maps-sjroloff

For this homework I assignment, I decided to build upon the exploration of the governor_state_toplines_2022 data set available at data/election-forecasts-2022 at master · fivethirtyeight/data (github.com).

I wanted to show the likelihood of certain states electing a Democrat or Republican governor in November 2022. Since governorship races are at the state level, I chose to display the states as the spatial unit.

A future improvement that I think would add more context is a hover over that displays a line graph of all historical polling (within the last election cycle).

library(tidyverse)

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
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✔ tibble  3.1.8     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.1
✔ readr   2.1.2     ✔ forcats 0.5.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(ggplot2)
library(dplyr)
library(plotly)

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

library(usmap)
gov_df<-readr::read_csv('governor_state_toplines_2022.csv', show_col_types = FALSE)

#head(gov_df)
#dim(gov_df)
#decide if the state is likely to have a democrat or republican governor
gov_df$Governorship_leans <- with(gov_df, ifelse(winner_Rparty < winner_Dparty, 'Democrat', 'Republican'))

# convert date field to date field
gov_df$forecastdate <- as.Date(gov_df$forecastdate, format = "%m/%d/%Y" )
#class(gov_df$forecastdate)

#filter to expression field so we don't have duplicate entries/state
gov_df_lite <- filter(gov_df, expression == '_lite')
#dim(gov_df_lite)
#head(gov_df_lite)

gov_df_lite$state<- strtrim(gov_df_lite$district, 2)



#make a groupby to calculate the likelihood of a democratic win in each state
gov_df_lite_grpby = gov_df_lite %>% group_by(state)  %>%
                    summarise(democratic_win  = mean(winner_Dparty),
                              .groups = 'drop')
 
#gov_df_lite_grpby


#make map
plot_usmap(data = gov_df_lite_grpby, values = 'democratic_win')+scale_fill_continuous(low = "red", high = "blue")+theme(legend.position = "right")+labs(title = "Democratic or Republican Leaning for Governor Races as of 9/17/2022", subtitle = "Blue represents a likely Democratic win; red represents a likely Republican win; 
states that are gray do not have a governorship race this year.")