#
library(stringr)
library(psych)
setwd("~/../practicum2")
source("common.R")
for(f in Sys.glob('data/irs/1*.csv')){
message('processing ',f)
fn=2000+as.numeric(str_match(f, '\\/(\\d+)')[,2])
d=read.csv(f,stringsAsFactors = F)
dt=read.csv(f,stringsAsFactors = F,colClasses = 'character')
d$COUNTYFIPS=dt$COUNTYFIPS ##repair
d$STATEFIPS=dt$STATEFIPS ##repair
d$fips=paste0(d$STATEFIPS,d$COUNTYFIPS)
rm(dt)
str(d)
d2=d[,c("STATE" , "COUNTYNAME" ,'fips' )]
d2$Year=fn
d2$num.returns=d$N1
d2$married.pct=d$MARS2/d$N1
d2$dependents.ratio=d$NUMDEP/d$N1
d2$adjusted.gross.income.avg=d$A00100/d$N1
d2$wages.avg=d$A00200/d$N1
d2$farming.ratio=d$SCHF/d$N1
d2$unemployed.ratio=d$N02300/d$N1
d2$dividends.ratio=d$N00600/d$N1
d2$business.ratio=d$N00900/d$N1
d2$realestate.ratio=d$N18500/d$N1 #indicator of ownership
d2$mortgage.ratio=d$N19300/d$N1 #indicator of ownership
d2$contributions.ratio=d$A19700/d$N1 #indicator of giving?
d2$taxcredits.ratio = d$N07100/d$N1
write.csv(d2,paste0("data/irsclean/",fn,"-irs-soi.csv"),row.names = F)
#summary(d2)
#describe(d2)
}
## processing data/irs/10incyallnoagi.csv
## 'data.frame': 3192 obs. of 75 variables:
## $ STATEFIPS : chr "01" "01" "01" "01" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ COUNTYFIPS: chr "000" "001" "003" "005" ...
## $ COUNTYNAME: chr "Alabama" "Autauga County" "Baldwin County" "Barbour County" ...
## $ AGI_STUB : int 0 0 0 0 0 0 0 0 0 0 ...
## $ N1 : num 2100274 24821 85312 10873 8639 ...
## $ MARS2 : num 785681 10183 37613 3367 3517 ...
## $ PREP : num 1273772 11995 47626 7821 5068 ...
## $ N2 : num 4389348 52230 175253 22943 18539 ...
## $ NUMDEP : num 1388578 17506 52276 7228 5921 ...
## $ A00100 : num 97604671 1141741 4331432 368337 326695 ...
## $ N00200 : num 1733560 21177 67180 9024 6801 ...
## $ A00200 : num 72276379 898499 2944594 281232 256627 ...
## $ N00300 : num 648344 7893 30721 2853 2115 ...
## $ A00300 : num 1508016 10302 76050 6459 3496 ...
## $ N00600 : num 280440 3179 16407 1184 619 ...
## $ A00600 : num 1438543 6121 85725 4808 1376 ...
## $ N00650 : num 246271 2739 14407 1087 507 ...
## $ A00650 : num 1103934 4010 61562 3810 781 ...
## $ N00900 : num 340738 3414 15786 1546 1268 ...
## $ A00900 : num 2882806 25106 168253 6718 8843 ...
## $ SCHF : num 49990 419 1196 469 203 ...
## $ N01000 : num 209185 2176 13113 890 494 ...
## $ A01000 : num 2125463 12678 107779 15870 6719 ...
## $ N01400 : num 155998 1624 9520 667 476 ...
## $ A01400 : num 2239541 17829 155146 8912 6161 ...
## $ N01700 : num 401412 4932 19457 1795 1417 ...
## $ A01700 : num 8250377 112072 440869 34269 23387 ...
## $ N02300 : num 177478 1641 6565 946 892 ...
## $ A02300 : num 956278 8967 38951 4599 4003 ...
## $ N02500 : num 239712 2823 13675 1199 897 ...
## $ A02500 : num 2662189 30999 160280 12728 8880 ...
## $ N03300 : num 7094 39 403 21 0 ...
## $ A03300 : num 130896 594 6531 345 0 ...
## $ N04470 : num 597252 7237 27586 2149 1774 ...
## $ A04470 : num 12580721 139264 611052 42163 32550 ...
## $ N18425 : num 472832 5747 19960 1587 1467 ...
## $ A18425 : num 2009415 19954 83744 5600 4874 ...
## $ N18450 : num 104431 1242 6595 473 253 ...
## $ A18450 : num 139590 2461 8687 1559 302 ...
## $ N18500 : num 502832 6212 23246 1748 1447 ...
## $ A18500 : num 646669 4726 30650 1755 825 ...
## $ N18300 : num 594024 7208 27422 2130 1763 ...
## $ A18300 : num 2954116 28713 128949 9862 6383 ...
## $ N19300 : num 470141 6091 21775 1522 1399 ...
## $ A19300 : num 4048346 52925 218889 11659 9636 ...
## $ N19700 : num 515552 6103 22828 1896 1507 ...
## $ A19700 : num 2830060 28805 103703 9528 6988 ...
## $ N04800 : num 1486268 18789 63304 6909 5659 ...
## $ A04800 : num 63024651 718586 2909193 210867 193657 ...
## $ N07100 : num 697410 9123 27176 3208 2642 ...
## $ A07100 : num 870595 11827 35888 3108 3188 ...
## $ N07220 : num 363507 5120 14516 1859 1710 ...
## $ A07220 : num 415146 6709 17776 1764 2083 ...
## $ N07180 : num 99047 1382 3900 372 344 ...
## $ A07180 : num 51585 772 1960 186 157 ...
## $ N07260 : num 112497 1163 3910 377 357 ...
## $ A07260 : num 95026 953 3440 316 303 ...
## $ N59660 : num 548773 5597 16531 3966 2416 ...
## $ A59660 : num 1379125 13789 37710 10186 5997 ...
## $ N59720 : num 500205 5130 14536 3682 2172 ...
## $ A59720 : num 1233638 12497 32902 9389 5337 ...
## $ N11070 : num 403905 3980 12531 2813 1676 ...
## $ A11070 : num 529032 5075 15948 3543 2066 ...
## $ N09600 : num 24706 129 1319 43 35 ...
## $ A09600 : num 127111 486 6542 155 142 ...
## $ N06500 : num 1253809 16201 55683 5455 4788 ...
## $ A06500 : num 10336547 103549 482358 29634 26954 ...
## $ N10300 : num 1403055 17539 61483 6199 5387 ...
## $ A10300 : num 10948191 109480 517828 31857 28910 ...
## $ N11901 : num 307476 3594 18120 1175 959 ...
## $ A11901 : num 1190657 13210 82619 3508 3154 ...
## $ N11902 : num 1724696 20634 63313 9366 7289 ...
## $ A11902 : num 5602752 61798 186866 29558 22279 ...
## $ fips : chr "01000" "01001" "01003" "01005" ...
## processing data/irs/11incyallnoagi.csv
## 'data.frame': 3193 obs. of 75 variables:
## $ STATEFIPS : chr "01" "01" "01" "01" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ COUNTYFIPS: chr "000" "001" "003" "005" ...
