library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.3     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.1     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors

library(dplyr)
library(ggplot2)

tuesdata <- tidytuesdayR::tt_load('2023-11-07')

--- Compiling #TidyTuesday Information for 2023-11-07 ----
--- There is 1 file available ---
--- Starting Download ---

    Downloading file 1 of 1: `house.csv`

--- Download complete ---

house <-tuesdata$house

cleandata<-house %>%
 filter(stage == "GEN" & year== "2022") %>%
  select(state, stage, party, candidatevotes) %>%
  drop_na(party) %>%
  filter(party  %in% c("DEMOCRAT", "REPUBLICAN")) 
  

#total votes for each party in each state 
  
totalvotes <- cleandata %>%
  group_by(state, party) %>%
  summarise(total = sum(candidatevotes)) %>%
  slice(which.max(total))

`summarise()` has grouped output by 'state'. You can override using the
`.groups` argument.

totalvotes <- totalvotes[!(totalvotes$state == "ALASKA" | totalvotes$state== "HAWAII"),]    

usmapp <- map_data("state")

#it has to match state in map_data function

totalvotes$state <- tolower(totalvotes$state)


electiondf <- left_join(totalvotes, usmapp, by = c("state" = "region"))


ggplot(data = electiondf, aes(x= long, y= lat, group = group, fill= party))+
  geom_polygon(color= "black", size = 0.1) +
    scale_fill_manual(values = c("DEMOCRAT" = "blue", "REPUBLICAN" = "red")) + # Set colors for parties
  labs(title = "Party with Most Votes in Each State") +
  theme_void()

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

#who won tht most votes in each state? 

mostvotes <- totalvotes %>%
    group_by(state) %>%
    top_n(1,total) 

#skipped Alaska and Hawaii because map_data functiom does not contain either


mostvotes <- mostvotes[!(mostvotes$state== "ALASKA" | mostvotes$state== "HAWAII"),]

#visualise it 

ggplot(data = mostvotes, aes(x= state, y= total, fill= party)) +
  geom_bar(stat = "identity", position = "dodge")