DEM 5093/7093
Ricardo Henrique Lowe, Jr., M.S.
02 February, 2021
In this project, I attempt to map poverty status among Black rural Texans by county. In order to find out which counties have the highest percent of Black inhabitants impoverished, I pull data from ACS 2019:
First, I pull Census api data.
#https://cran.r-project.org/web/packages/censusapi/vignettes/getting-started.html
#https://cran.r-project.org/web/packages/censusapi/censusapi.pdf
#install.packages("censusapi")
library(censusapi)##
## Attaching package: 'censusapi'## The following object is masked from 'package:methods':
##
## getFunctionapis <- listCensusApis()I look for my data variables from ACS 5yr 2019.
sahie_vars <- listCensusMetadata(
name = "acs/acs5",
vintage = 2019)
#sahie_vars[grep(x = sahie_vars$label, "poverty"), c("name", "label", "concept")]
#sahie_vars[grep(x = sahie_vars$label, "African"), c("name", "label", "concept")]
#sahie_vars[grep(x = sahie_vars$label, "Hispanic"), c("name", "label", "concept")]
#sahie_vars[grep(x = sahie_vars$label, "Black"), c("name", "label", "concept")]
#sahie_vars[grep(x = sahie_vars$concept, "TOTAL"), c("name", "label", "concept")]I tabulate my findings. Note that I limit the data to counties with total populations less than 60,000. The typical threshold for rural counties as defined by the State is less 50,000, but because we are using ACS data, I am being more liberal here to take into accounty margins of error (For example, Waller county has always been distinguished as a rural county, but recent 2019 estimates have the population exceeding 50,000 - which may disqualify it.)
sahie_national <- getCensus(
name = "acs/acs5",
vars = c("NAME","B17001B_002E","B03002_004E","B03002_014E","B03003_002E","B03003_003E"),
vintage = 2019,
region = "county:*")
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionsahie_national$Totalpop <- sahie_national$B03003_002E + sahie_national$B03003_003E
sahie_national$Blackpop <- sahie_national$B03002_004E + sahie_national$B03002_014E
pov <- sahie_national %>%
mutate(Blackpov=B17001B_002E,
Blackpop=Blackpop,
Totalpop=Totalpop,
Blperpov=Blackpov/Blackpop,
Blperpop=Blackpop/Totalpop)%>%
filter(state == 48 & Totalpop < 60000 & Blperpop>.15)%>%
select(NAME,Blackpov,Blackpop,Totalpop,Blperpov,Blperpop)%>%
arrange(desc(Blperpop))
pov ## NAME Blackpov Blackpop Totalpop Blperpov Blperpop
## 1 Houston County, Texas 1973 5888 22954 0.33508832 0.2565130
## 2 Waller County, Texas 2670 12911 51832 0.20680040 0.2490932
## 3 Falls County, Texas 1490 4233 17272 0.35199622 0.2450787
## 4 Morris County, Texas 843 2882 12373 0.29250520 0.2329265
## 5 Marion County, Texas 654 2294 10017 0.28509154 0.2290107
## 6 San Augustine County, Texas 502 1768 8286 0.28393665 0.2133720
## 7 Anderson County, Texas 1396 12124 57810 0.11514352 0.2097215
## 8 Robertson County, Texas 1060 3474 16990 0.30512378 0.2044732
## 9 Newton County, Texas 945 2823 13914 0.33475027 0.2028892
## 10 Shelby County, Texas 1747 4847 25349 0.36042913 0.1912107
## 11 Panola County, Texas 628 4032 23327 0.15575397 0.1728469
## 12 Cass County, Texas 1787 5195 30059 0.34398460 0.1728268
## 13 Limestone County, Texas 1006 4037 23417 0.24919495 0.1723961
## 14 Rusk County, Texas 1282 9075 53755 0.14126722 0.1688215
## 15 Red River County, Texas 581 2039 12171 0.28494360 0.1675294
## 16 Camp County, Texas 491 2136 12878 0.22986891 0.1658643
## 17 Jasper County, Texas 1828 5889 35506 0.31040924 0.1658593
## 18 Washington County, Texas 1181 5805 35163 0.20344531 0.1650883
## 19 Madison County, Texas 220 2332 14197 0.09433962 0.1642601
## 20 Freestone County, Texas 904 2995 19714 0.30183639 0.1519225
## 21 Grimes County, Texas 862 4244 27984 0.20311027 0.1516581#sahie_vars[grep(x = sahie_vars$name, "B03003_002E"), c("name", "label", "concept")]
povt <- sahie_national %>%
mutate(Blackpov=B17001B_002E,
Blackpop=Blackpop,
Totalpop=Totalpop,
Blperpov=Blackpov/Blackpop,
Blperpop=Blackpop/Totalpop)%>%
filter(state == 48 & Totalpop < 60000 & Blperpop>.15)%>%
select(NAME,Blackpov,Blackpop,Totalpop,Blperpov,Blperpop)%>%
arrange(desc(Blperpov))
