knitr::opts_chunk$set(echo = T)
library(tidyverse); library(tidycensus); library(sf); library(raster)
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library(reshape);library(tmap)
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## expand, smiths
library(kableExtra)
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## group_rows
library(choroplethr)
## Loading required package: acs
## Loading required package: XML
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## Attaching package: 'acs'
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library(choroplethrMaps)
library(mapview)
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library(stringr)
library(leaflet)
library(viridis)
## Loading required package: viridisLite
library(RColorBrewer)
library(censusapi)
##
## Attaching package: 'censusapi'
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## getFunction
census_api_key("dfbe50fd7620ed3bf0e87e29c6bc18ae92e8e9b0")
## To install your API key for use in future sessions, run this function with `install = TRUE`.
options(scipen = 999)
options(tigris_use_cache = TRUE)
2010 Variable
B02006_017 Estimate!!Total!!Vietnamese
B00001_001 Estimate!!Total
2019 variable
B02015_022 Estimate!!Total:!!Vietnamese
California Counties
2010 variables : B01003_001 General population
B02006_017 vietnamese population
2019 variables : B01003_001 General population
B02006_022 vietnamese population
oc10 <- get_acs(state = '06', geography = "county",
variables = c(General="B01003_001",Viet="B02006_017"), output = "wide",year = 2010) %>%
arrange(GEOID)
## Getting data from the 2006-2010 5-year ACS
oc19 <- get_acs(state = '06', geography = "county",
variables = c(General="B01003_001",Viet="B02015_022"), output = "wide",year = 2019) %>%
arrange(GEOID)
## Getting data from the 2015-2019 5-year ACS
oc19$general2010 <- oc10$GeneralE
oc19$viet2010 <- oc10$VietE
oc19$change <- oc19$GeneralE - oc10$GeneralE
oc19$Vchange <- oc19$VietE - oc10$VietE
oc19$NAME <- gsub(" County, California","",oc19$NAME)
oc19a <- oc19[,c("NAME","GeneralE","VietE","Vchange")]
colnames(oc19a) <- c("County","General","Vietnamese","Change from 2010")
oc19a %>% arrange(desc(Vietnamese)) %>%
kbl(caption = "2019 Vietnamese Population") %>%
kable_classic(full_width = F, html_font = "Cambria") %>%
column_spec(3, bold = T, color = "blue") %>%
column_spec(4, color = "blue")
2019 Vietnamese Population
|
County
|
General
|
Vietnamese
|
Change from 2010
|
|
Orange
|
3168044
|
209485
|
39229
|
|
Santa Clara
|
1927470
|
134164
|
15044
|
|
Los Angeles
|
10081570
|
93446
|
3015
|
|
San Diego
|
3316073
|
51824
|
8483
|
|
Alameda
|
1656754
|
34425
|
4961
|
|
Sacramento
|
1524553
|
28323
|
6140
|
|
Riverside
|
2411439
|
18115
|
1865
|
|
San Francisco
|
874961
|
15358
|
2047
|
|
San Bernardino
|
2149031
|
12184
|
-779
|
|
Contra Costa
|
1142251
|
10628
|
3725
|
|
San Joaquin
|
742603
|
8151
|
873
|
|
San Mateo
|
767423
|
4415
|
-102
|
|
Ventura
|
847263
|
4221
|
-382
|
|
Fresno
|
984521
|
3930
