U.S. Census Decennial data in California counties

Highlights:

2020 Hispanic Demographic Census
- Three states with largest population : California (15.4 Million), Texas (11.3 Million), Florida (5.5 Million)
- State with largest increase from year 2010: Texas with 2,376,780 increase
- State with largest decrease from year 2010: Porto Rico with 504,245 decrease
- California county with largest percentage: Imperial County with 85%
2020 Asian Demographic Census
- Three states with largest population : California (6.8 Million), New York (1.9 Million), Texas (1.6 Million)
- State with largest increase from year 2010: California with 1,087,060 increase
- State with largest decrease from year 2010: Hawaii with 229,069 decrease
2020 Black Demographic Census
- Three states with largest population : Texas (3.8 Million), Florida (3.7 Million), Georgia (3.5 Million)
- State with larger increase from year 2010: Texas with 444,689 increase
- State with larger decrease from year 2010: California with 329,710 decrease
2020 White Demographic Census
- Three states with largest population : California (24.8 Million), Texas (21.7 Million), Florida (16.4 Million)
- State with larger increase from year 2010: Texas with 1,876,762 increase
- State with larger decrease from year 2010: California with 1,492,244 decrease

Using Tidycensus R-Package as Data Analysis Tools

Tidycensus package (K. Walker and Herman 2021), first released in 2017, is an R package designed to facilitate the process of acquiring and working with US Census Bureau population data in the R environment. The package has two distinct goals:
- Data retrieval from Census
- Data manipulation and wrangling for analysis
- Two core functions that will be the basis of working with TidyCensus:
- get_acs(): used to pull data from the American Community Survey (ACS)
- get_decennial(): used to pull data from the Decennial Census

2020 Census Decennial Survey, as population percentage

# 2020 Decennial Census Variables
all_vars = c(
        all = "P2_001N",
        hisp = "P2_002N",
        white = "P2_005N",
        baa = "P2_006N",
        amin = "P2_007N",
        asian = "P2_008N",
        nhopi = "P2_009N",
        other = "P2_010N",
        multi = "P2_011N"
       )




pop2020 <- get_decennial(geography = "state", variables = all_vars, summary_var="P2_001N", year = 2020) %>% 
                         mutate(percent=round(100*(value/summary_value), digits=0)) %>%
                         arrange(as.numeric(GEOID))

## Getting data from the 2020 decennial Census

## Using the PL 94-171 Redistricting Data summary file

## Note: 2020 decennial Census data use differential privacy, a technique that
## introduces errors into data to preserve respondent confidentiality.
## ℹ Small counts should be interpreted with caution.
## ℹ See https://www.census.gov/library/fact-sheets/2021/protecting-the-confidentiality-of-the-2020-census-redistricting-data.html for additional guidance.
## This message is displayed once per session.

pop2020 %>% filter(variable=="hisp")  %>% select(2,4,6) %>% arrange(desc(percent)) %>% 
 gt() %>%   tab_header(
    title = md("Hispanic Population _ percentage"),
    subtitle = md("2020 Census"))

Hispanic Population _ percentage
2020 Census
NAME	value	percent
Puerto Rico	3249043	99
New Mexico	1010811	48
California	15579652	39
Texas	11441717	39
Arizona	2192253	31
Nevada	890257	29
Florida	5697240	26
Colorado	1263390	22
New Jersey	2002575	22
New York	3948032	20
Illinois	2337410	18
Connecticut	623293	17
Rhode Island	182101	17
Utah	492912	15
Oregon	588757	14
Washington	1059213	14
Idaho	239407	13
Kansas	382603	13
Massachusetts	887685	13
Maryland	729745	12
Nebraska	234715	12
Oklahoma	471931	12
Delaware	104290	11
District of Columbia	77652	11
North Carolina	1118596	11
Virginia	908749	11
Georgia	1123457	10
Hawaii	138923	10
Wyoming	59046	10
Arkansas	256847	9
Indiana	554191	8
Pennsylvania	1049615	8
Wisconsin	447290	8
Alaska	49824	7
Iowa	215986	7
Louisiana	322549	7
South Carolina	352838	7
Tennessee	479187	7
Michigan	564422	6
Minnesota	345640	6
Alabama	264047	5
Kentucky	207854	5
Missouri	303068	5
Mississippi	105220	4
Montana	45199	4
New Hampshire	59454	4
North Dakota	33412	4
Ohio	521308	4
South Dakota	38741	4
Maine	26609	2
Vermont	15504	2
West Virginia	34827	2

pop2020 %>% filter(variable=="white")  %>% select(2,4,6) %>% arrange(desc(percent)) %>% 
            gt() %>%   tab_header(
    title = md("White Population _ percentage"),
    subtitle = md("2020 Census"))

White Population _ percentage
2020 Census
NAME	value	percent
Maine	1228264	90
Vermont	573201	89
West Virginia	1598834	89
New Hampshire	1200649	87
Iowa	2638201	83
Montana	901318	83
North Dakota	636160	82
Kentucky	3664764	81
Wyoming	469664	81
South Dakota	705583	80
Idaho	1450523	79
Wisconsin	4634018	79
Minnesota	4353880	76
Missouri	4663907	76
Nebraska	1484687	76
Ohio	8954135	76
Indiana	5121004	75
Utah	2465355	75
Pennsylvania	9553417	73
Kansas	2122575	72
Michigan	7295651	72
Oregon	3036158	72
Tennessee	4900246	71
Arkansas	2063550	69
Rhode Island	754050	69
Massachusetts	4748897	68
Colorado	3760663	65
Washington	4918820	64
Alabama	3171351	63
Connecticut	2279232	63
South Carolina	3178552	62
Oklahoma	2407188	61
North Carolina	6312148	60
Delaware	579851	59
Virginia	5058363	59
Alaska	421758	58
Illinois	7472751	58
Louisiana	2596702	56
Mississippi	1639077	55
Arizona	3816547	53
Florida	11100503	52
New Jersey	4816381	52
New York	10598907	52
Georgia	5362156	50
Maryland	2913782	47
Nevada	1425952	46
Texas	11584597	40
District of Columbia	261771	38
New Mexico	772952	37
California	13714587	35
Hawaii	314365	22
Puerto Rico	24548	1

pop2020 %>% filter(variable=="asian")  %>% select(2,4,6) %>% arrange(desc(percent)) %>% 
            gt() %>%   tab_header(
    title = md("Asian Population _ percentage"),
    subtitle = md("2020 Census"))

Asian Population _ percentage
2020 Census
NAME	value	percent
Hawaii	531558	37
California	5978795	15
New Jersey	942921	10
Nevada	265991	9
New York	1916329	9
Washington	723062	9
Maryland	417962	7
Massachusetts	504900	7
Virginia	610612	7
Alaska	43449	6
Illinois	747280	6
Connecticut	170459	5
District of Columbia	33192	5
Minnesota	297460	5
Oregon	191797	5
Texas	1561518	5
Delaware	42398	4
Georgia	475680	4
Pennsylvania	506674	4
Arizona	248837	3
Colorado	195220	3
Florida	629626	3
Kansas	85225	3
Michigan	332288	3
Nebraska	52359	3
New Hampshire	35604	3
North Carolina	340059	3
Ohio	296604	3
Rhode Island	38367	3
Wisconsin	174267	3
Alabama	75918	2
Arkansas	51210	2
Indiana	166651	2
Iowa	75017	2
Kentucky	73843	2
Louisiana	85336	2
Missouri	132158	2
New Mexico	35261	2
North Dakota	13050	2
Oklahoma	89653	2
South Carolina	89394	2
South Dakota	13332	2
Tennessee	134302	2
Utah	78618	2
Vermont	11457	2
Idaho	26036	1
Maine	16668	1
Mississippi	32305	1
Montana	8077	1
West Virginia	14903	1
Wyoming	5037	1
Puerto Rico	2746	0

pop2020 %>% filter(variable=="baa")  %>% select(2,4,6) %>% arrange(desc(percent)) %>% 
            gt() %>%   tab_header(
    title = md("Black Population _ percentage"),
    subtitle = md("2020 Census"))