povt ## NAME Blackpov Blackpop Totalpop Blperpov Blperpop
## 1 Shelby County, Texas 1747 4847 25349 0.36042913 0.1912107
## 2 Falls County, Texas 1490 4233 17272 0.35199622 0.2450787
## 3 Cass County, Texas 1787 5195 30059 0.34398460 0.1728268
## 4 Houston County, Texas 1973 5888 22954 0.33508832 0.2565130
## 5 Newton County, Texas 945 2823 13914 0.33475027 0.2028892
## 6 Jasper County, Texas 1828 5889 35506 0.31040924 0.1658593
## 7 Robertson County, Texas 1060 3474 16990 0.30512378 0.2044732
## 8 Freestone County, Texas 904 2995 19714 0.30183639 0.1519225
## 9 Morris County, Texas 843 2882 12373 0.29250520 0.2329265
## 10 Marion County, Texas 654 2294 10017 0.28509154 0.2290107
## 11 Red River County, Texas 581 2039 12171 0.28494360 0.1675294
## 12 San Augustine County, Texas 502 1768 8286 0.28393665 0.2133720
## 13 Limestone County, Texas 1006 4037 23417 0.24919495 0.1723961
## 14 Camp County, Texas 491 2136 12878 0.22986891 0.1658643
## 15 Waller County, Texas 2670 12911 51832 0.20680040 0.2490932
## 16 Washington County, Texas 1181 5805 35163 0.20344531 0.1650883
## 17 Grimes County, Texas 862 4244 27984 0.20311027 0.1516581
## 18 Panola County, Texas 628 4032 23327 0.15575397 0.1728469
## 19 Rusk County, Texas 1282 9075 53755 0.14126722 0.1688215
## 20 Anderson County, Texas 1396 12124 57810 0.11514352 0.2097215
## 21 Madison County, Texas 220 2332 14197 0.09433962 0.1642601I make a prettier table.
library(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
##
## between, first, lastlibrary(kableExtra)##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rowscolnames(pov) <- c("County",
"Black Population in Poverty",
"Black Population",
"Total Population",
"% Black in Poverty",
"% of Population Black")
pov %>%
kbl(caption = "Poverty Status in Texas' Majority Black Rural Cities")%>%
add_header_above(c(" ", "Counts" = 3, "Percents" = 2),bold = T) %>%
kable_classic_2(full_width = T, html_font = "Times",font_size = 11) %>%
column_spec(1,width = "1.5in")| County | Black Population in Poverty | Black Population | Total Population | % Black in Poverty | % of Population Black |
|---|---|---|---|---|---|
| Houston County, Texas | 1973 | 5888 | 22954 | 0.3350883 | 0.2565130 |
| Waller County, Texas | 2670 | 12911 | 51832 | 0.2068004 | 0.2490932 |
| Falls County, Texas | 1490 | 4233 | 17272 | 0.3519962 | 0.2450787 |
| Morris County, Texas | 843 | 2882 | 12373 | 0.2925052 | 0.2329265 |
| Marion County, Texas | 654 | 2294 | 10017 | 0.2850915 | 0.2290107 |
| San Augustine County, Texas | 502 | 1768 | 8286 | 0.2839367 | 0.2133720 |
| Anderson County, Texas | 1396 | 12124 | 57810 | 0.1151435 | 0.2097215 |
| Robertson County, Texas | 1060 | 3474 | 16990 | 0.3051238 | 0.2044732 |
| Newton County, Texas | 945 | 2823 | 13914 | 0.3347503 | 0.2028892 |
| Shelby County, Texas | 1747 | 4847 | 25349 | 0.3604291 | 0.1912107 |
| Panola County, Texas | 628 | 4032 | 23327 | 0.1557540 | 0.1728469 |
