that has the first two character values from sr i.e. AP, SP and MP.
How can I do this?
Sample data
structure(list(date_received = c("11/30/2021 ", "11/30/2021 ",
"11/30/2021 ", "11/30/2021 ", "11/30/2021 ", "11/17/2021 ",
"12/3/2021 ", "12/3/2021 ", "12/13/2021 "), date_approved = c("11/30/2021",
"11/30/2021", "11/30/2021", "11/30/2021", "11/30/2021", "11/17/2021",
"12/3/2021", "12/3/2021", "12/3/2021"), sr = c("AP-21-080", "SP-21-081",
"AP-21-082", "SP-21-083", "MP-21-084", "AP-21-085", "AP-21-086",
"MP-21-087", "SP-21-088"), permit = c("AP1766856 Classroom C",
"AP1766858 Classroom A", "AP1766862 Landscape Area", "AP1766864 Classroom B",
"AO1766867", "06-SE-2420566", "06-E-2425187", "", "06-SM-2424110"
)), row.names = c(NA, -9L), class = c("tbl_df", "tbl", "data.frame"
))
Method 1
library(tidyverse)
df$permit_type= df%>% str_split_fixed(df$sr, "-", 2)
# Error
Error in str_split_fixed(., df$sr, "-", 2) :
unused argument (2)
Method 2
df$permit_type = df%>% str_extract(sr, "^.{2}")
# Error
Error in str_extract(., sr, "^.{2}") : unused argument ("^.{2}")
Method 3
df = df %>% mutate(permit_type = str_extract_all(sr, "\\b[a-z]{2}"))
# Returns permit_type with `Character(0)` values
>Solution :
For the last option, it should be uppercase characters ([A-Z]) instead of lowercase ([a-z]) as the input ‘sr’ column shows only uppercase. In addition, str_extract_all is used when there are multiple occurrences of the pattern and it returns a list (simplify = FALSE by default). Here, the example showed a single occurence, thus str_extract would be more useful as it returns a vector
library(dplyr)
library(stringr)
df %>%
mutate(permit_type = str_extract(sr, "\\b[A-Z]{2}"))
# A tibble: 9 × 5
date_received date_approved sr permit permit_type
<chr> <chr> <chr> <chr> <chr>
1 "11/30/2021 " 11/30/2021 AP-21-080 "AP1766856 Classroom C" AP
2 "11/30/2021 " 11/30/2021 SP-21-081 "AP1766858 Classroom A" SP
3 "11/30/2021 " 11/30/2021 AP-21-082 "AP1766862 Landscape Area" AP
4 "11/30/2021 " 11/30/2021 SP-21-083 "AP1766864 Classroom B" SP
5 "11/30/2021 " 11/30/2021 MP-21-084 "AO1766867" MP
6 "11/17/2021 " 11/17/2021 AP-21-085 "06-SE-2420566" AP
7 "12/3/2021 " 12/3/2021 AP-21-086 "06-E-2425187" AP
8 "12/3/2021 " 12/3/2021 MP-21-087 "" MP
9 "12/13/2021 " 12/3/2021 SP-21-088 "06-SM-2424110" SP
With str_split_fixed directly applying on the data, we can wrap the call within {}
df%>%
{str_split_fixed(.$sr, "-", 2)[,1]}
[1] "AP" "SP" "AP" "SP" "MP" "AP" "AP" "MP" "SP"
Similar issue in the second case
df%>%
{str_extract(.$sr, "^.{2}")}
[1] "AP" "SP" "AP" "SP" "MP" "AP" "AP" "MP" "SP"