Black Population _ percentage
2020 Census
NAME	value	percent
District of Columbia	282066	41
Mississippi	1079001	36
Georgia	3278119	31
Louisiana	1452420	31
Maryland	1795027	29
Alabama	1288159	26
South Carolina	1269031	25
Delaware	212960	22
North Carolina	2107526	20
Virginia	1578090	18
Tennessee	1083772	16
Arkansas	449884	15
Florida	3127052	15
Illinois	1775612	14
New York	2759022	14
Michigan	1358458	13
New Jersey	1154142	12
Ohio	1457180	12
Texas	3444712	12
Missouri	692774	11
Pennsylvania	1368978	11
Connecticut	360937	10
Indiana	637500	9
Nevada	291960	9
Kentucky	357764	8
Massachusetts	457055	7
Minnesota	392850	7
Oklahoma	283242	7
Kansas	163352	6
Wisconsin	366508	6
California	2119286	5
Nebraska	94405	5
Rhode Island	55386	5
Arizona	317161	4
Colorado	221310	4
Iowa	129321	4
Washington	296170	4
West Virginia	64749	4
Alaska	20731	3
North Dakota	26152	3
Hawaii	21877	2
Maine	25115	2
New Mexico	38330	2
Oregon	78658	2
South Dakota	17441	2
Idaho	14785	1
New Hampshire	18655	1
Utah	37192	1
Vermont	8649	1
Wyoming	4735	1
Montana	5077	0
Puerto Rico	4286	0

Population changes from 2010 Census to 2020 census

* Note of caution: when comparing data from year to year, it is important to be consistent in selecting data standards: 
**               ACS one-year estimate, or ACS 5-year estimate, or 10-year Decennial Survey

* survey application 
** 10-year Decennial surveys are used for general nation-wide analysis
** 5-year ACS surveys  are used for specific demographic analysis and county level geography

2010 Decenial census survey for states

vars10 <- c(Hispanic="P005010", Asian="P005006",White="P005003",Black="P005004")


pop2010 <- get_decennial(geography = "state", variables = vars10, year = 2010,
                    summary_var = "P005001", geometry = F) %>%
                    mutate(pct = round(100 * (value / summary_value),digits = 0))

## Getting data from the 2010 decennial Census

## Using Census Summary File 1

vars20 <- c(Hispanic="P2_002N", Asian="P1_006N",White="P1_003N",Black="P1_004N")
pop2020 <- get_decennial(geography = "state", variables = vars20, year = 2020,
                    summary_var = "P1_001N",  geometry = F) %>%
                    mutate(pct = round(100 * (value / summary_value),digits = 0))

## Getting data from the 2020 decennial Census

## Using the PL 94-171 Redistricting Data summary file

#----------------------------------------
hispanic2010 <- filter(pop2010,variable=="Hispanic") %>% arrange(desc(pct)) 
  
gt(hispanic2010[,c(2,4,6)]) %>%  
  tab_header(
    title = "Hispanic Population _ by percentage",
    subtitle = "2010 Census _ All states "
  )

Hispanic Population _ by percentage
2010 Census _ All states
NAME	value	pct
Puerto Rico	3688455	99
New Mexico	953403	46
California	14013719	38
Texas	9460921	38
Arizona	1895149	30
Nevada	716501	27
Florida	4223806	22
Colorado	1038687	21
New Jersey	1555144	18
New York	3416922	18
Illinois	2027578	16
Connecticut	479087	13
Utah	358340	13
Oregon	450062	12
Rhode Island	130655	12
Idaho	175901	11
Kansas	300042	11
Washington	755790	11
Massachusetts	627654	10
District of Columbia	54749	9
Georgia	853689	9
Hawaii	120842	9
Nebraska	167405	9
Oklahoma	332007	9
Wyoming	50231	9
Delaware	73221	8
Maryland	470632	8
North Carolina	800120	8
Virginia	631825	8
Alaska	39249	6
Arkansas	186050	6
Indiana	389707	6
Pennsylvania	719660	6
Wisconsin	336056	6
Iowa	151544	5
Minnesota	250258	5
South Carolina	235682	5
Tennessee	290059	5
Alabama	185602	4
Louisiana	192560	4
Michigan	436358	4
Missouri	212470	4
Kentucky	132836	3
Mississippi	81481	3
Montana	28565	3
New Hampshire	36704	3
Ohio	354674	3
South Dakota	22119	3
North Dakota	13467	2
Maine	16935	1
Vermont	9208	1
West Virginia	22268	1

#-------------------------------------
asian2010 <- filter(pop2010,variable=="Asian") %>% arrange(desc(pct)) 
gt(asian2010[,c(2,4,6)]) %>%  tab_header(
    title = "Asian Population _ by percentage",
    subtitle = "2010 Census _ All states"
  )

Asian Population _ by percentage
2010 Census _ All states
NAME	value	pct
Hawaii	513294	38
California	4775070	13
New Jersey	719827	8
Nevada	191047	7
New York	1406194	7
Washington	475634	7
Alaska	37459	5
Illinois	580586	5
Maryland	316694	5
Massachusetts	347495	5
Virginia	436298	5
Connecticut	134091	4
Minnesota	212996	4
Oregon	139436	4
Texas	948426	4
Arizona	170509	3
Colorado	135564	3
Delaware	28308	3
District of Columbia	20818	3
Georgia	311692	3
Pennsylvania	346288	3
Rhode Island	29988	3
Louisiana	69327	2
Florida	445216	2
Indiana	101444	2
Iowa	52597	2
Kansas	66967	2
Michigan	236490	2
Missouri	97221	2
Nebraska	31919	2
New Hampshire	28241	2
North Carolina	206579	2
Ohio	190765	2
Oklahoma	64154	2
Utah	54176	2
Wisconsin	128052	2
Alabama	52937	1
Arkansas	35647	1
Kentucky	48338	1
Idaho	18529	1
Maine	13442	1
Mississippi	25477	1
Montana	6138	1
New Mexico	26305	1
North Dakota	6839	1
South Carolina	58307	1
South Dakota	7553	1
Tennessee	90311	1
Vermont	7875	1
West Virginia	12285	1
Wyoming	4279	1
Puerto Rico	2930	0

white2010 <- filter(pop2010,variable=="White") %>% arrange(desc(pct)) 
gt(white2010[,c(2,4,6)]) %>%  tab_header(
    title = "White Population _ by percentage",
    subtitle = "2010 Census _ all states"
  )

White Population _ by percentage
2010 Census _ all states
NAME	value	pct
Maine	1254297	94
Vermont	590223	94
West Virginia	1726256	93
New Hampshire	1215050	92
Iowa	2701123	89
North Dakota	598007	89
Montana	868628	88
Kentucky	3745655	86
Wyoming	483874	86
South Dakota	689502	85
Idaho	1316243	84
Minnesota	4405142	83
Wisconsin	4738411	83
Indiana	5286453	82
Nebraska	1499753	82
Missouri	4850748	81
Ohio	9359263	81
Utah	2221719	80
Pennsylvania	10094652	79
Kansas	2230539	78
Oregon	3005848	78
Michigan	7569939	77
Massachusetts	4984800	76
Rhode Island	803685	76
Tennessee	4800782	76
Arkansas	2173469	75
Washington	4876804	73
Connecticut	2546262	71
Colorado	3520793	70
Oklahoma	2575381	69
Alabama	3204402	67
Delaware	586752	65
North Carolina	6223995	65
Virginia	5186450	65
Alaska	455320	64
Illinois	8167753	64
South Carolina	2962740	64
Louisiana	2734884	60
New Jersey	5214878	59
Arizona	3695647	58
Florida	10884722	58
Mississippi	1722287	58
New York	11304247	58
Georgia	5413920	56
Maryland	3157958	55
Nevada	1462081	54
Texas	11397345	45
California	14956253	40
New Mexico	833810	40
District of Columbia	209464	35
Hawaii	309343	23
Puerto Rico	26946	1

black2010 <- filter(pop2010,variable=="Black") %>% arrange(desc(pct)) 
gt(black2010[,c(2,4,6)]) %>%  tab_header(
    title = "Black Population _ by  percentage",
    subtitle = "2010 Census _ all states"
  )

