v10 <- load_variables(2010, "acs5", cache = TRUE)
View(v10)
v19 <- load_variables(2019, "acs5", cache = TRUE)
salvadoran19 <- v19 %>%
filter(str_detect(label,pattern="Salvadoran"))
salvadoran10 <- v10 %>%
filter(str_detect(label,pattern="Salvadoran"))
B03001_014 Estimate!!Total:!!Hispanic or Latino:!!Central American:!!Salvadoran
sal2019 <- get_acs(geography = "state",variables = "B03001_014",
year = 2019,geometry = F) %>%
arrange(GEOID)
## Getting data from the 2015-2019 5-year ACS
sal2010 <- get_acs(geography = "state",variables = "B03001_014",
year = 2010,geometry = F) %>%
arrange(GEOID)
## Getting data from the 2006-2010 5-year ACS
names(sal2019)[4] <- "year2019"
sal2019$year2010 <- sal2010$estimate
sal2019$change <- sal2019$year2019 - sal2019$year2010
sal2019[,c(2,4,6,7)] %>% arrange(desc(year2019)) %>%
kbl(caption = "Salvadoran American Population") %>%
kable_classic(full_width = F, html_font = "Cambria") %>%
column_spec(2:3, color = "blue") %>%
column_spec(4,bold = T, color = "blue")
Salvadoran American Population
|
NAME
|
year2019
|
year2010
|
change
|
|
California
|
715553
|
585541
|
130012
|
|
Texas
|
328905
|
225831
|
103074
|
|
Maryland
|
188211
|
120404
|
67807
|
|
New York
|
179245
|
141267
|
37978
|
|
Virginia
|
170670
|
113448
|
57222
|
|
Florida
|
75538
|
53158
|
22380
|
|
New Jersey
|
72631
|
53021
|
19610
|
|
Massachusetts
|
63516
|
37164
|
26352
|
|
North Carolina
|
54074
|
35069
|
19005
|
|
Georgia
|
45864
|
33875
|
11989
|
|
Nevada
|
44170
|
27305
|
16865
|
|
Arkansas
|
24241
|
12983
|
11258
|
|
Washington
|
20507
|
11525
|
8982
|
|
District of Columbia
|
19845
|
17884
|
1961
|
|
Illinois
|
19255
|
12903
|
6352
|
|
Colorado
|
17229
|
11849
|
5380
|
|
Arizona
|
15855
|
11428
|
4427
|
|
Tennessee
|
15266
|
10138
|
5128
|
|
Pennsylvania
|
14604
|
7119
|
7485
|
|
Utah
|
12901
|
7970
|
4931
|
|
Minnesota
|
12893
|
7631
|
5262
|
|
Indiana
|
12779
|
7103
|
5676
|
|
Ohio
|
10964
|
5306
|
5658
|
|
Connecticut
|
10364
|
6488
|
3876
|
|
Oregon
|
10362
|
6533
|
3829
|
|
Louisiana
|
8780
|
3870
|
4910
|
|
Kansas
|
7375
|
4840
|
2535
|
|
Iowa
|
7306
|
4993
|
2313
|
|
Nebraska
|
6667
|
5917
|
750
|
|
South Carolina
|
6345
|
3586
|
2759
|
|
Missouri
|
6294
|
4107
|
2187
|
|
Oklahoma
|
6268
|
2378
|
3890
|
|
Michigan
|
4559
|
2856
|
1703
|
|
Rhode Island
|
4288
|
3448
|
840
|
|
Kentucky
|
3843
|
2434
|
1409
|
|
New Mexico
|
3632
|
1615
|
2017
|
|
Alabama
|
3583
|
2038
|
1545
|
|
Wisconsin
|
2422
|
1612
|
810
|
|
Idaho
|
1919
|
869
|
1050
|
|
Delaware
|
1856
|
819
|
1037
|
|
South Dakota
|
1582
|
437
|
1145
|
|
Mississippi
|
1342
|
1007
|
335
|
|
West Virginia
|
1217
|
995
|
222
|
|
Alaska
|
1092
|
1062
|
30
|
|
New Hampshire
|
1043
|
774
|
269
|
|
Hawaii
|
786
|
910
|
-124
|
|
Maine
|
722
|
450
|
272
|
|
Wyoming
|
493
|
275
|
218
|
|
Puerto Rico
|
467
|
678
|
-211
|
|
Vermont
|
236
|
64
|
172
|
|
Montana
|
194
|
51
|
143
|
|
North Dakota
|
186
|
159
|
27
|
California counties
sal2019 <- get_acs(geography = "county",variables = "B03001_014",
state="06", year = 2019,geometry = F) %>%