## $ COUNTYNAME: chr "Alabama" "Autauga County" "Baldwin County" "Barbour County" ...
## $ AGI_STUB : int 0 0 0 0 0 0 0 0 0 0 ...
## $ N1 : num 2091218 24315 87236 10648 8049 ...
## $ MARS2 : num 778752 10151 37748 3322 3493 ...
## $ PREP : num 1253571 11900 46875 7410 5019 ...
## $ N2 : num 4340121 51513 175844 22062 17756 ...
## $ NUMDEP : num 1589968 18770 55954 8680 6554 ...
## $ A00100 : num 100308257 1149657 4556224 355824 331373 ...
## $ N00200 : num 1740617 20830 68542 8735 6897 ...
## $ A00200 : num 73819452 903517 3107119 279766 262943 ...
## $ N00300 : num 613319 7289 30310 2765 1991 ...
## $ A00300 : num 1234472 8125 67434 6774 2796 ...
## $ N00600 : num 276153 3086 16368 1190 626 ...
## $ A00600 : num 1377991 6940 87411 5860 1305 ...
## $ N00650 : num 245570 2742 14548 1104 509 ...
## $ A00650 : num 1010895 4612 60332 4677 773 ...
## $ N00900 : num 346394 3440 15992 1597 1210 ...
## $ A00900 : num 2933028 25228 172379 8609 8719 ...
## $ SCHF : num 48733 420 1188 444 217 ...
## $ N01000 : num 212509 2147 13680 978 493 ...
## $ A01000 : num 2332902 7992 147337 9364 2726 ...
## $ N01400 : num 161089 1743 9841 692 511 ...
## $ A01400 : num 2415698 21177 167777 8989 6445 ...
## $ N01700 : num 404829 5031 19808 1811 1421 ...
## $ A01700 : num 8606413 121079 462958 36437 24866 ...
## $ N02300 : num 149514 1385 5350 736 640 ...
## $ A02300 : num 714907 7093 27333 3382 2744 ...
## $ N02500 : num 249259 2932 14265 1231 907 ...
## $ A02500 : num 2827751 33493 173014 13411 9375 ...
## $ N03300 : num 7097 40 385 21 0.0001 ...
## $ A03300 : num 134294 721 6600 401 0.0001 ...
## $ N04470 : num 599369 7167 27440 2250 1792 ...
## $ A04470 : num 12769234 133814 616433 43331 39539 ...
## $ N18425 : num 475821 5689 20241 1641 1476 ...
## $ A18425 : num 2058790 20622 93223 7254 5239 ...
## $ N18450 : num 104624 1250 6309 499 260 ...
## $ A18450 : num 145563 1825 9988 1507 309 ...
## $ N18500 : num 494579 6075 22796 1757 1426 ...
## $ A18500 : num 636758 4682 28972 1791 805 ...
## $ N18300 : num 595721 7121 27256 2227 1783 ...
## $ A18300 : num 3006384 28418 137963 11129 6712 ...
## $ N19300 : num 460387 5915 21172 1493 1354 ...
## $ A19300 : num 3812392 49383 204268 11106 9024 ...
## $ N19700 : num 517680 6079 22633 1907 1554 ...
## $ A19700 : num 2941658 28738 107428 9558 7676 ...
## $ N04800 : num 1487647 18374 64121 6892 5587 ...
## $ A04800 : num 65015332 732202 3102762 208827 192542 ...
## $ N07100 : num 664438 8428 26093 3068 2478 ...
## $ A07100 : num 819378 10509 33627 2815 2874 ...
## $ N07220 : num 348252 4896 14098 1704 1601 ...
## $ A07220 : num 403951 6446 17391 1618 2009 ...
## $ N07180 : num 97828 1337 4000 371 335 ...
## $ A07180 : num 51303 734 1986 186 154 ...
## $ N07260 : num 60239 590 2097 227 194 ...
## $ A07260 : num 20955 168 806 88 57 ...
## $ N59660 : num 550074 5587 17308 3771 2334 ...
## $ A59660 : num 1413578 14050 40809 10059 6085 ...
## $ N59720 : num 502046 5089 15271 3483 2132 ...
## $ A59720 : num 1268146 12722 35832 9149 5522 ...
## $ N11070 : num 397229 3948 12552 2757 1604 ...
## $ A11070 : num 529531 4974 16256 3480 2009 ...
## $ N09600 : num 25940 144 1423 41 29 ...
## $ A09600 : num 137797 510 7144 336 124 ...
## $ N06500 : num 1262374 15958 56650 5442 4792 ...
## $ A06500 : num 10984677 107396 528715 29480 26998 ...
## $ N10300 : num 1414359 17335 62594 6282 5355 ...
## $ A10300 : num 11269687 112969 562341 31784 28678 ...
## $ N11901 : num 298684 3530 17500 1183 929 ...
## $ A11901 : num 1216196 11318 82124 3944 3220 ...
## $ N11902 : num 1699576 20011 63658 8999 6800 ...
## $ A11902 : num 5091418 56866 173706 28792 21033 ...
## $ fips : chr "01000" "01001" "01003" "01005" ...
## processing data/irs/12cyallnoagi.csv
## 'data.frame': 3193 obs. of 78 variables:
## $ STATEFIPS : chr "01" "01" "01" "01" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ COUNTYFIPS: chr "000" "001" "003" "005" ...