Black Population _ by percentage
2010 Census _ all states
NAME	value	pct
District of Columbia	301053	50
Mississippi	1093512	37
Louisiana	1442420	32
Georgia	2910800	30
Maryland	1674229	29
South Carolina	1279998	28
Alabama	1244437	26
Delaware	186782	21
North Carolina	2019854	21
Virginia	1523704	19
Tennessee	1049391	17
Arkansas	447102	15
Florida	2851100	15
Illinois	1832924	14
Michigan	1383756	14
New York	2783857	14
New Jersey	1125401	13
Ohio	1389115	12
Missouri	687149	11
Texas	2886825	11
Pennsylvania	1327091	10
Connecticut	335119	9
Indiana	582140	9
Kentucky	333075	8
Nevada	208058	8
Oklahoma	272071	7
California	2163804	6
Kansas	162700	6
Massachusetts	391693	6
Wisconsin	350898	6
Minnesota	269141	5
Rhode Island	51560	5
Arizona	239101	4
Colorado	188778	4
Nebraska	80959	4
Alaska	21949	3
Iowa	86906	3
Washington	229603	3
West Virginia	62122	3
New Mexico	35462	2
Oregon	64984	2
Hawaii	19904	1
Idaho	8875	1
Maine	15154	1
New Hampshire	13625	1
North Dakota	7720	1
South Dakota	9959	1
Utah	25951	1
Vermont	5943	1
Wyoming	4351	1
Montana	3743	0
Puerto Rico	4663	0

pop2010 <- get_acs(geography = "state",  
                   variables = c(Total="B02001_001", White="B02008_001",Hispanic="B03001_003",Asian="B02011_001", Black="B02009_001"), 
                         year = 2010) %>% arrange(as.numeric(GEOID))

## Getting data from the 2006-2010 5-year ACS

pop2020 <- get_acs(geography = "state",   
                   variables = c(Total="B02001_001", White="B02008_001",Hispanic="B03001_003",Asian="B02011_001", Black="B02009_001"), 
                         year = 2020) %>%   arrange(as.numeric(GEOID))

## Getting data from the 2016-2020 5-year ACS

pop2020$p2010 <- pop2010$estimate
pop2020$change <- pop2020$estimate - pop2010$estimate

pop2020 <- pop2020[-5]


filter(pop2020,variable=="Total") %>% select(c(2,4,6))  %>% gt() %>%   
  
    tab_header(
    title = md("Total Population Changes"),
    subtitle = md("2020 Census _ Change from 2010"))

Total Population Changes
2020 Census _ Change from 2010
NAME	estimate	change
Alabama	4893186	180535
Alaska	736990	45801
Arizona	7174064	927248
Arkansas	3011873	139189
California	39346023	2708733
Colorado	5684926	797865
Connecticut	3570549	24712
Delaware	967679	86401
District of Columbia	701974	117574
Florida	21216924	2705304
Georgia	10516579	1047764
Hawaii	1420074	86483
Idaho	1754367	227570
Illinois	12716164	-29195
Indiana	6696893	279495
Iowa	3150011	133744
Kansas	2912619	103290
Kentucky	4461952	176124
Louisiana	4664616	234676
Maine	1340825	13160
Maryland	6037624	341201
Massachusetts	6873003	395907
Michigan	9973907	21220
Minnesota	5600166	358252
Mississippi	2981835	39844
Missouri	6124160	201846
Montana	1061705	87966
Nebraska	1923826	124701
Nevada	3030281	396950
New Hampshire	1355244	41305
New Jersey	8885418	163841
New Mexico	2097021	83899
New York	19514849	285097
North Carolina	10386227	1115049
North Dakota	760394	100536
Ohio	11675275	162844
Oklahoma	3949342	274003
Oregon	4176346	414421
Pennsylvania	12794885	182180
Rhode Island	1057798	1409
South Carolina	5091517	580089
South Dakota	879336	79874
Tennessee	6772268	537300
Texas	28635442	4323551
Utah	3151239	494003
Vermont	624340	82
Virginia	8509358	667604
Washington	7512465	951168
West Virginia	1807426	-33376
Wisconsin	5806975	169028
Wyoming	581348	35769
Puerto Rico	3255642	-506680

total2020 <- filter(pop2020,variable=="Total") %>% select(c(2,4,6))  
total2020 %>%  group_by(estimate,change) %>% 
  summarise(total = sum(estimate),change=sum(change))

## `summarise()` has grouped output by 'estimate'. You can override using the
## `.groups` argument.

## # A tibble: 52 × 3
## # Groups:   estimate [52]
##    estimate change   total
##       <dbl>  <dbl>   <dbl>
##  1   581348  35769  581348
##  2   624340     82  624340
##  3   701974 117574  701974
##  4   736990  45801  736990
##  5   760394 100536  760394
##  6   879336  79874  879336
##  7   967679  86401  967679
##  8  1057798   1409 1057798
##  9  1061705  87966 1061705
## 10  1340825  13160 1340825
## # … with 42 more rows

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

filter(pop2020,variable=="Asian") %>% select(c(2,4,6)) %>%  arrange(desc(change)) %>% 
    gt() %>%  tab_header(
    title = md("Asian Population Changes"),
    subtitle = md("2020 Census _ Change from 2010")
  )

Asian Population Changes
2020 Census _ Change from 2010
NAME	estimate	change
California	6764118	1492806
Texas	1656166	651465
New York	1884346	392699
Washington	854617	295163
Florida	763613	232633
Virginia	691449	214694
New Jersey	948998	202836
Illinois	828847	199156
Georgia	515434	184564
Massachusetts	538409	170224
Pennsylvania	532335	161419
North Carolina	377854	150808
Ohio	344131	122999
Maryland	463308	117551
Arizona	326446	116968
Michigan	390937	112322
Minnesota	330738	100768
Nevada	320596	100272
Oregon	258188	84719
Indiana	195087	80378
Colorado	252624	79971
Wisconsin	203052	61742
Tennessee	155558	52661
Connecticut	196347	50453
Missouri	161495	48239
South Carolina	110663	43065
Utah	112368	41744
Hawaii	803266	39678
Iowa	97896	39094
Oklahoma	116701	38282
Kansas	110231	32698
Kentucky	88096	32536
Alabama	89031	27528
Nebraska	62516	25284
Louisiana	99667	22722
Arkansas	59949	20224
Alaska	64669	18026
Idaho	42595	15530
New Hampshire	47713	15474
District of Columbia	37847	14043
Delaware	45744	13962
New Mexico	48942	13623
Rhode Island	45472	10646
Mississippi	39237	10436
North Dakota	16362	8297
South Dakota	16062	6958
West Virginia	20784	6261
Maine	23244	6257
Montana	15622	5952
Vermont	14972	5510
Wyoming	9194	3589
Puerto Rico	8450	-6579

#------------------
filter(pop2020,variable=="Hispanic") %>% select(c(2,4,6))  %>% arrange(desc(change)) %>%
    gt() %>%  tab_header(
    title = md("Hispanic Population Changes"),
    subtitle = md("2020 Census _ Change from 2010")
  )

Hispanic Population Changes
2020 Census _ Change from 2010
NAME	estimate	change
Texas	11294257	2376780
California	15380929	1924772
Florida	5468826	1473502
Arizona	2260690	446016
New York	3720707	431827
New Jersey	1815078	346902
Pennsylvania	971813	315305
Washington	971522	279974
North Carolina	991051	267643
Illinois	2190696	250768
Colorado	1231126	247800
Massachusetts	828140	243165
Virginia	810770	235804
Georgia	1013057	228345
Nevada	875798	201901
Maryland	619418	189472
Connecticut	587212	140101
Oregon	552279	132084
Oklahoma	431467	129300
Ohio	459939	126920
Utah	446067	119261
New Mexico	1031788	117886
Tennessee	377162	115714
Indiana	475475	114003
Michigan	521203	97791
Wisconsin	408267	97718
South Carolina	296897	88143
Kansas	351602	75494
Minnesota	307675	73959
Louisiana	243372	70618
Missouri	262677	64007
Nebraska	214952	62981
Idaho	222967	61630
Arkansas	229629	61202
Iowa	194407	57475
Kentucky	167949	50601
Alabama	212951	50580
Rhode Island	168007	43575
Hawaii	152566	37133
District of Columbia	77981	26680
Mississippi	94342	24508
Delaware	91350	24435
New Hampshire	52792	17454
North Dakota	30325	17447
South Dakota	36088	15644
Alaska	53059	14666
Montana	41501	14241
Wyoming	58854	13072
West Virginia	28679	7724
Maine	23143	6100
Vermont	12518	3364
Puerto Rico	3212625	-504245