| Cass County, Texas | 1787 | 5195 | 30059 | 0.3439846 | 0.1728268 |
| Limestone County, Texas | 1006 | 4037 | 23417 | 0.2491949 | 0.1723961 |
| Rusk County, Texas | 1282 | 9075 | 53755 | 0.1412672 | 0.1688215 |
| Red River County, Texas | 581 | 2039 | 12171 | 0.2849436 | 0.1675294 |
| Camp County, Texas | 491 | 2136 | 12878 | 0.2298689 | 0.1658643 |
| Jasper County, Texas | 1828 | 5889 | 35506 | 0.3104092 | 0.1658593 |
| Washington County, Texas | 1181 | 5805 | 35163 | 0.2034453 | 0.1650883 |
| Madison County, Texas | 220 | 2332 | 14197 | 0.0943396 | 0.1642601 |
| Freestone County, Texas | 904 | 2995 | 19714 | 0.3018364 | 0.1519225 |
| Grimes County, Texas | 862 | 4244 | 27984 | 0.2031103 | 0.1516581 |
colnames(povt) <- c("County",
"Black Population in Poverty",
"Black Population",
"Total Population",
"% Black in Poverty",
"% of Population Black")
povt %>%
kbl(caption = "Poverty Status in Texas' Majority Black Rural Cities")%>%
add_header_above(c(" ", "Counts" = 3, "Percents" = 2),bold = T) %>%
kable_classic_2(full_width = T, html_font = "Times",font_size = 11) %>%
column_spec(1,width = "1.5in")| County | Black Population in Poverty | Black Population | Total Population | % Black in Poverty | % of Population Black |
|---|---|---|---|---|---|
| Shelby County, Texas | 1747 | 4847 | 25349 | 0.3604291 | 0.1912107 |
| Falls County, Texas | 1490 | 4233 | 17272 | 0.3519962 | 0.2450787 |
| Cass County, Texas | 1787 | 5195 | 30059 | 0.3439846 | 0.1728268 |
| Houston County, Texas | 1973 | 5888 | 22954 | 0.3350883 | 0.2565130 |
| Newton County, Texas | 945 | 2823 | 13914 | 0.3347503 | 0.2028892 |
| Jasper County, Texas | 1828 | 5889 | 35506 | 0.3104092 | 0.1658593 |
| Robertson County, Texas | 1060 | 3474 | 16990 | 0.3051238 | 0.2044732 |
| Freestone County, Texas | 904 | 2995 | 19714 | 0.3018364 | 0.1519225 |
| Morris County, Texas | 843 | 2882 | 12373 | 0.2925052 | 0.2329265 |
| Marion County, Texas | 654 | 2294 | 10017 | 0.2850915 | 0.2290107 |
| Red River County, Texas | 581 | 2039 | 12171 | 0.2849436 | 0.1675294 |
| San Augustine County, Texas | 502 | 1768 | 8286 | 0.2839367 | 0.2133720 |
| Limestone County, Texas | 1006 | 4037 | 23417 | 0.2491949 | 0.1723961 |
| Camp County, Texas | 491 | 2136 | 12878 | 0.2298689 | 0.1658643 |
| Waller County, Texas | 2670 | 12911 | 51832 | 0.2068004 | 0.2490932 |
| Washington County, Texas | 1181 | 5805 | 35163 | 0.2034453 | 0.1650883 |
| Grimes County, Texas | 862 | 4244 | 27984 | 0.2031103 | 0.1516581 |
| Panola County, Texas | 628 | 4032 | 23327 | 0.1557540 | 0.1728469 |
| Rusk County, Texas | 1282 | 9075 | 53755 | 0.1412672 | 0.1688215 |
| Anderson County, Texas | 1396 | 12124 | 57810 | 0.1151435 | 0.2097215 |
| Madison County, Texas | 220 | 2332 | 14197 | 0.0943396 | 0.1642601 |
Now, I want to map poverty status using Table 2, which arranges the data by percent of Black people in poverty.
