jp2 <- read.csv("https://raw.githubusercontent.com/taragonmd/data/master/drugrx-pearl2.csv")
str(jp2)## 'data.frame': 700 obs. of 4 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Recovered: Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ Drug : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ Gender : Factor w/ 2 levels "Men","Women": 1 1 1 1 1 1 1 1 1 1 ...tab2.rdg <- xtabs(~Recovered + Drug + Gender, data = jp2)
tab2.rdg## , , Gender = Men
##
## Drug
## Recovered No Yes
## No 36 6
## Yes 234 81
##
## , , Gender = Women
##
## Drug
## Recovered No Yes
## No 25 71
## Yes 55 192P1 <- (234+55)/(36+234+25+55);P1## [1] 0.8257143P2 <- (81+192)/(6+81+71+192); P2## [1] 0.78P3 <- 234/(36+234); P3## [1] 0.8666667P4 <- 81/(6+81); P4## [1] 0.9310345P5 <- 55/(55+25); P5## [1] 0.6875P6 <- 192/(71+192); P6## [1] 0.730038When looking at genders separately, people who take drugs had a higher possibility of recovery. However, when looking at the study population in a whole, not taking drugs is associated with higher possiblity of recovery. The results conflict with each other, and this is known as the Simpson’s paradox. I would suggest patients (for both men and women) to take the drug.
std89c <- read.csv("https://raw.githubusercontent.com/taragonmd/data/master/syphilis89c.csv", as.is = c(FALSE,FALSE,TRUE))
#a.
str(std89c)## 'data.frame': 44081 obs. of 3 variables:
## $ Sex : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 2 2 2 2 ...
## $ Race: Factor w/ 3 levels "Black","Other",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Age : chr "<=14" "<=14" "15-19" "15-19" ...head(std89c)## Sex Race Age
## 1 Male White <=14
## 2 Male White <=14
## 3 Male White 15-19
## 4 Male White 15-19
## 5 Male White 15-19
## 6 Male White 15-19lapply(std89c,table)## $Sex
##
## Female Male
## 18075 26006
##
## $Race
##
## Black Other White
## 35508 3956 4617
##
## $Age
##
## <=14 >55 15-19 20-24 25-29 30-34 35-44 45-54
## 230 1278 4378 10405 9610 8648 6901 2631#b. The Age variable is not ordered corretly because R converts the characters into factors with the levels in a alphabetical order automatically.
correct_age <- c("<=14","15-19","20-24","25-29","30-34","35-44","45-54",">55")
std89c$Age <- factor(std89c$Age, levels = correct_age)
table(std89c$Age)##
## <=14 15-19 20-24 25-29 30-34 35-44 45-54 >55
## 230 4378 10405 9610 8648 6901 2631 1278#c.
table(std89c$Race, std89c$Age, std89c$Sex)## , , = Female
##
##
## <=14 15-19 20-24 25-29 30-34 35-44 45-54 >55
## Black 165 2257 4503 3590 2628 1505 392 92
## Other 11 158 307 283 167 149 40 15
## White 14 253 475 433 316 243 55 24
##
## , , = Male
##
##
## <=14 15-19 20-24 25-29 30-34 35-44 45-54 >55
## Black 31 1412 4059 4121 4453 3858 1619 823
## Other 7 210 654 633 520 492 202 108
## White 2 88 407 550 564 654 323 216xtabs(~Race + Age + Sex, data = std89c)## , , Sex = Female
##
## Age
## Race <=14 15-19 20-24 25-29 30-34 35-44 45-54 >55
## Black 165 2257 4503 3590 2628 1505 392 92
## Other 11 158 307 283 167 149 40 15
## White 14 253 475 433 316 243 55 24
##
## , , Sex = Male
##
## Age
## Race <=14 15-19 20-24 25-29 30-34 35-44 45-54 >55
## Black 31 1412 4059 4121 4453 3858 1619 823
## Other 7 210 654 633 520 492 202 108
## White 2 88 407 550 564 654 323 216attach(std89c)
table(Race,Age,Sex)## , , Sex = Female
##
## Age
## Race <=14 15-19 20-24 25-29 30-34 35-44 45-54 >55
## Black 165 2257 4503 3590 2628 1505 392 92
## Other 11 158 307 283 167 149 40 15
## White 14 253 475 433 316 243 55 24
##
## , , Sex = Male
##
## Age
## Race <=14 15-19 20-24 25-29 30-34 35-44 45-54 >55
## Black 31 1412 4059 4121 4453 3858 1619 823
## Other 7 210 654 633 520 492 202 108
## White 2 88 407 550 564 654 323 216xtabs(~Race+Age+Sex)## , , Sex = Female
##
## Age
## Race <=14 15-19 20-24 25-29 30-34 35-44 45-54 >55
## Black 165 2257 4503 3590 2628 1505 392 92
## Other 11 158 307 283 167 149 40 15
## White 14 253 475 433 316 243 55 24
##
## , , Sex = Male
##
## Age
## Race <=14 15-19 20-24 25-29 30-34 35-44 45-54 >55
## Black 31 1412 4059 4121 4453 3858 1619 823
## Other 7 210 654 633 520 492 202 108
## White 2 88 407 550 564 654 323 216detach(std89c)
# When using attach(), the table() output contains field names. Without attachment of the data frame, table() doesn't return field names in its result. tab <- xtabs(~ Race + Age + Sex, data = std89c)
tab_sr <- apply(tab, c(1,3), sum); tab_sr## Sex
## Race Female Male
## Black 15132 20376
## Other 1130 2826
## White 1813 2804tab_sa <- apply(tab, c(2,3), sum); tab_sa## Sex
## Age Female Male
## <=14 190 40
## 15-19 2668 1710
## 20-24 5285 5120
## 25-29 4306 5304
## 30-34 3111 5537
## 35-44 1897 5004
## 45-54 487 2144
## >55 131 1147tab_ar <- apply(tab, c(1,2), sum); tab_ar## Age
## Race <=14 15-19 20-24 25-29 30-34 35-44 45-54 >55
## Black 196 3669 8562 7711 7081 5363 2011 915
## Other 18 368 961 916 687 641 242 123
## White 16 341 882 983 880 897 378 240tab_race <- apply(tab, 1, sum); tab_race## Black Other White
## 35508 3956 4617tab_age <- apply(tab, 2, sum); tab_age## <=14 15-19 20-24 25-29 30-34 35-44 45-54 >55
## 230 4378 10405 9610 8648 6901 2631 1278tab_sex <- apply(tab, 3, sum); tab_sex## Female Male
## 18075 26006rdis <- sweep(tab, c(1,3),tab_sr,"/"); rdis## , , Sex = Female
##
## Age
## Race <=14 15-19 20-24 25-29 30-34
## Black 0.0109040444 0.1491541105 0.2975812847 0.2372455723 0.1736716891
## Other 0.0097345133 0.1398230088 0.2716814159 0.2504424779 0.1477876106
## White 0.0077220077 0.1395477110 0.2619966906 0.2388306674 0.1742967457
## Age
## Race 35-44 45-54 >55
## Black 0.0994581020 0.0259053661 0.0060798308
## Other 0.1318584071 0.0353982301 0.0132743363
## White 0.1340319912 0.0303364589 0.0132377275
##
## , , Sex = Male
