library(tidyverse)
## ── Attaching packages ──────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 2.2.1 ✔ purrr 0.2.5
## ✔ tibble 1.4.2 ✔ dplyr 0.7.5
## ✔ tidyr 0.8.1 ✔ stringr 1.3.1
## ✔ readr 1.1.1 ✔ forcats 0.3.0
## ── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## Evictions data
evic <- read.csv("Downloads/cities.csv",
header=TRUE,
stringsAsFactors = FALSE)
names(evic)
## [1] "GEOID" "year"
## [3] "name" "parent.location"
## [5] "population" "poverty.rate"
## [7] "renter.occupied.households" "pct.renter.occupied"
## [9] "median.gross.rent" "median.household.income"
## [11] "median.property.value" "rent.burden"
## [13] "pct.white" "pct.af.am"
## [15] "pct.hispanic" "pct.am.ind"
## [17] "pct.asian" "pct.nh.pi"
## [19] "pct.multiple" "pct.other"
## [21] "eviction.filings" "evictions"
## [23] "eviction.rate" "eviction.filing.rate"
## [25] "low.flag" "imputed"
## [27] "subbed"
# ONC data
oncDF <- read.csv("~/Desktop/MATH239/OneNightCount.csv",
header=TRUE,
stringsAsFactors = FALSE)
oncG<-oncDF%>%
gather(City, Count, -c(Location,YEAR))
unique(oncG$City)
## [1] "SEATTLE" "KENT" "NORTH.END"
## [4] "EAST.SIDE" "SW.KING.CO" "WHITE.CNTR"
## [7] "FEDERAL.WAY" "RENTON" "NIGHT.OWL.BUSES"
## [10] "AUBURN" "VASHON.ISLAND" "TOTAL"
evic_onc<-evic%>%
filter(name %in% c("Auburn", "Federal Way",
"Kent", "Renton",
"Seattle", "Vashon",
"White Center"))
nameToONC<-data.frame(name=c("Auburn", "Federal Way",
"Kent", "Renton",
"Seattle", "Vashon",
"White Center"),
City=c("AUBURN", "FEDERAL.WAY",
"KENT", "RENTON",
"SEATTLE", "VASHON.ISLAND",
"WHITE.CNTR"))
evicCity<-evic_onc%>%
left_join(nameToONC, by="name")%>%
left_join(oncG)%>%
mutate(homeless_rate=Count/population)%>%
filter(Location=="TOTAL")
## Warning: Column `name` joining character vector and factor, coercing into
## character vector
## Joining, by = "City"
## Warning: Column `City` joining factor and character vector, coercing into
## character vector
head(evicCity)
## GEOID year name parent.location population poverty.rate
## 1 5303180 2000 Auburn Washington 40314 12.77
## 2 5303180 2000 Auburn Washington 40314 12.77
## 3 5303180 2000 Auburn Washington 40314 12.77
## 4 5303180 2000 Auburn Washington 40314 12.77
## 5 5303180 2000 Auburn Washington 40314 12.77
## 6 5303180 2000 Auburn Washington 40314 12.77
## renter.occupied.households pct.renter.occupied median.gross.rent
## 1 8427.06 45.8 639
## 2 8427.06 45.8 639
## 3 8427.06 45.8 639
## 4 8427.06 45.8 639
## 5 8427.06 45.8 639
## 6 8427.06 45.8 639
## median.household.income median.property.value rent.burden pct.white
## 1 39208 153400 26 79.92
## 2 39208 153400 26 79.92
## 3 39208 153400 26 79.92
## 4 39208 153400 26 79.92
## 5 39208 153400 26 79.92
## 6 39208 153400 26 79.92
## pct.af.am pct.hispanic pct.am.ind pct.asian pct.nh.pi pct.multiple
## 1 2.37 7.49 2.36 3.45 0.49 3.79
## 2 2.37 7.49 2.36 3.45 0.49 3.79
## 3 2.37 7.49 2.36 3.45 0.49 3.79
## 4 2.37 7.49 2.36 3.45 0.49 3.79
## 5 2.37 7.49 2.36 3.45 0.49 3.79
## 6 2.37 7.49 2.36 3.45 0.49 3.79
## pct.other eviction.filings evictions eviction.rate eviction.filing.rate
## 1 0.13 295.43 192.79 2.29 3.51
## 2 0.13 295.43 192.79 2.29 3.51
## 3 0.13 295.43 192.79 2.29 3.51
## 4 0.13 295.43 192.79 2.29 3.51
## 5 0.13 295.43 192.79 2.29 3.51
## 6 0.13 295.43 192.79 2.29 3.51
## low.flag imputed subbed City Location YEAR Count homeless_rate
## 1 1 0 0 AUBURN TOTAL 2016 110 0.002728581
## 2 1 0 0 AUBURN TOTAL 2015 132 0.003274297
## 3 1 0 0 AUBURN TOTAL 2014 97 0.002406112
## 4 1 0 0 AUBURN TOTAL 2013 57 0.001413901
## 5 1 0 0 AUBURN TOTAL 2012 44 0.001091432
## 6 1 0 0 AUBURN TOTAL 2011 45 0.001116238
this.year<-2016
thisDat<-evicCity%>%
filter(year==this.year)
