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Adding all data from Wyo website

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Alex Gebben Work 2025-10-02 16:13:34 -06:00
parent 116665e8f0
commit 2b907bf2ce
1 changed files with 37 additions and 1 deletions
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38
ARMA_Pop.r
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@ -17,11 +17,47 @@ plot(ARMA_POP,main="Lincoln County Population Forecast",xlab="Year",ylab="Popula
help("forecast") help("forecast")
####Employment to pop ratio ####Employment to pop ratio
EMP <- FRED_GET('LAUCN560230000000005','EMP') %>% inner_join(FRED_GET('WYLINC3POP','LN_POP')) %>% mutate(LN_POP=1000*LN_POP) EMP <- FRED_GET('LAUCN560230000000005','EMP') %>% inner_join(FRED_GET('WYLINC3POP','LN_POP')) %>% mutate(LN_POP=1000*LN_POP)
2900/3300
EMP <- EMP %>% mutate(RATIO=LN_POP/EMP) EMP <- EMP %>% mutate(RATIO=LN_POP/EMP)
ggplot(aes(x=YEAR,y=RATIO),data=EMP)+geom_line() ggplot(aes(x=YEAR,y=RATIO),data=EMP)+geom_line()
AVG_POP_RATIO <- mean(EMP$RATIO) AVG_POP_RATIO <- mean(EMP$RATIO)
SD_POP_RATIO <- sd(EMP$RATIO) SD_POP_RATIO <- sd(EMP$RATIO)
#####City level
#See data http://eadiv.state.wy.us/pop/
KEM <- c(3273,3523,3688,3667,3626,3637,3611,3388,3156,3040,3020,3029,2989,2959,2976,2963,2910,2807,2729,2690,2657,2608,2575,2561,2574,2579,2603,2640,2679,2692,2642,2597,2551,2575,2578,2554,2544,2499,2457,2435,2413,2445,2445,2404,2378)
DIAMOND <- c(1000,1070,1114,1101,1082,1078,1063,991,916,876,864,863,847,835,835,827,808,774,748,732,705,695,690,689,695,700,710,723,738,745,731,704,677,667,652,629,613,586,559,540,523,526,527,521,517)
AREA_POP <- KEM+DIAMOND
LN <- c(12177,13254,14031,14110,14111,14319,14384,13658,12875,12552,12625,12975,13124,13329,13759,14073,14206,14099,14114,14338,14621,14697,14858,15117,15539,15917,16429,17013,17629,18082,18083,17946,17822,18148,18346,18473,18766,18899,19042,19379,19658,20174,20690,20909,21000)
NO_CITY <- c(10095,10392,10747,10944,11043)
YEAR <- 1980:2024
DATA <- cbind(YEAR,LN,AREA_POP) %>% as_tibble %>% rename("Lincoln County"=LN,"Kemmerer & Diamondvile"=AREA_POP)
#Old daata:Period Ends in 1970
#See in part http://eadiv.state.wy.us/demog_data/cntycity_hist.htm
LN_OLD <- c(12487,10894,10286,9023,9018,8640)
LN_OLD <- cbind(seq(1920,1970,by=10),LN_OLD) %>% as_tibble
colnames(LN_OLD) <- c("YEAR","LN_POP")
KEM_OLD <- c(843,1517,1884,2026,1667,2028,2292)
KEM_OLD <- cbind(seq(1910,1970,by=10),KEM_OLD) %>% as_tibble
colnames(KEM_OLD) <- c("YEAR","KEM_POP")
DIAMOND_OLD <- c(696,726,812,586,415,398,485)
DIAMOND_OLD <- cbind(seq(1910,1970,by=10),DIAMOND_OLD) %>% as_tibble
colnames(DIAMOND_OLD) <- c("YEAR","DIA_POP")
OLD_DATA <- inner_join(KEM_OLD,DIAMOND_OLD) %>% full_join(LN_OLD)
GRAPH_DATA <- ts(DATA %>% select(-YEAR),start=c(1980),end=c(2024),frequency=1)
png("Population.png")
plot(GRAPH_DATA ,main="Regional Population Trends",type="b",lwd=4,col="blue")
dev.off()
lines(GRAPH_DATA,col="blue")
?plot
############## ##############
TS <- EMP %>% select(EMP) %>% ts(start=c(1990),end=c(2024),frequency=1) TS <- EMP %>% select(EMP) %>% ts(start=c(1990),end=c(2024),frequency=1)
BC <- BoxCox.lambda(TS) BC <- BoxCox.lambda(TS)