##
## Age
## Race <=14 15-19 20-24 25-29 30-34
## Black 0.0015213977 0.0692972124 0.1992049470 0.2022477424 0.2185414213
## Other 0.0024769993 0.0743099788 0.2314225053 0.2239915074 0.1840056617
## White 0.0007132668 0.0313837375 0.1451497860 0.1961483595 0.2011412268
## Age
## Race 35-44 45-54 >55
## Black 0.1893404005 0.0794562230 0.0403906557
## Other 0.1740976645 0.0714791224 0.0382165605
## White 0.2332382311 0.1151925820 0.0770328103cdis <- sweep(tab, c(2,3),tab_sa,"/"); cdis## , , Sex = Female
##
## Age
## Race <=14 15-19 20-24 25-29 30-34 35-44
## Black 0.86842105 0.84595202 0.85203406 0.83372039 0.84474446 0.79335793
## Other 0.05789474 0.05922039 0.05808893 0.06572225 0.05368049 0.07854507
## White 0.07368421 0.09482759 0.08987701 0.10055736 0.10157506 0.12809700
## Age
## Race 45-54 >55
## Black 0.80492813 0.70229008
## Other 0.08213552 0.11450382
## White 0.11293634 0.18320611
##
## , , Sex = Male
##
## Age
## Race <=14 15-19 20-24 25-29 30-34 35-44
## Black 0.77500000 0.82573099 0.79277344 0.77696078 0.80422612 0.77098321
## Other 0.17500000 0.12280702 0.12773438 0.11934389 0.09391367 0.09832134
## White 0.05000000 0.05146199 0.07949219 0.10369532 0.10186021 0.13069544
## Age
## Race 45-54 >55
## Black 0.75513060 0.71752398
## Other 0.09421642 0.09415867
## White 0.15065299 0.18831735jdis <- sweep(tab, 3, tab_sex,"/"); jdis## , , Sex = Female
##
## Age
## Race <=14 15-19 20-24 25-29 30-34
## Black 9.128631e-03 1.248686e-01 2.491286e-01 1.986169e-01 1.453942e-01
## Other 6.085754e-04 8.741355e-03 1.698479e-02 1.565698e-02 9.239281e-03
## White 7.745505e-04 1.399723e-02 2.627939e-02 2.395574e-02 1.748271e-02
## Age
## Race 35-44 45-54 >55
## Black 8.326418e-02 2.168741e-02 5.089903e-03
## Other 8.243430e-03 2.213001e-03 8.298755e-04
## White 1.344398e-02 3.042877e-03 1.327801e-03
##
## , , Sex = Male
##
## Age
## Race <=14 15-19 20-24 25-29 30-34
## Black 1.192033e-03 5.429516e-02 1.560794e-01 1.584634e-01 1.712297e-01
## Other 2.691687e-04 8.075060e-03 2.514804e-02 2.434054e-02 1.999539e-02
## White 7.690533e-05 3.383834e-03 1.565023e-02 2.114897e-02 2.168730e-02
## Age
## Race 35-44 45-54 >55
## Black 1.483504e-01 6.225486e-02 3.164654e-02
## Other 1.891871e-02 7.767438e-03 4.152888e-03
## White 2.514804e-02 1.242021e-02 8.305776e-03table(std89c)## , , Age = <=14
##
## Race
## Sex Black Other White
## Female 165 11 14
## Male 31 7 2
##
## , , Age = 15-19
##
## Race
## Sex Black Other White
## Female 2257 158 253
## Male 1412 210 88
##
## , , Age = 20-24
##
## Race
## Sex Black Other White
## Female 4503 307 475
## Male 4059 654 407
##
## , , Age = 25-29
##
## Race
## Sex Black Other White
## Female 3590 283 433
## Male 4121 633 550
##
## , , Age = 30-34
##
## Race
## Sex Black Other White
## Female 2628 167 316
## Male 4453 520 564
##
## , , Age = 35-44
##
## Race
## Sex Black Other White
## Female 1505 149 243
## Male 3858 492 654
##
## , , Age = 45-54
##
## Race
## Sex Black Other White
## Female 392 40 55
## Male 1619 202 323
##
## , , Age = >55
##
## Race
## Sex Black Other White
## Female 92 15 24
## Male 823 108 216data.frame(table(std89c))## Sex Race Age Freq
## 1 Female Black <=14 165
## 2 Male Black <=14 31
## 3 Female Other <=14 11
## 4 Male Other <=14 7
## 5 Female White <=14 14
## 6 Male White <=14 2
## 7 Female Black 15-19 2257
## 8 Male Black 15-19 1412
## 9 Female Other 15-19 158
## 10 Male Other 15-19 210
## 11 Female White 15-19 253
## 12 Male White 15-19 88
## 13 Female Black 20-24 4503
## 14 Male Black 20-24 4059
## 15 Female Other 20-24 307
## 16 Male Other 20-24 654
## 17 Female White 20-24 475
## 18 Male White 20-24 407
## 19 Female Black 25-29 3590
## 20 Male Black 25-29 4121
## 21 Female Other 25-29 283
## 22 Male Other 25-29 633
## 23 Female White 25-29 433
## 24 Male White 25-29 550
## 25 Female Black 30-34 2628
## 26 Male Black 30-34 4453
## 27 Female Other 30-34 167
## 28 Male Other 30-34 520
## 29 Female White 30-34 316
## 30 Male White 30-34 564
## 31 Female Black 35-44 1505
## 32 Male Black 35-44 3858
## 33 Female Other 35-44 149
## 34 Male Other 35-44 492
## 35 Female White 35-44 243
## 36 Male White 35-44 654
## 37 Female Black 45-54 392
## 38 Male Black 45-54 1619
## 39 Female Other 45-54 40
## 40 Male Other 45-54 202
## 41 Female White 45-54 55
## 42 Male White 45-54 323
## 43 Female Black >55 92
## 44 Male Black >55 823
## 45 Female Other >55 15
## 46 Male Other >55 108
## 47 Female White >55 24
## 48 Male White >55 216std89b <- read.csv("https://raw.githubusercontent.com/taragonmd/data/master/syphilis89b.csv")
expand_rows <- rep(1:48, std89b$Freq)
std89b_df <- std89b[expand_rows,]
head(std89b_df)## Sex Race Age Freq
## 1 Male White <=14 2
## 1.1 Male White <=14 2
## 2 Female White <=14 14
## 2.1 Female White <=14 14
## 2.2 Female White <=14 14
## 2.3 Female White <=14 14#1. Prints county names
url1 <- "https://raw.githubusercontent.com/taragonmd/data/master/calcounty.txt"
cty <- scan(url1, what="")
#2.1 Reads the whole data frame.
url2 <- "https://raw.githubusercontent.com/taragonmd/data/master/CalCounty2000.csv"
calpop <- read.csv(url2)
#2.2 Assign county names to each county number.
calpop$CtyName <- calpop$County
for( i in 1:length(cty)){
calpop$CtyName[calpop$County == i] <- cty[i]
}
#3. Categorizes age into different groups.
calpop$Agecat <- cut(calpop$Age, c(0,20,45,65,100), include.lowest = TRUE, right = FALSE)
#4. Creates a new variable that combines Asian and Pacific Islander together.
calpop$AsianPI <- calpop$Asian + calpop$Pac.Isl
#5. Renames variable "Hispanic" to "Latino", "Black" to "AfrAmer", and "Amer.Indian" to "AmerInd".
names(calpop)[c(6,9,10)] = c("Latino","AfrAmer","AmerInd")
#6. Select data for Alameda and San Francisco.