|
524
|
|
Solano
|
441829
|
3689
|
750
|
|
Kern
|
887641
|
2487
|
1198
|
|
Placer
|
385512
|
2073
|
907
|
|
Stanislaus
|
543194
|
2011
|
525
|
|
Yolo
|
217352
|
1979
|
-536
|
|
Sonoma
|
499772
|
1915
|
-518
|
|
Monterey
|
433410
|
1808
|
163
|
|
Santa Barbara
|
444829
|
1766
|
-215
|
|
Marin
|
259943
|
1098
|
-460
|
|
Santa Cruz
|
273962
|
776
|
155
|
|
San Luis Obispo
|
282165
|
676
|
-17
|
|
Tulare
|
461898
|
638
|
-399
|
|
Merced
|
271382
|
618
|
35
|
|
El Dorado
|
188563
|
584
|
487
|
|
Shasta
|
179212
|
531
|
179
|
|
Napa
|
139623
|
474
|
24
|
|
Butte
|
225817
|
443
|
-109
|
|
Sutter
|
96109
|
323
|
48
|
|
Kings
|
150691
|
299
|
96
|
|
Yuba
|
76360
|
275
|
75
|
|
Humboldt
|
135940
|
223
|
-129
|
|
Tehama
|
63912
|
207
|
57
|
|
Imperial
|
180701
|
155
|
32
|
|
Del Norte
|
27495
|
127
|
51
|
|
San Benito
|
60376
|
93
|
-41
|
|
Madera
|
155433
|
78
|
31
|
|
Mendocino
|
87224
|
58
|
-174
|
|
Calaveras
|
45514
|
53
|
32
|
|
Lake
|
64195
|
45
|
45
|
|
Lassen
|
30818
|
39
|
-50
|
|
Mono
|
14310
|
28
|
28
|
|
Colusa
|
21454
|
23
|
-3
|
|
Inyo
|
17977
|
16
|
16
|
|
Trinity
|
12700
|
16
|
-21
|
|
Nevada
|
99244
|
15
|
-143
|
|
Amador
|
38429
|
12
|
-51
|
|
Mariposa
|
17420
|
10
|
10
|
|
Alpine
|
1039
|
5
|
-18
|
|
Tuolumne
|
54045
|
5
|
-33
|
|
Plumas
|
18660
|
4
|
4
|
|
Glenn
|
27976
|
0
|
-72
|
|
Modoc
|
8907
|
0
|
-3
|
|
Sierra
|
3040
|
0
|
0
|
|
Siskiyou
|
43468
|
0
|
-88
|
Assembly Districts
oc10 <- get_acs(state = '06', geography = "state legislative district (lower 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 (lower 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 Population") %>%
kable_classic(full_width = F, html_font = "Cambria") %>%
column_spec(2, bold = T, color = "blue") %>%
column_spec(3, color = "blue")
2019 Vietnamese Population
|
NAME
|
VietE
|
Vchange
|
|
Assembly District 72
|
120193
|
110493
|
|
Assembly District 27
|
74837
|
71952
|
|
Assembly District 25
|
41943
|
40624
|
|
Assembly District 49
|
27640
|
-1414
|
|
Assembly District 65
|
26640
|
23902
|
|
Assembly District 69
|
26033
|
-3885
|
|
Assembly District 77
|
20003
|
17799
|
|
Assembly District 9
|
15367
|
9056
|
|
Assembly District 68
|
15211
|
-71719
|
|
Assembly District 74
|
13464
|
9447
|
|
Assembly District 18
|
12904
|
3755
|
|
Assembly District 79
|
12793
|
10788
|
|
Assembly District 20
|
12437
|
-6457
|
|
Assembly District 28
|
9882
|
-1226
|
|
Assembly District 48
|
9411
|
8075
|
|
Assembly District 19
|
8679
|
5840
|
|
Assembly District 17
|
7953
|
5271
|
|
Assembly District 13
|
7657
|
2322
|
|
Assembly District 45
|
7451
|
3899
|
|
Assembly District 8
|
7326
|
2917
|
|
Assembly District 29
|
6888
|
4666
|
|
Assembly District 55
|
6884
|
3023
|
|
Assembly District 80
|
6141
|
5589
|
|
Assembly District 60
|
5981
|
112
|
|
Assembly District 66
|
5806
|
888
|
|
Assembly District 15
|
5478
|
-634
|
|
Assembly District 52
|
5474
|
4621
|
|
Assembly District 67
|
5430
|
-12958
|
|
Assembly District 75
|
5368
|
-9223
|
|
Assembly District 24
|
