elections/EDA.R at master · JasonChan07/elections · GitHub

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library(data.table)
library(ggplot2)
library(dplyr)
library(stringr)
library(tidyr)

# Load data

## set working directory
setwd('/Users/jasonchan/Documents/DataProjects/elections')

## county election returns 2000-2016
election_returns <- fread('countypres_2000-2016.csv')

## county unemployment 2007-2018
county_unemployment <- fread('county-unemployment.csv')

## What were the pivot counties from 2008-2016?
## How large were the pivots (percentage swing)
## What demographics pivoted the most?
## What changes happend on a county-level from 2008-2016
## How did pivot counties vote in the midterms between 2008-2016
## How did pivot counties vote for governors?
## What was turnout like in these pivot counties


GetCountyReturns <- function(election_returns, county_unemployment) {

  # winning candidate for each county
  pivot_counties <- election_returns[year > 2004,
                       .SD[which.max(candidatevotes)],
                       by = .(year, state, county)]

  pivot_counties <- dcast(pivot_counties, state + county + FIPS ~ candidate, length)

  names(pivot_counties) <- str_replace_all(names(pivot_counties), c(" " = "."))

  # join pivot counties with county unemployment
  setkeyv(pivot_counties, 'FIPS')
  setkeyv(county_unemployment, 'FIPS')

  pivot_counties <- merge(pivot_counties, county_unemployment, all = FALSE)

  # remove unnecessary columns
  pivot_counties[, c('State',
                     'Area_name',
                     'Rural_urban_continuum_code_2013',
                     'Urban_influence_code_2013',
                     'Metro_2013') := NULL]

  # add indicator variable if county is pivot
  pivot_counties[ , pivot_indicator := as.factor(ifelse(Barack.Obama == 2 & Donald.Trump == 1, 1, 0))]

  return(pivot_counties)
}


counties <- GetCountyReturns(election_returns, county_unemployment)


# county descriptions
county_desc <- c('FIPS',
                 'state',
                 'county',
                 'pivot_indicator')

 # unemployment metrics
unemployment_metrics <- c('Civilian_labor_force_2007',
                       'Unemployed_2007',
                       'Civilian_labor_force_2008',
                       'Unemployed_2008',
                       'Civilian_labor_force_2009',
                       'Unemployed_2009',
                       'Civilian_labor_force_2010',
                       'Unemployed_2010',
                       'Civilian_labor_force_2011',
                       'Unemployed_2011',
                       'Civilian_labor_force_2012',
                       'Unemployed_2012',
                       'Civilian_labor_force_2013',
                       'Unemployed_2013',
                       'Civilian_labor_force_2014',
                       'Unemployed_2014',
                       'Civilian_labor_force_2015',
                       'Unemployed_2015',
                       'Civilian_labor_force_2016',
                       'Unemployed_2016',
                       'Civilian_labor_force_2017',
                       'Unemployed_2017',
                       'Civilian_labor_force_2018',
                       'Unemployed_2018'
                       )

counties_ue_wide <- counties[, c(county_desc, unemployment_metrics), with = FALSE]

# convert data from wide to long
counties_ue_long <- reshape(counties_ue_wide,
                            direction = 'long',
                            varying = unemployment_metrics,
                            timevar = 'year',
                            times = as.character(seq(2007, 2018, 1)),
                            v.names = c('unemployed', 'labor_force'),
                            idvar = county_desc)


# plot county unemployment rates