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snippet mygis_dt_merge_districts_2_precincts
	${1:my_geography} <- as.data.table(${1:my_geography})
	${2:my_new_geography} <- fram2[, .(geometry = st_union(geometry)), by = ${3:larger_district}]
	${2:my_new_geography} <- sf::st_sf(${2:my_new_geography})
	# test plot
	# ggplot(${2:my_new_geography}, aes(geometry=geometry)) +
	# geom_sf()

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#' Description
#' This file runs a live election-night forecast based on The Economist's pre-election forecasting model
#' available at projects.economist.com/us-2020-forecast/president.
#' It is resampling model based on https://pkremp.github.io/update_prob.html.
#' This script does not input any real election results! You will have to enter your picks/constraints manually (scroll to the bottom of the script).
#' 
#' Licence
#' This software is published by *[The Economist](https://www.economist.com)* under the [MIT licence](https://opensource.org/licenses/MIT). The data generated by *The Economist* are available under the [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).
#' The licences include only the data and the software authored by *The Economist*, and do not cover any *Economist* content or third-party data or content made available using the software. More information about licensing, syndication and the copyright of *Economist* content can be fou

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library(reactable)
library(dplyr)

red_pal <- function(x) rgb(colorRamp(c("#FFCDD2FF", "#C62828FF"))(x), maxColorValue = 255)
blue_pal <- function(x) rgb(colorRamp(c("#BBDEFBFF", "#1565C0FF"))(x), maxColorValue = 255)

mtcars %>%
  select(cyl, mpg) %>%
  reactable(
    pagination = FALSE,

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# 1. Scrape tables. You can do that with the Table Capture Chrome extension, or you can do it with R.

# Download the entire html document so I don't need to keep hammering the Wikipedia server


library(htmltab)
library(rvest)
library(purrr)
library(dplyr)

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library(dplyr)
library(tidyr)
library(janitor)
library(stringr)

starwars_garbage_data1 <- data.frame(
  stringsAsFactors = FALSE,
  v1 = c(
    "Character Name", "C-3PO", "Person-film ID", "2218529825", "7731900678",
    "123598423", "238952395", "6232048034", "3036308047",

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price_median1 <- readr::read_csv("https://raw.githubusercontent.com/smach/BU-dataviz-workshop-2019/master/data/zillow_data_median_sf_price.csv")
str(price_median1)
price_median2 <- read.csv("https://raw.githubusercontent.com/smach/BU-dataviz-workshop-2019/master/data/zillow_data_median_sf_price.csv")
str(price_median2)

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if(!require(pacman)){
    install.packages("pacman")
}
p_load(ggplot2, dplyr, janitor)
district <-c("A","B","C","A","B", "C")
money <-c(500,324,245,654,234, 232)
year <- c("2001", "2001", "2001", "2002", "2002", "2002")
df <- data.frame(district, money, year, stringsAsFactors = FALSE)
total_by_year <- df %>%
    group_by(year) %>%

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# Survey data file downloaded manually from the CDC https://www.cdc.gov/brfss/annual_data/annual_2017.html
surveydatafile <- "LLCP2017.ASC"

# I used datapasta package's df_paste() function to create the data frame below,
# after copying the metadata table into my clipboard. Metadata is here:
# https://www.cdc.gov/brfss/annual_data/2017/llcp_varlayout_17_onecolumn.html

column_meta_data <- data.frame(stringsAsFactors=FALSE,
   Starting.Column = c(1L, 17L, 19L, 19L, 21L, 23L, 32L, 36L, 36L, 63L, 64L,
                       65L, 66L, 67L, 68L, 69L, 71L, 73L, 75L, 76L, 77L, 78L,

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