5214
|
-16830
|
|
Assembly District 7
|
5114
|
2049
|
|
Assembly District 16
|
4878
|
-3556
|
|
Assembly District 57
|
4711
|
-2301
|
|
Assembly District 14
|
4684
|
1238
|
|
Assembly District 73
|
4338
|
1283
|
|
Assembly District 40
|
3959
|
-2388
|
|
Assembly District 11
|
3894
|
160
|
|
Assembly District 76
|
3873
|
-4369
|
|
Assembly District 62
|
3811
|
1335
|
|
Assembly District 78
|
3662
|
-6841
|
|
Assembly District 51
|
3511
|
-917
|
|
Assembly District 61
|
3456
|
-1888
|
|
Assembly District 38
|
3357
|
661
|
|
Assembly District 6
|
3266
|
1030
|
|
Assembly District 58
|
3205
|
1347
|
|
Assembly District 23
|
3146
|
-55238
|
|
Assembly District 30
|
2921
|
2480
|
|
Assembly District 41
|
2857
|
1345
|
|
Assembly District 22
|
2658
|
-12218
|
|
Assembly District 70
|
2642
|
-10853
|
|
Assembly District 54
|
2557
|
-424
|
|
Assembly District 4
|
2471
|
709
|
|
Assembly District 37
|
2136
|
138
|
|
Assembly District 47
|
2129
|
-28
|
|
Assembly District 63
|
2115
|
-2595
|
|
Assembly District 44
|
2107
|
-2330
|
|
Assembly District 43
|
2071
|
193
|
|
Assembly District 46
|
1947
|
1311
|
|
Assembly District 12
|
1860
|
-6685
|
|
Assembly District 10
|
1859
|
-8929
|
|
Assembly District 50
|
1810
|
863
|
|
Assembly District 34
|
1805
|
772
|
|
Assembly District 64
|
1615
|
-3445
|
|
Assembly District 39
|
1504
|
-212
|
|
Assembly District 53
|
1436
|
-1623
|
|
Assembly District 2
|
1380
|
414
|
|
Assembly District 42
|
1377
|
-176
|
|
Assembly District 35
|
1266
|
-2034
|
|
Assembly District 71
|
1249
|
-8076
|
|
Assembly District 3
|
1248
|
249
|
|
Assembly District 21
|
1170
|
-2606
|
|
Assembly District 36
|
982
|
-938
|
|
Assembly District 32
|
952
|
-183
|
|
Assembly District 56
|
895
|
-3810
|
|
Assembly District 33
|
844
|
-282
|
|
Assembly District 31
|
822
|
-318
|
|
Assembly District 1
|
651
|
-231
|
|
Assembly District 26
|
616
|
-2620
|
|
Assembly District 59
|
556
|
-1092
|
|
Assembly District 5
|
483
|
-2553
|
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
|
Zip codes : zcta
only 2019 data with zip code divisions are available
Population by California zip codes
zip19 <- get_acs( geography = "zcta",
variables = "B02015_022", year = 2019) %>%
arrange(GEOID)
## Getting data from the 2015-2019 5-year ACS
zip19$NAME <- as.numeric(gsub("ZCTA5","",zip19$NAME))
cazip <- zip19 %>% filter(NAME>90000 & NAME<96000)
cazip <- cazip[,c(2,4)]
colnames(cazip) <- c("Zipcode","Population")
top_n(cazip,n=100,Population) %>% arrange(desc(Population)) %>%
kbl(caption = "Top 100 Vietnamese Population by zip codes in California") %>%
kable_classic(full_width = F, html_font = "Cambria") %>%
column_spec(1:2, color = "blue")
Top 100 Vietnamese Population by zip codes in California
|
Zipcode
|
Population
|
|
92683
|
37293
|
|
92843
|
17880
|
|
95111
|
15609
|
|
92840
|
13251
|
|
92708
|
12508
|
|
95121
|
12244
|
|
92704
|
12159
|
|
92841
|
12045
|
|
95148
|
11361
|
|
92844
|
11265
|
|
95122
|
10557
|
|
95035
|
10225
|
|
92804
|
9809
|
|
92703
