Christian Testa ctesta01

Converting Categorical Variables to Dummy Variables and Vice-Versa

This document introduces a pair of functions which should be intuitive to tidyverse users and standardizes the process for converting categorical columns of data back and forth from a set of dummy variables (columns of TRUE/FALSE indicators for each of the categorical levels).

library(tidyverse)

	library(ggplot2)
	library(magrittr)
	library(rvest)
	library(dplyr)

	# url to fetch html from
	html_data <- "https://www.history.com/topics/black-history/black-history-milestones"

	# parse html
	html_text <- read_html(html_data)

	library(tidyverse)
	library(magrittr)
	library(geomtextpath)

	df_usa <- readr::read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/rolling-averages/us.csv")

	ma <- function(x, n = 31){stats::filter(x, rep(1 / n, n), sides = 2)}

	df_usa %<>% mutate(
	deaths_avg = as.numeric(ma(deaths_avg)),

	library(tuneR)
	library(dplyr)
	library(magrittr)
	library(ggplot2)
	library(gganimate)

	# advice from https://stackoverflow.com/questions/23310005/permission-denied-when-playing-wav-file
	# you may not need this (or need something else) for other operating systems
	setWavPlayer('/usr/bin/afplay')




	library(tidyverse)
	library(magrittr)
	library(lubridate)

	df <- readr::read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/rolling-averages/us.csv")

	library(urbnmapr)
	library(plotly)
	library(sf)


	# Sometimes when we have spatial polygons (sf objects) and rendering plots with
	# them is going unexpectedly slow, it's because the objects are defined in quite
	# high resolution. This can be great if you want to render a high resolution map,
	# but sometimes we don't need that much resolution and would like to simplify
	# the polygon shapes to make rendering visualizations faster.

	# animate a saddle differential equation system
	#
	# dX = -x - y
	# dY = x - y
	# dZ = .25 * z
	#
	# 25 trajectories are simulated and animated -- rendered using rgl in R
	#
	# Read more about rgl: https://dmurdoch.github.io/rgl/

	library(tidyverse)
	library(magrittr)

	# New York Times style 7-day moving average
	#
	# Use each value from the given vector and the prior 6 values
	# to compute a moving average where values less than 0 are dropped
	# both from the sum in the numerator and from the count of days
	# in the denominator.
	#

	install.packages("tigris")

	library(tigris)
	library(sp)

	fulton20 <- tracts(state = 'GA', county = c('Fulton'), year = 2020)
	fulton10 <- tracts(state = 'GA', county = c('Fulton'), year = 2010)
	fulton00 <- tracts(state = 'GA', county = c('Fulton'), year = 2000)

	library(leaflet)

	library(plotly)
	library(sf)
	library(dplyr)

	nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)

	N <- 10

	# make random data
	df <- lapply(1:N, function(x) nc) %>% bind_rows %>%