Final Project code DEM 7093
David Rodriguez
04 May, 2022
library(ipumsr)## Warning: package 'ipumsr' was built under R version 4.1.3nhgis_ddi <- read_ipums_codebook("C:/Users/drayr/Downloads/nhgis0002_csv.zip")
nhgis <- read_nhgis(data_file = "C:/Users/drayr/Downloads/nhgis0002_csv.zip")## Use of data from NHGIS is subject to conditions including that users should
## cite the data appropriately. Use command `ipums_conditions()` for more details.
##
##
## Reading data file...nhgis<-haven::zap_labels(nhgis)
names(nhgis)<-tolower(names(nhgis))
library(sf)## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUEholc <- st_read("C:/Users/drayr/Downloads/TXSanAntonio19XX/cartodb-query.shp")## Reading layer `cartodb-query' from data source
## `C:\Users\drayr\Downloads\TXSanAntonio19XX\cartodb-query.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 20 features and 3 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -98.57493 ymin: 29.35626 xmax: -98.43542 ymax: 29.49135
## Geodetic CRS: WGS 84Load some other packages
library(survey, quietly = T)## Warning: package 'survey' was built under R version 4.1.3library(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 4.1.3library(ggplot2, quietly = T)
library(tigris, quietly = T)
library(classInt, quietly = T)
library(tmap, quietly = T)
library(raster)Download geographic data for Census Tracts
options(tigris_class = "sf")
tract<-tracts(state = "TX",
county = 029,
year = 2020,
c = T)##
|
| | 0%
|
|= | 1%
|
|= | 2%
|
|== | 2%
|
|== | 3%
|
|=== | 4%
|
|=== | 5%
|
|==== | 5%
|
|==== | 6%
|
|===== | 7%
|
|===== | 8%
|
|====== | 8%
|
|====== | 9%
|
|======= | 10%
|
|======= | 11%
|
|======== | 11%
|
|======== | 12%
|
|========= | 12%
|
|========= | 13%
|
|========== | 14%
|
|========== | 15%
|
|=========== | 15%
|
|=========== | 16%
|
|============ | 17%
|
|============ | 18%
|
|============= | 18%
|
|============= | 19%
|
|============== | 19%
|
|============== | 20%
|
|=============== | 21%
|
|=============== | 22%
|
|================ | 23%
|
|================ | 24%
|
|================= | 24%
|
|================= | 25%
|
|================== | 26%
|
|=================== | 27%
|
|=================== | 28%
|
|==================== | 28%
|
|==================== | 29%
|
|===================== | 29%
|
|===================== | 30%
|
|====================== | 31%
|
|====================== | 32%
|
|======================= | 32%
|
|======================= | 33%
|
|======================= | 34%
|
|======================== | 34%
|
|======================== | 35%
|
|========================= | 35%
|
|========================= | 36%
|
|========================== | 37%
|
|=========================== | 38%
|
|=========================== | 39%
|
|============================ | 40%
|
|============================= | 41%
|
|============================= | 42%
|
|============================== | 42%
|
|============================== | 43%
|
|=============================== | 44%
|
|=============================== | 45%
|
|================================ | 45%
|
|================================ | 46%
|
|================================= | 47%
|
|================================== | 48%
|
|=================================== | 49%
|
|=================================== | 50%
|
|=================================== | 51%
|
|==================================== | 51%
|
|==================================== | 52%
|
|===================================== | 53%
|
|====================================== | 54%
|
|====================================== | 55%
|
|======================================= | 55%
|
|======================================= | 56%
|
|======================================== | 57%
|
|======================================== | 58%
|
|========================================= | 58%
|
|========================================= | 59%
|
|========================================== | 60%
|
|=========================================== | 61%
|
|=========================================== | 62%
|
|============================================ | 62%
|
|============================================ | 63%
|
|============================================ | 64%
|
|============================================= | 64%
|
|============================================= | 65%
|
|============================================== | 65%
|
|============================================== | 66%
|
|=============================================== | 66%
|
|=============================================== | 67%