|
9692
|
|
92126
|
8768
|
|
95132
|
8635
|
|
91770
|
7864
|
|
90680
|
6453
|
|
95127
|
6354
|
|
95133
|
6288
|
|
95112
|
5872
|
|
92105
|
5659
|
|
95116
|
4933
|
|
95131
|
4901
|
|
95136
|
4704
|
|
91732
|
4317
|
|
95624
|
4265
|
|
95828
|
4122
|
|
92655
|
4061
|
|
92115
|
3867
|
|
91733
|
3833
|
|
95123
|
3812
|
|
92647
|
3533
|
|
92802
|
3305
|
|
94606
|
3147
|
|
92111
|
3064
|
|
92646
|
3010
|
|
91776
|
2921
|
|
95138
|
2865
|
|
92801
|
2751
|
|
94587
|
2646
|
|
92129
|
2577
|
|
92706
|
2553
|
|
91801
|
2515
|
|
95757
|
2506
|
|
95829
|
2440
|
|
94544
|
2379
|
|
91731
|
2351
|
|
92648
|
2280
|
|
95823
|
2279
|
|
95209
|
2243
|
|
91706
|
2163
|
|
92626
|
2130
|
|
92806
|
2120
|
|
95758
|
2115
|
|
91335
|
2085
|
|
91306
|
2048
|
|
92563
|
2048
|
|
91790
|
2006
|
|
90260
|
1918
|
|
94538
|
1909
|
|
94122
|
1907
|
|
95135
|
1893
|
|
92620
|
1892
|
|
94501
|
1863
|
|
92707
|
1842
|
|
94577
|
1721
|
|
92602
|
1697
|
|
94112
|
1684
|
|
92805
|
1585
|
|
92503
|
1568
|
|
95051
|
1565
|
|
94568
|
1560
|
|
95037
|
1549
|
|
92127
|
1526
|
|
92117
|
1490
|
|
94560
|
1478
|
|
95212
|
1467
|
|
94134
|
1415
|
|
90031
|
1414
|
|
95824
|
1386
|
|
94565
|
1382
|
|
91754
|
1378
|
|
94539
|
1351
|
|
92630
|
1349
|
|
90620
|
1346
|
|
94536
|
1331
|
|
94601
|
1322
|
|
91763
|
1310
|
|
92618
|
1304
|
|
92780
|
1302
|
|
94806
|
1277
|
|
94541
|
1273
|
|
91304
|
1258
|
|
92886
|
1242
|
|
95125
|
1236
|
|
94089
|
1222
|
|
91007
|
1219
|
|
91744
|
1218
|
|
92124
|
1216
|
Population by Santa Clara County zip codes ( from 94022 to 95173)
sczip <- cazip %>% filter(Zipcode>94020 & Zipcode <95180)
top_n(sczip,n=100,Population) %>% arrange(desc(Population)) %>%
kbl(caption = "Top 100 Vietnamese Population by zip codes in Santa Clara County") %>%
kable_classic(full_width = F, html_font = "Cambria") %>%
column_spec(1:2, color = "blue")
Top 100 Vietnamese Population by zip codes in Santa Clara County
|
Zipcode
|
Population
|
|
95111
|
15609
|
|
95121
|
12244
|
|
95148
|
11361
|
|
95122
|
10557
|
|
95035
|
10225
|
|
95132
|
8635
|
|
95127
|
6354
|
|
95133
|
6288
|
|
95112
|
5872
|
|
95116
|
4933
|
|
95131
|
4901
|
|
95136
|
4704
|
|
95123
|
3812
|
|
94606
|
3147
|
|
95138
|
2865
|
|
94587
|
2646
|
|
94544
|
2379
|
|
94538
|
1909
|
|
94122
|
1907
|
|
95135
|
1893
|
|
94501
|
1863
|
|
94577
|
1721
|
|
94112
|
1684
|
|
95051
|
1565
|
|
94568
|
1560
|
|
95037
|
1549
|
|
94560
|
1478
|
|
94134
|
1415
|
|
94565
|
1382
|
|
94539
|
1351
|
|
94536
|
1331
|
|
94601
|
1322
|
|
94806
|
1277
|
|
94541
|
1273
|
|
95125
|
1236
|
|
94089
|
1222
|
|
95128
|
1210
|
|
95050
|
1160
|
|
94545
|
1149
|
|
94102
|
1064
|
|
95124
|
1064
|
|
94555
|
1055
|
|
95134
|
1052
|
|
95008
|
1037
|
|
95117
|
1027
|
|
94579
|
1016
|
|
94533
|
1002
|
|
94124
|
993
|
|
95120
|
988
|
|
95118
|
979
|
|
95014
|
959
|
|
94121
|
925
|
|
95054
|
894
|
|
94116
|
878
|
|
94109
|
869
|
|
94547
|
790
|
|
94580
|
787
|
|
94118
|
776
|
|
95129
|
773
|
|
94578
|
728
|
|
94085
|
698
|
|
95020
|
693
|
|
94531
|
664
|
|
94115
|
