HW2_State_Tracts

Save your API key to your working directory

use census_api_key(key = "yourkeyhere", install = T)

one time to install your key for use in tidycensus

library(tidycensus)

census_api_key(key ="f67be5a86e696eebf0c84a5a20d85f2c04f7e9ef", overwrite= T  )

## To install your API key for use in future sessions, run this function with `install = TRUE`.

options(tigris_use_cache = TRUE)

Load libraries

library(haven)
library(car)

## Loading required package: carData

library(lme4)

## Loading required package: Matrix

library(sjstats)
library(lmerTest)

## 
## Attaching package: 'lmerTest'

## The following object is masked from 'package:lme4':
## 
##     lmer

## The following object is masked from 'package:stats':
## 
##     step

library(MuMIn)

Look at available ACS variables

library(tidyverse); library(sf)

## -- Attaching packages ---------------------------------------------------------- tidyverse 1.2.1 --

## v ggplot2 3.0.0     v purrr   0.2.5
## v tibble  1.4.2     v dplyr   0.7.6
## v tidyr   0.8.1     v stringr 1.3.1
## v readr   1.1.1     v forcats 0.3.0

## -- Conflicts ------------------------------------------------------------- tidyverse_conflicts() --
## x tidyr::expand() masks Matrix::expand()
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
## x dplyr::recode() masks car::recode()
## x purrr::some()   masks car::some()

## Linking to GEOS 3.6.1, GDAL 2.2.3, proj.4 4.9.3

v10_Profile <- load_variables(2010, "acs5/profile", cache = TRUE) #demographic profile tables
v10_tables <- load_variables(2010 , "acs5", cache = TRUE) #all tables
#v10_sf1_tables <- load_variables(2010 , "sf1", cache = TRUE) #all tables for 2010 SF1

View(v10_Profile)

#Search for variables by 
v10_Profile[grep(x = v10_Profile$label, "owner"), c("name", "label")]

## # A tibble: 4 x 2
##   name       label                                                        
##   <chr>      <chr>                                                        
## 1 DP04_0004  Estimate!!HOUSING OCCUPANCY!!Homeowner vacancy rate          
## 2 DP04_0004P Percent!!HOUSING OCCUPANCY!!Homeowner vacancy rate           
## 3 DP04_0047  Estimate!!HOUSING TENURE!!Average household size of owner-oc~
## 4 DP04_0047P Percent!!HOUSING TENURE!!Average household size of owner-occ~

v10_Profile[grep(x = v10_Profile$label, "value"), c("name", "label")]

## # A tibble: 0 x 2
## # ... with 2 variables: name <chr>, label <chr>

Extract from ACS housing data for 2010 for TX Census Tracts

tx_acs<-get_acs(geography = "tract", state="TX", year = 2010,
                variables=c( "DP04_0089PE", "DP04_0080PE", "DP03_0002PE", "DP02_0061PE", "DP02_0003PE", "DP03_0119PE"), geometry = T, output = "wide")

## Getting data from the 2006-2010 5-year ACS
## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

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## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

## Getting data from the 2006-2010 5-year ACS

## Using the ACS Data Profile

names(tx_acs)

##  [1] "GEOID"       "NAME"        "DP04_0089PE" "DP04_0089PM" "DP04_0080PE"
##  [6] "DP04_0080PM" "DP03_0002PE" "DP03_0002PM" "DP02_0061PE" "DP02_0061PM"
## [11] "DP02_0003PE" "DP02_0003PM" "DP03_0119PE" "DP03_0119PM" "geometry"

#create a county FIPS code - 5 digit

tx_acs$county<-substr(tx_acs$GEOID, 1, 5)

#rename variables and filter missing cases
tx_acs2<-tx_acs%>%
  mutate(owner= DP04_0089PE, homevalue=DP04_0080PE, laborforce=DP03_0002PE, hsgrad=DP02_0061PE, hhchild= DP02_0003PE, poverty= DP03_0119PE) %>%
#  st_transform(crs = 102740)%>%
  filter(complete.cases(owner, homevalue, laborforce, hsgrad, hhchild, poverty))

class(tx_acs2)

## [1] "sf"         "tbl_df"     "tbl"        "data.frame"

Do families living in poverty have lower rates of homewownership than families who do not live in poverty, net of other demographic and socioeconomic factors?

