Jerry Shannon jshannon75

## walkerwarnock_2022.csv

          
            US Senate (Vote For 1)

            
              
              
              Herschel Junior Walker (Rep)
              
              
              
              
              Raphael Warnock (I) (Dem)
              
              
              
              
              Chase Oliver (Lib)

            
              County
              Registered Voters
              Election Day Votes
              Absentee by Mail Votes
              Advance Voting Votes
              Provisional Votes
              Total Votes
              Election Day Votes
              Absentee by Mail Votes
              Advance Voting Votes
              Provisional Votes
              Total Votes
              Election Day Votes
              Absentee by Mail Votes
              Advance Voting Votes
              Provisional Votes
              Total Votes
              Total

            
              Appling
              0
              2569
              224
              2550
              0
              5343
              411
              155
              694
              0
              1260
              28
              4
              33
              0
              65
              6668

            
              Atkinson
              0
              866
              33
              807
              0
              1706
              189
              22
              291
              0
              502
              15
              2
              10
              0
              27
              2235

            
              Bacon
              0
              829
              74
              2301
              0
              3204
              134
              41
              283
              0
              458
              17
              6
              25
              0
              48
              3710

            
              Baker
              0
              352
              35
              322
              2
              711
              163
              87
              272
              4
              526
              7
              0
              4
              1
              12
              1249

            
              Baldwin
              0
              2717
              296
              4403
              0
              7416
              2163
              818
              4343
              0
              7324
              107
              20
              87
              0
              214
              14954

            
              Banks
              0
              2275
              186
              3887
              0
              6348
              242
              84
              446
              0
              772
              71
              11
              77
              0
              159
              7279

            
              Barrow
              0
              8934
              662
              10531
              0
              20127
              3007
              691
              4534
              0
              8232
              450
              43
              400
              0
              893
              29252

## county_taxmigration
library(tidyverse)
library(tmap)
library(sf)

ctytax<-read_csv("countyinflow1920.csv") %>%
  mutate(ctyfips1=paste(y1_statefips,y1_countyfips,sep=""),
         ctyfips2=paste(y2_statefips,y2_countyfips,sep=""))

counties<-tigris::counties()

## Blue bird map
library(tidyverse)
library(extrafont)
library(sf)

ebird<-read_csv("ebird_GA_Sep2019.csv") #Individual observation data

species<-ebird %>%
  distinct(`COMMON NAME`)
species_blue<-species %>%
  filter(str_detect(tolower(`COMMON NAME`),"blue"))

## wh_tsp
#Code adapted from https://github.com/datawookie/travelling-salesman-map/blob/master/travelling-salesman-demo.R

library(tidyverse)
library(purrr)
library(ggmap)
library(gmapsdistance)
library(TSP)
library(sf)

# Due to changes in the Google Maps Terms of Service, this code will not work without first registering an API key.

## housingindex
library(tidycensus)
library(tidyverse)
library(sf)

#Select variables
v18<-load_variables(2018,"acs5",cache=TRUE)
vars<-v18 %>%
  filter(substr(name,1,6) %in% c("B25064","B03002","B25106","B19013"))
#write_csv(vars,"data/census_vars.csv") #Select just those variables I want
vars_sel<-read_csv("data/census_vars.csv")

## covdata_gifmap
library(tidyverse)
library(covdata)
library(tmap)
library(TTR)

ga_mobility<-apple_mobility %>%
  filter(sub_region=="Georgia" & geo_type=="county" &
           is.na(index)==FALSE) %>%
  group_by(region) %>%
  mutate(ra = runMean(index, 5)) %>%  ###Create a five day moving average

## covid_gifmap
library(tidyverse)
library(ggrepel)
library(gghighlight)
library(tidycensus)
library(sf)

dailydata<-read_csv("https://github.com/nytimes/covid-19-data/raw/master/us-counties.csv")

#This next step requires a census API
cty_pop<-get_acs(geography="county",variables = "B01001_001",geometry=TRUE) %>%

## lols_map.py
from os import path
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
import os

from wordcloud import WordCloud, STOPWORDS

d = path.dirname(__file__) if "__file__" in locals() else os.getcwd()

## meancenterweights
library(tidyverse)
library(sf)

#Join population data to tract boundaries and convert to point coordinates
tractdata<-read_csv("nhgis0105_2017_tract_racepov.csv") #Data on population downloaded from NHGIS
tracts<-st_read("nhgis0101_shapefile_tl2017_us_tract_2017/US_tract_2017.shp",stringsAsFactors=FALSE) %>% #Tract boundaries downloaded from NHGIS
  st_transform(4326)
tract_center<-tracts %>%
  st_centroid() %>%
  st_coordinates() %>%

