Veteran populations in California
Joe Long
2/22/2021
DP02PR_0069 Estimate!!VETERAN STATUS!!Civilian population 18 years and over
DP02_0069P Percent Estimate!!VETERAN STATUS!!Civilian population 18 years and over
#Search for variables by keywords in the label
v10_Profile <- load_variables(2010 , "acs5/profile", cache = TRUE) #demographic profile tables
v18_Profile <- load_variables(2018 , "acs5/profile", cache = TRUE) #demographic profile tables
v10_Profile[grep(x = v10_Profile$label, "VETERAN"), c("name", "label")]## # A tibble: 8 x 2
## name label
## <chr> <chr>
## 1 DP02_0068 Estimate!!VETERAN STATUS!!Civilian population 18 years and over
## 2 DP02_0068P Percent!!VETERAN STATUS!!Civilian population 18 years and over
## 3 DP02_0069 Estimate!!VETERAN STATUS!!Civilian veterans
## 4 DP02_0069P Percent!!VETERAN STATUS!!Civilian veterans
## 5 DP02PR_0068 Estimate!!VETERAN STATUS!!Civilian population 18 years and over
## 6 DP02PR_0068P Percent!!VETERAN STATUS!!Civilian population 18 years and over
## 7 DP02PR_0069 Estimate!!VETERAN STATUS!!Civilian veterans
## 8 DP02PR_0069P Percent!!VETERAN STATUS!!Civilian veteransv18_Profile[grep(x = v18_Profile$label, "VETERAN"), c("name", "label")]## # A tibble: 8 x 2
## name label
## <chr> <chr>
## 1 DP02_0068 Estimate!!VETERAN STATUS!!Civilian population 18 years and over
## 2 DP02_0068P Percent Estimate!!VETERAN STATUS!!Civilian population 18 years an~
## 3 DP02_0069 Estimate!!VETERAN STATUS!!Civilian population 18 years and over!!~
## 4 DP02_0069P Percent Estimate!!VETERAN STATUS!!Civilian population 18 years an~
## 5 DP02PR_0068 Estimate!!VETERAN STATUS!!Civilian population 18 years and over
## 6 DP02PR_006~ Percent Estimate!!VETERAN STATUS!!Civilian population 18 years an~
## 7 DP02PR_0069 Estimate!!VETERAN STATUS!!Civilian population 18 years and over!!~
## 8 DP02PR_006~ Percent Estimate!!VETERAN STATUS!!Civilian population 18 years an~v18_Profile[grep(x = v18_Profile$label, "DISABILITY"), c("name", "label")]## # A tibble: 32 x 2
## name label
## <chr> <chr>
## 1 DP02_0070 Estimate!!DISABILITY STATUS OF THE CIVILIAN NONINSTITUTIONALIZED ~
## 2 DP02_0070P Percent Estimate!!DISABILITY STATUS OF THE CIVILIAN NONINSTITUTIO~
## 3 DP02_0071 Estimate!!DISABILITY STATUS OF THE CIVILIAN NONINSTITUTIONALIZED ~
## 4 DP02_0071P Percent Estimate!!DISABILITY STATUS OF THE CIVILIAN NONINSTITUTIO~
## 5 DP02_0072 Estimate!!DISABILITY STATUS OF THE CIVILIAN NONINSTITUTIONALIZED ~
## 6 DP02_0072P Percent Estimate!!DISABILITY STATUS OF THE CIVILIAN NONINSTITUTIO~
## 7 DP02_0073 Estimate!!DISABILITY STATUS OF THE CIVILIAN NONINSTITUTIONALIZED ~
## 8 DP02_0073P Percent Estimate!!DISABILITY STATUS OF THE CIVILIAN NONINSTITUTIO~
## 9 DP02_0074 Estimate!!DISABILITY STATUS OF THE CIVILIAN NONINSTITUTIONALIZED ~
## 10 DP02_0074P Percent Estimate!!DISABILITY STATUS OF THE CIVILIAN NONINSTITUTIO~
## # ... with 22 more rowsus_vet<-get_acs(geography = "county",
year = 2018,
variables=c("DP02_0069P" ,"DP02_0069"),
geometry = F, output = "wide") %>% arrange(GEOID)## Getting data from the 2014-2018 5-year ACS## Using the ACS Data Profile## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.top_n(us_vet,n=20,DP02_0069E) %>%
ggplot(aes(x = DP02_0069E, y = reorder(NAME, DP02_0069E))) +
geom_point(color = "brown4", size = 3) +
labs(title = "Veteran population _ top 20 counties",
subtitle = "2019 Census American Community Survey",
y = "",
x = "Population estimate") 
top_n(us_vet,n=20,DP02_0069PE) %>%
ggplot(aes(x = DP02_0069PE, y = reorder(NAME, DP02_0069PE))) +
geom_point(color = "brown4", size = 3) +
labs(title = "Percent Veteran population _ top 20 counties",
subtitle = "2019 Census American Community Survey",
y = "",
x = "Percent Population estimate") 
2018 California
ca_vet<-get_acs(geography = "county",
state="06",
year = 2018,
variables=c("DP02_0069P" ,"DP02_0069"),
geometry = F, output = "wide") %>% arrange(GEOID)## Getting data from the 2014-2018 5-year ACS## Using the ACS Data Profiletop_n(ca_vet,n=20,DP02_0069E) %>%
mutate(NAME = gsub(" County, California", "", NAME)) %>%
ggplot(aes(x = DP02_0069E, y = reorder(NAME, DP02_0069E))) +
geom_point(color = "brown4", size = 3) +
labs(title = "Veteran population _ top 20 counties in California",
subtitle = "2018 Census American Community Survey",
y = "",
x = "Population estimate") 
us_vet<-get_acs(geography = "county",
year = 2018,
variables=c("DP02_0069P" ,"DP02_0069"),
geometry = F, output = "wide") %>% arrange(GEOID)## Getting data from the 2014-2018 5-year ACS