tx_acs2$ownerz<-scale(tx_acs2$owner, center = T, scale = T)
fit1<-lmer(ownerz~homevalue+laborforce+hsgrad+hhchild+poverty+(1|county), data=tx_acs2)
summary(fit1)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ownerz ~ homevalue + laborforce + hsgrad + hhchild + poverty +  
##     (1 | county)
##    Data: tx_acs2
## 
## REML criterion at convergence: 12967.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8511 -0.6348 -0.1029  0.5155  8.6860 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  county   (Intercept) 0.08004  0.2829  
##  Residual             0.69456  0.8334  
## Number of obs: 5150, groups:  county, 254
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)  2.352e-01  1.119e-01  4.727e+03   2.102   0.0356 *  
## homevalue   -4.990e-03  9.377e-04  4.700e+03  -5.322 1.08e-07 ***
## laborforce  -6.519e-03  1.440e-03  5.087e+03  -4.527 6.12e-06 ***
## hsgrad       1.314e-03  1.465e-03  4.854e+03   0.897   0.3698    
## hhchild      2.328e-02  1.040e-03  5.142e+03  22.382  < 2e-16 ***
## poverty     -3.446e-02  1.282e-03  4.931e+03 -26.883  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) homevl lbrfrc hsgrad hhchld
## homevalue  -0.230                            
## laborforce -0.844  0.200                     
## hsgrad     -0.436 -0.182  0.127              
## hhchild    -0.103  0.001 -0.198 -0.053       
## poverty    -0.132 -0.431  0.186 -0.112 -0.106

According to Model 1, yes, families who live below the poverty line have significantly lower rates of homeownership than families not living below poverty.

Do families living in poverty face a disadvantage in homeownership equally in every census tract in Texas?

fit2<-lmer(ownerz~homevalue+laborforce+hsgrad+hhchild+poverty+(1+poverty|county), data=tx_acs2)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 0.0026507 (tol =
## 0.002, component 1)

## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

summary(fit2)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: ownerz ~ homevalue + laborforce + hsgrad + hhchild + poverty +  
##     (1 + poverty | county)
##    Data: tx_acs2
## 
## REML criterion at convergence: 12919.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.6895 -0.6329 -0.1090  0.5037  8.5427 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr 
##  county   (Intercept) 0.180437 0.42478       
##           poverty     0.000211 0.01453  -0.88
##  Residual             0.682132 0.82591       
## Number of obs: 5150, groups:  county, 254
## 
## Fixed effects:
##               Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)  1.071e-01  1.168e-01  2.759e+03   0.916   0.3595    
## homevalue   -4.975e-03  9.348e-04  3.596e+03  -5.323 1.08e-07 ***
## laborforce  -6.386e-03  1.433e-03  4.961e+03  -4.455 8.57e-06 ***
## hsgrad       3.429e-03  1.501e-03  4.786e+03   2.285   0.0223 *  
## hhchild      2.332e-02  1.041e-03  5.033e+03  22.409  < 2e-16 ***
## poverty     -3.267e-02  2.226e-03  6.851e+01 -14.676  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) homevl lbrfrc hsgrad hhchld
## homevalue  -0.200                            
## laborforce -0.797  0.204                     
## hsgrad     -0.455 -0.208  0.122              
## hhchild    -0.096  0.007 -0.188 -0.061       
## poverty    -0.320 -0.277  0.060  0.070 -0.084
## convergence code: 0
## Model failed to converge with max|grad| = 0.0026507 (tol = 0.002, component 1)
## Model is nearly unidentifiable: very large eigenvalue
##  - Rescale variables?

According to Model 2, variance is present regarding poverty and census tracts in Texas. Yet Model 2 is not necessarily a better fit than Model 1.

rancoefs2<-ranef(fit2)
head(rancoefs2$county, n=10)

##       (Intercept)       poverty
## 48001  0.04261351 -0.0002266399
## 48003 -0.08180576  0.0024882730
## 48005  0.22207782 -0.0033557627
## 48007  0.27695567 -0.0055026340
## 48009 -0.18467004  0.0052570097
## 48011 -0.19071761  0.0054377329
## 48013  0.29926730 -0.0056330997
## 48015  0.05264104 -0.0007660562
## 48017  0.18480124 -0.0048168332
## 48019  0.19873915 -0.0036257534

Plot random slope model

par(mfrow=c(1,1))
plot(rancoefs2$county[, "(Intercept)"], rancoefs2$county[, "poverty"], main="Random effects for each census tract", xlab="Intercept", ylab="Poverty slope")

HW2_State_Tracts

Monica Cruz

September 24, 2018

Save your API key to your working directory

Load libraries

Look at available ACS variables

Extract from ACS housing data for 2010 for TX Census Tracts

Do families living in poverty have lower rates of homewownership than families who do not live in poverty, net of other demographic and socioeconomic factors?

According to Model 1, yes, families who live below the poverty line have significantly lower rates of homeownership than families not living below poverty.

Do families living in poverty face a disadvantage in homeownership equally in every census tract in Texas?

According to Model 2, variance is present regarding poverty and census tracts in Texas. Yet Model 2 is not necessarily a better fit than Model 1.

Plot random slope model