632
|
|
94132
|
603
|
|
94551
|
601
|
|
94591
|
593
|
|
94546
|
586
|
|
94080
|
579
|
|
95119
|
578
|
|
94520
|
571
|
|
94804
|
555
|
|
94553
|
553
|
|
94582
|
552
|
|
94086
|
535
|
|
94901
|
534
|
|
94127
|
533
|
|
95126
|
529
|
|
94602
|
499
|
|
94585
|
482
|
|
94509
|
478
|
|
94704
|
475
|
|
94103
|
451
|
|
94803
|
445
|
|
94087
|
442
|
|
94589
|
405
|
|
94621
|
400
|
|
94040
|
398
|
|
94603
|
393
|
|
94605
|
390
|
|
94114
|
387
|
|
94110
|
385
|
|
94607
|
382
|
|
94117
|
371
|
|
94502
|
368
|
|
95070
|
367
|
|
94609
|
356
|
|
95139
|
351
|
|
94619
|
330
|
|
94107
|
329
|
Population by Orange County zip codes ( from 90620 to 92899)
oczip <- cazip %>% filter(Zipcode>90600 & Zipcode <92900)
top_n(oczip,n=100,Population) %>% arrange(desc(Population)) %>%
kbl(caption = "Top 100 Vietnamese Population by zip codes in Orange County") %>%
kable_classic(full_width = F, html_font = "Cambria") %>%
column_spec(1:2, color = "blue")
Top 100 Vietnamese Population by zip codes in Orange County
|
Zipcode
|
Population
|
|
92683
|
37293
|
|
92843
|
17880
|
|
92840
|
13251
|
|
92708
|
12508
|
|
92704
|
12159
|
|
92841
|
12045
|
|
92844
|
11265
|
|
92804
|
9809
|
|
92703
|
9692
|
|
92126
|
8768
|
|
91770
|
7864
|
|
90680
|
6453
|
|
92105
|
5659
|
|
91732
|
4317
|
|
92655
|
4061
|
|
92115
|
3867
|
|
91733
|
3833
|
|
92647
|
3533
|
|
92802
|
3305
|
|
92111
|
3064
|
|
92646
|
3010
|
|
91776
|
2921
|
|
92801
|
2751
|
|
92129
|
2577
|
|
92706
|
2553
|
|
91801
|
2515
|
|
91731
|
2351
|
|
92648
|
2280
|
|
91706
|
2163
|
|
92626
|
2130
|
|
92806
|
2120
|
|
91335
|
2085
|
|
91306
|
2048
|
|
92563
|
2048
|
|
91790
|
2006
|
|
92620
|
1892
|
|
92707
|
1842
|
|
92602
|
1697
|
|
92805
|
1585
|
|
92503
|
1568
|
|
92127
|
1526
|
|
92117
|
1490
|
|
91754
|
1378
|
|
92630
|
1349
|
|
90620
|
1346
|
|
91763
|
1310
|
|
92618
|
1304
|
|
92780
|
1302
|
|
91304
|
1258
|
|
92886
|
1242
|
|
91007
|
1219
|
|
91744
|
1218
|
|
92124
|
1216
|
|
92869
|
1213
|
|
92782
|
1188
|
|
90650
|
1181
|
|
92649
|
1171
|
|
92123
|
1167
|
|
92612
|
1159
|
|
92606
|
1148
|
|
92870
|
1144
|
|
90703
|
1139
|
|
91755
|
1135
|
|
91766
|
1132
|
|
90630
|
1108
|
|
92131
|
1081
|
|
91780
|
1078
|
|
92879
|
1060
|
|
92880
|
1049
|
|
92656
|
1007
|
|
92705
|
982
|
|
92102
|
973
|
|
92882
|
968
|
|
92064
|
967
|
|
90805
|
966
|
|
92845
|
962
|
|
92069
|
923
|
|
92614
|
923
|
|
91709
|
909
|
|
92867
|
909
|
|
91311
|
900
|
|
92354
|
896
|
|
92507
|
886
|
|
91803
|
870
|
|
91791
|
868
|
|
92505
|
860
|
|
92027
|
830
|
|
92078
|
775
|
|
92604
|
773
|
|
92868
|
754
|
|
92596
|
753
|
|
92336
|
738
|
|
92122
|
736
|
|
92603
|
723
|
|
92865
|
715
|
|
90621
|
712
|
|
91745
|
706
|
|
91343
|
692
|
|
90706
|
685
|
|
91710
|
683
|
Population by Alameda County zip codes ( from 94501 to 94712)
alzip <- cazip %>% filter(Zipcode>94500 & Zipcode <94713)
top_n(alzip,n=75,Population) %>% arrange(desc(Population)) %>%
kbl(caption = "Top 75 Vietnamese Population by zip codes in Alameda County") %>%
kable_classic(full_width = F, html_font = "Cambria") %>%