arrange(GEOID)
## Getting data from the 2015-2019 5-year ACS
sal2010 <- get_acs(geography = "county",variables = "B03001_014",
state="06",year = 2010,geometry = F) %>%
arrange(GEOID)
## Getting data from the 2006-2010 5-year ACS
names(sal2019)[4] <- "year2019"
sal2019$year2010 <- sal2010$estimate
sal2019$change <- sal2019$year2019 - sal2019$year2010
sal2019[,c(2,4,6,7)] %>% arrange(desc(year2019)) %>%
kbl(caption = "Salvadoran American Population in California") %>%
kable_classic(full_width = F, html_font = "Cambria") %>%
column_spec(2:3, color = "blue") %>%
column_spec(4,bold = T, color = "blue")
Salvadoran American Population in California
|
NAME
|
year2019
|
year2010
|
change
|
|
Los Angeles County, California
|
425682
|
368626
|
57056
|
|
San Bernardino County, California
|
35290
|
25825
|
9465
|
|
Contra Costa County, California
|
30220
|
19006
|
11214
|
|
Orange County, California
|
28732
|
23935
|
4797
|
|
Riverside County, California
|
22850
|
17408
|
5442
|
|
San Mateo County, California
|
22694
|
18215
|
4479
|
|
Alameda County, California
|
21899
|
16040
|
5859
|
|
San Francisco County, California
|
15794
|
15432
|
362
|
|
Santa Clara County, California
|
14258
|
13225
|
1033
|
|
Fresno County, California
|
10507
|
6037
|
4470
|
|
Sacramento County, California
|
10419
|
6720
|
3699
|
|
Kern County, California
|
10249
|
9233
|
1016
|
|
San Diego County, California
|
9476
|
5744
|
3732
|
|
Ventura County, California
|
7436
|
4275
|
3161
|
|
Monterey County, California
|
6756
|
4261
|
2495
|
|
Solano County, California
|
6171
|
3748
|
2423
|
|
San Joaquin County, California
|
4898
|
3828
|
1070
|
|
Sonoma County, California
|
4609
|
2657
|
1952
|
|
Stanislaus County, California
|
4230
|
3069
|
1161
|
|
Marin County, California
|
3404
|
3413
|
-9
|
|
Tulare County, California
|
2499
|
1375
|
1124
|
|
Santa Barbara County, California
|
2463
|
1556
|
907
|
|
Santa Cruz County, California
|
1837
|
1017
|
820
|
|
Merced County, California
|
1599
|
1572
|
27
|
|
El Dorado County, California
|
1552
|
568
|
984
|
|
Placer County, California
|
1279
|
826
|
453
|
|
Madera County, California
|
1229
|
645
|
584
|
|
Yolo County, California
|
1204
|
1234
|
-30
|
|
Napa County, California
|
1099
|
1122
|
-23
|
|
Imperial County, California
|
939
|
541
|
398
|
|
Kings County, California
|
643
|
343
|
300
|
|
Butte County, California
|
483
|
610
|
-127
|
|
San Luis Obispo County, California
|
440
|
616
|
-176
|
|
Tuolumne County, California
|
320
|
60
|
260
|
|
Lake County, California
|
276
|
384
|
-108
|
|
Shasta County, California
|
248
|
281
|
-33
|
|
Tehama County, California
|
230
|
167
|
63
|
|
Humboldt County, California
|
228
|
109
|
119
|
|
Yuba County, California
|
210
|
133
|
77
|
|
San Benito County, California
|
155
|
130
|
25
|
|
Glenn County, California
|
137
|
76
|
61
|
|
Inyo County, California
|
120
|
91
|
29
|
|
Sutter County, California
|
113
|
230
|
-117
|
|
Plumas County, California
|
107
|
0
|
107
|
|
Nevada County, California
|
102
|
46
|
56
|
|