|
|=============================================== | 68%
|
|================================================ | 69%
|
|================================================= | 70%
|
|================================================= | 71%
|
|================================================== | 71%
|
|================================================== | 72%
|
|=================================================== | 72%
|
|=================================================== | 73%
|
|==================================================== | 74%
|
|==================================================== | 75%
|
|===================================================== | 75%
|
|===================================================== | 76%
|
|====================================================== | 76%
|
|====================================================== | 77%
|
|====================================================== | 78%
|
|======================================================= | 78%
|
|======================================================= | 79%
|
|======================================================== | 80%
|
|======================================================== | 81%
|
|========================================================= | 81%
|
|========================================================= | 82%
|
|========================================================== | 82%
|
|========================================================== | 83%
|
|=========================================================== | 84%
|
|=========================================================== | 85%
|
|============================================================ | 85%
|
|============================================================ | 86%
|
|============================================================= | 87%
|
|============================================================= | 88%
|
|============================================================== | 88%
|
|============================================================== | 89%
|
|=============================================================== | 89%
|
|=============================================================== | 90%
|
|=============================================================== | 91%
|
|================================================================ | 91%
|
|================================================================= | 92%
|
|================================================================= | 93%
|
|================================================================== | 94%
|
|=================================================================== | 95%
|
|=================================================================== | 96%
|
|==================================================================== | 97%
|
|==================================================================== | 98%
|
|===================================================================== | 98%
|
|===================================================================== | 99%
|
|======================================================================| 100%plot(tract["GEOID"],
main = "Census Tracts in San Antonio")
plot(holc["holc_grade"],
main = "HOLC graded areas: San Antonio 1935")
Prepare variables
nhgis2 <- nhgis %>%
mutate( tot=u7e001,
hisp=u7e002,
nh=u7e003,
nhblk=u7e006,
nhwht=u7e005,
GEOID=geocode,
cofips=countya) %>%
filter(statea=="48" & countya=="029")%>%
dplyr:: select(GEOID, tot, hisp, nh,nhblk,nhwht,cofips)
#We need the county-level totals for the total population and each race group
codat<-nhgis2%>%
summarise(co_total=sum(tot), co_wht=sum(nhwht), co_blk=sum(nhblk), cohisp=sum(hisp))
codat$cofips="029"
#we merge the county data back to the tract data by the county FIPS code
merged<-left_join(x=nhgis2,y=codat, by="cofips")
#have a look and make sure it looks ok
head(merged)#isolation index
tr.iso<-merged%>%
mutate(isoh=(hisp/cohisp * hisp/tot),
isob=(nhblk/co_blk * nhblk/tot),
isow=(nhwht/co_wht * nhwht/tot))%>%
group_by(cofips)#%>%
#summarise(iso_b= sum(isob, na.rm=T))
knitr::kable(head(nhgis2))| GEOID | tot | hisp | nh | nhblk | nhwht | cofips |
|---|---|---|---|---|---|---|
| 48029110100 | 3628 | 1639 | 1989 | 270 | 1400 | 029 |
| 48029110300 | 1950 | 1232 | 718 | 86 | 540 | 029 |
| 48029110500 | 1451 | 1191 | 260 | 80 | 139 | 029 |
| 48029110600 | 6664 | 4202 | 2462 | 811 | 1597 | 029 |
| 48029110700 | 1007 | 746 | 261 | 50 | 185 | 029 |
| 48029111000 | 2044 | 1135 | 909 | 115 | 668 | 029 |
join to geography