baindex <- calpop$CtyName == "Alameda" | calpop$CtyName == "San Francisco"
bapop <- calpop[baindex,]
bapop## County Year Sex Age White Latino Asian Pac.Islander AfrAmer AmerInd
## 1 1 2000 F 0 2751 2910 1921 63 1346 32
## 2 1 2000 F 1 2673 2837 1820 70 1343 37
## 3 1 2000 F 2 2654 2770 1930 64 1442 37
## 4 1 2000 F 3 2646 2798 1998 64 1455 39
## 5 1 2000 F 4 2780 2762 2029 85 1558 38
## 6 1 2000 F 5 2826 2738 1998 93 1632 45
## 7 1 2000 F 6 2871 2872 1984 78 1657 53
## 8 1 2000 F 7 2942 2837 1993 94 1732 51
## 9 1 2000 F 8 3087 2756 1957 85 1817 43
## 10 1 2000 F 9 3070 2603 2031 83 1819 44
## 11 1 2000 F 10 3076 2465 1967 89 1820 39
## 12 1 2000 F 11 3056 2315 1945 77 1802 40
## 13 1 2000 F 12 3167 2236 1897 71 1762 47
## 14 1 2000 F 13 3077 2168 1862 78 1570 40
## 15 1 2000 F 14 2979 2094 1736 82 1573 37
## 16 1 2000 F 15 3027 2023 1855 93 1524 47
## 17 1 2000 F 16 2972 1987 1869 79 1543 51
## 18 1 2000 F 17 2880 2045 1896 69 1552 33
## 19 1 2000 F 18 2787 2210 2056 93 1442 45
## 20 1 2000 F 19 2839 2312 2307 92 1376 41
## 21 1 2000 F 20 2870 2371 2522 86 1469 49
## 22 1 2000 F 21 2869 2417 2526 92 1462 51
## 23 1 2000 F 22 3079 2581 2399 86 1445 45
## 24 1 2000 F 23 3260 2629 2432 83 1490 50
## 25 1 2000 F 24 3430 2675 2573 77 1540 42
## 26 1 2000 F 25 3694 2771 2731 73 1597 48
## 27 1 2000 F 26 3701 2748 2790 74 1636 48
## 28 1 2000 F 27 3874 2861 2966 88 1696 48
## 29 1 2000 F 28 4037 2837 3022 76 1773 64
## 30 1 2000 F 29 4449 2734 3125 87 1915 66
## 31 1 2000 F 30 4978 2680 3324 86 2097 59
## 32 1 2000 F 31 4792 2540 3251 85 1847 51
## 33 1 2000 F 32 4521 2493 3062 101 1667 49
## 34 1 2000 F 33 4462 2383 2887 82 1719 47
## 35 1 2000 F 34 4612 2341 2916 73 1877 60
## 36 1 2000 F 35 5100 2362 3077 90 2110 67
## 37 1 2000 F 36 5252 2295 3195 92 2030 75
## 38 1 2000 F 37 5183 2172 3145 91 1909 71
## 39 1 2000 F 38 5330 2154 2923 90 1922 54
## 40 1 2000 F 39 5484 1961 2815 95 1924 61
## 41 1 2000 F 40 5586 1934 2847 99 2044 68
## 42 1 2000 F 41 5453 1790 2645 92 1872 60
## 43 1 2000 F 42 5460 1697 2607 83 1885 51
## 44 1 2000 F 43 5456 1569 2538 70 1754 54
## 45 1 2000 F 44 5394 1558 2484 70 1757 63
## 46 1 2000 F 45 5445 1532 2485 62 1835 60
## 47 1 2000 F 46 5328 1423 2337 68 1676 59
## 48 1 2000 F 47 5310 1306 2266 64 1765 55
## 49 1 2000 F 48 5204 1197 2190 51 1679 60
## 50 1 2000 F 49 5179 1181 2174 54 1629 55
## 51 1 2000 F 50 5274 1179 2246 69 1728 46
## 52 1 2000 F 51 5099 1126 2085 58 1597 64
## 53 1 2000 F 52 5206 1089 1870 43 1508 53
## 54 1 2000 F 53 5387 1001 1721 36 1433 52
## 55 1 2000 F 54 4387 884 1479 35 1186 46
## 56 1 2000 F 55 4132 841 1363 30 1164 40
## 57 1 2000 F 56 3921 759 1284 28 1059 34
## 58 1 2000 F 57 3776 755 1202 25 987 26
## 59 1 2000 F 58 3308 668 1166 20 973 30
## 60 1 2000 F 59 2915 659 1144 25 918 36
## 61 1 2000 F 60 2775 694 1103 29 884 32
## 62 1 2000 F 61 2658 603 1065 25 783 22
## 63 1 2000 F 62 2464 573 1082 26 751 15
## 64 1 2000 F 63 2274 558 1099 24 725 19
## 65 1 2000 F 64 2163 506 1079 24 668 18
## 66 1 2000 F 65 2219 505 1106 24 735 14
## 67 1 2000 F 66 2075 445 1065 26 650 9
## 68 1 2000 F 67 2016 462 986 18 637 17
## 69 1 2000 F 68 1994 468 958 21 577 15
## 70 1 2000 F 69 1984 459 949 15 589 12
## 71 1 2000 F 70 2154 465 943 12 648 10
## 72 1 2000 F 71 2074 396 888 13 593 11
## 73 1 2000 F 72 2046 392 841 18 588 12
## 74 1 2000 F 73 2160 397 815 15 613 13
## 75 1 2000 F 74 2307 368 824 12 638 12
## 76 1 2000 F 75 2301 336 767 11 628 13
## 77 1 2000 F 76 2272 317 715 13 585 15
## 78 1 2000 F 77 2209 302 615 8 541 10
## 79 1 2000 F 78 2246 276 539 4 521 9
## 80 1 2000 F 79 2127 248 533 5 490 7
## 81 1 2000 F 80 1980 216 455 6 501 9
## 82 1 2000 F 81 1800 201 393 7 441 5
## 83 1 2000 F 82 1679 178 347 9 414 5
## 84 1 2000 F 83 1542 158 307 4 358 6
## 85 1 2000 F 84 1445 143 281 3 300 8
## 86 1 2000 F 85 1355 127 262 2 303 7
## 87 1 2000 F 86 1294 108 234 2 263 5
## 88 1 2000 F 87 1113 110 188 3 213 8
## 89 1 2000 F 88 990 98 145 2 182 5
## 90 1 2000 F 89 861 68 130 2 149 3
## 91 1 2000 F 90 717 65 102 3 121 2
## 92 1 2000 F 91 580 52 78 2 102 2
## 93 1 2000 F 92 503 39 56 1 91 0
## 94 1 2000 F 93 438 43 46 0 76 1
## 95 1 2000 F 94 360 24 36 0 56 1
## 96 1 2000 F 95 254 21 34 0 50 0
## 97 1 2000 F 96 208 12 24 0 46 0
## 98 1 2000 F 97 146 13 18 0 39 0
## 99 1 2000 F 98 88 15 21 0 25 0
## 100 1 2000 F 99 87 10 14 1 22 0
## 101 1 2000 F 100 138 11 27 0 49 0
## 102 1 2000 M 0 2905 3025 2033 57 1344 30
## 103 1 2000 M 1 2788 2983 1985 76 1346 37
## 104 1 2000 M 2 2793 2913 2007 82 1452 30
## 105 1 2000 M 3 2841 2896 2132 77 1539 36
## 106 1 2000 M 4 2914 2877 2141 75 1620 32
## 107 1 2000 M 5 3082 2971 2178 95 1632 44
## 108 1 2000 M 6 3068 2904 2214 87 1720 38
## 109 1 2000 M 7 3113 2934 2128 82 1785 44
## 110 1 2000 M 8 3228 2801 2177 88 1800 55
## 111 1 2000 M 9 3282 2748 2108 81 1874 48
## 112 1 2000 M 10 3287 2669 2052 76 1968 48
## 113 1 2000 M 11 3214 2445 2110 89 1825 45
## 114 1 2000 M 12 3212 2398 2002 90 1706 49
## 115 1 2000 M 13 3174 2223 1876 86 1603 47
## 116 1 2000 M 14 3211 2168 1882 95 1561 40
## 117 1 2000 M 15 3120 2194 1963 79 1568 40
## 118 1 2000 M 16 3044 2244 1970 89 1553 34
## 119 1 2000 M 17 3125 2365 1947 76 1557 50
## 120 1 2000 M 18 3014 2559 2038 84 1488 42
## 121 1 2000 M 19 3031 2780 2322 93 1336 39
## 122 1 2000 M 20 3163 3058 2539 91 1347 37
## 123 1 2000 M 21 3075 3086 2454 84 1278 49
## 124 1 2000 M 22 3179 3161 2233 81 1178 53
## 125 1 2000 M 23 3261 3120 2266 70 1216 49
## 126 1 2000 M 24 3458 3257 2297 73 1271 36
## 127 1 2000 M 25 3752 3349 2494 70 1312 38