#------------------
filter(pop2020,variable=="White") %>% select(c(2,4,6))  %>% arrange(desc(change)) %>%
  gt() %>%  tab_header(
    title = md("White Population Changes"),
    subtitle = md("2020 Census _ Change from 2010")
  )

White Population Changes
2020 Census _ Change from 2010
NAME	estimate	change
Texas	21681317	3752456
Florida	16371086	1949076
California	24826054	1280089
North Carolina	7348103	751495
Colorado	4948790	730334
Arizona	5752282	724940
Washington	6007728	564173
Georgia	6353997	447211
South Carolina	3521154	428808
Tennessee	5380206	382181
Utah	2807476	379859
Virginia	6000367	368264
Oregon	3692435	349194
Idaho	1623030	180209
Oklahoma	3129278	166716
Indiana	5718307	145154
New Mexico	1627090	139261
Minnesota	4773429	134983
Massachusetts	5546174	131523
Missouri	5179826	128815
Kentucky	3965298	112071
Louisiana	2968078	98051
Nebraska	1711828	91844
District of Columbia	312609	82845
Kansas	2548409	82839
Arkansas	2382587	80504
Wisconsin	5076630	76424
Montana	970545	75073
Iowa	2894547	73855
North Dakota	674281	67040
Nevada	2080484	66104
Alabama	3411460	64185
South Dakota	762967	56879
Hawaii	604632	49240
Delaware	684795	43408
Illinois	9367474	38033
Wyoming	547044	35121
New Hampshire	1284067	26543
Alaska	529303	16373
Maine	1291593	3872
Michigan	8082811	1088
Maryland	3501814	-477
Vermont	600582	-6180
Rhode Island	877920	-7581
Mississippi	1774378	-13368
Ohio	9776587	-15331
New Jersey	6188854	-23545
West Virginia	1713884	-47098
Connecticut	2799956	-60568
Pennsylvania	10537652	-91106
New York	12894580	-175309
Puerto Rico	2246894	-817277

#------------------
filter(pop2020,variable=="Black") %>% select(c(2,4,6))  %>% arrange(desc(change)) %>%
  gt() %>%  tab_header(
    title = md("Black Population Changes"),
    subtitle = md("2020 Census _ Change from 2010")
  )

Black Population Changes
2020 Census _ Change from 2010
NAME	estimate	change
Texas	3834846	815111
Florida	3735264	673579
Georgia	3499486	551954
North Carolina	2392742	321041
California	2806900	226228
Maryland	1937445	200333
New York	3396120	195910
Virginia	1800436	184291
Pennsylvania	1634203	174119
Ohio	1673910	164798
Massachusetts	662313	163170
Minnesota	447354	142279
Arizona	425315	139221
Tennessee	1214063	136480
Louisiana	1560682	126802
Washington	425653	121040
South Carolina	1423031	118662
Indiana	735826	103856
Nevada	337681	101492
Alabama	1349127	90795
New Jersey	1335434	76479
Colorado	309449	75913
Connecticut	456214	71342
Kentucky	425351	60888
Missouri	784331	57879
Wisconsin	441868	53336
Iowa	156107	50373
Oklahoma	364516	48995
Mississippi	1149098	46686
Delaware	234353	37683
Oregon	124917	32384
Arkansas	493883	31780
Nebraska	117761	23415
Michigan	1524001	22463
Utah	59239	21078
Kansas	216216	21076
North Dakota	28675	19231
Rhode Island	91539	17572
District of Columbia	333114	17513
West Virginia	84768	13926
Hawaii	51286	13249
South Dakota	25975	13067
New Hampshire	32277	12432
New Mexico	62321	10853
Maine	27832	9347
Idaho	21346	6775
Illinois	1957092	5755
Alaska	37226	4882
Montana	12031	4720
Wyoming	10732	4445
Vermont	12384	3921
Puerto Rico	547012	-21025

U.S. counties with Hispanic Majority ( greater than 50%)

vars20 <- c(Hispanic="P2_002N", Asian="P1_006N",White="P1_003N",Black="P1_004N")


us2020 <- get_decennial(geography = "county", variables = vars20, year = 2020,
                    summary_var = "P1_001N", geometry = F) %>%
                    mutate(pct = round(100 * (value / summary_value),digits = 0))

## Getting data from the 2020 decennial Census

## Using the PL 94-171 Redistricting Data summary file

us2020 <- us2020 %>% filter(!str_detect(NAME, regex("\\ Puerto Rico", ignore_case = TRUE)))
us2020$NAME <- gsub(" County","",us2020$NAME)
us2020 <- us2020 %>% separate(NAME,sep = "," , c('County', 'state'))
hispanic2020 <- filter(us2020,variable=="Hispanic") %>%  filter(pct>50) %>% arrange(desc(pct)) 
gt(hispanic2020[,c(2,3,7)]) %>%  tab_header(
    title = "Hispanic Population _ by county percentage",
    subtitle = "2020 Census California"
  )

Hispanic Population _ by county percentage
2020 Census California
County	state	pct
Starr	Texas	98
Webb	Texas	95
Maverick	Texas	95
Zapata	Texas	94
Hidalgo	Texas	92
Zavala	Texas	92
Cameron	Texas	89
Brooks	Texas	88
Jim Hogg	Texas	88
Dimmit	Texas	87
Willacy	Texas	87
Imperial	California	85
Reeves	Texas	85
El Paso	Texas	83
Santa Cruz	Arizona	83
Duval	Texas	81
Presidio	Texas	81
Val Verde	Texas	80
Jim Wells	Texas	79
Mora	New Mexico	79
Frio	Texas	77
Guadalupe	New Mexico	77
Culberson	Texas	75
Deaf Smith	Texas	75
Kenedy	Texas	75
San Miguel	New Mexico	75
La Salle	Texas	74
Kleberg	Texas	71
Pecos	Texas	71
Uvalde	Texas	70
Miami-Dade	Florida	69
Crane	Texas	68
Reagan	Texas	67
Doña Ana	New Mexico	67
Rio Arriba	New Mexico	67
Tulare	California	66
Bailey	Texas	66
Parmer	Texas	66
Luna	New Mexico	66
Seward	Kansas	66
Castro	Texas	65
Yoakum	Texas	65
Adams	Washington	64
Atascosa	Texas	64
Hudspeth	Texas	64
Yuma	Arizona	64
Colusa	California	62
Merced	California	62
Bee	Texas	62
Crockett	Texas	62
Sutton	Texas	62
San Benito	California	61
Ector	Texas	61
Nueces	Texas	61
Winkler	Texas	61
Lea	New Mexico	61
Madera	California	60
Monterey	California	60
Hale	Texas	60
Cochran	Texas	60
Valencia	New Mexico	60
Bexar	Texas	59
Moore	Texas	59
Kings	California	57
Costilla	Colorado	57
Floyd	Texas	57
Lamb	Texas	57
Chaves	New Mexico	57
Hidalgo	New Mexico	57
Ford	Kansas	57
Hendry	Florida	56
Garza	Texas	56
San Patricio	Texas	56
Terry	Texas	56
Andrews	Texas	56
Caldwell	Texas	56
Kern	California	55
Bronx	New York	55
Crosby	Texas	55
Ochiltree	Texas	55
Fresno	California	54
San Bernardino	California	54
Franklin	Washington	54
Osceola	Florida	54
Dawson	Texas	54
Upton	Texas	54
Ward	Texas	54
Karnes	Texas	53
Grant	Kansas	53
Dallam	Texas	52
Schleicher	Texas	52
Finney	Kansas	52
Conejos	Colorado	51
Yakima	Washington	51
Texas	Oklahoma	51
Taos	New Mexico	51

