I decided to pick a data set regarding the senate race fundamentals. Using dplyr and stringr, I created a new column "state_ID for just the two character initials of states. I also filtered based on what I believe are the most competitive states this cycle.
library(tidyverse)## -- Attaching packages --------------------------------------------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.3 v dplyr 1.0.2
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## -- Conflicts ------------------------------------------------------------------------------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()senFunURL <- "https://projects.fivethirtyeight.com/2020-general-data/senate_fundamentals.csv"
senFun <- read.csv(file = senFunURL, header = TRUE, sep = ",")
senFun <- senFun %>%
dplyr::mutate(state_ID = str_extract(district, "^[:alpha:]{2}")) %>%
filter(state_ID == "ME" | state_ID == "MI" | state_ID == "AL" | state_ID == "CO" | state_ID == "IA" | state_ID == "GA" | state_ID == "AZ" | state_ID == "NC" | state_ID == "SC")