## $ COUNTYNAME: chr "Alabama" "Autauga County" "Baldwin County" "Barbour County" ...
## $ AGI_STUB : int 0 0 0 0 0 0 0 0 0 0 ...
## $ N1 : num 2050090 23700 88490 10050 7900 ...
## $ MARS1 : num 810190 8800 35520 3630 2680 ...
## $ MARS2 : num 774210 10100 38380 3280 3430 ...
## $ MARS4 : num 427560 4310 12620 2990 1650 ...
## $ PREP : num 1238940 11820 47190 7320 4990 ...
## $ N2 : num 4241420 50580 177290 21110 17450 ...
## $ NUMDEP : num 1535340 18370 55770 8230 6450 ...
## $ A00100 : num 104129915 1206063 4899665 371840 344287 ...
## $ N00200 : num 1716600 20310 69690 8330 6790 ...
## $ A00200 : num 75281821 913638 3252973 273242 273744 ...
## $ N00300 : num 560890 7090 27940 2580 1940 ...
## $ A00300 : num 996850 7153 55549 4016 2004 ...
## $ N00600 : num 276830 3200 16800 1140 590 ...
## $ A00600 : num 1733897 8099 101760 5641 1293 ...
## $ N00650 : num 247730 2830 14990 1060 480 ...
## $ A00650 : num 1347830 5798 75656 4569 920 ...
## $ N00900 : num 337840 3330 16150 1630 1160 ...
## $ A00900 : num 3018562 22301 178028 11472 8722 ...
## $ SCHF : num 46930 400 1200 450 200 ...
## $ N01000 : num 216800 2340 14410 910 470 ...
## $ A01000 : num 3587580 37005 250614 10034 3945 ...
## $ N01400 : num 163370 1790 10260 720 510 ...
## $ A01400 : num 2536921 23016 177059 10362 6175 ...
## $ N01700 : num 416530 5410 20660 1900 1380 ...
## $ A01700 : num 9019145 129498 488452 37073 25295 ...
## $ N02300 : num 127030 1250 4500 690 540 ...
## $ A02300 : num 500843 5278 19706 3364 1893 ...
## $ N02500 : num 261270 3090 15120 1290 970 ...
## $ A02500 : num 3110766 37258 191665 14787 10056 ...
## $ N03300 : num 6860 50 460 20 0 30 0 0 100 0 ...
## $ A03300 : num 134950 828 7980 430 0 ...
## $ N04470 : num 581300 6840 27040 2120 1720 ...
## $ A00101 : num 62386864 647822 3050674 173162 148838 ...
## $ A04470 : num 12568349 130654 606077 40966 31820 ...
## $ N18425 : num 460600 5390 20060 1510 1450 ...
## $ A18425 : num 2124557 20716 98307 5205 4954 ...
## $ N18450 : num 99330 1240 6130 530 230 ...
## $ A18450 : num 139306 1952 9083 1182 304 ...
## $ N18500 : num 473110 5760 22170 1690 1330 ...
## $ A18500 : num 614945 4496 28169 1656 772 ...
## $ N18300 : num 574510 6780 26830 2080 1710 ...
## $ A18300 : num 3056384 28491 143288 8723 6494 ...
## $ N19300 : num 436640 5550 20480 1430 1300 ...
## $ A19300 : num 3471625 44415 186202 10103 8289 ...
## $ N19700 : num 502050 5830 22480 1820 1500 ...
## $ A19700 : num 3039256 29536 113195 9738 7662 ...
## $ N04800 : num 1469630 17810 65390 6430 5680 ...
## $ A04800 : num 68597241 778737 3398744 212157 208835 ...
## $ N09600 : num 25280 150 1510 40 30 ...
## $ A09600 : num 136128 582 7980 270 148 ...
## $ N07100 : num 620410 7910 25590 2690 2420 ...
## $ A07100 : num 837113 9870 32423 2454 2783 ...
## $ N07180 : num 98270 1390 4090 370 350 ...
## $ A07180 : num 51870 755 1988 179 147 ...
## $ N07220 : num 346650 4930 14140 1590 1650 ...
## $ A07220 : num 400518 6429 17364 1547 2021 ...
## $ N07260 : num 34150 340 1330 110 130 ...
## $ A07260 : num 13001 105 549 30 59 ...
## $ N59660 : num 537300 5440 17780 3560 2250 ...
## $ A59660 : num 1417546 13813 43075 9638 6022 ...
## $ N59720 : num 488680 4940 15630 3280 2060 ...
## $ A59720 : num 1275498 12544 37963 8750 5492 ...
## $ N11070 : num 381510 3900 12360 2610 1550 ...
## $ A11070 : num 505877 4922 15931 3354 1987 ...
## $ N06500 : num 1268390 15610 58160 5260 4910 ...
## $ A06500 : num 11586119 116291 587167 31173 28396 ...
## $ N10300 : num 1413690 16960 64080 6090 5410 ...
## $ A10300 : num 12209093 121963 621440 33556 30233 ...
## $ N11901 : num 310220 3510 17630 1180 1000 ...
## $ A11901 : num 1418852 11151 92499 4628 4104 ...
## $ N11902 : num 1651260 19460 64920 8390 6620 ...
## $ A11902 : num 4838708 54297 175887 25548 19610 ...
## $ fips : chr "01000" "01001" "01003" "01005" ...
## processing data/irs/13incyallnoagi.csv
## 'data.frame': 3193 obs. of 116 variables:
## $ STATEFIPS : chr "01" "01" "01" "01" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ COUNTYFIPS: chr "000" "001" "003" "005" ...