2010 population data for California counties

vars10 <- c(Hispanic="P005010", Asian="P005006",White="P005003",Black="P005004")


ca2010 <- get_decennial(geography = "county", variables = vars10, year = 2010,
                    summary_var = "P005001", state = "CA", geometry = F) %>%
                    mutate(pct = round(100 * (value / summary_value),digits = 0))

## Getting data from the 2010 decennial Census

## Using Census Summary File 1

ca2010$NAME <- gsub(", California","",ca2010$NAME)

hispanic2010 <- filter(ca2010,variable=="Hispanic") %>% arrange(desc(pct)) 
gt(hispanic2010[,c(2,4,6)]) %>%  tab_header(
    title = "Hispanic Population _ by county percentage",
    subtitle = "2010 Census California"
  )

Hispanic Population _ by county percentage
2010 Census California
NAME	value	pct
Imperial County	140271	80
Tulare County	268065	61
San Benito County	31186	56
Colusa County	11804	55
Merced County	140485	55
Monterey County	230003	55
Madera County	80992	54
Kings County	77866	51
Fresno County	468070	50
Kern County	413033	49
San Bernardino County	1001145	49
Los Angeles County	4687889	48
Riverside County	995257	45
Santa Barbara County	181687	43
Stanislaus County	215658	42
Ventura County	331567	40
San Joaquin County	266341	39
Glenn County	10539	37
Orange County	1012973	34
Napa County	44010	32
San Diego County	991348	32
Santa Cruz County	84092	32
Yolo County	60953	30
Sutter County	27251	29
Santa Clara County	479210	27
Mono County	3762	26
San Mateo County	182502	25
Yuba County	18051	25
Sonoma County	120430	25
Contra Costa County	255560	24
Solano County	99356	24
Alameda County	339889	23
Mendocino County	19505	22
Sacramento County	306196	22
Tehama County	13906	22
San Luis Obispo County	55973	21
Inyo County	3597	19
Del Norte County	5093	18
Lassen County	6117	18
Lake County	11088	17
Marin County	39069	15
San Francisco County	121774	15
Butte County	31116	14
Modoc County	1342	14
Placer County	44710	13
Amador County	4756	12
El Dorado County	21875	12
Tuolumne County	5918	11
Calaveras County	4703	10
Humboldt County	13211	10
Siskiyou County	4615	10
Mariposa County	1676	9
Nevada County	8439	9
Plumas County	1605	8
Shasta County	14878	8
Sierra County	269	8
Alpine County	84	7
Trinity County	959	7

#-------------------------------------
asian2010 <- filter(ca2010,variable=="Asian") %>% arrange(desc(pct)) 
gt(asian2010[,c(2,4,6)]) %>%  tab_header(
    title = "Asian Population _ by county percentage",
    subtitle = "2010 Census California"
  )

Asian Population _ by county percentage
2010 Census California
NAME	value	pct
San Francisco County	265700	33
Santa Clara County	565466	32
Alameda County	390524	26
San Mateo County	175934	24
Orange County	532477	18
Contra Costa County	148881	14
Los Angeles County	1325671	14
Sacramento County	198944	14
San Joaquin County	94547	14
Solano County	59027	14
Sutter County	13442	14
Yolo County	25640	13
San Diego County	328058	11
Fresno County	86856	9
Merced County	18183	7
Napa County	8986	7
Yuba County	4710	7
Ventura County	54099	7
Monterey County	23777	6
San Bernardino County	123978	6
Placer County	19963	6
Riverside County	125921	6
Marin County	13577	5
Santa Barbara County	19591	5
Stanislaus County	24712	5
Butte County	8921	4
Kern County	33100	4
Santa Cruz County	10658	4
Sonoma County	17777	4
Del Norte County	938	3
Kings County	5339	3
El Dorado County	6143	3
San Luis Obispo County	8106	3
Tulare County	14204	3
Glenn County	674	2
Humboldt County	2854	2
Madera County	2533	2
Mendocino County	1402	2
San Benito County	1298	2
Shasta County	4297	2
Colusa County	267	1
Alpine County	7	1
Amador County	396	1
Calaveras County	529	1
Imperial County	2201	1
Inyo County	229	1
Mariposa County	201	1
Lake County	695	1
Lassen County	337	1
Modoc County	70	1
Mono County	191	1
Nevada County	1124	1
Plumas County	127	1
Siskiyou County	528	1
Tehama County	625	1
Trinity County	93	1
Tuolumne County	530	1
Sierra County	12	0

white2010 <- filter(ca2010,variable=="White") %>% arrange(desc(pct)) 
gt(white2010[,c(2,4,6)]) %>%  tab_header(
    title = "White Population _ by county percentage",
    subtitle = "2010 Census California"
  )

White Population _ by county percentage
2010 Census California
NAME	value	pct
Sierra County	2855	88
Nevada County	85477	87
Plumas County	17015	85
Calaveras County	38074	84
Trinity County	11518	84
Mariposa County	15192	83
Shasta County	146044	82
Tuolumne County	45325	82
Amador County	30325	80
El Dorado County	144689	80
Modoc County	7649	79
Siskiyou County	35683	79
Humboldt County	103958	77
Placer County	265294	76
Butte County	165416	75
Lake County	47938	74
Alpine County	852	73
Marin County	183830	73
Tehama County	45603	72
San Luis Obispo County	191696	71
Mendocino County	60249	69
Mono County	9687	68
Lassen County	23270	67
Inyo County	12296	66
Sonoma County	320027	66
Del Norte County	18513	65
Santa Cruz County	156397	60
Yuba County	42416	59
Glenn County	15717	56
Napa County	76967	56
Sutter County	47782	50
Yolo County	100240	50
Ventura County	400868	49
Contra Costa County	500923	48
Sacramento County	687166	48
San Diego County	1500047	48
Santa Barbara County	203122	48
Stanislaus County	240423	47
Orange County	1328499	44
San Francisco County	337451	42
San Mateo County	303609	42
Solano County	168628	41
Colusa County	8524	40
Riverside County	869068	40
Kern County	323794	39
Madera County	57380	38
San Benito County	21154	38
San Joaquin County	245919	36
Kings County	53879	35
Santa Clara County	626909	35
Alameda County	514559	34
Fresno County	304522	33
Monterey County	136435	33
San Bernardino County	677598	33
Tulare County	143935	33
Merced County	81599	32
Los Angeles County	2728321	28
Imperial County	23927	14

black2010 <- filter(ca2010,variable=="Black") %>% arrange(desc(pct)) 
gt(black2010[,c(2,4,6)]) %>%  tab_header(
    title = "Black Population _ by county percentage",
    subtitle = "2010 Census California"
  )

Black Population _ by county percentage
2010 Census California
NAME	value	pct
Solano County	58743	14
Alameda County	184126	12
Sacramento County	139949	10
Contra Costa County	93604	9
Lassen County	2790	8
Los Angeles County	815086	8
San Bernardino County	170700	8
Kings County	10314	7
San Joaquin County	48540	7
Riverside County	130823	6
San Francisco County	46781	6
Fresno County	45005	5
Kern County	45377	5
San Diego County	146600	5
Del Norte County	967	3
Imperial County	5114	3
Madera County	5009	3
Marin County	6621	3
Merced County	8785	3
Monterey County	11300	3
San Mateo County	18763	3
Yuba County	2122	3
Stanislaus County	13065	3
Amador County	938	2
Lake County	1186	2
Napa County	2440	2
San Luis Obispo County	5128	2
Santa Barbara County	7242	2
Santa Clara County	42331	2
Sutter County	1713	2
Ventura County	13082	2
Yolo County	4752	2
Tuolumne County	1114	2
Colusa County	168	1
Butte County	3133	1
Calaveras County	355	1
Glenn County	192	1
Humboldt County	1393	1
El Dorado County	1296	1
Inyo County	102	1
Mariposa County	129	1
Orange County	44000	1
Mendocino County	544	1
Modoc County	77	1
San Benito County	355	1
Placer County	4427	1
Plumas County	181	1
Siskiyou County	552	1
Santa Cruz County	2304	1
Shasta County	1438	1
Sonoma County	6769	1
Tehama County	349	1
Tulare County	5497	1
Alpine County	0	0
Mono County	42	0
Nevada County	341	0
Sierra County	5	0
Trinity County	45	0