Mono County, California
|
93
|
165
|
-72
|
|
Colusa County, California
|
73
|
286
|
-213
|
|
Mendocino County, California
|
64
|
246
|
-182
|
|
Siskiyou County, California
|
49
|
123
|
-74
|
|
Amador County, California
|
45
|
70
|
-25
|
|
Lassen County, California
|
44
|
92
|
-48
|
|
Calaveras County, California
|
37
|
21
|
16
|
|
Modoc County, California
|
27
|
46
|
-19
|
|
Mariposa County, California
|
22
|
0
|
22
|
|
Del Norte County, California
|
11
|
10
|
1
|
|
Trinity County, California
|
2
|
53
|
-51
|
|
Alpine County, California
|
0
|
0
|
0
|
|
Sierra County, California
|
0
|
0
|
0
|
state Assembly Districts
sal2019 <- get_acs(geography = "state legislative district (lower chamber)",variables = "B03001_014",
state = "06",year = 2019) %>%
arrange(GEOID)
## Getting data from the 2015-2019 5-year ACS
sal2010 <- get_acs(geography = "state legislative district (lower chamber)",variables = "B03001_014",
state="06",year = 2010,geometry = F) %>%
arrange(GEOID)
## Getting data from the 2006-2010 5-year ACS
names(sal2019)[4] <- "year2019"
sal2019$year2010 <- sal2010$estimate
sal2019$change <- sal2019$year2019 - sal2019$year2010
sal2019$NAME <- gsub("2018","", sal2019$NAME)
sal2019$NAME <- gsub(", California","", sal2019$NAME)
sal2019$NAME <- gsub("[[:punct:]]","", sal2019$NAME)
sal2019[,c(2,4,6,7)] %>% arrange(desc(year2019)) %>%
kbl(caption = "Salvadoran American Population \nCalifornia Assembly Districts") %>%
kable_classic(full_width = F, html_font = "Cambria") %>%
column_spec(2:3, color = "blue") %>%
column_spec(4,bold = T, color = "blue")
Salvadoran American Population California Assembly Districts
|
NAME
|
year2019
|
year2010
|
change
|
|
Assembly District 59
|
54496
|
4163
|
50333
|
|
Assembly District 53
|
44963
|
2451
|
42512
|
|
Assembly District 46
|
43627
|
30551
|
13076
|
|
Assembly District 39
|
33049
|
38236
|
-5187
|
|
Assembly District 51
|
26349
|
13201
|
13148
|
|
Assembly District 54
|
23775
|
4330
|
19445
|
|
Assembly District 45
|
22561
|
37314
|
-14753
|
|
Assembly District 64
|
22140
|
4415
|
17725
|
|
Assembly District 63
|
21148
|
4709
|
16439
|
|
Assembly District 36
|
21074
|
17266
|
3808
|
|
Assembly District 62
|
18001
|
10419
|
7582
|
|
Assembly District 15
|
15753
|
3353
|
12400
|
|
Assembly District 58
|
15368
|
9810
|
5558
|
|
Assembly District 43
|
15359
|
13396
|
1963
|
|
Assembly District 14
|
14230
|
8327
|
5903
|
|
Assembly District 47
|
12468
|
18976
|
-6508
|
|
Assembly District 19
|
12232
|
10135
|
2097
|
|
Assembly District 18
|
11653
|
6064
|
5589
|
|
Assembly District 52
|
11436
|
22988
|
-11552
|
|
Assembly District 22
|
11350
|
3715
|
7635
|
|
Assembly District 48
|
11317
|
50897
|
-39580
|
|
Assembly District 17
|
11189
|
3569
|
7620
|
|
Assembly District 57
|
10432
|
10362
|
70
|
|
Assembly District 69
|
9862
|
7808
|
2054
|
|
Assembly District 38
|
8561
|
8323
|
238
|
|
Assembly District 31
|
8193
|
3644
|
4549
|
|
Assembly District 41
|
7968
|
2436
|
5532
|
|
Assembly District 70
|
7462
|
1959
|
5503
|
|
Assembly District 61
|
7422
|
