Categorizing Data Based on Text Value in New Column [duplicate] - r

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How to join (merge) data frames (inner, outer, left, right)
(13 answers)
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I'm trying to take an existing data frame that has a column for state and add a new column called Region depending on what the row's state is. So for example any row that has "CA" should be categorized "West" and any row that has "IL" should be Midwest. There are 4 regions: West, South, Midwest, and Northeast.
I had tried doing this separately in 4 code chunks like this:
south <- c("FL", "KY", "GA", "TX", "MS", "SC", "NC", "AL", "LA", "AR", "TN", "VA", "DC", "MD", "DE", "WV") #16 states
south.mdata <- mdata %>% filter(state %in% south) #1832 locations
south.byyear <- south.mdata %>% group_by(Year) %>% summarize(s.total = n())
south.total <- data %>% filter(state %in% south) %>% group_by(Year) %>% summarize(yearly.total = n())
But this seems repetitive and not the most efficient way to do this. Plus I'd like to be able to group_by both Year and Region so I can compare across regions.
I'm having trouble implementing this and the first thing that comes to mind is to do some sort of if/else loop using filter but I know loops aren't really R's style.
The original data looks like this:
Field.1 ID title description streetaddress city state
1 74 DE074 Cork 'n' Bottle Route 14, 1 mile south of town Rehoboth Beach DE
2 75 DE075 Cork 'n' Bottle Route 14, 1 mile south of town Rehoboth Beach DE
3 23 DE023 Dog House 1200 DuPont Hwy. Wilmington DE
4 19 DE019 Dog House 1200 DuPont Hwy Wilmington DE
5 26 DE026 Dog House 1200 Dupont Wilmington DE
6 65 DE065 Henlopen Hotel Bar Boardwalk & Surf Rehoboth Beach DE
amenityfeatures type Year notes lon lat
1 (M),(R) Restaurant 1977 <NA> -75.07601 38.72095
2 (M),(R) Restaurant 1976 <NA> -75.07601 38.72095
3 (M),(R) Restaurant 1975 <NA> -75.58243 39.68839
4 (M),(R) Restaurant 1976 <NA> -75.58243 39.68839
5 (M),(R) Restaurant 1974 <NA> -75.58723 39.76705
6 (M) Bars/Clubs,Hotel 1972 <NA> -75.07712 38.72280
status
1 Location could not be verified. General city or location coordinates used.
2 Location could not be verified. General city or location coordinates used.
3 Google Verified Location
4 Google Verified Location
5 Google Verified Location
6 Verified Location
I want to add a new column called "Region" that would loop through each row, look at the state, and then add a value to Region.
Any suggestions on the right syntax to do something like this would be so appreciated! Thanks so much!

Answer 1

This is a snippet of what the solution suggested by Gregor’s comment could look like.
library(tidyverse)
orig_data <-
tribble(~ID, ~state,
1, "CA",
2, "FL",
3, "DE")
region_lookup <-
tribble(~state, ~region,
"CA", "west",
"FL", "south",
"DE", "south")
left_join(orig_data, region_lookup)
#> Joining, by = "state"
#> # A tibble: 3 x 3
#> ID state region
#> <dbl> <chr> <chr>
#> 1 1 CA west
#> 2 2 FL south
#> 3 3 DE south
Created on 2020-11-02 by the reprex package (v0.3.0)

Answer 2

the simplest solution would be a join. Therefore you need a data.frame/tibble that has all the states an regions. Fortunately the data is already in base R:
library(dplyr)
# build the tibble of state abbrevitation and region from base R data
state_region <- dplyr::tibble(state.abb, state.region)
# join it on your data.frame/tibble
ORIGINAL_DATA %>%
dplyr::left_join(state_region, by = c("state" = "state.abb"))
Now you should have a new column called "state.region" that you can group by. Be aware that the states have to be in upper case.