geo1<-left_join(tract, tr.iso, by=c("GEOID"= "GEOID"))
head(geo1)geo2<-geo1%>%
mutate(
area=st_area(.))
geo2<-st_transform(geo2, crs=2278)
holc<-st_transform(holc, crs=2278)Map layout
tmap_mode("view")## tmap mode set to interactive viewingholctract<-st_intersection(holc,geo2)## Warning: attribute variables are assumed to be spatially constant throughout all
## geometriesholcAB<-holc%>%
filter(holc_grade== "A" | holc_grade=="B" )
holctractAB<-st_intersection(holcAB,geo2)## Warning: attribute variables are assumed to be spatially constant throughout all
## geometriesholcCD<-holc%>%
filter(holc_grade== "C" | holc_grade=="D" )
holctractCD<-st_intersection(holcCD,geo2)## Warning: attribute variables are assumed to be spatially constant throughout all
## geometriesholcA<-holc%>%
filter(holc_grade=="A")
holctractA<-st_intersection(holcA,geo2)## Warning: attribute variables are assumed to be spatially constant throughout all
## geometriesholcB<-holc%>%
filter(holc_grade=="B")
holctractB<-st_intersection(holcB,geo2)## Warning: attribute variables are assumed to be spatially constant throughout all
## geometriesholcC<-holc%>%
filter(holc_grade=="C")
holctractC<-st_intersection(holcC,geo2)## Warning: attribute variables are assumed to be spatially constant throughout all
## geometriesholcD<-holc%>%
filter(holc_grade=="D")
holctractD<-st_intersection(holcD,geo2)## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries#tmaptools::palette_explorer()
tmap_mode("view")## tmap mode set to interactive viewing#Hispanic isolation
tm_shape(holctract)+
tm_polygons(alpha=0, palette = "-viridis", n = 10, contrast = c(0.42, 1))+
tm_shape(holc)+
tm_polygons("holc_grade", alpha = .5, border.col = "red", lty= "solid")+
tm_layout(title="HOLC grades Layered on SA 2020 Census Tracts",
title.size =1.5,
legend.frame = TRUE,
title.position = c('right', 'top'))# tm_shape(holctract)+
# tm_polygons("isoh", palette = "-viridis", n = 10, contrast = c(0, 0.52))+
# tm_shape(holcCD)+
# tm_polygons("holc_grade",palette = "black", alpha = .3, border.col = "red", lty= "solid", ltw = 100)
#
#
# tm_shape(holctractAB)+
# tm_polygons("isoh")+
# tm_shape(holcAB)+
# tm_polygons("holc_grade", alpha = 0, border.col = "red", lty= "solid")
#
# tm_shape(holctractCD)+
# tm_polygons("isoh")+
# tm_shape(holcCD)+
# tm_polygons("holc_grade", alpha = 0, border.col = "red", lty= "solid")
#
# #White isolation
# tm_shape(holctract)+
# tm_polygons("isow", palette = "-viridis", n = 10, contrast = c(0, 0.52))+
# tm_shape(holcAB)+
# tm_polygons("holc_grade",palette = "black", alpha = .3, border.col = "red", lty= "solid", ltw = 100)
#
#
# tm_shape(holctractAB)+
# tm_polygons("isow")+
# tm_shape(holcAB)+
# tm_polygons("holc_grade", alpha = 0, border.col = "red", lty= "solid")
#
# tm_shape(holctractCD)+
# tm_polygons("isow")+
# tm_shape(holcCD)+
# tm_polygons("holc_grade", alpha = 0, border.col = "red", lty= "solid")
#
# tm_shape(holctractD)+
# tm_polygons("hisp")+
# tm_shape(holcD)+
# tm_polygons("holc_grade", alpha = 0, border.col = "red", lty= "solid")Make a map layout
#tmaptools::palette_explorer()
#Hispanic isolation
tmap_mode("plot")## tmap mode set to plottingp1<-tm_shape(holctract)+
tm_polygons(c("isoh"),
title=c("Hispanic Isolation "),
palette="Blues",
style="quantile",
n=5)+
tm_scale_bar()+
tm_layout(title="Hispanic Isolation Index by Census Tract 2020",
title.size =1.5,
legend.frame = TRUE,
title.position = c('right', 'top'))+
tm_compass()+
tm_format("World",
legend.position = c("left", "bottom"),
main.title.position =c("center"))+
tm_shape(holcCD)+
tm_polygons("holc_grade",palette = "red", alpha = .3, border.col = "red", lty= "solid", ltw = 100)
p2<-tm_shape(holctract)+
tm_polygons(c("isow"),
title=c("White Isolation "),
palette="Greens",
style="quantile",
n=5)+
tm_scale_bar()+
tm_layout(title="White Isolation Index by Census Tract 2020",
title.size =1.5,
legend.frame = TRUE,
title.position = c('right', 'top'))+
tm_compass()+
tm_format("World",
legend.position = c("left", "bottom"),
main.title.position =c("center"))+
tm_shape(holcAB)+
tm_polygons("holc_grade",palette = "red", alpha = .3, border.col = "red", lty= "solid", ltw = 100)
tmap_arrange(p1,p2, nrow=1)
---
title: "Final Project code DEM 7093"
author: "David Rodriguez"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
   html_document:
    df_print: paged
    fig_height: 7
    fig_width: 7
    toc: yes
    toc_float: yes
    code_download: true
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r}
library(ipumsr)