10090
|
-2668
|
|
Assembly District 20
|
7159
|
3314
|
3845
|
|
Assembly District 33
|
6604
|
1613
|
4991
|
|
Assembly District 11
|
6491
|
9312
|
-2821
|
|
Assembly District 24
|
6426
|
3171
|
3255
|
|
Assembly District 10
|
6399
|
2076
|
4323
|
|
Assembly District 40
|
6370
|
25655
|
-19285
|
|
Assembly District 49
|
6242
|
7540
|
-1298
|
|
Assembly District 30
|
5677
|
4165
|
1512
|
|
Assembly District 66
|
5395
|
4574
|
821
|
|
Assembly District 32
|
5375
|
5126
|
249
|
|
Assembly District 60
|
5348
|
2193
|
3155
|
|
Assembly District 34
|
4935
|
2201
|
2734
|
|
Assembly District 29
|
4704
|
2482
|
2222
|
|
Assembly District 27
|
4699
|
2960
|
1739
|
|
Assembly District 68
|
4566
|
4815
|
-249
|
|
Assembly District 65
|
4498
|
3584
|
914
|
|
Assembly District 7
|
4481
|
4050
|
431
|
|
Assembly District 50
|
4444
|
21090
|
-16646
|
|
Assembly District 44
|
4212
|
9564
|
-5352
|
|
Assembly District 67
|
4192
|
2286
|
1906
|
|
Assembly District 25
|
4149
|
1739
|
2410
|
|
Assembly District 56
|
3777
|
7507
|
-3730
|
|
Assembly District 21
|
3765
|
5266
|
-1501
|
|
Assembly District 13
|
3600
|
7715
|
-4115
|
|
Assembly District 42
|
3158
|
4950
|
-1792
|
|
Assembly District 8
|
3123
|
3093
|
30
|
|
Assembly District 12
|
3061
|
11033
|
-7972
|
|
Assembly District 72
|
3036
|
4328
|
-1292
|
|
Assembly District 55
|
3026
|
7017
|
-3991
|
|
Assembly District 37
|
2913
|
3633
|
-720
|
|
Assembly District 5
|
2904
|
1982
|
922
|
|
Assembly District 73
|
2835
|
1233
|
1602
|
|
Assembly District 74
|
2753
|
825
|
1928
|
|
Assembly District 26
|
2654
|
3012
|
-358
|
|
Assembly District 4
|
2650
|
1535
|
1115
|
|
Assembly District 9
|
2618
|
2303
|
315
|
|
Assembly District 28
|
2500
|
3127
|
-627
|
|
Assembly District 23
|
2334
|
4715
|
-2381
|
|
Assembly District 6
|
2174
|
4688
|
-2514
|
|
Assembly District 79
|
2031
|
927
|
1104
|
|
Assembly District 76
|
1879
|
664
|
1215
|
|
Assembly District 35
|
1726
|
1299
|
427
|
|
Assembly District 75
|
1666
|
875
|
791
|
|
Assembly District 16
|
1537
|
6125
|
-4588
|
|
Assembly District 2
|
1419
|
1243
|
176
|
|
Assembly District 3
|
1189
|
885
|
304
|
|
Assembly District 77
|
1146
|
729
|
417
|
|
Assembly District 1
|
1095
|
1111
|
-16
|
|
Assembly District 78
|
1071
|
1001
|
70
|
|
Assembly District 71
|
1055
|
2420
|
-1365
|
|
Assembly District 80
|
1054
|
3883
|
-2829
|
sal2019[,c(2,4)] %>%
top_n(n=25, year2019) %>%
arrange(desc(year2019)) %>%
ggplot(aes(x = year2019, y = reorder(NAME, year2019))) +
geom_point(color = "red", size = 3) +
labs(title = "Salvadoran American population in California",
subtitle = "2019 American Community Survey",
y = "",
x = "")
State Senate Districts
sal2019 <- get_acs(geography = "state legislative district (upper chamber)",variables = "B03001_014",
state = "06",year = 2019) %>%
arrange(GEOID)
## Getting data from the 2015-2019 5-year ACS
sal2010 <- get_acs(geography = "state legislative district (upper chamber)",variables = "B03001_014",
state="06",year = 2010,geometry = F) %>%
arrange(GEOID)
