library(leaflet)
home_icon <- makeIcon(
  iconUrl = "https://img.icons8.com/metro/26/000000/home.png",
  iconWidth = 31*215/230, iconHeight = 31,
  iconAnchorX = 31*215/230/2, iconAnchorY = 16)

my_map <- df %>% leaflet() %>%
  setView(lng = -117.98, lat = 33.75, zoom = 10) %>%
  addTiles() %>%
  addMarkers(lat= 33.7508, lng=-117.9897, popup="Westminster, CA", icon = home_icon) 
 
my_map

v15_Profile <- load_variables(year = 2015 , dataset = "acs5/profile",
cache = TRUE) #demographic profile tables
#Open the data for examination
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~

#Search for variables by keywords in the label
#v15_Profile[grep(x = v15_Profile$label, "Median household"), c("name", "label")]

# Vietnamese as race
viet_variables <- v15_Profile[grep(x = v15_Profile$label, "Vietnamese"), c("name", "label")]
viet_variables

## # A tibble: 2 x 2
##   name       label                                      
##   <chr>      <chr>                                      
## 1 DP05_0045  Estimate!!RACE!!One race!!Asian!!Vietnamese
## 2 DP05_0045P Percent!!RACE!!One race!!Asian!!Vietnamese

# B23010_001    Estimate!!Total PRESENCE OF OWN CHILDREN UNDER 18 YEARS OLD
#   B00001_001 total population
# B14007_002    Estimate!!Total!!Enrolled in school

acs5/subject

Orange county, California Vietnamese population

library(kableExtra)

## 
## Attaching package: 'kableExtra'

## The following object is masked from 'package:dplyr':
## 
##     group_rows

oc_18_viet<-get_acs(geography = "tract", state="CA", county = "059",
        year = 2018, variables=c( "B02015_022") , geometry = T, output = "wide")

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

oc_17_viet<-get_acs(geography = "tract", state="CA", county = "059",
        year = 2017, variables=c( "B02015_022") , geometry = T, output = "wide")

## Getting data from the 2013-2017 5-year ACS

oc_16_viet<-get_acs(geography = "tract", state="CA", county = "059",
        year = 2016, variables=c( "B02015_022") , geometry = T, output = "wide")

## Getting data from the 2012-2016 5-year ACS

oc_15_viet<-get_acs(geography = "tract", state="CA", county = "059",
        year = 2015, variables=c( "B02015_022") , geometry = T, output = "wide")

## Getting data from the 2011-2015 5-year ACS

oc_18_viet <- arrange(oc_18_viet,GEOID)
oc_17_viet <- arrange(oc_17_viet,GEOID)
oc_16_viet <- arrange(oc_16_viet,GEOID)
oc_15_viet <- arrange(oc_15_viet,GEOID)

oc_years <- oc_18_viet[,2]
oc_years$'2015' <- oc_15_viet$B02015_022E
oc_years$'2016' <- oc_16_viet$B02015_022E
oc_years$'2017' <- oc_17_viet$B02015_022E
oc_years$'2018' <- oc_18_viet$B02015_022E

oc_years$NAME <- gsub("Census Tract", "", oc_years$NAME) 
oc_years$NAME <- gsub(", Orange County, California","",oc_years$NAME) 
top_oc <- top_n(oc_years,10,oc_years$`2018`)
top_oc <- as.data.frame(top_oc)
top_oc <- select(top_oc,-2)

kable(top_oc) %>%
   kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
   add_header_above(c("Census Tract"=2,"" ,"Yearly" = 2)) %>%
  add_footnote(c("Line 3 : decline in 2016", "Line 7: decline in 2016"), notation = "symbol") %>%
   row_spec(1:10, bold = F, color = "black", background = "lightyellow") %>% 
row_spec(c(3,7), bold = T, color = "red", background = "lightblue")

Census Tract			Yearly
NAME	2015	2016	2017	2018
887.02	2643	2912	3283	3153
889.01	4325	4329	4640	4360
889.03	4952	4743	4898	5075
889.04	3263	3454	3609	4065
889.05	3769	4029	3831	3904
890.01	3743	3805	4032	3970
996.01	3323	2561	2524	3274
997.01	3336	3357	3582	3755
997.02	3478	3420	3697	3715
998.03	2590	2757	2726	3220
^* Line 3 : decline in 2016
^† Line 7: decline in 2016