nhgis_ddi <- read_ipums_codebook("C:/Users/drayr/Downloads/nhgis0002_csv.zip")

nhgis <- read_nhgis(data_file = "C:/Users/drayr/Downloads/nhgis0002_csv.zip")
nhgis<-haven::zap_labels(nhgis) 
names(nhgis)<-tolower(names(nhgis))

library(sf)
holc <- st_read("C:/Users/drayr/Downloads/TXSanAntonio19XX/cartodb-query.shp")


```

## Load some other packages
```{r, message=FALSE}
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(raster)

```


### Download geographic data for Census Tracts

```{r}
options(tigris_class = "sf")
tract<-tracts(state = "TX",
              county = 029,
             year = 2020,
             c = T)


plot(tract["GEOID"],
     main = "Census Tracts in San Antonio")


plot(holc["holc_grade"],
     main = "HOLC graded areas: San Antonio 1935")

```


## Prepare variables

```{r}
nhgis2 <- nhgis %>%
  mutate( tot=u7e001,
    hisp=u7e002,
    nh=u7e003,
    nhblk=u7e006,
    nhwht=u7e005,
    GEOID=geocode,
    cofips=countya) %>%
  filter(statea=="48" & countya=="029")%>%
   dplyr:: select(GEOID, tot, hisp, nh,nhblk,nhwht,cofips)


#We need the county-level totals for the total population and each race group
codat<-nhgis2%>%
  summarise(co_total=sum(tot), co_wht=sum(nhwht), co_blk=sum(nhblk), cohisp=sum(hisp))
codat$cofips="029"

#we merge the county data back to the tract data by the county FIPS code
merged<-left_join(x=nhgis2,y=codat, by="cofips")
#have a look and make sure it looks ok
head(merged)

#isolation index
tr.iso<-merged%>%
  mutate(isoh=(hisp/cohisp * hisp/tot),
         isob=(nhblk/co_blk * nhblk/tot),
         isow=(nhwht/co_wht * nhwht/tot))%>%
  group_by(cofips)#%>%
  #summarise(iso_b= sum(isob, na.rm=T))

knitr::kable(head(nhgis2))

```


## join to geography

```{r}

geo1<-left_join(tract, tr.iso, by=c("GEOID"= "GEOID"))
head(geo1)

geo2<-geo1%>%
   mutate( 
    area=st_area(.))   

geo2<-st_transform(geo2, crs=2278)
holc<-st_transform(holc, crs=2278)
```

## Map layout

```{r}
tmap_mode("view")

holctract<-st_intersection(holc,geo2)

holcAB<-holc%>%
  filter(holc_grade== "A" | holc_grade=="B" )
holctractAB<-st_intersection(holcAB,geo2)

holcCD<-holc%>%
  filter(holc_grade== "C" | holc_grade=="D" )
holctractCD<-st_intersection(holcCD,geo2)



holcA<-holc%>%
  filter(holc_grade=="A")
holctractA<-st_intersection(holcA,geo2)

holcB<-holc%>%
  filter(holc_grade=="B")
holctractB<-st_intersection(holcB,geo2)

holcC<-holc%>%
  filter(holc_grade=="C")
holctractC<-st_intersection(holcC,geo2)

holcD<-holc%>%
  filter(holc_grade=="D")
holctractD<-st_intersection(holcD,geo2)


```