## $ COUNTYNAME: chr "Alabama" "Autauga County" "Baldwin County" "Barbour County" ...
## $ AGI_STUB : int 0 0 0 0 0 0 0 0 0 0 ...
## $ N1 : num 2048400 23690 89970 10070 8000 ...
## $ MARS1 : num 816130 8940 36320 3740 2820 ...
## $ MARS2 : num 770530 10000 38880 3160 3370 ...
## $ MARS4 : num 422330 4250 12720 3030 1700 ...
## $ PREP : num 1212800 11400 47180 7190 4880 ...
## $ N2 : num 4214440 50190 179450 20910 17470 ...
## $ NUMDEP : num 1510500 18080 56120 8100 6490 ...
## $ A00100 : num 103760136 1199480 4965837 404981 361441 ...
## $ N02650 : num 2042960 23640 89740 10040 7990 ...
## $ A02650 : num 105120016 1211914 5045468 409272 365867 ...
## $ N00200 : num 1716020 20240 70630 8280 6890 ...
## $ A00200 : num 76117842 917228 3352604 271105 274279 ...
## $ N00300 : num 519400 6790 26200 2370 1730 ...
## $ A00300 : num 847210 7349 48498 3112 1793 ...
## $ N00600 : num 271980 3020 16670 1100 560 ...
## $ A00600 : num 1530363 8042 91176 5123 1209 ...
## $ N00650 : num 245780 2730 15080 1030 480 ...
## $ A00650 : num 1139480 5717 65822 4128 822 ...
## $ N00700 : num 338400 4080 14030 1030 1050 ...
## $ A00700 : num 322653 3197 14228 840 720 ...
## $ N00900 : num 336170 3330 16540 1690 1180 ...
## $ A00900 : num 2963959 23660 184406 11092 7281 ...
## $ N01000 : num 227670 2410 15130 950 510 ...
## $ A01000 : num 2688098 12469 171239 12138 4440 ...
## $ N01400 : num 165080 1840 10470 680 500 ...
## $ A01400 : num 2477109 23249 179460 9259 6847 ...
## $ N01700 : num 420490 5410 21030 1850 1460 ...
## $ A01700 : num 9385128 134988 514678 37295 26854 ...
## $ SCHF : num 46330 390 1170 450 200 ...
## $ N02300 : num 98280 960 3550 560 410 ...
## $ A02300 : num 371772 3950 14509 1970 1404 ...
## $ N02500 : num 273260 3240 15990 1370 1010 ...
## $ A02500 : num 3377504 40104 211482 16431 10871 ...
## $ N26270 : num 95110 790 7050 430 220 ...
## $ A26270 : num 5593449 29639 360124 9355 14936 ...
## $ N02900 : num 464170 5320 23420 1950 1470 ...
## $ A02900 : num 1356174 12440 79631 4292 3536 ...
## $ N03220 : num 51160 650 2300 270 190 ...
## $ A03220 : num 12914 156 582 73 43 ...
## $ N03300 : num 6790 40 470 0 0 30 0 0 90 0 ...
## $ A03300 : num 140440 746 8674 0 0 ...
## $ N03270 : num 44950 400 3340 180 140 ...
## $ A03270 : num 276085 2287 18820 997 698 ...
## $ N03150 : num 28470 400 1600 100 70 ...
## $ A03150 : num 125439 1642 7872 474 242 ...
## $ N03210 : num 124300 1690 5990 310 280 ...
## $ A03210 : num 123921 1708 5800 320 267 ...
## $ N03230 : num 20510 300 780 50 50 ...
## $ A03230 : num 51378 768 1899 117 117 ...
## $ N03240 : num 5310 50 360 20 0 40 0 0 60 0 ...
## $ A03240 : num 101917 604 4394 208 0 ...
## $ N04470 : num 551130 6370 25900 1940 1580 ...
## $ A04470 : num 11831485 122386 579820 38538 29723 ...
## $ A00101 : num 58630796 590637 2925893 160346 152852 ...
## $ N18425 : num 439590 5050 19320 1340 1320 ...
## $ A18425 : num 2134434 19391 100903 4641 5219 ...
## $ N18450 : num 92830 1100 5710 530 220 ...
## $ A18450 : num 134110 1680 8663 1301 282 ...
## $ N18500 : num 446380 5320 21270 1520 1220 ...
## $ A18500 : num 603613 4212 27581 1562 750 ...
## $ N18300 : num 546150 6320 25650 1930 1570 ...
## $ A18300 : num 3040635 26610 144306 8075 6652 ...
## $ N19300 : num 408500 5070 19330 1270 1120 ...
## $ A19300 : num 3055891 37494 166216 8677 7013 ...
## $ N19700 : num 479270 5480 21710 1700 1370 ...
## $ A19700 : num 3027746 29051 114541 9683 8199 ...
## $ N04800 : num 1469200 17900 66560 6410 5760 ...
## $ A04800 : num 68235339 767841 3474916 226102 218728 ...
## $ N05800 : num 1460520 17820 65960 6350 5730 ...
## $ A05800 : num 12446559 123444 644059 39894 33107 ...
## $ N09600 : num 20540 100 1300 40 20 ...
## $ A09600 : num 94010 397 5496 134 54 ...
## $ N07100 : num 630220 7870 26140 2720 2450 ...
## $ A07100 : num 773132 9844 33670 2471 2821 ...
## $ N07300 : num 59670 590 3950 200 70 ...
## $ A07300 : num 77671 197 4352 24 5 ...
## $ N07180 : num 97240 1380 4090 360 320 ...
## $ A07180 : num 51440 756 2088 173 145 ...
## $ N07230 : num 146340 1810 4930 560 420 ...
## $ A07230 : num 153140 2051 5164 566 461 ...
## $ N07240 : num 111670 1290 3810 530 460 ...
## $ A07240 : num 19897 229 691 102 85 ...
## $ N07220 : num 337120 4830 14010 1510 1640 ...
## $ A07220 : num 392472 6257 17278 1481 2017 ...
## $ N07260 : num 47910 490 1920 170 160 ...
## $ A07260 : num 18455 166 812 73 52 ...
## $ N09400 : num 246630 2400 12770 1220 890 ...
## $ A09400 : num 601842 5373 34711 2375 1644 ...
## $ N10600 : num 1955660 22890 83910 9550 7640 ...
## $ A10600 : num 16222622 164698 745980 57613 47162 ...
## $ N59660 : num 535970 5400 17880 3420 2280 ...
## $ A59660 : num 1437610 14108 44330 9718 6148 ...
## $ N59720 : num 486010 4840 15590 3190 2080 ...
## $ A59720 : num 1285256 12736 38478 8774 5592 ...
## [list output truncated]
## processing data/irs/14incyallnoagi.csv
## 'data.frame': 3192 obs. of 129 variables:
## $ STATEFIPS : chr "01" "01" "01" "01" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ COUNTYFIPS: chr "000" "001" "003" "005" ...