2020 population data for CA counties

vars20 <- c(Hispanic="P2_002N", Asian="P1_006N",White="P1_003N",Black="P1_004N")


ca2020 <- get_decennial(geography = "county", variables = vars20, year = 2020,
                    summary_var = "P1_001N", state = "CA", geometry = F) %>%
                    mutate(pct = round(100 * (value / summary_value),digits = 0))

## Getting data from the 2020 decennial Census

## Using the PL 94-171 Redistricting Data summary file

ca2020$NAME <- gsub(", California","",ca2020$NAME)

hispanic2020 <- filter(ca2020,variable=="Hispanic") %>% arrange(desc(pct)) 
gt(hispanic2020[,c(2,4,6)]) %>%  tab_header(
    title = "Hispanic Population _ by county percentage",
    subtitle = "2020 Census California"
  )

Hispanic Population _ by county percentage
2020 Census California
NAME	value	pct
Imperial County	153027	85
Tulare County	309895	66
Colusa County	13476	62
Merced County	173857	62
San Benito County	39241	61
Madera County	93178	60
Monterey County	265321	60
Kings County	86607	57
Kern County	499158	55
Fresno County	540743	54
San Bernardino County	1170913	54
Riverside County	1202295	50
Los Angeles County	4804763	48
Stanislaus County	265978	48
Santa Barbara County	210584	47
Glenn County	12541	43
Ventura County	365285	43
San Joaquin County	325725	42
Napa County	48829	35
Santa Cruz County	94299	35
Orange County	1086834	34
San Diego County	1119629	34
Yolo County	71700	33
Sutter County	31568	32
Sonoma County	141438	29
Yuba County	23520	29
Solano County	128155	28
Contra Costa County	314900	27
Tehama County	17938	27
Mono County	3507	27
Mendocino County	23933	26
San Mateo County	191386	25
Santa Clara County	487357	25
Sacramento County	374434	24
San Luis Obispo County	67921	24
Alameda County	393749	23
Lassen County	7531	23
Inyo County	4399	23
Lake County	15442	23
Butte County	40112	19
Marin County	49410	19
Del Norte County	5321	19
San Francisco County	136761	16
Placer County	60628	15
Amador County	6014	15
El Dorado County	26459	14
Humboldt County	18535	14
Modoc County	1259	14
Tuolumne County	7124	13
Calaveras County	5865	13
Siskiyou County	5527	13
Mariposa County	2140	12
Sierra County	377	12
Shasta County	19730	11
Nevada County	10416	10
Plumas County	1897	10
Alpine County	84	7
Trinity County	937	6

hispanic2020 <- filter(ca2020,variable=="Hispanic") %>% arrange(desc(value)) 
gt(hispanic2020[,c(2,4,6)]) %>%  tab_header(
    title = "Hispanic Population _ by population",
    subtitle = "2020 Census California"
  )

Hispanic Population _ by population
2020 Census California
NAME	value	pct
Los Angeles County	4804763	48
Riverside County	1202295	50
San Bernardino County	1170913	54
San Diego County	1119629	34
Orange County	1086834	34
Fresno County	540743	54
Kern County	499158	55
Santa Clara County	487357	25
Alameda County	393749	23
Sacramento County	374434	24
Ventura County	365285	43
San Joaquin County	325725	42
Contra Costa County	314900	27
Tulare County	309895	66
Stanislaus County	265978	48
Monterey County	265321	60
Santa Barbara County	210584	47
San Mateo County	191386	25
Merced County	173857	62
Imperial County	153027	85
Sonoma County	141438	29
San Francisco County	136761	16
Solano County	128155	28
Santa Cruz County	94299	35
Madera County	93178	60
Kings County	86607	57
Yolo County	71700	33
San Luis Obispo County	67921	24
Placer County	60628	15
Marin County	49410	19
Napa County	48829	35
Butte County	40112	19
San Benito County	39241	61
Sutter County	31568	32
El Dorado County	26459	14
Mendocino County	23933	26
Yuba County	23520	29
Shasta County	19730	11
Humboldt County	18535	14
Tehama County	17938	27
Lake County	15442	23
Colusa County	13476	62
Glenn County	12541	43
Nevada County	10416	10
Lassen County	7531	23
Tuolumne County	7124	13
Amador County	6014	15
Calaveras County	5865	13
Siskiyou County	5527	13
Del Norte County	5321	19
Inyo County	4399	23
Mono County	3507	27
Mariposa County	2140	12
Plumas County	1897	10
Modoc County	1259	14
Trinity County	937	6
Sierra County	377	12
Alpine County	84	7

#-----------------------
             
asian2020 <- filter(ca2020,variable=="Asian") %>% arrange(desc(pct)) 
gt(asian2020[,c(2,4,6)]) %>%  tab_header(
    title = "Asian Population _ by county percentage",
    subtitle = "2020 Census California"
  )

Asian Population _ by county percentage
2020 Census California
NAME	value	pct
Santa Clara County	759030	39
San Francisco County	296505	34
Alameda County	545261	32
San Mateo County	230242	30
Orange County	706813	22
Contra Costa County	217823	19
Sacramento County	281733	18
San Joaquin County	139323	18
Sutter County	18234	18
Solano County	72766	16
Los Angeles County	1499984	15
Yolo County	30392	14
Trinity County	2222	14
San Diego County	410752	12
Fresno County	113328	11
Placer County	35500	9
Napa County	10753	8
San Bernardino County	182287	8
Ventura County	64923	8
Merced County	20715	7
Riverside County	171243	7
Yuba County	5774	7
Marin County	16431	6
Monterey County	26680	6
Santa Barbara County	26549	6
Stanislaus County	34778	6
Butte County	10533	5
El Dorado County	9200	5
Kern County	46777	5
Santa Cruz County	12553	5
Sonoma County	22845	5
Kings County	5923	4
San Benito County	2423	4
San Luis Obispo County	10402	4
Tulare County	17194	4
Humboldt County	3615	3
Madera County	3907	3
Del Norte County	840	3
Shasta County	5978	3
Imperial County	3049	2
Lassen County	494	2
Mariposa County	298	2
Mendocino County	1788	2
Tehama County	1027	2
Calaveras County	743	2
Glenn County	647	2
Siskiyou County	888	2
Alpine County	12	1
Colusa County	276	1
Modoc County	61	1
Nevada County	1427	1
Plumas County	152	1
Tuolumne County	816	1
Mono County	163	1
Amador County	582	1
Inyo County	282	1
Lake County	1004	1
Sierra County	7	0

white2020 <- filter(ca2020,variable=="White") %>% arrange(desc(pct)) 
gt(white2020[,c(2,4)]) %>%  tab_header(
    title = "White Population _ by county percentage",
    subtitle = "2020 Census California"
  )