#------------change

oc_17_viet <- arrange(oc_17_viet,GEOID)
oc_18_viet <- arrange(oc_18_viet,GEOID)

oc_18_viet$change <- oc_18_viet$B02015_022E - oc_17_viet$B02015_022E


#--- change general population
oc_18_change<-get_acs(geography = "tract", state="CA", county = "059",
        year = 2018, variables=c( "B23010_001") , geometry = T, output = "wide")

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

oc_17_change<-get_acs(geography = "tract", state="CA", county = "059",
        year = 2017, variables=c( "B23010_001") , geometry = T, output = "wide")

## Getting data from the 2013-2017 5-year ACS

oc_17_change <- arrange(oc_17_change,GEOID)
oc_18_change <- arrange(oc_18_change,GEOID)

oc_18_change$change <- oc_18_change$B23010_001E - oc_17_change$B23010_001E

#--------------- General population + Vietnamese population and general family count
# B05003_003    Estimate!!Total!!Male!!Under 18 years
# B05003_014    Estimate!!Total!!Female!!Under 18 years

oc_18 <- get_acs(
      geography = "tract",
      variables = c( Total="B01003_001",Viet="B02015_022",
                     M_under18="B05003_003", F_under18="B05003_014", 
                     Family="B14007_002",Age_median="B01002D_001"),
      state = "CA", county = "059",
      year = 2018, output = "wide",
      geometry = TRUE ) %>%
      arrange(GEOID)

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

oc_17 <- get_acs(
      geography = "tract",
      variables = c( Total="B01003_001",Viet="B02015_022",
                      M_under18="B05003_003", F_under18="B05003_014", 
                     Family="B14007_002",Age_median="B01002D_001"),
      state = "CA", county = "059",
      year = 2017, output = "wide",
      geometry = TRUE) %>%
      arrange(GEOID)

## Getting data from the 2013-2017 5-year ACS

oc_18$totalchange <- oc_18$TotalE - oc_17$TotalE
oc_18$vchange <- oc_18$VietE - oc_17$VietE
oc_18$familychange <- oc_18$FamilyE - oc_17$FamilyE
oc_18$agechange <- oc_18$Age_medianE - oc_17$Age_medianE
oc_18$studentchange <- oc_18$M_under18E+oc_18$F_under18E -oc_17$M_under18E - oc_17$F_under18E

oc_18$NAME <- gsub("Census Tract", "", oc_18$NAME) 
oc_18$NAME <- gsub(", Orange County, California","",oc_18$NAME)                         

top_viet <- top_n(oc_18,10,vchange)
top_viet %>%
   ggplot(aes(y = vchange, x = reorder(NAME, vchange))) +
   geom_point(color = "red", size = 3) +
   labs(title = "Census tracts with highest increase in Vietnamese population",
       subtitle = "2013-2017 American Community Survey",
       x = "Census Tracts",
       y = "Population count")

top_viet <- as.data.frame(top_viet)
top_viet[,c(2,16,17,18,19,20)] %>% arrange(-vchange) %>%
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  row_spec(1:5, bold = T, color = "black", background = "lightblue")

NAME	totalchange	vchange	familychange	agechange	studentchange
996.01	-41	750	-22	0.0	-214
998.03	115	494	-57	-2.3	45
889.04	252	456	137	0.9	92
889.02	376	427	169	-0.5	41
881.07	-24	391	72	-0.5	-33
877.01	75	367	23	0.6	-165
524.20	-554	327	159	0.2	-133
992.29	-258	303	4	-0.4	-131
748.03	-150	297	-206	2.0	-292
992.27	42	283	58	0.8	4

#-------------
bottom_change <- top_n(oc_18,10,-vchange)

bottom_change <- as.data.frame(bottom_change)
bottom_change[,c(2,16,17,18,19,20)] %>% arrange(vchange) %>%
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  row_spec(1:5, bold = T, color = "black", background = "#DeF7E9")

NAME	totalchange	vchange	familychange	agechange	studentchange
863.06	-183	-611	114	17.0	99
878.02	-239	-563	-181	2.3	25
885.02	240	-547	69	5.6	106
999.05	-73	-393	-116	2.5	-8
992.26	-244	-372	-118	0.4	-189
869.01	-153	-370	-97	4.7	-145
884.01	-459	-305	-298	1.3	-325
755.15	1834	-287	988	-2.4	61
889.01	-127	-280	24	-1.2	-10
886.01	234	-266	148	2.2	-4

bottom_change %>%
   ggplot(aes(y = vchange, x = reorder(NAME, vchange))) +
   geom_point(color = "blue", size = 3) +
   labs(title = "Census tracts with largest decline in Vietnamese population",
       subtitle = "Illustration by Kim Phan ",  
       x = "Census Tracts",
       y = "Population count")