## $ COUNTYNAME: chr "Alabama" "Autauga County" "Baldwin County" "Barbour County" ...
## $ AGI_STUB : int 0 0 0 0 0 0 0 0 0 0 ...
## $ N1 : num 2046400 23820 90890 9730 7980 ...
## $ MARS1 : num 822280 9100 37140 3540 2760 ...
## $ MARS2 : num 765870 9960 38890 3090 3350 ...
## $ MARS4 : num 418380 4230 12790 2980 1720 ...
## $ PREP : num 1196960 11260 47460 6940 4880 ...
## $ N2 : num 4183470 50060 179880 20300 17320 ...
## $ NUMDEP : num 1486080 17910 55660 7860 6340 ...
## $ TOTAL_VITA: num 45780 510 1620 40 30 ...
## $ VITA : num 29720 150 400 40 30 ...
## $ TCE : num 16050 350 1240 0 0 ...
## $ A00100 : num 107890713 1237474 5245901 374572 365681 ...
## $ N02650 : num 2040290 23760 90590 9690 7960 ...
## $ A02650 : num 109294525 1250116 5331680 378961 369326 ...
## $ N00200 : num 1708600 20330 70750 8080 6880 ...
## $ A00200 : num 77903018 944288 3432927 271548 280863 ...
## $ N00300 : num 504910 7420 25420 2220 1610 ...
## $ A00300 : num 778755 6447 45680 2859 1571 ...
## $ N00600 : num 270770 3060 16840 1090 500 ...
## $ A00600 : num 1731839 9362 107181 5688 1514 ...
## $ N00650 : num 247470 2750 15360 1010 450 ...
## $ A00650 : num 1304289 6617 78506 4560 1011 ...
## $ N00700 : num 323820 3880 13550 900 1010 ...
## $ A00700 : num 337538 3237 14846 872 750 ...
## $ N00900 : num 340020 3430 16910 1600 1180 ...
## $ A00900 : num 2979940 24817 188235 10234 8071 ...
## $ N01000 : num 229150 2420 15400 940 480 ...
## $ A01000 : num 3986748 21361 277215 7473 4972 ...
## $ N01400 : num 168750 1890 10850 700 520 ...
## $ A01400 : num 2679053 23765 194892 10450 6764 ...
## $ N01700 : num 423450 5510 21410 1830 1430 ...
## $ A01700 : num 9828189 140991 536510 39021 27576 ...
## $ SCHF : num 45800 410 1180 440 190 1110 200 410 720 280 ...
## $ N02300 : num 72370 660 2620 440 280 ...
## $ A02300 : num 217367 2122 8242 1683 965 ...
## $ N02500 : num 282850 3320 16620 1360 1040 ...
## $ A02500 : num 3635563 43004 229345 16754 11494 ...
## $ N26270 : num 95500 830 7100 400 210 770 110 230 1410 370 ...
## $ A26270 : num 5608287 29244 352679 10425 16664 ...
## $ N02900 : num 466170 5310 23820 1980 1480 ...
## $ A02900 : num 1403812 12642 85779 4389 3645 ...
## $ N03220 : num 49010 620 2270 280 180 ...
## $ A03220 : num 12402 151 571 69 44 ...
## $ N03300 : num 6540 50 450 0 0 30 0 0 90 0 ...
## $ A03300 : num 140594 750 9255 0 0 ...
## $ N03270 : num 45450 410 3370 170 150 ...
## $ A03270 : num 297208 2426 21082 1007 732 ...
## $ N03150 : num 28370 380 1590 90 80 ...
## $ A03150 : num 127723 1551 7806 464 271 ...
## $ N03210 : num 129120 1750 6260 350 300 ...
## $ A03210 : num 135346 1821 6366 375 293 ...
## $ N03230 : num 19300 250 710 60 40 160 20 50 400 70 ...
## $ A03230 : num 48835 605 1674 138 115 ...
## $ N03240 : num 5230 50 330 0 0 40 0 0 60 0 ...
## $ A03240 : num 107084 445 5105 0 0 ...
## $ N04470 : num 532720 6100 25230 1870 1540 ...
## $ A04470 : num 11601502 119624 578400 36858 29938 ...
## $ A00101 : num 60271119 591641 3031140 158195 151734 ...
## $ N18425 : num 429440 4920 19150 1320 1280 ...
## $ A18425 : num 2092769 19013 99206 4805 4855 ...
## $ N18450 : num 87110 990 5380 510 220 ...
## $ A18450 : num 123708 1463 8263 1211 304 ...
## $ N18500 : num 430800 5090 20680 1440 1170 ...
## $ A18500 : num 598983 4049 27465 1499 725 ...
## $ N18300 : num 529360 6070 25090 1840 1530 ...
## $ A18300 : num 2977934 25839 140874 8062 6810 ...
## $ N19300 : num 390510 4850 18580 1150 1100 ...
## $ A19300 : num 2846999 34957 154805 7441 6574 ...
## $ N19700 : num 467050 5280 21340 1650 1340 ...
## $ A19700 : num 3124370 29509 121362 9391 7480 ...
## $ N04800 : num 1477890 18120 67540 6260 5720 ...
## $ A04800 : num 71936625 802296 3682828 215438 224461 ...
## $ N05800 : num 1473560 18110 67150 6250 5730 ...
## $ A05800 : num 13181728 129008 678871 34112 38221 ...
## $ N09600 : num 21240 100 1310 40 30 ...
## $ A09600 : num 98238 298 5623 141 86 ...
## $ N05780 : num 20430 210 1230 100 70 ...
## $ A05780 : num 15371 176 1082 86 52 ...
## $ N07100 : num 627820 7880 26320 2660 2450 ...
## $ A07100 : num 740106 9841 32966 2410 2824 ...
## $ N07300 : num 63560 640 4330 200 80 ...
## $ A07300 : num 48996 152 4657 28 5 ...
## $ N07180 : num 96890 1370 4160 350 320 ...
## $ A07180 : num 51448 755 2143 165 143 ...
## $ N07230 : num 145740 1770 4900 560 420 ...
## $ A07230 : num 153197 2026 5278 566 444 ...
## $ N07240 : num 118660 1360 4120 560 490 ...
## $ A07240 : num 21050 245 714 104 84 ...
## $ N07220 : num 332960 4760 13860 1490 1630 ...
## $ A07220 : num 388503 6122 17065 1458 2001 ...
## $ N07260 : num 41000 420 1620 140 170 430 70 100 970 450 ...
## $ A07260 : num 17894 133 756 50 63 ...
## $ N09400 : num 249710 2510 13210 1220 870 ...
## $ A09400 : num 615307 5572 36403 2449 1672 ...
## $ N85770 : num 41160 390 2260 190 130 ...
## [list output truncated]
## processing data/irs/15incyallnoagi.csv
## 'data.frame': 3192 obs. of 133 variables:
## $ STATEFIPS : chr "01" "01" "01" "01" ...
## $ STATE : chr "AL" "AL" "AL" "AL" ...
## $ COUNTYFIPS: chr "000" "001" "003" "005" ...
## $ COUNTYNAME: chr "Alabama" "Autauga County" "Baldwin County" "Barbour County" ...
## $ AGI_STUB : int 0 0 0 0 0 0 0 0 0 0 ...