Shelby County, which according to my findings has the largest percent of Blacks. I decide to map the top 4 poorest Black counties (Shelby, Falls, Cass and Houston).
library(tidycensus)
library(tidyverse)
library(sf)
library(ggplot2)
library(mapview)
library(RColorBrewer)
library(tmap)
library(tmap)
library(tmaptools)
library(classInt)
library(patchwork)
v15_Profile <- load_variables(2019 , "acs5/profile", cache = TRUE) #demographic profile tables
#Search for variables by keywords in the label
#v15_Profile[grep(x = v15_Profile$label, "POVERTY"), c("name", "label")]
#v15_Profile[grep(x = v15_Profile$label, "Black"), c("name", "label", "concept")]
#v15_Profile[grep(x = v15_Profile$label, "Black"), c("name", "label")]
#v15_Profile[grep(x = v15_Profile$label, "POVERTY"), c("name", "label", "concept")]
head(v15_Profile)## # A tibble: 6 x 3
## name label concept
## <chr> <chr> <chr>
## 1 DP02_00… Estimate!!HOUSEHOLDS BY TYPE!!Total hou… SELECTED SOCIAL CHARACTERIS…
## 2 DP02_00… Percent!!HOUSEHOLDS BY TYPE!!Total hous… SELECTED SOCIAL CHARACTERIS…
## 3 DP02_00… Estimate!!HOUSEHOLDS BY TYPE!!Total hou… SELECTED SOCIAL CHARACTERIS…
## 4 DP02_00… Percent!!HOUSEHOLDS BY TYPE!!Total hous… SELECTED SOCIAL CHARACTERIS…
## 5 DP02_00… Estimate!!HOUSEHOLDS BY TYPE!!Total hou… SELECTED SOCIAL CHARACTERIS…
## 6 DP02_00… Percent!!HOUSEHOLDS BY TYPE!!Total hous… SELECTED SOCIAL CHARACTERIS…I want to map Black poverty for the county and the actual tracts.