## 128 1 2000 M 26 3715 3352 2566 75 1256 39
## 129 1 2000 M 27 3910 3374 2811 82 1317 47
## 130 1 2000 M 28 4100 3351 2897 74 1340 42
## 131 1 2000 M 29 4555 3481 2988 82 1491 43
## 132 1 2000 M 30 5165 3412 3078 84 1731 56
## 133 1 2000 M 31 4873 3032 2995 85 1579 59
## 134 1 2000 M 32 4627 2944 2827 75 1441 70
## 135 1 2000 M 33 4641 2817 2701 78 1452 41
## 136 1 2000 M 34 4823 2802 2749 84 1558 46
## 137 1 2000 M 35 5355 2815 3011 86 1762 74
## 138 1 2000 M 36 5464 2561 3048 104 1624 58
## 139 1 2000 M 37 5444 2384 3001 95 1617 48
## 140 1 2000 M 38 5550 2306 2722 90 1666 48
## 141 1 2000 M 39 5580 2104 2611 84 1651 65
## 142 1 2000 M 40 5856 2175 2779 98 1823 55
## 143 1 2000 M 41 5537 1984 2652 90 1613 57
## 144 1 2000 M 42 5635 1843 2509 84 1529 56
## 145 1 2000 M 43 5492 1777 2412 69 1464 53
## 146 1 2000 M 44 5535 1688 2351 74 1460 44
## 147 1 2000 M 45 5520 1630 2381 72 1591 53
## 148 1 2000 M 46 5316 1416 2226 63 1491 46
## 149 1 2000 M 47 5327 1276 2044 56 1403 49
## 150 1 2000 M 48 5207 1235 1993 55 1300 47
## 151 1 2000 M 49 4939 1140 1929 58 1289 44
## 152 1 2000 M 50 5217 1163 2032 56 1453 38
## 153 1 2000 M 51 5066 1036 1870 59 1332 39
## 154 1 2000 M 52 5127 998 1744 53 1237 42
## 155 1 2000 M 53 5282 914 1525 49 1146 52
## 156 1 2000 M 54 4377 799 1346 40 975 37
## 157 1 2000 M 55 4066 797 1256 36 937 28
## 158 1 2000 M 56 3884 720 1154 41 911 38
## 159 1 2000 M 57 3892 657 1054 30 866 27
## 160 1 2000 M 58 3334 625 949 21 770 32
## 161 1 2000 M 59 2890 558 980 25 751 32
## 162 1 2000 M 60 2754 554 1023 23 754 24
## 163 1 2000 M 61 2573 521 926 26 697 19
## 164 1 2000 M 62 2403 457 904 25 606 18
## 165 1 2000 M 63 2152 457 863 15 615 11
## 166 1 2000 M 64 2006 449 823 16 613 11
## 167 1 2000 M 65 1985 426 854 22 588 14
## 168 1 2000 M 66 1851 385 829 18 499 12
## 169 1 2000 M 67 1813 366 782 17 490 4
## 170 1 2000 M 68 1786 362 754 20 471 5
## 171 1 2000 M 69 1735 361 751 15 460 7
## 172 1 2000 M 70 1749 333 733 12 432 7
## 173 1 2000 M 71 1672 333 672 17 390 9
## 174 1 2000 M 72 1648 311 634 10 405 10
## 175 1 2000 M 73 1655 278 619 7 411 11
## 176 1 2000 M 74 1618 252 561 4 435 7
## 177 1 2000 M 75 1599 268 578 8 388 7
## 178 1 2000 M 76 1577 254 528 7 346 8
## 179 1 2000 M 77 1498 200 476 4 344 9
## 180 1 2000 M 78 1526 191 408 3 346 5
## 181 1 2000 M 79 1384 194 361 3 314 7
## 182 1 2000 M 80 1256 147 368 3 312 5
## 183 1 2000 M 81 1118 118 274 2 227 3
## 184 1 2000 M 82 1002 98 232 2 209 4
## 185 1 2000 M 83 884 82 218 5 162 3
## 186 1 2000 M 84 810 75 174 3 161 2
## 187 1 2000 M 85 777 62 139 4 142 3
## 188 1 2000 M 86 659 58 138 2 108 2
## 189 1 2000 M 87 513 54 119 1 87 2
## 190 1 2000 M 88 411 52 105 1 77 0
## 191 1 2000 M 89 342 36 88 2 67 3
## 192 1 2000 M 90 280 29 64 1 49 3
## 193 1 2000 M 91 231 29 43 0 33 2
## 194 1 2000 M 92 165 23 48 0 20 1
## 195 1 2000 M 93 125 10 36 1 20 0
## 196 1 2000 M 94 107 10 22 0 24 0
## 197 1 2000 M 95 73 7 13 0 17 0
## 198 1 2000 M 96 49 3 10 1 14 1
## 199 1 2000 M 97 32 3 10 0 12 0
## 200 1 2000 M 98 20 3 7 0 9 0
## 201 1 2000 M 99 17 2 4 0 6 0
## 202 1 2000 M 100 27 1 8 0 12 0
## 7475 38 2000 F 0 1248 782 949 26 279 9
## 7476 38 2000 F 1 1012 693 936 22 285 8
## 7477 38 2000 F 2 784 667 936 22 266 9
## 7478 38 2000 F 3 731 694 950 29 283 11
## 7479 38 2000 F 4 660 656 982 35 307 7
## 7480 38 2000 F 5 622 669 1097 30 320 8
## 7481 38 2000 F 6 582 659 1102 35 380 9
## 7482 38 2000 F 7 602 675 1148 25 387 6
## 7483 38 2000 F 8 616 677 1168 22 395 12
## 7484 38 2000 F 9 608 671 1152 38 386 6
## 7485 38 2000 F 10 651 654 1133 36 398 7
## 7486 38 2000 F 11 639 592 1205 31 361 8
## 7487 38 2000 F 12 623 616 1184 25 391 11
## 7488 38 2000 F 13 651 612 1130 26 358 11
## 7489 38 2000 F 14 626 572 1148 33 348 5
## 7490 38 2000 F 15 633 588 1213 28 368 5
## 7491 38 2000 F 16 621 600 1260 30 353 6
## 7492 38 2000 F 17 617 631 1297 37 367 9
## 7493 38 2000 F 18 822 738 1254 42 370 12
## 7494 38 2000 F 19 1007 805 1404 45 384 14
## 7495 38 2000 F 20 1157 857 1573 39 361 14
## 7496 38 2000 F 21 1336 865 1619 40 350 15
## 7497 38 2000 F 22 2114 943 1779 40 342 17
## 7498 38 2000 F 23 3278 1032 2006 31 380 16
## 7499 38 2000 F 24 3993 1125 2195 31 379 22
## 7500 38 2000 F 25 4590 1202 2323 40 376 20
## 7501 38 2000 F 26 4747 1185 2394 40 362 24
## 7502 38 2000 F 27 4872 1170 2458 41 357 20
## 7503 38 2000 F 28 5259 1166 2418 37 409 26
## 7504 38 2000 F 29 5721 1198 2415 39 430 28
## 7505 38 2000 F 30 6054 1230 2448 42 484 24
## 7506 38 2000 F 31 5140 1067 2223 40 422 25
## 7507 38 2000 F 32 4370 1017 2071 41 392 25
## 7508 38 2000 F 33 3934 930 1951 30 376 20
## 7509 38 2000 F 34 3622 890 1973 35 394 20
## 7510 38 2000 F 35 3577 891 2100 34 428 18
## 7511 38 2000 F 36 3136 858 2107 31 422 18
## 7512 38 2000 F 37 2739 782 2113 33 441 23
## 7513 38 2000 F 38 2560 758 1962 26 444 18
## 7514 38 2000 F 39 2457 711 1877 32 429 23
## 7515 38 2000 F 40 2524 748 1973 32 489 26
## 7516 38 2000 F 41 2230 761 1901 31 440 29
## 7517 38 2000 F 42 2112 713 1909 20 486 18
## 7518 38 2000 F 43 2123 676 1908 19 456 19
## 7519 38 2000 F 44 2121 683 1943 16 435 18
## 7520 38 2000 F 45 2219 709 1971 23 485 22
## 7521 38 2000 F 46 2111 619 1893 17 459 21
## 7522 38 2000 F 47 2223 589 1874 15 433 14
## 7523 38 2000 F 48 2222 553 1866 18 405 12
## 7524 38 2000 F 49 2188 528 1924 19 388 18
## 7525 38 2000 F 50 2327 574 2206 25 407 20
## 7526 38 2000 F 51 2212 530 2091 15 392 21
## 7527 38 2000 F 52 2284 472 1800 17 396 21
## 7528 38 2000 F 53 2341 441 1600 20 357 17
## 7529 38 2000 F 54 1914 441 1408 19 313 16
## 7530 