mod_home<-lm(homeless_rate~median.household.income+eviction.rate, data=thisDat)
summary(mod_home)
##
## Call:
## lm(formula = homeless_rate ~ median.household.income + eviction.rate,
## data = thisDat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.833e-03 -5.216e-04 1.819e-05 4.622e-04 1.933e-03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.213e-03 9.844e-04 7.328 9.94e-10 ***
## median.household.income -5.822e-08 1.418e-08 -4.107 0.000132 ***
## eviction.rate -2.413e-03 2.928e-04 -8.243 3.09e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0008106 on 56 degrees of freedom
## (11 observations deleted due to missingness)
## Multiple R-squared: 0.5497, Adjusted R-squared: 0.5336
## F-statistic: 34.18 on 2 and 56 DF, p-value: 1.986e-10
mod_home1<-lm(homeless_rate~population+median.household.income+poverty.rate, data=thisDat)
summary(mod_home1)
##
## Call:
## lm(formula = homeless_rate ~ population + median.household.income +
## poverty.rate, data = thisDat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0012830 -0.0005731 -0.0001098 0.0003178 0.0030235
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.916e-02 3.519e-03 5.444 1.25e-06 ***
## population 5.267e-09 6.232e-10 8.452 1.61e-11 ***
## median.household.income -2.378e-07 4.204e-08 -5.656 5.74e-07 ***
## poverty.rate -3.768e-04 1.001e-04 -3.766 0.000406 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0007843 on 55 degrees of freedom
## (11 observations deleted due to missingness)
## Multiple R-squared: 0.586, Adjusted R-squared: 0.5635
## F-statistic: 25.95 on 3 and 55 DF, p-value: 1.361e-10
mod_home2<-lm(homeless_rate~poverty.rate+eviction.rate, data=thisDat)
summary(mod_home2)
##
## Call:
## lm(formula = homeless_rate ~ poverty.rate + eviction.rate, data = thisDat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0015765 -0.0007216 0.0000269 0.0004056 0.0018401
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.285e-03 3.932e-04 5.811 3.08e-07 ***
## poverty.rate 1.287e-04 3.828e-05 3.361 0.0014 **
## eviction.rate -2.372e-03 3.092e-04 -7.671 2.69e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0008435 on 56 degrees of freedom
## (11 observations deleted due to missingness)
## Multiple R-squared: 0.5125, Adjusted R-squared: 0.4951
## F-statistic: 29.43 on 2 and 56 DF, p-value: 1.837e-09
mod_home3<-lm(homeless_rate~rent.burden+eviction.rate, data=thisDat)
summary(mod_home3)
##
## Call:
## lm(formula = homeless_rate ~ rent.burden + eviction.rate, data = thisDat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.554e-03 -6.567e-04 3.170e-06 3.910e-04 1.775e-03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.320e-03 1.954e-03 0.676 0.502
## rent.burden 6.634e-05 6.497e-05 1.021 0.312
## eviction.rate -2.070e-03 3.422e-04 -6.048 1.27e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0009161 on 56 degrees of freedom
## (11 observations deleted due to missingness)
## Multiple R-squared: 0.4248, Adjusted R-squared: 0.4043
## F-statistic: 20.68 on 2 and 56 DF, p-value: 1.882e-07
mod_home4<-lm(homeless_rate~rent.burden+eviction.rate, data=thisDat)
summary(mod_home4)
##
## Call:
## lm(formula = homeless_rate ~ rent.burden + eviction.rate, data = thisDat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.554e-03 -6.567e-04 3.170e-06 3.910e-04 1.775e-03
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.320e-03 1.954e-03 0.676 0.502
## rent.burden 6.634e-05 6.497e-05 1.021 0.312
## eviction.rate -2.070e-03 3.422e-04 -6.048 1.27e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0009161 on 56 degrees of freedom
## (11 observations deleted due to missingness)
## Multiple R-squared: 0.4248, Adjusted R-squared: 0.4043
## F-statistic: 20.68 on 2 and 56 DF, p-value: 1.882e-07