White Population _ by county percentage
2020 Census California
NAME	value
Nevada County	85604
Plumas County	16608
Sierra County	2703
Calaveras County	36315
Tuolumne County	44207
Mariposa County	13385
Modoc County	6772
Shasta County	142899
El Dorado County	146624
Amador County	31104
Siskiyou County	33597
Humboldt County	98095
Trinity County	11627
Butte County	149557
Placer County	288586
Mono County	9349
San Luis Obispo County	198338
Lake County	46858
Alpine County	814
Marin County	179377
Tehama County	44926
Mendocino County	59510
Lassen County	21066
Sonoma County	306684
Del Norte County	17193
Inyo County	11752
Santa Cruz County	160565
Yuba County	46590
Napa County	76158
Glenn County	15753
Ventura County	428677
San Diego County	1633129
Yolo County	107304
Santa Barbara County	224748
Sutter County	46810
Stanislaus County	256602
Sacramento County	715722
Colusa County	9364
Contra Costa County	501697
Orange County	1383257
Madera County	65248
San Benito County	27230
Kern County	371734
Riverside County	995627
San Francisco County	361382
Kings County	61226
San Mateo County	300188
Solano County	175768
Tulare County	186255
Fresno County	374678
Merced County	104534
Monterey County	158879
San Bernardino County	782691
San Joaquin County	267339
Los Angeles County	3259427
Santa Clara County	622617
Alameda County	523836
Imperial County	47537

black2020 <- filter(ca2020,variable=="Black") %>% arrange(desc(pct)) 
gt(black2020[,c(2,4,6)]) %>%  tab_header(
    title = "Black Population _ by county percentage",
    subtitle = "2020 Census California"
  )

Black Population _ by county percentage
2020 Census California
NAME	value	pct
Solano County	62157	14
Alameda County	164879	10
Sacramento County	152795	10
Contra Costa County	101485	9
San Bernardino County	184558	8
San Joaquin County	60351	8
Los Angeles County	794364	8
Lassen County	2277	7
Kern County	50130	6
Kings County	9023	6
Riverside County	156477	6
Fresno County	48707	5
San Diego County	155813	5
San Francisco County	46725	5
Yuba County	3052	4
Madera County	4596	3
Merced County	9158	3
Yolo County	6164	3
Amador County	1236	3
Del Norte County	855	3
Stanislaus County	15913	3
Butte County	3644	2
Imperial County	4362	2
Marin County	6339	2
Monterey County	9943	2
Napa County	2443	2
Orange County	53842	2
Placer County	6890	2
San Luis Obispo County	4610	2
San Mateo County	15707	2
Santa Clara County	44966	2
Sonoma County	7615	2
Sutter County	1982	2
Tuolumne County	1009	2
Lake County	1199	2
Santa Barbara County	7374	2
Ventura County	15330	2
Alpine County	10	1
Colusa County	198	1
El Dorado County	1537	1
Humboldt County	1879	1
Mariposa County	108	1
Mendocino County	642	1
Modoc County	67	1
Plumas County	103	1
San Benito County	634	1
Santa Cruz County	3150	1
Tehama County	420	1
Tulare County	6668	1
Mono County	74	1
Calaveras County	364	1
Glenn County	177	1
Inyo County	97	1
Shasta County	1912	1
Siskiyou County	496	1
Nevada County	460	0
Sierra County	7	0
Trinity County	71	0

Population shifts by county

a2020 <- arrange(asian2020,as.numeric(GEOID)) 
h2020 <- arrange(hispanic2020,as.numeric(GEOID)) 
w2020 <- arrange(white2020,as.numeric(GEOID)) 
b2020 <- arrange(black2020,as.numeric(GEOID)) 
#--------------------------
a2010 <- arrange(asian2010,as.numeric(GEOID)) 
h2010 <- arrange(hispanic2010,as.numeric(GEOID)) 
w2010 <- arrange(white2010,as.numeric(GEOID)) 
b2010 <- arrange(black2010,as.numeric(GEOID)) 
#--------------------------

a_change <- a2010[,c(1:2,4)]

a_change$year2020 <- a2020$value
colnames(a_change) <- c("GEOID","County","Y2010","Y2020")
a_change$change <- round( 100 * (a_change$Y2020 - a_change$Y2010)/a_change$Y2010,digits=0)
a_change <- a_change %>% filter(Y2010 >1000 ) %>% arrange(desc(change))

a_change[-1] %>% kbl(caption = "Asian Population Trends\n, cities with more than 1000 population") %>% kable_styling(latex_options = "center_position")

Asian Population Trends , cities with more than 1000 population
County	Y2010	Y2020	change
San Benito County	1298	2423	87
Placer County	19963	35500	78
Madera County	2533	3907	54
El Dorado County	6143	9200	50
San Bernardino County	123978	182287	47
San Joaquin County	94547	139323	47
Contra Costa County	148881	217823	46
Sacramento County	198944	281733	42
Kern County	33100	46777	41
Stanislaus County	24712	34778	41
Alameda County	390524	545261	40
Imperial County	2201	3049	39
Shasta County	4297	5978	39
Riverside County	125921	171243	36
Santa Barbara County	19591	26549	36
Sutter County	13442	18234	36
Santa Clara County	565466	759030	34
Orange County	532477	706813	33
San Mateo County	175934	230242	31
Fresno County	86856	113328	30
Sonoma County	17777	22845	29
Mendocino County	1402	1788	28
San Luis Obispo County	8106	10402	28
Humboldt County	2854	3615	27
Nevada County	1124	1427	27
San Diego County	328058	410752	25
Solano County	59027	72766	23
Yuba County	4710	5774	23
Marin County	13577	16431	21
Tulare County	14204	17194	21
Napa County	8986	10753	20
Ventura County	54099	64923	20
Yolo County	25640	30392	19
Butte County	8921	10533	18
Santa Cruz County	10658	12553	18
Merced County	18183	20715	14
Los Angeles County	1325671	1499984	13
Monterey County	23777	26680	12
San Francisco County	265700	296505	12
Kings County	5339	5923	11

#---------------------------
h_change <- h2010[,c(1:2,4)]
h_change$y2020 <- h2020$value
colnames(h_change) <- c("GEOID","County","Y2010","Y2020")
h_change$change <- round(100 * (h_change$Y2020 - h_change$Y2010)/h_change$Y2010, digits = 0)
h_change <- h_change %>% filter(Y2010 >1000 )%>% arrange(desc(change))
h_change[-1] %>% kbl(caption = "Hispanic Population Trends\n, cities with more than 1000 population") %>% kable_styling(latex_options = "center_position")

Hispanic Population Trends , cities with more than 1000 population
County	Y2010	Y2020	change
Humboldt County	13211	18535	40
Lake County	11088	15442	39
Placer County	44710	60628	36
Shasta County	14878	19730	33
Yuba County	18051	23520	30
Butte County	31116	40112	29
Solano County	99356	128155	29
Tehama County	13906	17938	29
Mariposa County	1676	2140	28
Amador County	4756	6014	26
Marin County	39069	49410	26
San Benito County	31186	39241	26
Calaveras County	4703	5865	25
Merced County	140485	173857	24
Contra Costa County	255560	314900	23
Lassen County	6117	7531	23
Mendocino County	19505	23933	23
Nevada County	8439	10416	23
Stanislaus County	215658	265978	23
Inyo County	3597	4399	22
Sacramento County	306196	374434	22
San Joaquin County	266341	325725	22
El Dorado County	21875	26459	21
Kern County	413033	499158	21
Riverside County	995257	1202295	21
San Luis Obispo County	55973	67921	21
Siskiyou County	4615	5527	20
Tuolumne County	5918	7124	20
Glenn County	10539	12541	19
Plumas County	1605	1897	18
Yolo County	60953	71700	18
San Bernardino County	1001145	1170913	17
Sonoma County	120430	141438	17
Alameda County	339889	393749	16
Fresno County	468070	540743	16
Santa Barbara County	181687	210584	16
Sutter County	27251	31568	16
Tulare County	268065	309895	16
Madera County	80992	93178	15
Monterey County	230003	265321	15
Colusa County	11804	13476	14
San Diego County	991348	1119629	13
San Francisco County	121774	136761	12
Santa Cruz County	84092	94299	12
Kings County	77866	86607	11
Napa County	44010	48829	11
Ventura County	331567	365285	10
Imperial County	140271	153027	9
Orange County	1012973	1086834	7
San Mateo County	182502	191386	5
Del Norte County	5093	5321	4
Los Angeles County	4687889	4804763	2
Santa Clara County	479210	487357	2
Modoc County	1342	1259	-6
Mono County	3762	3507	-7