column_spec(1:2, color = "blue")
Top 75 Vietnamese Population by zip codes in Alameda County
|
Zipcode
|
Population
|
|
94606
|
3147
|
|
94587
|
2646
|
|
94544
|
2379
|
|
94538
|
1909
|
|
94501
|
1863
|
|
94577
|
1721
|
|
94568
|
1560
|
|
94560
|
1478
|
|
94565
|
1382
|
|
94539
|
1351
|
|
94536
|
1331
|
|
94601
|
1322
|
|
94541
|
1273
|
|
94545
|
1149
|
|
94555
|
1055
|
|
94579
|
1016
|
|
94533
|
1002
|
|
94547
|
790
|
|
94580
|
787
|
|
94578
|
728
|
|
94531
|
664
|
|
94551
|
601
|
|
94591
|
593
|
|
94546
|
586
|
|
94520
|
571
|
|
94553
|
553
|
|
94582
|
552
|
|
94602
|
499
|
|
94585
|
482
|
|
94509
|
478
|
|
94704
|
475
|
|
94589
|
405
|
|
94621
|
400
|
|
94603
|
393
|
|
94605
|
390
|
|
94607
|
382
|
|
94502
|
368
|
|
94609
|
356
|
|
94619
|
330
|
|
94610
|
325
|
|
94542
|
316
|
|
94608
|
302
|
|
94564
|
295
|
|
94550
|
280
|
|
94566
|
276
|
|
94534
|
264
|
|
94703
|
262
|
|
94583
|
246
|
|
94558
|
237
|
|
94521
|
228
|
|
94595
|
215
|
|
94523
|
210
|
|
94503
|
205
|
|
94549
|
204
|
|
94611
|
203
|
|
94563
|
194
|
|
94506
|
188
|
|
94513
|
183
|
|
94552
|
161
|
|
94597
|
155
|
|
94561
|
144
|
|
94588
|
142
|
|
94510
|
137
|
|
94709
|
136
|
|
94519
|
129
|
|
94526
|
128
|
|
94702
|
114
|
|
94598
|
110
|
|
94706
|
105
|
|
94518
|
89
|
|
94592
|
83
|
|
94530
|
81
|
|
94705
|
70
|
|
94596
|
64
|
|
94618
|
62
|
Population by San Diego County zip codes ( from 91901 to 92199)
sdzip <- cazip %>% filter(Zipcode>91900 & Zipcode <92200)
top_n(sdzip,n=75,Population) %>% arrange(desc(Population)) %>%
kbl(caption = "Top 75 Vietnamese Population by zip codes in San Diego County") %>%
kable_classic(full_width = F, html_font = "Cambria") %>%
column_spec(1:2, color = "blue")
Top 75 Vietnamese Population by zip codes in San Diego County
|
Zipcode
|
Population
|
|
92126
|
8768
|
|
92105
|
5659
|
|
92115
|
3867
|
|
92111
|
3064
|
|
92129
|
2577
|
|
92127
|
1526
|
|
92117
|
1490
|
|
92124
|
1216
|
|
92123
|
1167
|
|
92131
|
1081
|
|
92102
|
973
|
|
92064
|
967
|
|
92069
|
923
|
|
92027
|
830
|
|
92078
|
775
|
|
92122
|
736
|
|
92026
|
682
|
|
92058
|
668
|
|
92114
|
656
|
|
92128
|
639
|
|
92056
|
631
|
|
92120
|
623
|
|
92130
|
613
|
|
91941
|
540
|
|
91945
|
539
|
|
92104
|
498
|
|
92010
|
466
|
|
91913
|
443
|
|
92011
|
443
|
|
92025
|
419
|
|
91902
|
393
|
|
92154
|
390
|
|
91911
|
363
|
|
92009
|
361
|
|
92139
|
339
|
|
92119
|
334
|
|
92083
|
295
|
|
91942
|
294
|
|
92014
|
276
|
|
92057
|
271
|
|
92081
|
268
|
|
92082
|
255
|
|
92071
|
243
|
|
91950
|
238
|
|
92116
|
236
|
|
92029
|
231
|
|
91977
|
221
|
|
92121
|
210
|
|
92101
|
208
|
|
92113
|
207
|
|
92109
|
204
|
|
92021
|
188
|
|
92110
|
182
|
|
92020
|
180
|
|
91910
|
168
|
|
92024
|
157
|
|
91914
|
140
|
|
92065
|
122
|
|
92107
|
121
|
|
92054
|
114
|
|
92019
|
110
|
|
91915
|
108
|
|
92037
|
104
|
|
92084
|
85
|
|
92108
|
82
|
|
92055
|
67
|
|
92075
|
66
|
|
92103
|
66
|
|
91935
|
56
|
|
91978
|
52
|
|
92106
|
51
|
|
92008
|
39
|
|
91932
|
38
|
|
92040
|
38
|
|
92118
|
36
|