38 2000 F 55 1793 420 1322 14 291 17
## 7531 38 2000 F 56 1695 381 1187 11 319 9
## 7532 38 2000 F 57 1619 379 1127 10 316 10
## 7533 38 2000 F 58 1413 385 1183 13 273 9
## 7534 38 2000 F 59 1277 365 1220 15 283 8
## 7535 38 2000 F 60 1265 373 1305 10 317 9
## 7536 38 2000 F 61 1213 368 1249 11 261 6
## 7537 38 2000 F 62 1164 353 1320 11 263 8
## 7538 38 2000 F 63 1098 356 1293 15 259 12
## 7539 38 2000 F 64 1039 348 1329 12 273 12
## 7540 38 2000 F 65 979 348 1325 10 259 6
## 7541 38 2000 F 66 932 339 1362 4 234 6
## 7542 38 2000 F 67 937 309 1289 5 245 6
## 7543 38 2000 F 68 958 298 1213 9 224 7
## 7544 38 2000 F 69 1016 335 1317 9 231 7
## 7545 38 2000 F 70 1087 320 1396 10 232 4
## 7546 38 2000 F 71 1081 303 1278 8 231 5
## 7547 38 2000 F 72 1129 281 1260 8 243 9
## 7548 38 2000 F 73 1138 254 1277 8 254 5
## 7549 38 2000 F 74 1208 260 1262 5 237 4
## 7550 38 2000 F 75 1257 245 1135 3 258 4
## 7551 38 2000 F 76 1261 223 996 4 249 4
## 7552 38 2000 F 77 1265 242 913 6 240 8
## 7553 38 2000 F 78 1198 232 855 3 239 5
## 7554 38 2000 F 79 1168 193 773 3 199 3
## 7555 38 2000 F 80 1091 211 718 2 196 2
## 7556 38 2000 F 81 995 156 628 2 171 2
## 7557 38 2000 F 82 925 145 540 5 159 2
## 7558 38 2000 F 83 882 139 487 3 157 3
## 7559 38 2000 F 84 839 126 454 3 156 4
## 7560 38 2000 F 85 870 137 395 3 136 4
## 7561 38 2000 F 86 815 120 343 1 119 4
## 7562 38 2000 F 87 748 118 305 1 88 6
## 7563 38 2000 F 88 644 85 271 2 77 4
## 7564 38 2000 F 89 548 65 221 3 69 1
## 7565 38 2000 F 90 475 58 177 1 50 1
## 7566 38 2000 F 91 408 48 153 2 48 1
## 7567 38 2000 F 92 310 39 122 4 44 1
## 7568 38 2000 F 93 278 36 105 1 34 2
## 7569 38 2000 F 94 215 31 85 0 25 0
## 7570 38 2000 F 95 151 23 65 0 22 1
## 7571 38 2000 F 96 106 14 52 0 9 0
## 7572 38 2000 F 97 86 10 45 0 8 0
## 7573 38 2000 F 98 67 9 31 0 6 0
## 7574 38 2000 F 99 50 10 20 0 7 0
## 7575 38 2000 F 100 98 11 47 1 16 0
## 7576 38 2000 M 0 1245 850 981 29 269 12
## 7577 38 2000 M 1 1043 749 921 27 279 8
## 7578 38 2000 M 2 876 723 941 24 314 4
## 7579 38 2000 M 3 790 721 1054 31 327 7
## 7580 38 2000 M 4 732 696 1080 31 335 8
## 7581 38 2000 M 5 708 713 1141 35 336 8
## 7582 38 2000 M 6 683 732 1147 38 332 9
## 7583 38 2000 M 7 627 713 1123 40 330 11
## 7584 38 2000 M 8 661 688 1172 34 389 8
## 7585 38 2000 M 9 633 696 1211 29 382 10
## 7586 38 2000 M 10 669 692 1263 39 409 9
## 7587 38 2000 M 11 671 644 1276 41 383 8
## 7588 38 2000 M 12 648 651 1263 37 356 8
## 7589 38 2000 M 13 616 591 1245 29 385 8
## 7590 38 2000 M 14 654 585 1270 33 388 5
## 7591 38 2000 M 15 653 624 1303 34 342 7
## 7592 38 2000 M 16 653 670 1343 45 346 7
## 7593 38 2000 M 17 686 778 1365 37 360 7
## 7594 38 2000 M 18 765 902 1357 32 346 10
## 7595 38 2000 M 19 868 1062 1411 29 351 11
## 7596 38 2000 M 20 1076 1190 1546 26 348 16
## 7597 38 2000 M 21 1247 1189 1529 21 328 19
## 7598 38 2000 M 22 1749 1308 1653 25 302 19
## 7599 38 2000 M 23 2783 1351 1829 41 322 21
## 7600 38 2000 M 24 3630 1378 2020 35 342 23
## 7601 38 2000 M 25 4593 1575 2155 39 351 30
## 7602 38 2000 M 26 4913 1529 2190 28 345 37
## 7603 38 2000 M 27 5117 1554 2251 25 351 34
## 7604 38 2000 M 28 5729 1602 2252 31 403 36
## 7605 38 2000 M 29 6456 1613 2210 32 428 38
## 7606 38 2000 M 30 7205 1767 2294 38 501 37
## 7607 38 2000 M 31 6476 1533 2102 29 483 39
## 7608 38 2000 M 32 5829 1412 1879 31 446 35
## 7609 38 2000 M 33 5315 1285 1763 31 439 33
## 7610 38 2000 M 34 5116 1369 1810 30 443 38
## 7611 38 2000 M 35 5331 1332 1940 37 539 40
## 7612 38 2000 M 36 4780 1244 1877 35 499 35
## 7613 38 2000 M 37 4246 1056 1934 36 500 34
## 7614 38 2000 M 38 4045 994 1813 38 502 35
## 7615 38 2000 M 39 3703 990 1762 36 535 35
## 7616 38 2000 M 40 3803 1005 1850 41 586 37
## 7617 38 2000 M 41 3307 914 1740 26 549 32
## 7618 38 2000 M 42 3229 858 1732 25 570 22
## 7619 38 2000 M 43 3156 825 1734 24 522 33
## 7620 38 2000 M 44 2964 732 1737 17 477 24
## 7621 38 2000 M 45 3117 756 1814 19 522 24
## 7622 38 2000 M 46 2914 689 1696 18 495 28
## 7623 38 2000 M 47 2830 657 1643 19 474 21
## 7624 38 2000 M 48 2846 617 1546 18 495 27
## 7625 38 2000 M 49 2840 603 1639 15 486 21
## 7626 38 2000 M 50 2974 590 1916 22 536 25
## 7627 38 2000 M 51 2702 490 1811 14 474 19
## 7628 38 2000 M 52 2697 481 1569 14 415 20
## 7629 38 2000 M 53 2732 450 1328 13 391 24
## 7630 38 2000 M 54 2334 402 1118 10 315 17
## 7631 38 2000 M 55 2199 370 1109 6 323 13
## 7632 38 2000 M 56 1999 353 1007 10 294 17
## 7633 38 2000 M 57 1849 316 937 15 311 16
## 7634 38 2000 M 58 1608 286 972 9 257 19
## 7635 38 2000 M 59 1496 307 957 10 268 11
## 7636 38 2000 M 60 1478 302 1046 10 293 16
## 7637 38 2000 M 61 1392 275 1023 6 262 13
## 7638 38 2000 M 62 1325 260 998 3 257 8
## 7639 38 2000 M 63 1234 230 997 9 253 10
## 7640 38 2000 M 64 1128 240 1027 11 231 5
## 7641 38 2000 M 65 1133 256 1104 10 242 8
## 7642 38 2000 M 66 1035 237 1139 10 209 5
## 7643 38 2000 M 67 988 205 1077 10 205 5
## 7644 38 2000 M 68 967 196 975 4 175 6
## 7645 38 2000 M 69 1044 226 1005 7 178 5
## 7646 38 2000 M 70 1086 228 1038 8 192 6
## 7647 38 2000 M 71 1015 207 955 10 167 3
## 7648 38 2000 M 72 992 194 913 9 182 2
## 7649 38 2000 M 73 972 182 900 6 180 4
## 7650 38 2000 M 74 935 177 876 6 163 4
## 7651 38 2000 M 75 1000 164 912 6 137 2
## 7652 38 2000 M 76 962 149 810 5 140 1
## 7653 38 2000 M 77 836 133 821 5 138 2
## 7654 38 2000 M 78 825 124 736 3 138 2
## 7655 38 2000 M 79 794 99 579 3 127 1
## 7656 38 2000 M 80 741 96 538 1 130 2
## 7657 38 2000 M 81 648 81 418 2 111 3
## 7658 38 2000 M 82 578 83 335 3 96 2
## 7659 38 2000 