knitr::opts_chunk$set(echo = T)
library(tidyverse); library(tidycensus); library(sf); library(raster)
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## v tibble 3.1.1 v dplyr 1.0.6
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## v readr 1.4.0 v forcats 0.5.1
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## Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1
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##
## Attaching package: 'raster'
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## select
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## extract
library(reshape);library(tmap)
##
## Attaching package: 'reshape'
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## expand, smiths
library(kableExtra)
##
## Attaching package: 'kableExtra'
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## group_rows
library(choroplethr)
## Loading required package: acs
## Loading required package: XML
##
## Attaching package: 'acs'
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## combine
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library(choroplethrMaps)
library(mapview)
## GDAL version >= 3.1.0 | setting mapviewOptions(fgb = TRUE)
library(stringr)
library(leaflet)
library(viridis)
## Loading required package: viridisLite
library(RColorBrewer)
library(censusapi)
##
## Attaching package: 'censusapi'
## The following object is masked from 'package:methods':
##
## getFunction
census_api_key("dfbe50fd7620ed3bf0e87e29c6bc18ae92e8e9b0")
## To install your API key for use in future sessions, run this function with `install = TRUE`.
options(scipen = 999)
options(tigris_use_cache = TRUE)
California Counties
2010 variables : B01003_001 General population
B02006_010 Estimate!!Total!!Korean
2019 variables : B01003_001 General population
B02015_012 Estimate!!Total:!!Korean
library(dplyr)
oc10 <- get_acs(state = '06', geography = "county",
variables = c(General="B01003_001",Korean="B02006_010"), output = "wide",year = 2010) %>%
arrange(GEOID)
## Getting data from the 2006-2010 5-year ACS
oc19 <- get_acs(state = '06', geography = "county",
variables = c(General="B01003_001",Korean="B02015_012"), output = "wide",year = 2019) %>%
arrange(GEOID)
## Getting data from the 2015-2019 5-year ACS
oc19$general2010 <- oc10$GeneralE
oc19$k2010 <- oc10$KoreanE
oc19$change <- oc19$GeneralE - oc10$GeneralE
oc19$Kchange <- oc19$KoreanE - oc10$KoreanE
oc19$NAME <- gsub(" County, California","",oc19$NAME)
oc19a <- oc19[,c("NAME","KoreanE","Kchange")]
colnames(oc19a) <- c("County","Korean","Change")
oc19a %>% arrange(desc(Korean)) %>%
kbl(caption = "2019 Korean Population") %>%
kable_classic(full_width = F, html_font = "Cambria") %>%
column_spec(2, bold = T, color = "blue") %>%
column_spec(3, color = "blue")
2019 Korean Population
|
County
|
Korean
|
Change
|
|
Los Angeles
|
209336
|
-1530
|
|
Orange
|
93676
|
12021
|
|
Santa Clara
|
30137
|
1471
|
|
San Diego
|
22779
|
2872
|
|
Alameda
|
19561
|
-192
|
|
San Bernardino
|
14758
|
3356
|
|
Riverside
|
11390
|
1231
|
|
San Francisco
|
10759
|
839
|
|
Contra Costa
|
9439
|
1212
|
|
San Mateo
|
6851
|
909
|
|
Sacramento
|
6705
|
-129
|
|
Ventura
|
4307
|
707
|
|
Monterey
|
2617
|
-325
|
|
Yolo
|
1996
|
4
|
|
Fresno
|
1841
|
132
|
|
Santa Barbara
|
1789
|
-329
|
|
Kern
|
1641
|
-618
|
|
Solano
|
1544
|
162
|
|
San Joaquin
|
1510
|
-115
|
|
Placer
|
1320
|
202
|
|
Sonoma
|
1289
|
-88
|
|
Marin
|
1099
|
-98
|
|
Tulare
|
865
|
648
|
|
San Luis Obispo
|
815
|
-18
|
|
Imperial
|
777
|
139
|
|
Stanislaus
|
738
|
15
|
|
Santa Cruz
|
700
|
4
|
|
El Dorado
|
650
|
18
|
|
Butte
|
580
|
259
|
|
Merced