## Using the ACS Data Profile2010 California DP02_0069
vet_2018<-get_acs(geography = "county",
state="06",
year = 2018,
variables=c("DP02_0069P" ,"DP02_0069"),
geometry = F, output = "wide") %>% arrange(GEOID)## Getting data from the 2014-2018 5-year ACS## Using the ACS Data Profilevet_2010<-get_acs(geography = "county",
state="06",
year = 2010,
variables=c("DP02_0069P" ,"DP02_0069"),
geometry = F, output = "wide") %>% arrange(GEOID)## Getting data from the 2006-2010 5-year ACS
## Using the ACS Data Profilevet_2018$P2010 <- vet_2010$DP02_0069E
vet_2018$change <- vet_2018$DP02_0069E - vet_2010$DP02_0069E
top_n(vet_2018,n=-20,change) %>%
mutate(NAME = gsub(" County, California", "", NAME)) %>%
ggplot(aes(x = DP02_0069E, y = reorder(NAME, DP02_0069E))) +
geom_point(color = "red", size = 3) +
labs(title = "Declines in Veteran population _ top 20 counties in California",
subtitle = "Census variable # DP02_0069\nFrom 2010 to 2018 ", caption = "Illustration by Joe Long",
y = "",
x = "Population declines") 
change <- select(vet_2018,c(2,8))
top_n(change,n=-20,change) %>% arrange(change)%>% kable() %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
column_spec(2, T, color = "blue" ) | NAME | change |
|---|---|
| Los Angeles County, California | -97666 |
| Orange County, California | -32772 |
| San Diego County, California | -24520 |
| San Bernardino County, California | -23872 |
| Santa Clara County, California | -21252 |
| Sacramento County, California | -21054 |
| Alameda County, California | -19879 |
| Riverside County, California | -19308 |
| Contra Costa County, California | -15312 |
| San Mateo County, California | -11020 |
| Kern County, California | -10510 |
| Ventura County, California | -10292 |
| San Francisco County, California | -9040 |
| Fresno County, California | -9021 |
| San Joaquin County, California | -7887 |
| Sonoma County, California | -6825 |
| San Luis Obispo County, California | -6187 |
| Butte County, California | -6073 |
| Santa Barbara County, California | -6058 |
| Stanislaus County, California | -5853 |
top_n(change,n=-10,change) %>% arrange(change) %>%
ggplot(aes(x=reorder(NAME,change),y=change,fill=NAME)) +
geom_bar(stat = "identity") +
labs(title = "Declines _ Veteran Population by top ten CA counties ",
subtitle = "From 2010 to 2018 Census Survey Variable# DP02_0069",
x="County", y="count",
caption = "Data source: Census data ACS\nIllustration by @JoeLongSanDiego")+
theme(legend.position = "none")+
coord_flip()
library(tmap)## Warning: replacing previous import 'sf::st_make_valid' by
## 'lwgeom::st_make_valid' when loading 'tmap'library(tmaptools)
ca_vet<-get_acs(geography = "county",
state="06",
year = 2018,
variables=c("DP02_0069P" ,"DP02_0069"),
geometry = T, output = "wide") %>% arrange(GEOID)## Getting data from the 2014-2018 5-year ACS## Using the ACS Data Profiletm_shape(ca_vet)+
tm_polygons("DP02_0069E", title="Number of persons", palette="Blues", style="quantile", n=10 ,legend.hist=T)+
tm_format("World", title="Veteran Population in California Counties", legend.outside=T)+
tm_scale_bar()+
tm_compass()
tm_shape(ca_vet)+
tm_polygons("DP02_0069PE", title="% in county population", palette="Blues", style="jenks", n=5,legend.hist=T )+
tm_format("World", title="Percentage _ Veteran Population in California Counties", legend.outside=T)+
tm_scale_bar()+
tm_compass()
Orange county
oc_vet<-get_acs(geography = "tract",
state="06",
county = "059",
year = 2018,
variables=c("DP02_0069P" ,"DP02_0069"),
geometry = T, output = "wide") %>% arrange(GEOID)## Getting data from the 2014-2018 5-year ACS## Using the ACS Data Profileoc_vet$DP02_0069E <- na.omit(oc_vet$DP02_0069E)
tm_shape(oc_vet)+
tm_polygons("DP02_0069E", title="Population estimate", palette="Blues", style="quantile", n=5 ,legend.hist=T)+
tm_format("World", title="Veteran Population by Census tracts in Orange county, CA", legend.outside=T)+
tm_scale_bar()+
tm_compass()## Warning: The shape oc_vet contains empty units.
tm_shape(oc_vet)+
tm_polygons("DP02_0069PE", title="Population estimate", palette="Blues", style="quantile", n=8 ,legend.hist=T)+
tm_format("World", title="Percentage Population_ Veteran Population by Census tracts in Orange county, CA", legend.outside=T)+
tm_scale_bar()+
tm_compass()## Warning: The shape oc_vet contains empty units.
library(mapview)
library(RColorBrewer)
pal <- colorRampPalette(brewer.pal(6, "Blues")) #set colors
mapview(oc_vet["DP02_0069E"], col.regions=pal, legend=T,map.types="OpenStreetMap",
layer.name="% Veteran in Census tract population")