## osmmap.R
library(tidyverse)
library(osmdata)
library(sf)
library(tigris)
library(wordcloud2)
library(tmap)

#Load city outline
city="Atlanta"
US Senate (Vote For 1)
		Herschel Junior Walker (Rep)					Raphael Warnock (I) (Dem)					Chase Oliver (Lib)
County	Registered Voters	Election Day Votes	Absentee by Mail Votes	Advance Voting Votes	Provisional Votes	Total Votes	Election Day Votes	Absentee by Mail Votes	Advance Voting Votes	Provisional Votes	Total Votes	Election Day Votes	Absentee by Mail Votes	Advance Voting Votes	Provisional Votes	Total Votes	Total
Appling	0	2569	224	2550	0	5343	411	155	694	0	1260	28	4	33	0	65	6668
Atkinson	0	866	33	807	0	1706	189	22	291	0	502	15	2	10	0	27	2235
Bacon	0	829	74	2301	0	3204	134	41	283	0	458	17	6	25	0	48	3710
Baker	0	352	35	322	2	711	163	87	272	4	526	7	0	4	1	12	1249
Baldwin	0	2717	296	4403	0	7416	2163	818	4343	0	7324	107	20	87	0	214	14954
Banks	0	2275	186	3887	0	6348	242	84	446	0	772	71	11	77	0	159	7279
Barrow	0	8934	662	10531	0	20127	3007	691	4534	0	8232	450	43	400	0	893	29252
	library(tidyverse)
	library(tmap)
	library(sf)

	ctytax<-read_csv("countyinflow1920.csv") %>%
	mutate(ctyfips1=paste(y1_statefips,y1_countyfips,sep=""),
	ctyfips2=paste(y2_statefips,y2_countyfips,sep=""))

	counties<-tigris::counties()
	library(tidyverse)
	library(extrafont)
	library(sf)

	ebird<-read_csv("ebird_GA_Sep2019.csv") #Individual observation data

	species<-ebird %>%
	distinct(`COMMON NAME`)
	species_blue<-species %>%
	filter(str_detect(tolower(`COMMON NAME`),"blue"))
	#Code adapted from https://github.com/datawookie/travelling-salesman-map/blob/master/travelling-salesman-demo.R

	library(tidyverse)
	library(purrr)
	library(ggmap)
	library(gmapsdistance)
	library(TSP)
	library(sf)

	# Due to changes in the Google Maps Terms of Service, this code will not work without first registering an API key.
	library(tidycensus)
	library(tidyverse)
	library(sf)

	#Select variables
	v18<-load_variables(2018,"acs5",cache=TRUE)
	vars<-v18 %>%
	filter(substr(name,1,6) %in% c("B25064","B03002","B25106","B19013"))
	#write_csv(vars,"data/census_vars.csv") #Select just those variables I want
	vars_sel<-read_csv("data/census_vars.csv")
	library(tidyverse)
	library(covdata)
	library(tmap)
	library(TTR)

	ga_mobility<-apple_mobility %>%
	filter(sub_region=="Georgia" & geo_type=="county" &
	is.na(index)==FALSE) %>%
	group_by(region) %>%
	mutate(ra = runMean(index, 5)) %>% ###Create a five day moving average
	library(tidyverse)
	library(ggrepel)
	library(gghighlight)
	library(tidycensus)
	library(sf)

	dailydata<-read_csv("https://github.com/nytimes/covid-19-data/raw/master/us-counties.csv")

	#This next step requires a census API
	cty_pop<-get_acs(geography="county",variables = "B01001_001",geometry=TRUE) %>%
	from os import path
	from PIL import Image
	import numpy as np
	import matplotlib.pyplot as plt
	import os

	from wordcloud import WordCloud, STOPWORDS

	d = path.dirname(__file__) if "__file__" in locals() else os.getcwd()
	library(tidyverse)
	library(sf)

	#Join population data to tract boundaries and convert to point coordinates
	tractdata<-read_csv("nhgis0105_2017_tract_racepov.csv") #Data on population downloaded from NHGIS
	tracts<-st_read("nhgis0101_shapefile_tl2017_us_tract_2017/US_tract_2017.shp",stringsAsFactors=FALSE) %>% #Tract boundaries downloaded from NHGIS
	st_transform(4326)
	tract_center<-tracts %>%
	st_centroid() %>%
	st_coordinates() %>%
	library(tidyverse)
	library(osmdata)
	library(sf)
	library(tigris)
	library(wordcloud2)
	library(tmap)

	#Load city outline
	city="Atlanta"