## $ N1 : num 2053620 23960 93140 9590 7960 ...
## $ MARS1 : num 834300 9130 38310 3560 2760 ...
## $ MARS2 : num 763210 10020 39800 3020 3360 ...
## $ MARS4 : num 415730 4290 12900 2860 1750 ...
## $ PREP : num 1172420 10960 46970 6590 4670 ...
## $ N2 : num 4166050 50220 183680 19710 17270 ...
## $ NUMDEP : num 1467040 17800 56550 7540 6300 ...
## $ TOTAL_VITA: num 45580 510 1700 30 50 ...
## $ VITA : num 28830 130 350 30 50 ...
## $ TCE : num 16740 370 1350 0 0 ...
## $ VITA_EIC : num 7720 0 90 0 0 40 0 40 360 0 ...
## $ RAL : num 15400 70 300 240 20 40 130 200 780 170 ...
## $ RAC : num 457090 5150 15100 2860 2110 ...
## $ ELDERLY : num 477220 5160 27460 2410 1640 ...
## $ A00100 : num 111789331 1291011 5585227 376661 379445 ...
## $ N02650 : num 2046690 23880 92800 9540 7940 ...
## $ A02650 : num 113236900 1304385 5671834 381264 383693 ...
## $ N00200 : num 1710160 20360 72260 7860 6900 ...
## $ A00200 : num 80323944 979232 3646995 271680 289194 ...
## $ N00300 : num 502130 7210 25980 2110 1600 ...
## $ A00300 : num 763242 5850 45974 2548 1509 ...
## $ N00600 : num 268410 3030 17040 1040 520 ...
## $ A00600 : num 1724024 9391 105811 5639 1643 ...
## $ N00650 : num 246340 2740 15600 950 440 ...
## $ A00650 : num 1351071 7109 79788 4741 1073 ...
## $ N00700 : num 318950 3770 13570 910 1030 ...
## $ A00700 : num 340193 3235 15614 805 843 ...
## $ N00900 : num 345310 3490 17480 1650 1210 ...
## $ A00900 : num 3128573 28135 208501 9281 7614 ...
## $ N01000 : num 228720 2520 15770 890 440 ...
## $ A01000 : num 4203762 17134 243919 8629 3829 ...
## $ N01400 : num 171560 1930 11320 720 480 ...
## $ A01400 : num 2797936 25374 211185 10959 6800 ...
## $ N01700 : num 421830 5520 21820 1790 1370 ...
## $ A01700 : num 10091228 147466 563755 38421 28038 ...
## $ SCHF : num 45220 380 1140 430 210 ...
## $ N02300 : num 58030 480 2090 290 290 ...
## $ A02300 : num 174080 1545 6902 928 776 ...
## $ N02500 : num 290730 3480 17480 1390 1040 ...
## $ A02500 : num 3860827 46432 248600 17714 11776 ...
## $ N26270 : num 95770 800 7290 400 210 ...
## $ A26270 : num 6243919 33397 426752 10712 21667 ...
## $ N02900 : num 467340 5420 24400 2000 1480 ...
## $ A02900 : num 1447569 13374 86607 4603 4248 ...
## $ N03220 : num 47860 580 2200 260 160 ...
## $ A03220 : num 12168 150 553 67 42 ...
## $ N03300 : num 6490 30 430 0 0 40 0 0 80 0 ...
## $ A03300 : num 148557 771 9048 0 0 ...
## $ N03270 : num 45600 400 3570 160 130 530 50 210 670 130 ...
## $ A03270 : num 310419 2549 22257 1137 759 ...
## $ N03150 : num 27310 340 1550 90 80 ...
## $ A03150 : num 124317 1426 7754 480 272 ...
## $ N03210 : num 132870 1830 6550 330 320 ...
## $ A03210 : num 141658 1987 6715 360 342 ...
## $ N03230 : num 19400 240 700 70 50 150 30 40 360 70 ...
## $ A03230 : num 49224 617 1742 155 148 ...
## $ N03240 : num 5460 50 350 0 0 40 0 0 60 0 ...
## $ A03240 : num 116127 554 5254 0 0 ...
## $ N04470 : num 532830 6140 26000 1780 1500 ...
## $ A04470 : num 12016758 122492 594750 37082 29067 ...
## $ A00101 : num 62810906 611108 3235385 158386 155807 ...
## $ N18425 : num 430430 4950 20050 1230 1240 ...
## $ A18425 : num 2224129 19859 109052 4517 5415 ...
## $ N18450 : num 86540 990 5200 480 220 ...
## $ A18450 : num 124298 1491 7865 1237 266 ...
## $ N18500 : num 428350 5090 21040 1380 1130 ...
## $ A18500 : num 617767 4178 29281 1398 696 ...
## $ N18300 : num 529720 6110 25830 1770 1510 ...
## $ A18300 : num 3132011 27065 152430 7653 6709 ...
## $ N19300 : num 385610 4830 18880 1120 1020 ...
## $ A19300 : num 2772904 34062 155403 7166 6010 ...
## $ N19700 : num 465790 5290 21830 1570 1320 ...
## $ A19700 : num 3379436 30270 126880 9454 8624 ...
## $ N04800 : num 1492170 18380 69680 6110 5760 ...
## $ A04800 : num 75074369 841758 3957754 218839 235282 ...
## $ N05800 : num 1492870 18380 69550 6120 5790 ...
## $ A05800 : num 13898089 136829 746462 34616 41302 ...
## $ N09600 : num 22380 120 1450 40 30 ...
## $ A09600 : num 104881 400 6511 171 105 ...
## $ N05780 : num 41550 420 2450 190 130 ...
## $ A05780 : num 29586 295 2063 127 63 ...
## $ N07100 : num 631420 7980 27020 2560 2490 ...
## $ A07100 : num 738700 9773 34642 2353 2895 ...
## $ N07300 : num 63450 670 4400 230 80 ...
## $ A07300 : num 42234 84 4665 24 9 ...
## $ N07180 : num 97460 1400 4250 320 320 ...
## $ A07180 : num 52777 783 2242 155 152 ...
## $ N07230 : num 145890 1790 5100 500 430 ...
## $ A07230 : num 154923 2078 5504 524 467 ...
## $ N07240 : num 122430 1380 4280 550 540 ...
## $ A07240 : num 22009 245 787 98 105 ...
## $ N07220 : num 331830 4780 14220 1480 1660 ...
## $ A07220 : num 386740 6155 17377 1431 2055 ...
## $ N07260 : num 39130 410 1410 130 130 ...
## [list output truncated]
Noticed a lot of skew
summary(d2)