M 83 524 45 305 3 64 1
## 7660 38 2000 M 84 518 45 295 1 53 2
## 7661 38 2000 M 85 462 50 248 1 45 1
## 7662 38 2000 M 86 387 48 203 2 42 2
## 7663 38 2000 M 87 346 35 166 3 39 1
## 7664 38 2000 M 88 293 30 148 1 40 1
## 7665 38 2000 M 89 238 30 141 0 32 2
## 7666 38 2000 M 90 187 29 110 0 15 2
## 7667 38 2000 M 91 145 15 87 0 15 0
## 7668 38 2000 M 92 117 15 70 0 16 0
## 7669 38 2000 M 93 86 10 45 0 16 0
## 7670 38 2000 M 94 60 8 40 1 12 1
## 7671 38 2000 M 95 49 7 23 0 9 0
## 7672 38 2000 M 96 31 9 11 0 9 0
## 7673 38 2000 M 97 22 5 12 1 3 0
## 7674 38 2000 M 98 13 2 8 1 0 0
## 7675 38 2000 M 99 15 0 8 0 2 0
## 7676 38 2000 M 100 28 8 17 0 7 0
## Multirace CtyName Agecat AsianPI
## 1 711 Alameda [0,20) 1984
## 2 574 Alameda [0,20) 1890
## 3 579 Alameda [0,20) 1994
## 4 588 Alameda [0,20) 2062
## 5 575 Alameda [0,20) 2114
## 6 576 Alameda [0,20) 2091
## 7 548 Alameda [0,20) 2062
## 8 514 Alameda [0,20) 2087
## 9 545 Alameda [0,20) 2042
## 10 537 Alameda [0,20) 2114
## 11 524 Alameda [0,20) 2056
## 12 489 Alameda [0,20) 2022
## 13 475 Alameda [0,20) 1968
## 14 463 Alameda [0,20) 1940
## 15 433 Alameda [0,20) 1818
## 16 431 Alameda [0,20) 1948
## 17 402 Alameda [0,20) 1948
## 18 384 Alameda [0,20) 1965
## 19 388 Alameda [0,20) 2149
## 20 384 Alameda [0,20) 2399
## 21 361 Alameda [20,45) 2608
## 22 357 Alameda [20,45) 2618
## 23 350 Alameda [20,45) 2485
## 24 356 Alameda [20,45) 2515
## 25 356 Alameda [20,45) 2650
## 26 328 Alameda [20,45) 2804
## 27 318 Alameda [20,45) 2864
## 28 318 Alameda [20,45) 3054
## 29 320 Alameda [20,45) 3098
## 30 333 Alameda [20,45) 3212
## 31 338 Alameda [20,45) 3410
## 32 308 Alameda [20,45) 3336
## 33 293 Alameda [20,45) 3163
## 34 281 Alameda [20,45) 2969
## 35 280 Alameda [20,45) 2989
## 36 313 Alameda [20,45) 3167
## 37 288 Alameda [20,45) 3287
## 38 272 Alameda [20,45) 3236
## 39 277 Alameda [20,45) 3013
## 40 295 Alameda [20,45) 2910
## 41 285 Alameda [20,45) 2946
## 42 273 Alameda [20,45) 2737
## 43 265 Alameda [20,45) 2690
## 44 254 Alameda [20,45) 2608
## 45 239 Alameda [20,45) 2554
## 46 233 Alameda [45,65) 2547
## 47 219 Alameda [45,65) 2405
## 48 227 Alameda [45,65) 2330
## 49 199 Alameda [45,65) 2241
## 50 182 Alameda [45,65) 2228
## 51 199 Alameda [45,65) 2315
## 52 178 Alameda [45,65) 2143
## 53 172 Alameda [45,65) 1913
## 54 166 Alameda [45,65) 1757
## 55 146 Alameda [45,65) 1514
## 56 133 Alameda [45,65) 1393
## 57 127 Alameda [45,65) 1312
## 58 124 Alameda [45,65) 1227
## 59 106 Alameda [45,65) 1186
## 60 91 Alameda [45,65) 1169
## 61 96 Alameda [45,65) 1132
## 62 86 Alameda [45,65) 1090
## 63 87 Alameda [45,65) 1108
## 64 79 Alameda [45,65) 1123
## 65 72 Alameda [45,65) 1103
## 66 71 Alameda [65,100] 1130
## 67 69 Alameda [65,100] 1091
## 68 65 Alameda [65,100] 1004
## 69 62 Alameda [65,100] 979
## 70 62 Alameda [65,100] 964
## 71 63 Alameda [65,100] 955
## 72 54 Alameda [65,100] 901
## 73 49 Alameda [65,100] 859
## 74 48 Alameda [65,100] 830
## 75 51 Alameda [65,100] 836
## 76 43 Alameda [65,100] 778
## 77 40 Alameda [65,100] 728
## 78 42 Alameda [65,100] 623
## 79 41 Alameda [65,100] 543
## 80 48 Alameda [65,100] 538
## 81 42 Alameda [65,100] 461
## 82 28 Alameda [65,100] 400
## 83 23 Alameda [65,100] 356
## 84 17 Alameda [65,100] 311
## 85 16 Alameda [65,100] 284
## 86 12 Alameda [65,100] 264
## 87 13 Alameda [65,100] 236
## 88 9 Alameda [65,100] 191
## 89 9 Alameda [65,100] 147
## 90 7 Alameda [65,100] 132
## 91 8 Alameda [65,100] 105
## 92 4 Alameda [65,100] 80
## 93 3 Alameda [65,100] 57
## 94 3 Alameda [65,100] 46
## 95 3 Alameda [65,100] 36
## 96 3 Alameda [65,100] 34
## 97 2 Alameda [65,100] 24
## 98 1 Alameda [65,100] 18
## 99 1 Alameda [65,100] 21
## 100 1 Alameda [65,100] 15
## 101 1 Alameda [65,100] 27
## 102 755 Alameda [0,20) 2090
## 103 606 Alameda [0,20) 2061
## 104 602 Alameda [0,20) 2089
## 105 585 Alameda [0,20) 2209
## 106 576 Alameda [0,20) 2216
## 107 596 Alameda [0,20) 2273
## 108 550 Alameda [0,20) 2301
## 109 532 Alameda [0,20) 2210
## 110 528 Alameda [0,20) 2265
## 111 550 Alameda [0,20) 2189
## 112 524 Alameda [0,20) 2128
## 113 478 Alameda [0,20) 2199
## 114 470 Alameda [0,20) 2092
## 115 453 Alameda [0,20) 1962
## 116 453 Alameda [0,20) 1977
## 117 387 Alameda [0,20) 2042
## 118 379 Alameda [0,20) 2059
## 119 362 Alameda [0,20) 2023
## 120 364 Alameda [0,20) 2122
## 121 352 Alameda [0,20) 2415
## 122 312 Alameda [20,45) 2630
## 123 305 Alameda [20,45) 2538
## 124 292 Alameda [20,45) 2314
## 125 257 Alameda [20,45) 2336
## 126 284 Alameda [20,45) 2370
## 127 301 Alameda [20,45) 2564
## 128 297 Alameda [20,45) 2641
## 129 286 Alameda [20,45) 2893
## 130 293 Alameda [20,45) 2971
## 131 309 Alameda [20,45) 3070
## 132 308 Alameda [20,45) 3162
## 133 283 Alameda [20,45) 3080
## 134 257 Alameda [20,45) 2902
## 135 256 Alameda [20,45) 2779
## 136 267 Alameda [20,45) 2833
## 137 271 Alameda [20,45) 3097
## 138 262 Alameda [20,45) 3152
## 139 253 Alameda [20,45) 3096
## 140 253 Alameda [20,45) 2812
## 141 257 Alameda [20,45) 2695
## 142 279 Alameda [20,45) 2877
## 143 238 Alameda [20,45) 2742
## 144 243 Alameda [20,45) 2593
## 145 225 Alameda [20,45) 2481
## 146 207 Alameda [20,45) 2425
## 147 220 Alameda [45,65) 2453
## 148 204 Alameda [45,65) 2289
## 149 174 Alameda [45,65) 2100
## 150 164 Alameda [45,65) 2048
## 151 159 Alameda [45,65) 1987
## 152 162 Alameda [45,65) 2088
## 153 151 Alameda [45,65) 1929
## 154 147 Alameda [45,65) 1797
## 155 147 Alameda [45,65) 1574
## 156 120 Alameda [45,65) 1386
## 157 126 Alameda [45,65) 1292
## 158 113 Alameda [45,65) 1195
## 159 101 