## Getting data from the 2006-2010 5-year ACS
names(sal2019)[4] <- "year2019"
sal2019$year2010 <- sal2010$estimate
sal2019$change <- sal2019$year2019 - sal2019$year2010
sal2019$NAME <- gsub("2018","", sal2019$NAME)
sal2019$NAME <- gsub(", California","", sal2019$NAME)
sal2019$NAME <- gsub("[[:punct:]]","", sal2019$NAME)
sal2019[,c(2,4,6,7)] %>% arrange(desc(year2019)) %>%
kbl(caption = "Salvadoran American Population \nCalifornia State Senate Districts") %>%
kable_classic(full_width = F, html_font = "Cambria") %>%
column_spec(2:3, color = "blue") %>%
column_spec(4,bold = T, color = "blue")
Salvadoran American Population California State Senate Districts
|
NAME
|
year2019
|
year2010
|
change
|
|
State Senate District 30
|
84126
|
29322
|
54804
|
|
State Senate District 18
|
76012
|
6922
|
69090
|
|
State Senate District 24
|
73685
|
20485
|
53200
|
|
State Senate District 33
|
39712
|
4156
|
35556
|
|
State Senate District 35
|
32017
|
4099
|
27918
|
|
State Senate District 21
|
30524
|
18167
|
12357
|
|
State Senate District 27
|
28063
|
18462
|
9601
|
|
State Senate District 9
|
27747
|
13320
|
14427
|
|
State Senate District 20
|
23904
|
69403
|
-45499
|
|
State Senate District 11
|
23266
|
7780
|
15486
|
|
State Senate District 32
|
22500
|
19865
|
2635
|
|
State Senate District 22
|
18322
|
73275
|
-54953
|
|
State Senate District 13
|
17931
|
9255
|
8676
|
|
State Senate District 25
|
16876
|
30150
|
-13274
|
|
State Senate District 7
|
14962
|
13075
|
1887
|
|
State Senate District 12
|
13507
|
6544
|
6963
|
|
State Senate District 31
|
12770
|
9638
|
3132
|
|
State Senate District 34
|
11640
|
14245
|
-2605
|
|
State Senate District 10
|
11136
|
10022
|
1114
|
|
State Senate District 3
|
10438
|
13271
|
-2833
|
|
State Senate District 23
|
9549
|
6322
|
3227
|
|
State Senate District 14
|
9011
|
3262
|
5749
|
|
State Senate District 26
|
8749
|
64272
|
-55523
|
|
State Senate District 29
|
8541
|
8713
|
-172
|
|
State Senate District 16
|
7914
|
9345
|
-1431
|
|
State Senate District 28
|
7861
|
8589
|
-728
|
|
State Senate District 6
|
7602
|
4888
|
2714
|
|
State Senate District 15
|
7358
|
4294
|
3064
|
|
State Senate District 37
|
7319
|
8338
|
-1019
|
|
State Senate District 19
|
7204
|
4293
|
2911
|
|
State Senate District 17
|
6813
|
23947
|
-17134
|
|
State Senate District 2
|
6598
|
5119
|
1479
|
|
State Senate District 5
|
6592
|
6663
|
-71
|
|
State Senate District 36
|
4714
|
2596
|
2118
|
|
State Senate District 8
|
4401
|
20446
|
-16045
|
|
State Senate District 1
|
3953
|
2952
|
1001
|
|
State Senate District 4
|
3700
|
2181
|
1519
|
|
State Senate District 40
|
3119
|
3988
|
-869
|
|
State Senate District 39
|
2933
|
1696
|
1237
|
|
State Senate District 38
|
2484
|
2181
|
303
|
sal2019[,c(2,4)] %>%
top_n(n=25, year2019) %>%
arrange(desc(year2019)) %>%
ggplot(aes(x = year2019, y = reorder(NAME, year2019))) +
geom_point(color = "red", size = 3) +
labs(title = "Salvadoran American population in California",
subtitle = "2019 American Community Survey",
y = "",
x = "")