## STATE COUNTYNAME fips Year
## Length:3192 Length:3192 Length:3192 Min. :2015
## Class :character Class :character Class :character 1st Qu.:2015
## Mode :character Mode :character Mode :character Median :2015
## Mean :2015
## 3rd Qu.:2015
## Max. :2015
## num.returns married.pct dependents.ratio
## Min. : 30 Min. :0.0000 Min. :0.0000
## 1st Qu.: 4660 1st Qu.:0.3764 1st Qu.:0.5466
## Median : 11410 Median :0.4203 Median :0.6211
## Mean : 93341 Mean :0.4117 Mean :0.6400
## 3rd Qu.: 32005 3rd Qu.:0.4573 3rd Qu.:0.7191
## Max. :17758580 Max. :0.6667 Max. :1.4472
## adjusted.gross.income.avg wages.avg farming.ratio
## Min. : 21.60 Min. : 0.00 Min. :0.00000
## 1st Qu.: 43.45 1st Qu.: 30.34 1st Qu.:0.01384
## Median : 49.14 Median : 34.01 Median :0.03870
## Mean : 52.33 Mean : 36.04 Mean :0.06390
## 3rd Qu.: 57.34 3rd Qu.: 39.32 3rd Qu.:0.08882
## Max. :248.35 Max. :109.81 Max. :0.51429
## unemployed.ratio dividends.ratio business.ratio realestate.ratio
## Min. :0.00000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.02683 1st Qu.:0.1115 1st Qu.:0.1384 1st Qu.:0.1192
## Median :0.03981 Median :0.1595 Median :0.1609 Median :0.1636
## Mean :0.04585 Mean :0.1631 Mean :0.1646 Mean :0.1799
## 3rd Qu.:0.05743 3rd Qu.:0.2062 3rd Qu.:0.1856 3rd Qu.:0.2305
## Max. :0.35802 Max. :0.7207 Max. :0.4074 Max. :0.5252
## mortgage.ratio contributions.ratio taxcredits.ratio
## Min. :0.00000 Min. : 0.0000 Min. :0.0000
## 1st Qu.:0.09054 1st Qu.: 0.5955 1st Qu.:0.2779
## Median :0.12955 Median : 0.8292 Median :0.2965
## Mean :0.14659 Mean : 0.9504 Mean :0.2965
## 3rd Qu.:0.19115 3rd Qu.: 1.1439 3rd Qu.:0.3158
## Max. :0.46486 Max. :19.6464 Max. :0.4129
hist(d2$unemployed.ratio)
plot(density(asinh(d2$contributions.ratio)))
May need to transform or winsor data With outliers:
moments::skewness(d2[,-1*(1:5)])
## married.pct dependents.ratio
## -0.8448543 0.8036882
## adjusted.gross.income.avg wages.avg
## 3.1765319 2.0793214
## farming.ratio unemployed.ratio
## 1.9453960 3.3453933
## dividends.ratio business.ratio
## 1.0053472 0.9410124
## realestate.ratio mortgage.ratio
## 0.8189409 0.8572887
## contributions.ratio taxcredits.ratio
## 9.0811802 -0.3123149
Without outliers via winsoring:
moments::skewness(winsor(d2[,-1*(1:5)]))
## married.pct dependents.ratio
## -0.125195422 0.195697991
## adjusted.gross.income.avg wages.avg
## 0.229422439 0.255601066
## farming.ratio unemployed.ratio
## 0.430669260 0.182699028
## dividends.ratio business.ratio
## -0.011278442 0.082777416
## realestate.ratio mortgage.ratio
## 0.244112009 0.264294342
## contributions.ratio taxcredits.ratio
## 0.202489986 0.008845928
De-leveraged outliers via transformation:
moments::skewness(apply(d2[,-1*(1:5)],2,asinh))
## married.pct dependents.ratio
## -0.9379107 0.5404818
## adjusted.gross.income.avg wages.avg
## 1.0638145 -0.9786635
## farming.ratio unemployed.ratio
## 1.8941685 3.2748984
## dividends.ratio business.ratio
## 0.8650462 0.8991994
## realestate.ratio mortgage.ratio
## 0.7669676 0.8165621
## contributions.ratio taxcredits.ratio
## 1.1165750 -0.3647279
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.4.2
##
## Attaching package: 'dplyr'
## The following object is masked _by_ '.GlobalEnv':
##
## coalesce
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(choroplethr)
## Warning: package 'choroplethr' was built under R version 3.4.2
## Loading required package: acs
## Warning: package 'acs' was built under R version 3.4.2
## Loading required package: XML
##
## Attaching package: 'acs'
## The following object is masked from 'package:dplyr':
##
## combine
## The following object is masked from 'package:base':
##
## apply
library(choroplethrMaps)
## Warning: package 'choroplethrMaps' was built under R version 3.4.2
Helpful text: https://www.gislounge.com/mapping-county-demographic-data-in-r/
for(n in names(d2[,-1*(1:5)])){
print(county_choropleth(title=paste0(' ',n),data.frame(region=as.numeric(d2$fips),
value=d2[[n]])))
}
## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 1000, 2000, 2158, 4000, 5000,
## 6000, 8000, 9000, 10000, 11000, 12000, 13000, 15000, 16000, 17000, 18000,
## 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000,
## 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000,
## 39000, 40000, 41000, 42000, 44000, 45000, 46000, 46102, 47000, 48000,
## 49000, 50000, 51000, 53000, 54000, 55000, 56000
## Warning in self$bind(): The following regions were missing and are being
## set to NA: 46113, 15005, 51515, 2270
## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 1000, 2000, 2158, 4000, 5000,
## 6000, 8000, 9000, 10000, 11000, 12000, 13000, 15000, 16000, 17000, 18000,
## 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000,
## 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000,
## 39000, 40000, 41000, 42000, 44000, 45000, 46000, 46102, 47000, 48000,
## 49000, 50000, 51000, 53000, 54000, 55000, 56000
## Warning in self$bind(): The following regions were missing and are being
## set to NA: 46113, 15005, 51515, 2270
## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 1000, 2000, 2158, 4000, 5000,
## 6000, 8000, 9000, 10000, 11000, 12000, 13000, 15000, 16000, 17000, 18000,
## 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000,
## 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000,
## 39000, 40000, 41000, 42000, 44000, 45000, 46000, 46102, 47000, 48000,
## 49000, 50000, 51000, 53000, 54000, 55000, 56000
## Warning in super$initialize(map.df, user.df): The following regions were
## missing and are being set to NA: 46113, 15005, 51515, 2270
## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 1000, 2000, 2158, 4000, 5000,