Alameda [45,65) 1084
## 160 98 Alameda [45,65) 970
## 161 77 Alameda [45,65) 1005
## 162 83 Alameda [45,65) 1046
## 163 72 Alameda [45,65) 952
## 164 64 Alameda [45,65) 929
## 165 66 Alameda [45,65) 878
## 166 58 Alameda [45,65) 839
## 167 51 Alameda [65,100] 876
## 168 49 Alameda [65,100] 847
## 169 53 Alameda [65,100] 799
## 170 46 Alameda [65,100] 774
## 171 45 Alameda [65,100] 766
## 172 35 Alameda [65,100] 745
## 173 39 Alameda [65,100] 689
## 174 34 Alameda [65,100] 644
## 175 35 Alameda [65,100] 626
## 176 35 Alameda [65,100] 565
## 177 32 Alameda [65,100] 586
## 178 25 Alameda [65,100] 535
## 179 26 Alameda [65,100] 480
## 180 29 Alameda [65,100] 411
## 181 30 Alameda [65,100] 364
## 182 28 Alameda [65,100] 371
## 183 24 Alameda [65,100] 276
## 184 15 Alameda [65,100] 234
## 185 8 Alameda [65,100] 223
## 186 11 Alameda [65,100] 177
## 187 10 Alameda [65,100] 143
## 188 10 Alameda [65,100] 140
## 189 7 Alameda [65,100] 120
## 190 5 Alameda [65,100] 106
## 191 6 Alameda [65,100] 90
## 192 6 Alameda [65,100] 65
## 193 3 Alameda [65,100] 43
## 194 3 Alameda [65,100] 48
## 195 3 Alameda [65,100] 37
## 196 2 Alameda [65,100] 22
## 197 1 Alameda [65,100] 13
## 198 2 Alameda [65,100] 11
## 199 2 Alameda [65,100] 10
## 200 0 Alameda [65,100] 7
## 201 0 Alameda [65,100] 4
## 202 1 Alameda [65,100] 8
## 7475 267 San Francisco [0,20) 975
## 7476 202 San Francisco [0,20) 958
## 7477 174 San Francisco [0,20) 958
## 7478 183 San Francisco [0,20) 979
## 7479 156 San Francisco [0,20) 1017
## 7480 143 San Francisco [0,20) 1127
## 7481 132 San Francisco [0,20) 1137
## 7482 145 San Francisco [0,20) 1173
## 7483 153 San Francisco [0,20) 1190
## 7484 139 San Francisco [0,20) 1190
## 7485 131 San Francisco [0,20) 1169
## 7486 124 San Francisco [0,20) 1236
## 7487 132 San Francisco [0,20) 1209
## 7488 126 San Francisco [0,20) 1156
## 7489 124 San Francisco [0,20) 1181
## 7490 127 San Francisco [0,20) 1241
## 7491 115 San Francisco [0,20) 1290
## 7492 119 San Francisco [0,20) 1334
## 7493 122 San Francisco [0,20) 1296
## 7494 126 San Francisco [0,20) 1449
## 7495 117 San Francisco [20,45) 1612
## 7496 114 San Francisco [20,45) 1659
## 7497 139 San Francisco [20,45) 1819
## 7498 177 San Francisco [20,45) 2037
## 7499 181 San Francisco [20,45) 2226
## 7500 199 San Francisco [20,45) 2363
## 7501 204 San Francisco [20,45) 2434
## 7502 202 San Francisco [20,45) 2499
## 7503 212 San Francisco [20,45) 2455
## 7504 221 San Francisco [20,45) 2454
## 7505 208 San Francisco [20,45) 2490
## 7506 179 San Francisco [20,45) 2263
## 7507 163 San Francisco [20,45) 2112
## 7508 147 San Francisco [20,45) 1981
## 7509 136 San Francisco [20,45) 2008
## 7510 146 San Francisco [20,45) 2134
## 7511 134 San Francisco [20,45) 2138
## 7512 117 San Francisco [20,45) 2146
## 7513 103 San Francisco [20,45) 1988
## 7514 95 San Francisco [20,45) 1909
## 7515 104 San Francisco [20,45) 2005
## 7516 94 San Francisco [20,45) 1932
## 7517 95 San Francisco [20,45) 1929
## 7518 87 San Francisco [20,45) 1927
## 7519 82 San Francisco [20,45) 1959
## 7520 83 San Francisco [45,65) 1994
## 7521 78 San Francisco [45,65) 1910
## 7522 78 San Francisco [45,65) 1889
## 7523 76 San Francisco [45,65) 1884
## 7524 66 San Francisco [45,65) 1943
## 7525 79 San Francisco [45,65) 2231
## 7526 74 San Francisco [45,65) 2106
## 7527 69 San Francisco [45,65) 1817
## 7528 66 San Francisco [45,65) 1620
## 7529 58 San Francisco [45,65) 1427
## 7530 55 San Francisco [45,65) 1336
## 7531 55 San Francisco [45,65) 1198
## 7532 47 San Francisco [45,65) 1137
## 7533 35 San Francisco [45,65) 1196
## 7534 35 San Francisco [45,65) 1235
## 7535 41 San Francisco [45,65) 1315
## 7536 38 San Francisco [45,65) 1260
## 7537 34 San Francisco [45,65) 1331
## 7538 40 San Francisco [45,65) 1308
## 7539 37 San Francisco [45,65) 1341
## 7540 35 San Francisco [65,100] 1335
## 7541 26 San Francisco [65,100] 1366
## 7542 23 San Francisco [65,100] 1294
## 7543 28 San Francisco [65,100] 1222
## 7544 26 San Francisco [65,100] 1326
## 7545 26 San Francisco [65,100] 1406
## 7546 27 San Francisco [65,100] 1286
## 7547 33 San Francisco [65,100] 1268
## 7548 29 San Francisco [65,100] 1285
## 7549 28 San Francisco [65,100] 1267
## 7550 33 San Francisco [65,100] 1138
## 7551 25 San Francisco [65,100] 1000
## 7552 23 San Francisco [65,100] 919
## 7553 24 San Francisco [65,100] 858
## 7554 14 San Francisco [65,100] 776
## 7555 17 San Francisco [65,100] 720
## 7556 18 San Francisco [65,100] 630
## 7557 17 San Francisco [65,100] 545
## 7558 12 San Francisco [65,100] 490
## 7559 11 San Francisco [65,100] 457
## 7560 11 San Francisco [65,100] 398
## 7561 13 San Francisco [65,100] 344
## 7562 11 San Francisco [65,100] 306
## 7563 9 San Francisco [65,100] 273
## 7564 5 San Francisco [65,100] 224
## 7565 4 San Francisco [65,100] 178
## 7566 5 San Francisco [65,100] 155
## 7567 5 San Francisco [65,100] 126
## 7568 5 San Francisco [65,100] 106
## 7569 3 San Francisco [65,100] 85
## 7570 3 San Francisco [65,100] 65
## 7571 2 San Francisco [65,100] 52
## 7572 1 San Francisco [65,100] 45
## 7573 1 San Francisco [65,100] 31
## 7574 0 San Francisco [65,100] 20
## 7575 1 San Francisco [65,100] 48
## 7576 278 San Francisco [0,20) 1010
## 7577 213 San Francisco [0,20) 948
## 7578 187 San Francisco [0,20) 965
## 7579 182 San Francisco [0,20) 1085
## 7580 159 San Francisco [0,20) 1111
## 7581 133 San Francisco [0,20) 1176
## 7582 134 San Francisco [0,20) 1185
## 7583 138 San Francisco [0,20) 1163
## 7584 132 San Francisco [0,20) 1206
## 7585 126 San Francisco [0,20) 1240
## 