```{r}

#tmaptools::palette_explorer()
tmap_mode("view")
#Hispanic isolation

tm_shape(holctract)+
  tm_polygons(alpha=0, palette = "-viridis", n = 10, contrast = c(0.42, 1))+
  tm_shape(holc)+
  tm_polygons("holc_grade", alpha = .5, border.col = "red", lty= "solid")+
  tm_layout(title="HOLC grades Layered on SA 2020 Census Tracts",
            title.size =1.5,
            legend.frame = TRUE,
            title.position = c('right', 'top'))

# tm_shape(holctract)+
#    tm_polygons("isoh", palette = "-viridis", n = 10, contrast = c(0, 0.52))+
#   tm_shape(holcCD)+
#   tm_polygons("holc_grade",palette = "black", alpha = .3, border.col = "red", lty= "solid", ltw = 100)
# 
# 
# tm_shape(holctractAB)+
#   tm_polygons("isoh")+
#   tm_shape(holcAB)+
#   tm_polygons("holc_grade", alpha = 0, border.col = "red", lty= "solid")
# 
# tm_shape(holctractCD)+
#   tm_polygons("isoh")+
#   tm_shape(holcCD)+
#   tm_polygons("holc_grade", alpha = 0, border.col = "red", lty= "solid")
# 
# #White isolation
# tm_shape(holctract)+
#    tm_polygons("isow", palette = "-viridis", n = 10, contrast = c(0, 0.52))+
#   tm_shape(holcAB)+
#   tm_polygons("holc_grade",palette = "black", alpha = .3, border.col = "red", lty= "solid", ltw = 100)
# 
# 
# tm_shape(holctractAB)+
#   tm_polygons("isow")+
#   tm_shape(holcAB)+
#   tm_polygons("holc_grade", alpha = 0, border.col = "red", lty= "solid")
# 
# tm_shape(holctractCD)+
#   tm_polygons("isow")+
#   tm_shape(holcCD)+
#   tm_polygons("holc_grade", alpha = 0, border.col = "red", lty= "solid")
# 
# tm_shape(holctractD)+
#   tm_polygons("hisp")+
#   tm_shape(holcD)+
#   tm_polygons("holc_grade", alpha = 0, border.col = "red", lty= "solid")

```

## Make a map layout

```{r, fig.height=10, fig.width=12}


#tmaptools::palette_explorer()
#Hispanic isolation

tmap_mode("plot")
p1<-tm_shape(holctract)+
  tm_polygons(c("isoh"),
              title=c("Hispanic Isolation "),
              palette="Blues",
              style="quantile",
              n=5)+
  tm_scale_bar()+
  tm_layout(title="Hispanic Isolation Index by Census Tract 2020",
            title.size =1.5,
            legend.frame = TRUE,
            title.position = c('right', 'top'))+
  tm_compass()+
  tm_format("World",
            legend.position =  c("left", "bottom"),
            main.title.position =c("center"))+
    tm_shape(holcCD)+
  tm_polygons("holc_grade",palette = "red", alpha = .3, border.col = "red", lty= "solid", ltw = 100)


p2<-tm_shape(holctract)+
  tm_polygons(c("isow"),
              title=c("White Isolation "),
              palette="Greens",
              style="quantile",
              n=5)+
  tm_scale_bar()+
  tm_layout(title="White Isolation Index by Census Tract 2020",
            title.size =1.5,
            legend.frame = TRUE,
            title.position = c('right', 'top'))+
  tm_compass()+
  tm_format("World",
            legend.position =  c("left", "bottom"),
            main.title.position =c("center"))+
    tm_shape(holcAB)+
  tm_polygons("holc_grade",palette = "red", alpha = .3, border.col = "red", lty= "solid", ltw = 100)


tmap_arrange(p1,p2, nrow=1)


```