|
518
|
144
|
|
Napa
|
486
|
-53
|
|
Kings
|
331
|
146
|
|
Mendocino
|
276
|
243
|
|
Shasta
|
182
|
-104
|
|
San Benito
|
162
|
123
|
|
Sutter
|
116
|
-311
|
|
Lake
|
102
|
52
|
|
Humboldt
|
101
|
-61
|
|
Tuolumne
|
88
|
-18
|
|
Madera
|
87
|
35
|
|
Del Norte
|
53
|
-22
|
|
Yuba
|
50
|
-15
|
|
Calaveras
|
40
|
40
|
|
Amador
|
37
|
37
|
|
Nevada
|
37
|
-75
|
|
Plumas
|
32
|
32
|
|
Lassen
|
24
|
13
|
|
Tehama
|
21
|
-46
|
|
Trinity
|
11
|
-14
|
|
Colusa
|
8
|
5
|
|
Mariposa
|
6
|
6
|
|
Siskiyou
|
3
|
-42
|
|
Inyo
|
2
|
-53
|
|
Alpine
|
0
|
0
|
|
Glenn
|
0
|
-17
|
|
Modoc
|
0
|
0
|
|
Mono
|
0
|
0
|
|
Sierra
|
0
|
0
|
Assembly Districts
oc10 <- get_acs(state = '06', geography = "state legislative district (lower chamber)",
variables = c(General="B01003_001",Korean="B02006_010"), output = "wide",year = 2010) %>%
arrange(GEOID)
## Getting data from the 2006-2010 5-year ACS
oc19 <- get_acs(state = '06', geography = "state legislative district (lower chamber)",
variables = c(General="B01003_001",Korean="B02015_012"), output = "wide",year = 2019) %>%
arrange(GEOID)
## Getting data from the 2015-2019 5-year ACS
oc19$Korean10 <- oc10$KoreanE
oc19$change <- oc19$KoreanE - oc10$KoreanE
oc19$NAME <- gsub(", California","",oc19$NAME)
oc19$NAME <- gsub("2018","",oc19$NAME)
oc19$NAME <- gsub("[[:punct:]]","",oc19$NAME)
oc19[,c(2,5,8)] %>% arrange(desc(KoreanE)) %>%
kbl(caption = "2019 Korean Population") %>%
kable_classic(full_width = F, html_font = "Cambria") %>%
column_spec(2, bold = T, color = "blue") %>%
column_spec(3, color = "blue")
2019 Korean Population
|
NAME
|
KoreanE
|
change
|
|
Assembly District 53
|
52573
|
35144
|
|
Assembly District 65
|
38703
|
37476
|
|
Assembly District 66
|
24716
|
21756
|
|
Assembly District 55
|
22650
|
19087
|
|
Assembly District 68
|
17682
|
8747
|
|
Assembly District 43
|
17470
|
4555
|
|
Assembly District 38
|
12155
|
-3271
|
|
Assembly District 74
|
12044
|
9665
|
|
Assembly District 28
|
10993
|
9566
|
|
Assembly District 58
|
10987
|
3740
|
|
Assembly District 54
|
10793
|
4418
|
|
Assembly District 50
|
10774
|
8876
|
|
Assembly District 77
|
10394
|
8831
|
|
Assembly District 16
|
10098
|
6349
|
|
Assembly District 57
|
9448
|
7123
|
|
Assembly District 24
|
9011
|
2713
|
|
Assembly District 25
|
8018
|
7431
|
|
Assembly District 72
|
7625
|
-11926
|
|
Assembly District 41
|
7534
|
4016
|
|
Assembly District 15
|
6667
|
1455
|
|
Assembly District 51
|
6494
|
906
|
|
Assembly District 45
|
6472
|
-1716
|
|
Assembly District 49
|
6307
|
3251
|
|
Assembly District 17
|
6230
|
5503
|
|
Assembly District 19
|
5226
|
1144
|
|
Assembly District 22
|
5195
|
-7228
|
|
Assembly District 20
|
4909
|
-912
|
|
Assembly District 73
|
4791
|
1717
|
|
Assembly District 46
|
4596
|
-6302
|
|
Assembly District 29
|
4573
|
3185
|
|
Assembly District 60
|
4519
|
-14879
|
|
Assembly District 78
|
4092
|
1610
|
|
Assembly District 52
|
3893
|
3628
|
|
Assembly District 39
|
3725
|
1927
|
|
Assembly District 40
|
3694
|
-3107
|
|
Assembly District 70
|
3647
|
-13585
|
|
Assembly District 69
|
3340
|
1555
|
|
Assembly District 18
|
3258
|
-1783
|
|
Assembly District 62
|
3209
|
2131
|
|
Assembly District 79
|
2965
|
1943
|
|
Assembly District 8
|
2744
|
-271
|
|
Assembly District 6
|
2721
|
1063
|
|
Assembly District 44
|
2658
|
-7975
|
|
Assembly District 4
|
2614
|
1123
|
|
Assembly District 36
|
2563
|
324
|
|
Assembly District 14
|
2514
|
-5042
|
|
Assembly District 27
|
2406
|
-539
|
|
Assembly District 59
|
2236
|
-3050
|
|
Assembly District 75
|
2182
|
-7026
|
|
Assembly District 67
|
2137
|
-13129
|
|
Assembly District 76
|
2114
|
