Vietnamese Population in Santa Clara county
Joe Long
1/26/2021
#acs.lookup(endyear=2012, span=1, table.number="B01001", keyword="Female")
v15 <- load_variables(2015, "acs5", cache = TRUE)
View(v15)
v19 <- load_variables(2019, "acs5", cache = TRUE)
View(v19)
us_components <- get_estimates(geography = "state", product = "components")
us_components## # A tibble: 624 x 4
## NAME GEOID variable value
## <chr> <chr> <chr> <dbl>
## 1 Alabama 01 BIRTHS 59637
## 2 Alaska 02 BIRTHS 11335
## 3 Arizona 04 BIRTHS 86765
## 4 Arkansas 05 BIRTHS 38779
## 5 California 06 BIRTHS 500353
## 6 Colorado 08 BIRTHS 66345
## 7 Connecticut 09 BIRTHS 36319
## 8 Delaware 10 BIRTHS 11026
## 9 District of Columbia 11 BIRTHS 9652
## 10 Florida 12 BIRTHS 221755
## # ... with 614 more rowsunique(us_components$variable)## [1] "BIRTHS" "DEATHS" "DOMESTICMIG"
## [4] "INTERNATIONALMIG" "NATURALINC" "NETMIG"
## [7] "RBIRTH" "RDEATH" "RDOMESTICMIG"
## [10] "RINTERNATIONALMIG" "RNATURALINC" "RNETMIG"net_migration <- get_estimates(geography = "county",
variables = "RNETMIG",
geometry = TRUE,
shift_geo = TRUE)## Please note: Alaska and Hawaii are being shifted and are not to scale.library(tidycensus)
sc2015 <- get_estimates(
geography = "county",
product = "characteristics",
breakdown = c("RACE"),
breakdown_labels = TRUE,
state="06",
year = 2015) %>% filter(GEOID=="06085")
sc2018 <- get_estimates(
geography = "county",
product = "characteristics",
breakdown = c("RACE"),
breakdown_labels = TRUE,
state="06",
year = 2018) %>% filter(GEOID=="06085")
sc2015 <- arrange(sc2015,GEOID)
sc2018 <- arrange(sc2018,GEOID)
sc2018$change <- sc2018$value -sc2015$valueDP05_0050 Vietnamese
DP05_0050P Percent!!RACE!!Total population!!One race!!Asian!!Vietnamese
cs_2019 <- load_variables(2019, dataset = "acs1/profile")
viet <- cs_2019 %>%
filter(str_detect(label,pattern="Vietnamese"))
viet2019 <- get_acs(geography = "tract",variables = "DP05_0050",
state = "06",county = "085",year = 2019,geometry = TRUE) %>%
arrange(GEOID)## Getting data from the 2015-2019 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%viet2015 <- get_acs(geography = "tract",variables = "DP05_0050",
state = "06",county = "085",year = 2015) %>%
arrange(GEOID)## Getting data from the 2011-2015 5-year ACS
## Using the ACS Data Profileviet2019$change <- viet2019$estimate - viet2015$estimate
#his <- cs_2019 %>% filter(str_detect(label,pattern="HISPANIC"))
#asian <- cs_2019 %>% filter(str_detect(label,pattern="Asian"))ggplot(viet2019, aes(fill = change)) +
geom_sf(color="white")+
labs(title = "Vietnamese Population changes from 2015 to 2018 _ Census tracts",
subtitle = "Santa Clara County, CA")+
theme_void()
viet2019 <- arrange(viet2019,desc(change))
viet2019$NAME <- gsub(", Santa Clara County, California","",viet2019$NAME)
viet2019$NAME <- gsub("Census Tract","",viet2019$NAME)
top <- viet2019[1:25,]
top %>% ggplot( aes(fill=NAME, y=change, x=reorder(NAME,change))) +
geom_bar(position="dodge", stat="identity") +
theme(legend.position = "none")+
geom_text(aes(label=change), size=3,position=position_dodge(width=1)) + coord_flip() +
labs(title = "Population Increase _ from 2015 to 2018",subtitle = "Vietnamese Population",
x="Census tract",y="Santa Clara County, California")
top2 <- viet2019[26:50,]
top2 %>% ggplot( aes(fill=NAME, y=change, x=reorder(NAME,change))) +
geom_bar(position="dodge", stat="identity") +
theme(legend.position = "none")+
geom_text(aes(label=change), size=3,position=position_dodge(width=1)) + coord_flip() +
labs(title = "Population Increase _ from 2015 to 2018",subtitle = "Vietnamese Population",
x="Census tract",y="Santa Clara County, California")
top3 <- viet2019[51:75,]
top3 %>% ggplot( aes(fill=NAME, y=change, x=reorder(NAME,change))) +
geom_bar(position="dodge", stat="identity") +
theme(legend.position = "none")+
geom_text(aes(label=change), size=3,position=position_dodge(width=1)) + coord_flip() +
labs(title = "Population Increase _ from 2015 to 2018",subtitle = "Vietnamese Population",
x="Census tract",y="Santa Clara County, California")
top4 <- viet2019[76:100,]
top4 %>% ggplot( aes(fill=NAME, y=change, x=reorder(NAME,change))) +
geom_bar(position="dodge", stat="identity") +
theme(legend.position = "none")+
geom_text(aes(label=change), size=3,position=position_dodge(width=1)) + coord_flip() +
labs(title = "Population Increase _ from 2015 to 2018",subtitle = "Vietnamese Population",
x="Census tract",y="Santa Clara County, California")
top5 <- viet2019[101:125,]
top5 %>% ggplot( aes(fill=NAME, y=change, x=reorder(NAME,change))) +
geom_bar(position="dodge", stat="identity") +
theme(legend.position = "none")+
geom_text(aes(label=change), size=3,position=position_dodge(width=1)) + coord_flip() +
labs(title = "Population Increase _ from 2015 to 2018",subtitle = "Vietnamese Population",
x="Census tract",y="Santa Clara County, California")
viet2019[126:150,] %>%
ggplot( aes(fill=NAME, y=change, x=reorder(NAME,change))) +
geom_bar(position="dodge", stat="identity") +
theme(legend.position = "none")+
geom_text(aes(label=change), size=3,position=position_dodge(width=1)) + coord_flip() +
labs(title = "Population Increase _ from 2015 to 2018",subtitle = "Vietnamese Population",
x="Census tract",y="Santa Clara County, California")