Total population by counties

## Getting data from the 2014-2018 5-year ACS

NAME estimate
Los Angeles 10098052
San Diego 3302833
Orange 3164182
Riverside 2383286
San Bernardino 2135413
Santa Clara 1922200
Alameda 1643700
Sacramento 1510023
Contra Costa 1133247
Fresno 978130
Kern 883053
San Francisco 870044
Ventura 848112
San Mateo 765935
San Joaquin 732212
Stanislaus 539301
Sonoma 501317
Tulare 460477
Santa Barbara 443738
Solano 438530
Monterey 433212
Placer 380077
San Luis Obispo 281455
Santa Cruz 273765
Merced 269075
Marin 260295
Butte 227075
Yolo 214977
El Dorado 186661
Imperial 180216
Shasta 179085
Madera 155013
Kings 150075
Napa 140530
Humboldt 135768
Nevada 99092
Sutter 95872
Mendocino 87422
Yuba 75493
Lake 64148
Tehama 63373
San Benito 59416
Tuolumne 53932
Calaveras 45235
Siskiyou 43540
Amador 37829
Lassen 31185
Glenn 27897
Del Norte 27424
Colusa 21464
Plumas 18699
Inyo 18085
Mariposa 17540
Mono 14174
Trinity 12862
Modoc 8938
Sierra 2930
Alpine 1146

Median Housing prices

From the 2013-2017 5-year ACS

## Getting data from the 2014-2018 5-year ACS

## Median Income in California From the 2013-2017 5-year ACS

## Warning in (function (endyear, span = 5, dataset = "acs", keyword, table.name, : temporarily downloading and using archived XML variable lookup files;
##   since this is *much* slower, recommend running
##   acs.tables.install()
## Warning in (function (endyear, span = 5, dataset = "acs", keyword, table.name, : temporarily downloading and using archived XML variable lookup files;
##   since this is *much* slower, recommend running
##   acs.tables.install()
## Warning in (function (endyear, span = 5, dataset = "acs", keyword, table.name, : temporarily downloading and using archived XML variable lookup files;
##   since this is *much* slower, recommend running
##   acs.tables.install()
## Warning in (function (endyear, span = 5, dataset = "acs", keyword, table.name, : temporarily downloading and using archived XML variable lookup files;
##   since this is *much* slower, recommend running
##   acs.tables.install()
## Warning in acs.fetch(endyear = endyear, span = span, geography =
## geography[[1]], : NAs introduced by coercion
## Warning in (function (endyear, span = 5, dataset = "acs", keyword, table.name, : temporarily downloading and using archived XML variable lookup files;
##   since this is *much* slower, recommend running
##   acs.tables.install()
## Warning in self$bind(): The following regions were missing and are being set to
## NA: 6073009902

Asian American population in California and San Diego County

ca <- get_acs(geography = "county", variables = "B02001_005", state = '06')
## Getting data from the 2014-2018 5-year ACS
ca %>%
mutate(NAME = gsub(" County, California", "", NAME)) %>%
 ggplot(aes(x = estimate, y = reorder(NAME, estimate))) +
 geom_point(color = "red", size = 1) +
  labs(title = "Asian Population by county in California",
       subtitle = "2013-2017 American Community Survey",
       y = "",
       x = "ACS estimate")

#------------------------------

vars <- load_variables(2017, "acs5", cache = TRUE) %>%
        mutate(table_id = str_sub(name, 1, 6))

asian <- vars %>%
         filter(table_id == "B02001_005")
    
sd_asian <- get_acs(geography = "tract", variables = "B02001_005",
                state = '06', county = '073', geometry = TRUE)
## Getting data from the 2014-2018 5-year ACS
ggplot(sd_asian, aes(fill = desc(estimate), color = estimate)) +
  geom_sf(colour="white") +
  coord_sf(crs = 26914) +
  labs(title = "Asian Population in San Diego county, by Census tract",
       subtitle = "2013-2017 ACS",
       y = "",
       x = "")

language <- read.csv("E:/Census 2020/SanDiego/Non-English Speakers.csv", stringsAsFactors=T)

viet <- filter(language,Language.Spoken.at.Home=="Vietnamese" )

ggplot(viet, aes(x = Year, y=Languages.Spoken)) + 
  geom_bar(stat = "identity",fill = "darkblue") +
  labs(title = "Vietnamese Language Spoken at Home",
       subtitle = "2016-2018 American Community Survey",
       y = "",
       x = "ACS estimate")

language18 <- filter(language,Year==2018 )
top18 <- top_n(language18,n=10,Languages.Spoken) %>% 
                  arrange(desc(Languages.Spoken))

ggplot(top18, aes(x = reorder(Language.Spoken.at.Home,desc(Languages.Spoken)), y=Languages.Spoken)) + 
  geom_bar(stat = "identity",fill = "darkblue") +
  labs(title = "Language Spoken at Home",
       subtitle = "2016-2018 American Community Survey",
       y = "",
       x = "ACS estimate")+
  coord_flip()

Median Income in San Diego county

From the 2013-2017 5-year ACS

## Getting data from the 2014-2018 5-year ACS
Census_Tract Median_Income
215 196299
83.13 192083
83.28 187989
83.11 182188
171.07 176635
170.29 172554
83.24 166383
83.10 165395
171.09 164327
83.66 163274
95.04 163000
173.03 162500
83.33 161411
83.65 158750
83.03 158427
170.45 154079
83.31 153690
113 152500
83.37 150806
83.27 150536 

## Getting data from the 2014-2018 5-year ACS

## California Median Income

## Getting data from the 2014-2018 5-year ACS

#————Income for San Diego

## Getting data from the 2014-2018 5-year ACS

```