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

oc_18 %>% ggplot() +
geom_sf(aes(fill = -VietE)) +
coord_sf(datum = NA)

#-------------------
bottom_viet <- top_n(oc_18,10,-vchange)
bottom_viet %>%
  ggplot(aes(y = vchange, x = reorder(NAME, vchange))) +
  geom_point(color = "red", size = 3) +
  labs(title = "Census tracts with highest decline in  Vietnamese population",
       subtitle = "Illustration by Kim Phan ",
       x = "Census Tracts",
       y = "Number ")

oc_18 %>% ggplot() +geom_sf(aes(fill = -vchange)) +
coord_sf(datum = NA)

library(data.table)

## 
## Attaching package: 'data.table'

## The following objects are masked from 'package:dplyr':
## 
##     between, first, last

library(dplyr)
library(formattable)

## Warning: package 'formattable' was built under R version 3.6.3

library(tidyr)
library(kableExtra)
#Set a few color variables to make our table more visually appealing

customGreen0 = "#DeF7E9"
customGreen = "#71CA97"
customRed = "#ff7f7f"
oc_18 <- as.data.frame(oc_18)
oc_18[1:40,c(2,5,16,17,18,19,20)] %>% arrange(-VietE) %>%
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  row_spec(1:20, bold = T, color = "black", background = "#DeF7E9") %>%
  row_spec(21:40, bold = T, color = "black", background = "lightblue")

NAME	VietE	totalchange	vchange	familychange	agechange	studentchange
115.02	272	62	0	-123	-1.8	15
15.01	216	-43	-43	-13	-0.2	57
16.02	188	-162	135	-70	-0.5	56
111.02	178	-77	54	-83	-0.5	-71
19.01	140	-127	-8	2	0.5	-13
15.05	132	82	-56	-148	-0.3	90
114.03	112	10	66	-67	-0.8	-28
19.03	111	78	21	-68	2.0	53
13.01	76	-13	-6	-55	0.4	-25
18.01	59	142	-121	146	9.6	23
17.04	53	141	44	157	3.5	-124
17.07	51	-233	38	-21	-0.1	-71
114.02	44	145	35	57	-3.1	94
110	39	89	9	0	-1.3	18
11.03	36	-13	5	-55	0.1	-69
111.01	31	-47	13	-89	0.1	-31
112	27	98	27	72	-24.6	57
17.05	20	-236	-1	-93	6.7	-107
115.03	19	30	-3	-35	-7.9	24
17.06	18	23	9	83	-2.3	98
18.02	14	-265	-3	-419	6.8	-225
19.02	13	52	-10	125	-0.7	102
15.06	12	-205	-2	-76	-2.1	-1
14.04	10	-54	4	64	0.4	-76
15.07	10	90	10	141	-1.5	24
11.02	9	19	1	-167	1.4	-14
14.01	9	-147	9	-265	3.1	-35
14.03	9	-63	-4	-27	0.0	-45
113	9	181	7	-168	1.4	-82
13.03	8	43	3	-57	-2.7	-18
12.02	6	-72	-1	-21	1.0	-66
16.01	5	9	0	-46	-0.3	-122
12.01	4	26	-2	187	0.1	72
11.01	0	232	-3	-56	-1.4	-43
13.04	0	155	0	7	-13.2	22
14.02	0	-36	0	95	-1.9	28
15.03	0	-537	-8	-111	8.1	-224
15.04	0	-61	0	-88	-0.5	126
17.08	0	116	0	-37	0.4	-43
114.01	0	227	0	84	-0.5	62

oc_18_viet %>%                      
ggplot( aes(fill = -B02015_022E, color = B02015_022E)) +
geom_sf() +
coord_sf(datum = NA) +
  ggtitle("Vietnamese population",
  subtitle = "Orange County California, 2018 ")+
 
 
  guides(color=FALSE)+
  theme_minimal()