## 6000, 8000, 9000, 10000, 11000, 12000, 13000, 15000, 16000, 17000, 18000,
## 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000,
## 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000,
## 39000, 40000, 41000, 42000, 44000, 45000, 46000, 46102, 47000, 48000,
## 49000, 50000, 51000, 53000, 54000, 55000, 56000
## Warning in super$initialize(map.df, user.df): The following regions were
## missing and are being set to NA: 46113, 15005, 51515, 2270
## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 1000, 2000, 2158, 4000, 5000,
## 6000, 8000, 9000, 10000, 11000, 12000, 13000, 15000, 16000, 17000, 18000,
## 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000,
## 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000,
## 39000, 40000, 41000, 42000, 44000, 45000, 46000, 46102, 47000, 48000,
## 49000, 50000, 51000, 53000, 54000, 55000, 56000
## Warning in super$initialize(map.df, user.df): The following regions were
## missing and are being set to NA: 46113, 15005, 51515, 2270
## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 1000, 2000, 2158, 4000, 5000,
## 6000, 8000, 9000, 10000, 11000, 12000, 13000, 15000, 16000, 17000, 18000,
## 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000,
## 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000,
## 39000, 40000, 41000, 42000, 44000, 45000, 46000, 46102, 47000, 48000,
## 49000, 50000, 51000, 53000, 54000, 55000, 56000
## Warning in super$initialize(map.df, user.df): The following regions were
## missing and are being set to NA: 46113, 15005, 51515, 2270
## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 1000, 2000, 2158, 4000, 5000,
## 6000, 8000, 9000, 10000, 11000, 12000, 13000, 15000, 16000, 17000, 18000,
## 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000,
## 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000,
## 39000, 40000, 41000, 42000, 44000, 45000, 46000, 46102, 47000, 48000,
## 49000, 50000, 51000, 53000, 54000, 55000, 56000
## Warning in super$initialize(map.df, user.df): The following regions were
## missing and are being set to NA: 46113, 15005, 51515, 2270
## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 1000, 2000, 2158, 4000, 5000,
## 6000, 8000, 9000, 10000, 11000, 12000, 13000, 15000, 16000, 17000, 18000,
## 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000,
## 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000,
## 39000, 40000, 41000, 42000, 44000, 45000, 46000, 46102, 47000, 48000,
## 49000, 50000, 51000, 53000, 54000, 55000, 56000
## Warning in super$initialize(map.df, user.df): The following regions were
## missing and are being set to NA: 46113, 15005, 51515, 2270
## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 1000, 2000, 2158, 4000, 5000,
## 6000, 8000, 9000, 10000, 11000, 12000, 13000, 15000, 16000, 17000, 18000,
## 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000,
## 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000,
## 39000, 40000, 41000, 42000, 44000, 45000, 46000, 46102, 47000, 48000,
## 49000, 50000, 51000, 53000, 54000, 55000, 56000
## Warning in super$initialize(map.df, user.df): The following regions were
## missing and are being set to NA: 46113, 15005, 51515, 2270
## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 1000, 2000, 2158, 4000, 5000,
## 6000, 8000, 9000, 10000, 11000, 12000, 13000, 15000, 16000, 17000, 18000,
## 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000,
## 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000,
## 39000, 40000, 41000, 42000, 44000, 45000, 46000, 46102, 47000, 48000,
## 49000, 50000, 51000, 53000, 54000, 55000, 56000
## Warning in super$initialize(map.df, user.df): The following regions were
## missing and are being set to NA: 46113, 15005, 51515, 2270
## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 1000, 2000, 2158, 4000, 5000,
## 6000, 8000, 9000, 10000, 11000, 12000, 13000, 15000, 16000, 17000, 18000,
## 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000,
## 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000,
## 39000, 40000, 41000, 42000, 44000, 45000, 46000, 46102, 47000, 48000,
## 49000, 50000, 51000, 53000, 54000, 55000, 56000
## Warning in super$initialize(map.df, user.df): The following regions were
## missing and are being set to NA: 46113, 15005, 51515, 2270
## Warning in super$initialize(map.df, user.df): Your data.frame contains the
## following regions which are not mappable: 1000, 2000, 2158, 4000, 5000,
## 6000, 8000, 9000, 10000, 11000, 12000, 13000, 15000, 16000, 17000, 18000,
## 19000, 20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000,
## 29000, 30000, 31000, 32000, 33000, 34000, 35000, 36000, 37000, 38000,
## 39000, 40000, 41000, 42000, 44000, 45000, 46000, 46102, 47000, 48000,
## 49000, 50000, 51000, 53000, 54000, 55000, 56000
## Warning in super$initialize(map.df, user.df): The following regions were
## missing and are being set to NA: 46113, 15005, 51515, 2270
Another option for county maps: https://stackoverflow.com/questions/25875877/remove-border-lines-in-ggplot-map-choropleth https://www.arilamstein.com/blog/2015/07/02/exploring-the-demographics-of-ferguson-missouri/
# end of file