#https://spatialanalysis.github.io/lab_tutorials/4_R_Mapping.html
Shelby_acs <-get_acs(geography = "tract",
state="TX",
county = c("Shelby"),
year = 2018,
variables=c( "DP03_0119PE",
"DP05_0078P","DP05_0078") ,
geometry = T, output = "wide")## Getting data from the 2014-2018 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.## Using the ACS Data Profile##
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|======================================================================| 100%#create a county FIPS code - 5 digit
Shelby_acs$county<-substr(Shelby_acs$GEOID, 1, 5)
Shelby_acs$tract <-substr(Shelby_acs$NAME, 7, 17)
#rename variables and filter missing cases
Shelby_acs2V<-Shelby_acs%>%
mutate(pblack= DP05_0078PE, ppov=DP03_0119PE) %>%
# st_transform(crs = 102740)%>%
na.omit()
shlpov <- tm_shape(Shelby_acs2V) +
tm_fill("ppov", palette="BuPu") +
tm_borders(col="black",lwd=.85,lty=1)+#lwd = thickness, lty = line type (1-solid,2-dash,3-dotted)
#tm_text("tract", size = .45, remove.overlap = TRUE)+
tm_legend(show=FALSE)+
tm_layout(frame=FALSE)
shlpov_bytract <- tm_shape(Shelby_acs2V) +
tm_fill("ppov", title="Poverty (%)", palette="BuPu") +
tm_borders(col="black",lwd=.85,lty=1)+ #lwd = thickness, lty = line type (1-solid,2-dash,3-dotted)
tm_text("DP05_0078E", size = .85, col = "black", fontface = "bold", remove.overlap = TRUE)+
tm_facets(by = "tract")+
tm_layout(legend.position = c("right", "bottom"))
# tm_layout(main.title = "Poverty Estimates Shelby County (2019)", main.title.size = 0.9, main.title.position = c("left","top"),
#frame = FALSE)
#shlpbl <- tm_shape(Shelby_acs2V) +
#tm_fill("pblack", palette="BuPu") +
#tm_borders(col="black",lwd=.85,lty=1)+#lwd = thickness, lty = line type (1-solid,2-dash,3-dotted)
##tm_text("tract", size = .45, remove.overlap = TRUE)+
#tm_legend(show=FALSE)+
#tm_layout(frame=FALSE)
#shlpbl_bytract <- tm_shape(Shelby_acs2V) +
#tm_fill("pblack", title="Black (%)", palette="BuPu") +
#tm_borders(col="black",lwd=.85,lty=1)+#lwd = thickness, lty = line type (1-solid,2-dash,3-dotted)
#tm_text("DP05_0078E", size = .85, col = "black", fontface = "bold", remove.overlap = TRUE)+
#tm_facets(by = "tract")+
#tm_layout(legend.position = c("right", "bottom"))
##tm_layout(main.title = "Percent Black Shelby County (2019)", main.title.size = 0.9, main.title.position = c("left","top"),
#frame = FALSE)
#tmap_arrange(shlpov,shlpov_bytract)
#tmap_arrange(shlpbl,shlpbl_bytract)
#https://stackoverflow.com/questions/10776139/r-grid-layout-title
library(grid)
grid.newpage()
pushViewport(viewport(layout = grid.layout(2, 2, heights = unit(c(0.5, 5, 5), "null"))))
print(shlpov, vp=viewport(layout.pos.row = 2, layout.pos.col = 1))
print(shlpov_bytract, vp=viewport(layout.pos.row = 2, layout.pos.col = 2))
grid.text("Shelby County Poverty Estimates (ACS 2018)", vp = viewport(layout.pos.row = 1, layout.pos.col = 1:2))
#grid.newpage()
#pushViewport(viewport(layout = grid.layout(2, 2, heights = unit(c(0.5, 5, 5), "null"))))
#print(shlpbl, vp=viewport(layout.pos.row = 2, layout.pos.col = 1))
#print(shlpbl_bytract, vp=viewport(layout.pos.row = 2, layout.pos.col = 2))
#grid.text("Shelby County Percent Black Estimates (ACS 2018)", vp = viewport(layout.pos.row = 1, layout.pos.col = 1:2))#https://spatialanalysis.github.io/lab_tutorials/4_R_Mapping.html