#----------------------------
w_change <- w2010[,c(1:2,4)]
w_change$year2020 <- w2020$value
colnames(w_change) <- c("GEOID","County","Y2010","Y2020")
w_change$change <- round(  100 * (w_change$Y2020 - w_change$Y2010)/w_change$Y2010, digits = 0)
w_change <- w_change %>% filter(Y2010 >1000 )%>% arrange(desc(change))
w_change[-1] %>% kbl(caption = "White Population Trends\n, cities with more than 1000 population") %>% kable_styling(latex_options = "center_position")

White Population Trends , cities with more than 1000 population
County	Y2010	Y2020	change
Imperial County	23927	47537	99
San Benito County	21154	27230	29
Tulare County	143935	186255	29
Merced County	81599	104534	28
Fresno County	304522	374678	23
Los Angeles County	2728321	3259427	19
Monterey County	136435	158879	16
San Bernardino County	677598	782691	16
Kern County	323794	371734	15
Riverside County	869068	995627	15
Kings County	53879	61226	14
Madera County	57380	65248	14
Santa Barbara County	203122	224748	11
Colusa County	8524	9364	10
Yuba County	42416	46590	10
Placer County	265294	288586	9
San Diego County	1500047	1633129	9
San Joaquin County	245919	267339	9
San Francisco County	337451	361382	7
Stanislaus County	240423	256602	7
Ventura County	400868	428677	7
Yolo County	100240	107304	7
Orange County	1328499	1383257	4
Sacramento County	687166	715722	4
Solano County	168628	175768	4
Amador County	30325	31104	3
San Luis Obispo County	191696	198338	3
Santa Cruz County	156397	160565	3
Alameda County	514559	523836	2
El Dorado County	144689	146624	1
Trinity County	11518	11627	1
Contra Costa County	500923	501697	0
Glenn County	15717	15753	0
Nevada County	85477	85604	0
Mendocino County	60249	59510	-1
Napa County	76967	76158	-1
San Mateo County	303609	300188	-1
Santa Clara County	626909	622617	-1
Tehama County	45603	44926	-1
Lake County	47938	46858	-2
Marin County	183830	179377	-2
Plumas County	17015	16608	-2
Shasta County	146044	142899	-2
Sutter County	47782	46810	-2
Tuolumne County	45325	44207	-2
Mono County	9687	9349	-3
Inyo County	12296	11752	-4
Sonoma County	320027	306684	-4
Calaveras County	38074	36315	-5
Sierra County	2855	2703	-5
Humboldt County	103958	98095	-6
Siskiyou County	35683	33597	-6
Del Norte County	18513	17193	-7
Lassen County	23270	21066	-9
Butte County	165416	149557	-10
Modoc County	7649	6772	-11
Mariposa County	15192	13385	-12

#----------------------------
b_change <- b2010[,c(1:2,4)]
b_change$year2020 <- b2020$value
b_change$change <- round(100 * (b_change$year2020 - b_change$value)/b_change$year2020, digits = 0)
colnames(b_change) <- c("GEOID","County","Y2010","Y2020","Change")
b_change <- b_change %>% filter(Y2010 >1000 )%>% arrange(desc(Change))
b_change[-1] %>% kbl(caption = "Black Population Trends\n, cities with more than 1000 population") %>% kable_styling(latex_options = "center_position")

Black Population Trends , cities with more than 1000 population
County	Y2010	Y2020	Change
Placer County	4427	6890	36
Yuba County	2122	3052	30
Santa Cruz County	2304	3150	27
Humboldt County	1393	1879	26
Shasta County	1438	1912	25
Yolo County	4752	6164	23
San Joaquin County	48540	60351	20
Orange County	44000	53842	18
Stanislaus County	13065	15913	18
Tulare County	5497	6668	18
El Dorado County	1296	1537	16
Riverside County	130823	156477	16
Ventura County	13082	15330	15
Butte County	3133	3644	14
Sutter County	1713	1982	14
Sonoma County	6769	7615	11
Kern County	45377	50130	9
Contra Costa County	93604	101485	8
Fresno County	45005	48707	8
Sacramento County	139949	152795	8
San Bernardino County	170700	184558	8
San Diego County	146600	155813	6
Santa Clara County	42331	44966	6
Solano County	58743	62157	5
Merced County	8785	9158	4
Santa Barbara County	7242	7374	2
Lake County	1186	1199	1
Napa County	2440	2443	0
San Francisco County	46781	46725	0
Los Angeles County	815086	794364	-3
Marin County	6621	6339	-4
Madera County	5009	4596	-9
Tuolumne County	1114	1009	-10
San Luis Obispo County	5128	4610	-11
Alameda County	184126	164879	-12
Kings County	10314	9023	-14
Monterey County	11300	9943	-14
Imperial County	5114	4362	-17
San Mateo County	18763	15707	-19
Lassen County	2790	2277	-23

racevars <- c(White = "P2_005N", 
              Black = "P2_006N", 
              Asian = "P2_008N", 
              Hispanic = "P2_002N")
ca <- get_decennial(
  geography = "county",
  variables = racevars,
  state = "06",
  geometry = TRUE,
  summary_var = "P2_001N",
  year = 2020
)

## Getting data from the 2020 decennial Census

## Using the PL 94-171 Redistricting Data summary file

ca %>% filter(variable=="Hispanic") %>%
  mutate(percent = 100 * (value / summary_value)) %>%
  ggplot(aes(fill = percent)) +
  
  geom_sf(color = "white") +
  theme_void() + 
  scale_fill_viridis_c() + 
  labs(fill = "Hispanic % of population\nCalifornia County\n(2020 Census )")

ca %>% filter(variable=="White") %>%
  mutate(percent = 100 * (value / summary_value)) %>%
  ggplot(aes(fill = percent)) +
  
  geom_sf(color = "white") +
  theme_void() + 
  scale_fill_viridis_c() + 
  labs(fill = "White % of population\nCalifornia county\n(2020 Census )")

ca %>% filter(variable=="Asian") %>%
  mutate(percent = 100 * (value / summary_value)) %>%
  ggplot(aes(fill = percent)) +
  
  geom_sf(color = "white") +
  theme_void() + 
  scale_fill_viridis_c() + 
  labs(fill = "Asian % of population\nCalifornia County\n(2020 Census)")

racevars <- c(White = "P2_005N", 
              Black = "P2_006N", 
              Asian = "P2_008N", 
              Hispanic = "P2_002N")

oc <- get_decennial(
  geography = "tract",
  variables = racevars,
  state = "06",
  county = "059",
  geometry = TRUE,
  summary_var = "P2_001N",
  year = 2020
)

## Getting data from the 2020 decennial Census

## Using the PL 94-171 Redistricting Data summary file

oc %>% filter(variable=="Hispanic") %>%
  mutate(percent = 100 * (value / summary_value)) %>%
  ggplot(aes(fill = percent)) +
  
  geom_sf(color = "white") +
  theme_void() + 
  scale_fill_viridis_c() + 
  labs(fill = "Hispanic % of population\nOrange County, CA\n(2020 Census tracts)")

oc %>% filter(variable=="White") %>%
  mutate(percent = 100 * (value / summary_value)) %>%
  ggplot(aes(fill = percent)) +
  
  geom_sf(color = "white") +
  theme_void() + 
  scale_fill_viridis_c() + 
  labs(fill = "White % of population\nOrange County, CA\n(2020 Census tracts)")

oc %>% filter(variable=="Asian") %>%
  mutate(percent = 100 * (value / summary_value)) %>%
  ggplot(aes(fill = percent)) +
  
  geom_sf(color = "white") +
  theme_void() + 
  scale_fill_viridis_c() + 
  labs(fill = "Asian % of population\nOrange County, CA\n(2020 Census tracts)")

U.S. Census Decennial data in California counties

Joe Long JoeSanDiego2000@Gmail.com

June 10, 2022

Highlights:

Using Tidycensus R-Package as Data Analysis Tools

2020 Census Decennial Survey, as population percentage

Population changes from 2010 Census to 2020 census

2010 Decenial census survey for states

U.S. counties with Hispanic Majority ( greater than 50%)

2010 population data for California counties

2020 population data for CA counties

Population shifts by county