DEM 7093 HW7

library(ipumsr)

## Warning: package 'ipumsr' was built under R version 4.1.3

ddi <- read_ipums_ddi("C:/Users/drayr/OneDrive/Desktop/DEM Spring 2022/DEM 7093/HW7/usa_00010.xml")
data <- read_ipums_micro(ddi)

## Use of data from IPUMS USA is subject to conditions including that users should
## cite the data appropriately. Use command `ipums_conditions()` for more details.

data<-haven::zap_labels(data) 
names(data)<-tolower(names(data))

Load some other packages

library(survey, quietly = T)

## Warning: package 'survey' was built under R version 4.1.3

library(tidyverse, quietly = T)
library(car, quietly = T)

## Warning: package 'car' was built under R version 4.1.3

## Warning: package 'carData' was built under R version 4.1.3

library(ggplot2, quietly = T)
library(tigris, quietly = T)
library(classInt, quietly = T)
library(tmap, quietly = T)

Download geographic data for Public Use Microdata Areas

The Public Use Microdata Area is the lowest level of geography in the PUMS data. They correspond to greographic ares of ~ 100,000 people.

options(tigris_class = "sf")
pumas<-pumas(state = "CA",
             year = 2019,
             cb = T)

## 
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plot(pumas["GEOID10"],
     main = "Public Use Microdata Areas in California")

mapview::mapview(pumas, zcol= "GEOID10")

Prepare variables

data$pwt <- data$perwt
data$hwt <- data$hhwt

#citizenship
data$mig<-Recode(data$migrate1, recodes = "1='Same House'; 2:4='mover'; 3:4='notcitizen';else=NA ")

Generate survey design object

Here we identify the person weights and the survey design variables.

des<-svydesign(ids = ~cluster,
               strata = ~ strata,
               weights = ~pwt,
               data = data)

perform survey estimation for PUMAs

puma_est_mig<-svyby(formula = ~mig,
                    by = ~puma,
                    design = des,
                    FUN=svymean,
                    na.rm = TRUE )

head(puma_est_mig)

join to geography

pumas$puma<-as.numeric(pumas$PUMACE10)

geo1<-left_join(pumas, puma_est_mig, by=c("puma"= "puma"))
head(geo1)

Map estimates

Same House by PUMA

tmap_mode("view")

## tmap mode set to interactive viewing

geo1%>%
  tm_shape()+
  tm_polygons("migSame House",
              style="kmeans",
              n=8,
              legend.hist = TRUE) +
  tm_layout(legend.outside = TRUE,
            title = "Same House in California PUMAs \n 2015-2019")