Falls_acs <-get_acs(geography = "tract",
state="TX",
county = c("Falls"),
year = 2018,
variables=c( "DP03_0119PE",
"DP05_0078P","DP05_0078") ,
geometry = T, output = "wide")## Getting data from the 2014-2018 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.## Using the ACS Data Profile#create a county FIPS code - 5 digit
Falls_acs$county<-substr(Falls_acs$GEOID, 1, 5)
Falls_acs$tract <-substr(Falls_acs$NAME, 7, 14)
#rename variables and filter missing cases
Falls_acs2V<-Falls_acs%>%
mutate(pblack= DP05_0078PE, ppov=DP03_0119PE) %>%
# st_transform(crs = 102740)%>%
na.omit()
falpov <- tm_shape(Falls_acs2V) +
tm_fill("ppov", palette="BuPu") +
tm_borders(col="black",lwd=.85,lty=1)+#lwd = thickness, lty = line type (1-solid,2-dash,3-dotted)
#tm_text("tract", size = .45, remove.overlap = TRUE)+
tm_legend(show=FALSE)+
tm_layout(frame=FALSE)
falpov_bytract <- tm_shape(Falls_acs2V) +
tm_fill("ppov", title="Poverty (%)", palette="BuPu") +
tm_borders(col="black",lwd=.85,lty=1)+ #lwd = thickness, lty = line type (1-solid,2-dash,3-dotted)
tm_text("DP05_0078E", size = .85, col = "black", fontface = "bold", remove.overlap = TRUE)+
tm_facets(by = "tract")+
tm_layout(legend.position = c("right", "bottom"))
# tm_layout(main.title = "Poverty Estimates Falls County (2019)", main.title.size = 0.9, main.title.position = c("left","top"),
#frame = FALSE)
#falpbl <- tm_shape(Falls_acs2V) +
#tm_fill("pblack", palette="BuPu") +
#tm_borders(col="black",lwd=.85,lty=1)+#lwd = thickness, lty = line type (1-solid,2-dash,3-dotted)
##tm_text("tract", size = .45, remove.overlap = TRUE)+
#tm_legend(show=FALSE)+
#tm_layout(frame=FALSE)
#falpbl_bytract <- tm_shape(Falls_acs2V) +
#tm_fill("pblack", title="Black (%)", palette="BuPu") +
#tm_borders(col="black",lwd=.85,lty=1)+#lwd = thickness, lty = line type (1-solid,2-dash,3-dotted)
#tm_text("DP05_0078E", size = .85, col = "black", fontface = "bold", remove.overlap = TRUE)+
#tm_facets(by = "tract")+
#tm_layout(legend.position = c("right", "bottom"))
#tm_layout(main.title = "Percent Black Falls County (2019)", main.title.size = 0.9, main.title.position = c("left","top"),
#frame = FALSE)
#tmap_arrange(falpov,falpov_bytract)
#tmap_arrange(falpbl,falpbl_bytract)
#https://stackoverflow.com/questions/10776139/r-grid-layout-title
library(grid)
grid.newpage()
pushViewport(viewport(layout = grid.layout(2, 2, heights = unit(c(0.5, 5, 5), "null"))))
print(falpov, vp=viewport(layout.pos.row = 2, layout.pos.col = 1))
print(falpov_bytract, vp=viewport(layout.pos.row = 2, layout.pos.col = 2))
grid.text("Falls County Poverty Estimates (ACS 2018)", vp = viewport(layout.pos.row = 1, layout.pos.col = 1:2))
#grid.newpage()
#pushViewport(viewport(layout = grid.layout(2, 2, heights = unit(c(0.5, 5, 5), "null"))))
#print(falpbl, vp=viewport(layout.pos.row = 2, layout.pos.col = 1))
#print(falpbl_bytract, vp=viewport(layout.pos.row = 2, layout.pos.col = 2))
#grid.text("Falls County Percent Black Estimates (ACS 2018)", vp = viewport(layout.pos.row = 1, layout.pos.col = 1:2))#https://spatialanalysis.github.io/lab_tutorials/4_R_Mapping.html
Cass_acs <-get_acs(geography = "tract",
