Post Election Reflection

library(geofacet)
library(glmnet)

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library(usmap)
library(ggpubr)

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library(ggthemes)
library(haven)
library(kableExtra)
library(maps)
library(mgcv)

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library(mgcViz)

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library(RColorBrewer)
library(scales)
library(sf)

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library(spData)

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library(stargazer)

## 
## Please cite as:

##  Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.

##  R package version 5.2.3. https://CRAN.R-project.org/package=stargazer

library(tidygeocoder)
library(tidyverse)

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library(tigris)

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library(tmap)

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library(tmaptools)
library(viridis)

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library(broom)
library(knitr)
library(dplyr)
library(Metrics)

state_pres_vote <- read_csv("state_votes_pres_2024.csv")

## New names:
## Rows: 52 Columns: 42
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (42): FIPS, Geographic Name, Geographic Subtype, Total Vote, Kamala D. H...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
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state_pres_vote <- state_pres_vote |>
  select(state = "Geographic Name", Trump = "Donald J. Trump", Harris = "Kamala D. Harris") |>
  mutate(
    Trump = as.numeric(Trump),
    Harris = as.numeric(Harris),
    D2pv = (Harris/(Trump + Harris))*100
  ) |> 
  drop_na()

## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `Trump = as.numeric(Trump)`.
## Caused by warning:
## ! NAs introduced by coercion
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.

my_predictions <- read_csv("my_predictions.csv")

## Rows: 7 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): state
## dbl (1): my_prediction
## 
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## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

my_predictions <- my_predictions|>
  left_join(state_pres_vote, by = c("state")) |>
  select(state, D2pv, my_prediction) |> 
  filter(state %in% c("Arizona", "North Carolina", "Nevada", "Pennsylvania", "Michigan", "Wisconsin", "Georgia"))

my_predictions

## # A tibble: 7 × 3
##   state           D2pv my_prediction
##   <chr>          <dbl>         <dbl>
## 1 Arizona         47.2          50.2
## 2 Georgia         48.9          50.0
## 3 Michigan        49.3          49.0
## 4 Nevada          48.4          49.7
## 5 North Carolina  48.3          49.3
## 6 Pennsylvania    49.0          48.9
## 7 Wisconsin       49.5          48.6

Mse <- mse(my_predictions$D2pv, my_predictions$my_prediction)
Mse

## [1] 2.104484

Rmse <- rmse(my_predictions$D2pv, my_predictions$my_prediction)
Rmse 

## [1] 1.450684

Mae <- mae(my_predictions$D2pv, my_predictions$my_prediction)
Mae

## [1] 1.131759

Bias <- bias(my_predictions$D2pv, my_predictions$my_prediction)
Bias

## [1] -0.759456

state_level_analysis <- my_predictions |>
  group_by(state) |> 
  mutate(
    mse = mse(D2pv, my_prediction),
    rmse = rmse(D2pv, my_prediction),
    mae = mae(D2pv, my_prediction),
    bias = bias(D2pv, my_prediction)
  )

kable(state_level_analysis)