P0030001 Total population: P0030002 White alone P0030003 Black or African American alone P0030004 American Indian and Alaska Native alone P0030005 Asian alone P0030006 Native Hawaiian and Other Pacific Islander alone P0030007 Some Other Race alone P0030008 Two or More Races —————————— P005003 RACE 18+:one race:White alone P5. Race For The Population 18 Years And Over P005004 RACE 18+:one race:Bl/AfAm alone P5. Race For The Population 18 Years And Over P005005 RACE 18+:one race:AmInd/AK alone P5. Race For The Population 18 Years And Over P005006 RACE 18+:one race:Asian alone P5. Race For The Population 18 Years And Over P005007 RACE 18+:one race:HI alone P5. Race For The Population 18 Years And Over P005008 RACE 18+:one race:Other alone P5. Race For The Population 18 Years And Over P005009 RACE 18+:Total 2+races P5. Race For The Population 18 Years And Over P005010 RACE 18+:Total 2 races P5. Race For The Population 18 Years And Over P005011 RACE 18+:2 races:White;Bl/AfAm

library(viridis)

## Loading required package: viridisLite

library(totalcensus)


# read data of Asian population in each block
# B05006_076    Estimate!!Total!!Asia!!South Eastern Asia!!Vietnam origin
# B03002_001    Estimate!!Total Hispanic origin
# B02015_001    Estimate!!Total Asian origin


oc_18<-get_acs(geography = "tract", state="CA", county = "059", year = 2018, 
               variables=c( Asian="B02015_001",Hispanic="B03002_001",Vietnamese="B05006_076") , 
               geometry = T, output = "wide")

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

ggplot(oc_18, aes(fill = -VietnameseE)) +
  geom_sf(color = "red") +
   labs(title = "Vietnamese population",
       subtitle = "Illustration by Kim Phan ")+
    theme_void()

ggplot(oc_18, aes(fill = -HispanicE)) +
  labs(title = "Hispanic population",
       subtitle = "Illustration by Kim Phan ")+
  geom_sf(color = "black") +
  theme_void()

ggplot(oc_18, aes(fill = -AsianE)) +
  labs(title = "Asian population",
       subtitle = "Illustration by Kim Phan ")+
  geom_sf(color = "yellow") +
  theme_void()

San Diego county, California Vietnamese population

sd_18_viet<-get_acs(geography = "tract", state="CA", county = "073",
        year = 2018, variables=c( "B02015_022") , geometry = T, output = "wide")

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

sd_17_viet<-get_acs(geography = "tract", state="CA", county = "073",
        year = 2017, variables=c( "B02015_022") , geometry = T, output = "wide")

## Getting data from the 2013-2017 5-year ACS

#------------change
sd_17_viet$value = sd_17_viet$B02015_022E
sd_18_viet$value = sd_18_viet$B02015_022E
sd_17_viet <- arrange(sd_17_viet,GEOID)
sd_18_viet <- arrange(sd_18_viet,GEOID)
sd_18_viet$change <- sd_18_viet$value - sd_17_viet$value

gone <- filter(oc_18_viet,GEOID=="06059990100")

top_sd <- top_n(sd_18_viet,20,value)
top_sd <-top_sd %>%
  mutate(NAME = gsub("Census Tract", "", NAME)) %>%
  mutate(NAME = gsub(", San Diego County, California", "", NAME)) 
  
top_sd %>%
  ggplot(aes(y = value, x = reorder(NAME, value))) +
  geom_point(color = "red", size = 3) +
  labs(title = "Top 20 Census tracts with highest Vietnamese population in San Diego County",
       subtitle = "2013-2017 American Community Survey",
       x = "Census Tracts",
       y = "Population count")

sd_18_viet %>% ggplot() +
geom_sf(aes(fill = -B02015_022E)) +
coord_sf(datum = NA)

change_sd <- top_n(sd_18_viet,20,change)
bottom_sd <- top_n(sd_18_viet,20,-change)

change_sd <-change_sd %>%
  mutate(NAME = gsub("Census Tract", "", NAME)) %>%
  mutate(NAME = gsub(", San Diego County, California", "", NAME)) 
  
change_sd %>%
  ggplot(aes(y = value, x = reorder(NAME, value))) +
  geom_point(color = "red", size = 3) +
  labs(title = "Top 20 Census tracts with highest CHANGE in \nVietnamese population in San Diego County",
       subtitle = "2013-2017 American Community Survey",
       x = "Census Tracts",
       y = "Population count")

Census data on Vietnamese population changes in Orange County

Kim Phan, UCR Graduate Student

March 2020

acs5/subject