state="TX",
county = c("Cass"),
year = 2018,
variables=c( "DP03_0119PE",
"DP05_0078P","DP05_0078") ,
geometry = T, output = "wide")## Getting data from the 2014-2018 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.## Using the ACS Data Profile#create a county FIPS code - 5 digit
Cass_acs$county<-substr(Cass_acs$GEOID, 1, 5)
Cass_acs$tract <-substr(Cass_acs$NAME, 7, 17)
#rename variables and filter missing cases
Cass_acs2V<-Cass_acs%>%
mutate(pblack= DP05_0078PE, ppov=DP03_0119PE) %>%
# st_transform(crs = 102740)%>%
na.omit()
caspov <- tm_shape(Cass_acs2V) +
tm_fill("ppov", palette="BuPu") +
tm_borders(col="black",lwd=.85,lty=1)+#lwd = thickness, lty = line type (1-solid,2-dash,3-dotted)
#tm_text("tract", size = .45, remove.overlap = TRUE)+
tm_legend(show=FALSE)+
tm_layout(frame=FALSE)
caspov_bytract <- tm_shape(Cass_acs2V) +
tm_fill("ppov", title="Poverty (%)", palette="BuPu") +
tm_borders(col="black",lwd=.85,lty=1)+ #lwd = thickness, lty = line type (1-solid,2-dash,3-dotted)
tm_text("DP05_0078E", size = .85, col = "black", fontface = "bold", remove.overlap = TRUE)+
tm_facets(by = "tract")+
tm_layout(legend.position = c("right", "bottom"))
# tm_layout(main.title = "Poverty Estimates Cass County (2019)", main.title.size = 0.9, main.title.position = c("left","top"),
#frame = casSE)
#caspbl <- tm_shape(Cass_acs2V) +
#tm_fill("pblack", palette="BuPu") +
#tm_borders(col="black",lwd=.85,lty=1)+#lwd = thickness, lty = line type (1-solid,2-dash,3-dotted)
##tm_text("tract", size = .45, remove.overlap = TRUE)+
#tm_legend(show=casSE)+
#tm_layout(frame=casSE)
#caspbl_bytract <- tm_shape(Cass_acs2V) +
#tm_fill("pblack", title="Black (%)", palette="BuPu") +
#tm_borders(col="black",lwd=.85,lty=1)+#lwd = thickness, lty = line type (1-solid,2-dash,3-dotted)
#tm_text("DP05_0078E", size = .85, col = "black", fontface = "bold", remove.overlap = TRUE)+
#tm_facets(by = "tract")+
#tm_layout(legend.position = c("right", "bottom"))
#tm_layout(main.title = "Percent Black Cass County (2019)", main.title.size = 0.9, main.title.position = c("left","top"),
#frame = casSE)
#tmap_arrange(caspov,caspov_bytract)
#tmap_arrange(caspbl,caspbl_bytract)
#https://stackoverflow.com/questions/10776139/r-grid-layout-title
library(grid)
grid.newpage()
pushViewport(viewport(layout = grid.layout(2, 2, heights = unit(c(0.5, 5, 5), "null"))))
print(caspov, vp=viewport(layout.pos.row = 2, layout.pos.col = 1))
print(caspov_bytract, vp=viewport(layout.pos.row = 2, layout.pos.col = 2))
grid.text("Cass County Poverty Estimates (ACS 2018)", vp = viewport(layout.pos.row = 1, layout.pos.col = 1:2))
#grid.newpage()
#pushViewport(viewport(layout = grid.layout(2, 2, heights = unit(c(0.5, 5, 5), "null"))))
#print(caspbl, vp=viewport(layout.pos.row = 2, layout.pos.col = 1))
#print(caspbl_bytract, vp=viewport(layout.pos.row = 2, layout.pos.col = 2))
#grid.text("Cass County Percent Black Estimates (ACS 2018)", vp = viewport(layout.pos.row = 1, layout.pos.col = 1:2))#https://spatialanalysis.github.io/lab_tutorials/4_R_Mapping.html
Houston_acs <-get_acs(geography = "tract",
state="TX",
county = c("Houston"),
year = 2018,
variables=c( "DP03_0119PE",
"DP05_0078P","DP05_0078") ,