-29
|
|
Assembly District 37
|
2062
|
-1461
|
|
Assembly District 61
|
1886
|
-8
|
|
Assembly District 64
|
1878
|
-1501
|
|
Assembly District 9
|
1751
|
518
|
|
Assembly District 63
|
1751
|
-3205
|
|
Assembly District 23
|
1746
|
-915
|
|
Assembly District 10
|
1674
|
-1394
|
|
Assembly District 48
|
1563
|
-34756
|
|
Assembly District 80
|
1505
|
309
|
|
Assembly District 42
|
1500
|
-9298
|
|
Assembly District 33
|
1457
|
-233
|
|
Assembly District 34
|
1407
|
852
|
|
Assembly District 7
|
1405
|
-256
|
|
Assembly District 56
|
1402
|
-20731
|
|
Assembly District 47
|
1386
|
-10472
|
|
Assembly District 35
|
1373
|
-503
|
|
Assembly District 11
|
1314
|
-912
|
|
Assembly District 30
|
1301
|
895
|
|
Assembly District 13
|
1184
|
-4039
|
|
Assembly District 2
|
1052
|
10
|
|
Assembly District 26
|
916
|
1
|
|
Assembly District 12
|
772
|
-4653
|
|
Assembly District 3
|
767
|
402
|
|
Assembly District 71
|
732
|
-5850
|
|
Assembly District 21
|
679
|
-6330
|
|
Assembly District 5
|
540
|
-1927
|
|
Assembly District 32
|
460
|
-1529
|
|
Assembly District 1
|
319
|
-153
|
|
Assembly District 31
|
101
|
-245
|
Senate Districts
oc10 <- get_acs(state = '06', geography = "state legislative district (upper chamber)",
variables = c(General="B01003_001",Viet="B02006_017"), output = "wide",year = 2010) %>%
arrange(GEOID)
## Getting data from the 2006-2010 5-year ACS
oc19 <- get_acs(state = '06', geography = "state legislative district (upper chamber)",
variables = c(General="B01003_001",Viet="B02015_022"), output = "wide",year = 2019) %>%
arrange(GEOID)
## Getting data from the 2015-2019 5-year ACS
oc19$change <- oc19$GeneralE - oc10$GeneralE
oc19$Vchange <- oc19$VietE - oc10$VietE
oc19$NAME <- gsub(", California","",oc19$NAME)
oc19$NAME <- gsub("2018","",oc19$NAME)
oc19$NAME <- gsub("[[:punct:]]","",oc19$NAME)
oc19[,c(2,5,8)] %>% arrange(desc(VietE)) %>%
kbl(caption = "2019 Vietnamese American Population") %>%
kable_classic(full_width = F, html_font = "Cambria") %>%
column_spec(2, bold = T, color = "blue") %>%
column_spec(3, color = "blue")
2019 Vietnamese American Population
|
NAME
|
VietE
|
Vchange
|
|
State Senate District 34
|
144963
|
53766
|
|
State Senate District 15
|
81899
|
74714
|
|
State Senate District 10
|
49935
|
8555
|
|
State Senate District 22
|
38613
|
32282
|
|
State Senate District 29
|
33322
|
24498
|
|
State Senate District 37
|
28892
|
20028
|
|
State Senate District 39
|
27689
|
6122
|
|
State Senate District 6
|
24188
|
12339
|
|
State Senate District 9
|
18424
|
5491
|
|
State Senate District 17
|
17263
|
14163
|
|
State Senate District 11
|
16620
|
-170
|
|
State Senate District 27
|
11209
|
4893
|
|
State Senate District 38
|
10503
|
31
|
|
State Senate District 5
|
10066
|
-3104
|
|
State Senate District 40
|
9899
|
5032
|
|
State Senate District 31
|
9437
|
722
|
|
State Senate District 7
|
9316
|
3055
|
|
State Senate District 35
|
9188
|
-45878
|
|
State Senate District 36
|
8226
|
-1916
|
|
State Senate District 32
|
8201
|
310
|
|
State Senate District 13
|
7824
|
-66584
|
|
State Senate District 28
|
7756
|
2494
|
|
State Senate District 20
|
7603
|
157
|
|
State Senate District 3
|
6604
|
-889
|
|
State Senate District 26
|
5402
|
2555
|
|
State Senate District 23
|
5081
|
-333
|
|
State Senate District 4
|
5068
|
2584
|
|
State Senate District 24
|
4924
|
-23266
|
|
State Senate District 25
|
4806
|
-2447
|
|
State Senate District 8
|
4290
|
-7324
|
|
State Senate District 18
|
3980
|
1618
|
|
State Senate District 33
|
3611
|
-16540
|
|
State Senate District 19
|
3439
|
-1633
|
|
State Senate District 2
|
3083
|
-1107
