sysilviakim

# Swirl and swirl lessons installation guide
install.packages("swirl")
library(swirl) # load the swirl package

## To start a swirl lesson, 
## Type in `swirl()`

## These courses contain some useful lessons to do via swirl.
install_course_github("kosukeimai", "qss-swirl")
install_course("R Programming")

sysilviakim

library(tidyverse)
library(Kmisc)
## https://www.pewresearch.org/internet/fact-sheet/social-media/

# Read tables quickly ==========================================================
pew_01 <- read.table(
  text = "Facebook	Instagram	LinkedIn
Total	69%	40%	28%
Men	61%	36%	31%
Women	77%	44%	26%

sysilviakim

library(tidyverse)
library(rvest)
library(Kmisc)

url <- paste0(
  "https://www.presidency.ucsb.edu/statistics/data/", 
  "voter-turnout-in-presidential-elections"
)

vap <- read_html(url) %>%

sysilviakim

library(tidyverse)
library(readxl)
library(janitor)

download.file(
  url =
    "https://www2.census.gov/programs-surveys/cps/tables/p20/585/table04a.xlsx",
  destfile = "turnout2020.xlsx",
  mode = "wb"
)

sysilviakim

library(dplyr)
library(ggplot2)
library(ggridges)
library(Kmisc)

# Brookings Party Unity Votes ==================================================
url <-
  "https://www.brookings.edu/wp-content/uploads/2017/01/vitalstats_ch8_tbl3.csv"
df <- read.csv(url)

sysilviakim

renv::init()
install.packages("devtools")
install.packages("remotes")
install.packages("colorspace")
library(remotes)
install_github(
  "sysilviakim/Kmisc", INSTALL_opts = c("--no-multiarch"), dependencies = TRUE
)
Kmisc::proj_skeleton()

sysilviakim

# Library and data import ======================================================
library(tidyverse)
library(scales)
library(ggrepel)
library(Kmisc) # devtools::install_github("sysilviakim/Kmisc")
options(digits = 5, scipen = 999)

# MEDSL Data: U.S. President 1976–2016
# https://doi.org/10.7910/DVN/42MVDX
# This data file contains constituency (state_po-level) returns for elections

sysilviakim

library(tidyverse)

set.seed(100)
df <- tibble(
  n = seq(1e5),
  x = rbinom(1e5, 1, 0.5), 
  y = cumsum(x) / n
)

## In base R, plot(df$n, df$y)

sysilviakim

library(dplyr)
library(tibble)
library(rvest)
library(xtable)

url_redist <- "https://ballotpedia.org/State-by-state_redistricting_procedures"
state_df <- read_html(url_redist) %>%
  html_table(fill = TRUE) %>%
  ## Fourth table is the congressional redistricting table, after manual insp.
  .[[4]] %>%

sysilviakim

library(tidyverse)
library(rvest)
library(jsonlite)
library(xtable)

## How many Electoral College electors per state? ==============================
## Page as is for Sep 1, 2020
url_270 <- "https://www.270towin.com"
electors <- read_html(url_270) %>%
  html_nodes("script") %>%