geometry = T, output = "wide")## Getting data from the 2014-2018 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.## Using the ACS Data Profile#create a county FIPS code - 5 digit
Houston_acs$county<-substr(Houston_acs$GEOID, 1, 5)
Houston_acs$tract <-substr(Houston_acs$NAME, 7, 17)
#rename variables and filter missing houes
Houston_acs2V<-Houston_acs%>%
mutate(pblack= DP05_0078PE, ppov=DP03_0119PE) %>%
# st_transform(crs = 102740)%>%
na.omit()
houpov <- tm_shape(Houston_acs2V) +
tm_fill("ppov", palette="BuPu") +
tm_borders(col="black",lwd=.85,lty=1)+#lwd = thickness, lty = line type (1-solid,2-dash,3-dotted)
#tm_text("tract", size = .45, remove.overlap = TRUE)+
tm_legend(show=FALSE)+
tm_layout(frame=FALSE)
houpov_bytract <- tm_shape(Houston_acs2V) +
tm_fill("ppov", title="Poverty (%)", palette="BuPu") +
tm_borders(col="black",lwd=.85,lty=1)+ #lwd = thickness, lty = line type (1-solid,2-dash,3-dotted)
tm_text("DP05_0078E", size = .85, col = "black", fontface = "bold", remove.overlap = TRUE)+
tm_facets(by = "tract")+
tm_layout(legend.position = c("right", "bottom"))
# tm_layout(main.title = "Poverty Estimates Houston County (2019)", main.title.size = 0.9, main.title.position = c("left","top"),
#frame = HoustonE)
#houpbl <- tm_shape(Houston_acs2V) +
#tm_fill("pblack", palette="BuPu") +
#tm_borders(col="black",lwd=.85,lty=1)+#lwd = thickness, lty = line type (1-solid,2-dash,3-dotted)
##tm_text("tract", size = .45, remove.overlap = TRUE)+
#tm_legend(show=HoustonE)+
#tm_layout(frame=HoustonE)
#houpbl_bytract <- tm_shape(Houston_acs2V) +
#tm_fill("pblack", title="Black (%)", palette="BuPu") +
#tm_borders(col="black",lwd=.85,lty=1)+#lwd = thickness, lty = line type (1-solid,2-dash,3-dotted)
#tm_text("DP05_0078E", size = .85, col = "black", fontface = "bold", remove.overlap = TRUE)+
#tm_facets(by = "tract")+
#tm_layout(legend.position = c("right", "bottom"))
#tm_layout(main.title = "Percent Black Houston County (2019)", main.title.size = 0.9, main.title.position = c("left","top"),
#frame = HoustonE)
#tmap_arrange(houpov,houpov_bytract)
#tmap_arrange(houpbl,houpbl_bytract)
#https://stackoverflow.com/questions/10776139/r-grid-layout-title
library(grid)
grid.newpage()
pushViewport(viewport(layout = grid.layout(2, 2, heights = unit(c(0.5, 5, 5), "null"))))
print(houpov, vp=viewport(layout.pos.row = 2, layout.pos.col = 1))
print(houpov_bytract, vp=viewport(layout.pos.row = 2, layout.pos.col = 2))
grid.text("Houston County Poverty Estimates (ACS 2018)", vp = viewport(layout.pos.row = 1, layout.pos.col = 1:2))
#grid.newpage()
#pushViewport(viewport(layout = grid.layout(2, 2, heights = unit(c(0.5, 5, 5), "null"))))
#print(houpbl, vp=viewport(layout.pos.row = 2, layout.pos.col = 1))
#print(houpbl_bytract, vp=viewport(layout.pos.row = 2, layout.pos.col = 2))
#grid.text("Houston County Percent Black Estimates (ACS 2018)", vp = viewport(layout.pos.row = 1, layout.pos.col = 1:2))#library(mapview)
#library(RColorBrewer)
#pal <- colorRampPalette(brewer.pal(6, "Blues")) #set colors
#
#
#top4_acs <-get_acs(geography = "tract",
# state="TX",
# county = c("Shelby","Falls","Cass","Houston"),
# year = 2018,
# variables=c( "DP03_0119PE",
# "DP05_0078P","DP05_0078") ,
# geometry = T, output = "wide")
#
#mapView(top4_acs)