|
|
State Senate District 1
|
3000
|
-6164
|
|
State Senate District 16
|
2322
|
665
|
|
State Senate District 21
|
2269
|
-8091
|
|
State Senate District 30
|
2016
|
-1957
|
|
State Senate District 14
|
1719
|
-1858
|
|
State Senate District 12
|
1694
|
-302
|
Congressional Districts
oc10 <- get_acs(state = '06', geography = "congressional district",
variables = c(General="B01003_001",Viet="B02006_017"), output = "wide",year = 2010) %>%
arrange(GEOID)
## Getting data from the 2006-2010 5-year ACS
oc19 <- get_acs(state = '06', geography = "congressional district",
variables = c(General="B01003_001",Viet="B02015_022"), output = "wide",year = 2019) %>%
arrange(GEOID)
## Getting data from the 2015-2019 5-year ACS
oc19$change <- oc19$GeneralE - oc10$GeneralE
oc19$Vchange <- oc19$VietE - oc10$VietE
oc19$NAME <- gsub("116th Congress","",oc19$NAME)
oc19$NAME <- gsub(", California","",oc19$NAME)
oc19$NAME <- gsub("[[:punct:]]","",oc19$NAME)
oc19[,c(2,5,8)] %>% arrange(desc(VietE)) %>%
kbl(caption = "2019 Vietnamese American Population") %>%
kable_classic(full_width = F, html_font = "Cambria") %>%
column_spec(2, bold = T, color = "blue") %>%
column_spec(3, color = "blue")
2019 Vietnamese American Population
|
NAME
|
VietE
|
Vchange
|
|
Congressional District 19
|
82836
|
80683
|
|
Congressional District 48
|
75925
|
59549
|
|
Congressional District 47
|
64334
|
1815
|
|
Congressional District 17
|
46607
|
44379
|
|
Congressional District 46
|
43295
|
-12878
|
|
Congressional District 27
|
21887
|
15302
|
|
Congressional District 52
|
21124
|
14212
|
|
Congressional District 45
|
19893
|
14936
|
|
Congressional District 7
|
18409
|
12975
|
|
Congressional District 32
|
18295
|
-5455
|
|
Congressional District 15
|
15179
|
-25965
|
|
Congressional District 13
|
14780
|
-3669
|
|
Congressional District 12
|
13073
|
6800
|
|
Congressional District 53
|
11517
|
-2320
|
|
Congressional District 39
|
10616
|
6676
|
|
Congressional District 18
|
10326
|
8051
|
|
Congressional District 42
|
10039
|
2084
|
|
Congressional District 51
|
9845
|
4131
|
|
Congressional District 6
|
9829
|
5962
|
|
Congressional District 30
|
9285
|
5363
|
|
Congressional District 43
|
8470
|
4257
|
|
Congressional District 9
|
8382
|
-733
|
|
Congressional District 38
|
7548
|
1121
|
|
Congressional District 11
|
7024
|
-1878
|
|
Congressional District 35
|
6362
|
1414
|
|
Congressional District 14
|
6230
|
-593
|
|
Congressional District 49
|
5557
|
721
|
|
Congressional District 41
|
5479
|
2296
|
|
Congressional District 50
|
5308
|
-9058
|
|
Congressional District 31
|
4956
|
1026
|
|
Congressional District 3
|
4922
|
-6219
|
|
Congressional District 5
|
4807
|
-6220
|
|
Congressional District 34
|
4397
|
2136
|
|
Congressional District 33
|
3986
|
2149
|
|
Congressional District 22
|
3394
|
1956
|
|
Congressional District 10
|
3128
|
-1281
|
|
Congressional District 26
|
3118
|
-4015
|
|
Congressional District 28
|
2856
|
155
|
|
Congressional District 25
|
2824
|
-327
|
|
Congressional District 20
|
2806
|
1689
|
|
Congressional District 4
|
2706
|
1021
|
|
Congressional District 29
|
2619
|
-10946
|
|
Congressional District 37
|
2579
|
-2750
|
|
Congressional District 24
|
2443
|
-529
|
|
Congressional District 44
|
2235
|
-6834
|
|
Congressional District 36
|
2150
|
-1438
|
|
Congressional District 23
|
2107
|
-1862
|
|
Congressional District 2
|
2059
|
307
|
|
Congressional District 8
|
1695
|
-9053
|
|
Congressional District 16
|
1645
|
-69366
|
|
Congressional District 1
|
1275
|
-2474
|
|
Congressional District 40
|
1121
|
-28909
|
|
Congressional District 21
|
1062
|
-1883
|