Homework 7

#Perform an extract of the 2019 5 Year ACS data:

library(ipumsr)

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

ddi <- read_ipums_ddi("C:/Users/shahi/OneDrive - University of Texas at San Antonio/Desktop/Spring'22/Dem 5093 (GIS)/usa_00006.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) #necessary to avoid problems with "labelled" data class
names(data)<-tolower(names(data))

Load some other packages

library(survey, quietly = T)
library(tidyverse, quietly = T)
library(car, quietly = T)
library(ggplot2, quietly = T)
library(tigris, quietly = T)
library(classInt, quietly = T)
library(tmap, quietly = T)
library(janitor,quietly = T)

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

library(mapview, quietly= T)
library(knitr, quietly = T)

#Using these data, create estimates for Californian PUMAs of the % of the population that lived in the same house last year:

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

Here I recode several demographic variables

data$pwt <- data$perwt
data$hwt <- data$hhwt
#Migration
data$migration<- Recode(data$migrate1, recodes = "1=1; 2:4=0; 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)

puma_est_migration<-svyby( ~I(migration==1),
                    by = ~puma,
                    design = des,
                    FUN=svymean,
                    na.rm = TRUE ) %>% 
  clean_names() %>% 
  mutate(Pctsh=round((i_migration_1_true*100),1), Pctnsh=round((i_migration_1_false*100),1)) %>% 
  rename(propnsh=i_migration_1_false,
         propsh=i_migration_1_true,
         Stderr=se_i_migration_1_true) %>% 
  select(puma,Pctsh,Pctnsh,Stderr)

join to geography

pumas<- pumas(state = "CA",
              year = 2019,
              cb= T) %>% 
  mutate(puma=as.numeric(PUMACE10))

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

Map estimates:

Residintial stability rates by PUMA

tmap_mode("view")

## tmap mode set to interactive viewing

tm_basemap("OpenStreetMap.Mapnik")

  tm_shape(geo1)+
  tm_polygons("Pctsh",
              style="kmeans",
              title=c("Percent Estimates"),
              palette="Blues",
              n=8,
              legend.hist = TRUE) +
  tm_layout(legend.outside = TRUE,
            title = "Percent of the population living in the same house in California PUMAs \n 2015-2019",
            title.size =1.5,
            legend.frame = TRUE,
            ) +  tm_compass(position = c("left","top")) + tm_format("World",
            legend.position =  c("left", "bottom"),
            main.title.position =c("center")) + tm_scale_bar(position = c("left","bottom"))

## Compass not supported in view mode.

## legend.postion is used for plot mode. Use view.legend.position in tm_view to set the legend position in view mode.