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Wokring on age comparison in Kemmerer and Lincoln

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Alex Gebben Work 2025-10-30 16:58:53 -06:00
parent fa11049040
commit 06b8a858e2
2 changed files with 46 additions and 1 deletions
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3
2_Result_Analysis.r
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@ -24,5 +24,6 @@ YEARS <- 2023:(2023+NUM_YEARS_PROJECTED)
GRAPH_DATA$Percentile <- factor(GRAPH_DATA$Percentile,levels=rev(c('2.5%','5%','10%','25%','40%','60%','75%','90%','95%','97.5%'))) GRAPH_DATA$Percentile <- factor(GRAPH_DATA$Percentile,levels=rev(c('2.5%','5%','10%','25%','40%','60%','75%','90%','95%','97.5%')))
GRAPH <- ggplot(data=GRAPH_DATA)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`2.5%`,ymax=`97.5%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`5%`,ymax=`95%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`10%`,ymax=`90%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`25%`,ymax=`75%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`40%`,ymax=`60%`),alpha=ALPHA,fill=COLOR)+geom_line(aes(x=Year,y=Population,group=Percentile,color=Percentile))+geom_line(data=HIST,aes(x=Year,y=Population),color='black',size=0.75)+geom_line(data=MEDIAN_PRED,aes(x=Year,y=Population),color='black',linetype=4,size=0.75)+ scale_x_continuous(breaks = c(seq(1940, 2070, by = 10)))+ scale_y_continuous(breaks = seq(0, 35000, by = 5000))+theme_bw()+ggtitle("Lincoln County, Wyoming Population Forecast") GRAPH <- ggplot(data=GRAPH_DATA)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`2.5%`,ymax=`97.5%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`5%`,ymax=`95%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`10%`,ymax=`90%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`25%`,ymax=`75%`),alpha=ALPHA,fill=COLOR)+geom_ribbon(data=FAN_DATA,aes(x=Year,ymin=`40%`,ymax=`60%`),alpha=ALPHA,fill=COLOR)+geom_line(aes(x=Year,y=Population,group=Percentile,color=Percentile))+geom_line(data=HIST,aes(x=Year,y=Population),color='black',size=0.75)+geom_line(data=MEDIAN_PRED,aes(x=Year,y=Population),color='black',linetype=4,size=0.75)+ scale_x_continuous(breaks = c(seq(1940, 2070, by = 10)))+ scale_y_continuous(breaks = seq(0, 35000, by = 5000))+theme_bw()+ggtitle("Lincoln County, Wyoming Population Forecast")
GRAPH GRAPH
ggsave("Lincoln_Forecast.png",GRAPH) length(RES$SIM_ID %>% unique)
ggsave(Lincoln_Forecast.png",GRAPH)

44
Zip_Code.r Normal file
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@ -0,0 +1,44 @@
library(tidyverse)
library(tidycensus)
library(zipcodeR)
KEY <- '30e13ab22563318ff59286e433099f4174d4edd4'
TRACTS <- get_tracts(search_city('Diamondville','WY')$zipcode) %>% full_join(get_tracts(search_city('Kemmerer','WY')$zipcode))
TRACTS
census_api_key(KEY, install = TRUE)
vars <- c("P2_002N")
get_decennial(geography="tract",variables=vars,state='WY',county='lincoln',GEOID='56023978400')
vars <- c("B01001_004","B01001_005")
get_acs(geography="tract",variables=vars,state='WY',county='lincoln',GEOID='56023978400')
######MALE
VARS <-c(
#Under_Five
'B01001_003E',
#5-9
'B01001_004E',
#10-14
'B01001_005E',
#15-17
'B01001_006E',
#18-19
'B01001_007E',
#20
'B01001_008E',
#21
'B01001_009E',
#22-24
'B01001_010E',
#25-29
'B01001_011E',
#85+
'B01001_025E')
TEMP <- get_acs(geography="tract",variables=VARS,state='WY',county='lincoln')
KEM <- TEMP %>% filter(GEOID=='56023978400')
KEM
19/sum(KEM$estimate)
LN <- TEMP %>% filter(GEOID!='56023978400')
LN
19/sum(KEM$estimate)