7586 129 San Francisco [0,20) 1302
## 7587 133 San Francisco [0,20) 1317
## 7588 121 San Francisco [0,20) 1300
## 7589 109 San Francisco [0,20) 1274
## 7590 119 San Francisco [0,20) 1303
## 7591 123 San Francisco [0,20) 1337
## 7592 108 San Francisco [0,20) 1388
## 7593 105 San Francisco [0,20) 1402
## 7594 110 San Francisco [0,20) 1389
## 7595 112 San Francisco [0,20) 1440
## 7596 105 San Francisco [20,45) 1572
## 7597 103 San Francisco [20,45) 1550
## 7598 131 San Francisco [20,45) 1678
## 7599 145 San Francisco [20,45) 1870
## 7600 162 San Francisco [20,45) 2055
## 7601 171 San Francisco [20,45) 2194
## 7602 172 San Francisco [20,45) 2218
## 7603 186 San Francisco [20,45) 2276
## 7604 196 San Francisco [20,45) 2283
## 7605 198 San Francisco [20,45) 2242
## 7606 213 San Francisco [20,45) 2332
## 7607 199 San Francisco [20,45) 2131
## 7608 170 San Francisco [20,45) 1910
## 7609 154 San Francisco [20,45) 1794
## 7610 144 San Francisco [20,45) 1840
## 7611 160 San Francisco [20,45) 1977
## 7612 165 San Francisco [20,45) 1912
## 7613 141 San Francisco [20,45) 1970
## 7614 126 San Francisco [20,45) 1851
## 7615 132 San Francisco [20,45) 1798
## 7616 140 San Francisco [20,45) 1891
## 7617 125 San Francisco [20,45) 1766
## 7618 119 San Francisco [20,45) 1757
## 7619 111 San Francisco [20,45) 1758
## 7620 98 San Francisco [20,45) 1754
## 7621 106 San Francisco [45,65) 1833
## 7622 101 San Francisco [45,65) 1714
## 7623 96 San Francisco [45,65) 1662
## 7624 80 San Francisco [45,65) 1564
## 7625 84 San Francisco [45,65) 1654
## 7626 100 San Francisco [45,65) 1938
## 7627 78 San Francisco [45,65) 1825
## 7628 62 San Francisco [45,65) 1583
## 7629 65 San Francisco [45,65) 1341
## 7630 57 San Francisco [45,65) 1128
## 7631 51 San Francisco [45,65) 1115
## 7632 43 San Francisco [45,65) 1017
## 7633 42 San Francisco [45,65) 952
## 7634 43 San Francisco [45,65) 981
## 7635 38 San Francisco [45,65) 967
## 7636 40 San Francisco [45,65) 1056
## 7637 32 San Francisco [45,65) 1029
## 7638 33 San Francisco [45,65) 1001
## 7639 35 San Francisco [45,65) 1006
## 7640 34 San Francisco [45,65) 1038
## 7641 29 San Francisco [65,100] 1114
## 7642 19 San Francisco [65,100] 1149
## 7643 23 San Francisco [65,100] 1087
## 7644 20 San Francisco [65,100] 979
## 7645 22 San Francisco [65,100] 1012
## 7646 30 San Francisco [65,100] 1046
## 7647 24 San Francisco [65,100] 965
## 7648 28 San Francisco [65,100] 922
## 7649 29 San Francisco [65,100] 906
## 7650 23 San Francisco [65,100] 882
## 7651 20 San Francisco [65,100] 918
## 7652 20 San Francisco [65,100] 815
## 7653 17 San Francisco [65,100] 826
## 7654 18 San Francisco [65,100] 739
## 7655 16 San Francisco [65,100] 582
## 7656 15 San Francisco [65,100] 539
## 7657 15 San Francisco [65,100] 420
## 7658 12 San Francisco [65,100] 338
## 7659 10 San Francisco [65,100] 308
## 7660 9 San Francisco [65,100] 296
## 7661 9 San Francisco [65,100] 249
## 7662 7 San Francisco [65,100] 205
## 7663 9 San Francisco [65,100] 169
## 7664 5 San Francisco [65,100] 149
## 7665 4 San Francisco [65,100] 141
## 7666 3 San Francisco [65,100] 110
## 7667 2 San Francisco [65,100] 87
## 7668 2 San Francisco [65,100] 70
## 7669 3 San Francisco [65,100] 45
## 7670 3 San Francisco [65,100] 41
## 7671 2 San Francisco [65,100] 23
## 7672 1 San Francisco [65,100] 11
## 7673 0 San Francisco [65,100] 13
## 7674 0 San Francisco [65,100] 9
## 7675 0 San Francisco [65,100] 8
## 7676 1 San Francisco [65,100] 17#7. Creates some lables.
agelabs <- names(table(bapop$Agecat))
sexlabs <- c("Female","Male")
racen <- c("White","AfrAmer","AsianPI","Latino","Multirace","AmerInd")
ctylabs <- names(table(bapop$CtyName))
#8. Sum up data for Alameda and SF.
bapop2 <- aggregate(bapop[,racen],list(Agecat = bapop$Agecat, Sex = bapop$Sex, County = bapop$CtyName),sum)
bapop2## Agecat Sex County White AfrAmer AsianPI Latino Multirace
## 1 [0,20) F Alameda 58160 31765 40653 49738 10120
## 2 [20,45) F Alameda 112326 44437 72923 58553 7658
## 3 [45,65) F Alameda 82205 24948 33236 18534 2922
## 4 [65,100] F Alameda 49762 12834 16004 7548 1014
## 5 [0,20) M Alameda 61446 32277 42922 53097 10102
## 6 [20,45) M Alameda 115745 36976 69053 69233 6795
## 7 [45,65) M Alameda 81332 20737 29841 17402 2506
## 8 [65,100] M Alameda 33994 8087 11855 5416 711
## 9 [0,20) F San Francisco 14355 6986 23265 13251 2940
## 10 [20,45) F San Francisco 85766 10284 52479 23458 3656
## 11 [45,65) F San Francisco 35617 6890 31478 9184 1144
## 12 [65,100] F San Francisco 27215 5172 23044 5773 554
## 13 [0,20) M San Francisco 14881 6959 24541 14480 2851
## 14 [20,45) M San Francisco 105798 11111 48379 31605 3766
## 15 [45,65) M San Francisco 43694 7352 26404 8674 1220
## 16 [65,100] M San Francisco 20072 3329 17190 3428 450
## AmerInd
## 1 839
## 2 1401
## 3 822
## 4 246
## 5 828
## 6 1263
## 7 687
## 8 156
## 9 173
## 10 526
## 11 282
## 12 121
## 13 165
## 14 782
## 15 354
## 16 76#9. Temp matrix of counts.
tmp <- as.matrix(cbind(bapop2[1:4,racen],bapop2[5:8,racen], bapop2[9:12,racen],bapop2[13:16,racen]));tmp## White AfrAmer AsianPI Latino Multirace AmerInd White AfrAmer AsianPI
## 1 58160 31765 40653 49738 10120 839 61446 32277 42922
## 2 112326 44437 72923 58553 7658 1401 115745 36976 69053
## 3 82205 24948 33236 18534 2922 822 81332 20737 29841
## 4 49762 12834 16004 7548 1014 246 33994 8087 11855
## Latino Multirace AmerInd White AfrAmer AsianPI Latino Multirace AmerInd
## 1 53097 10102 828 14355 6986 23265 13251 2940 173
## 2 69233 6795 1263 85766 10284 52479 23458 3656 526
## 3 17402 2506 687 35617 6890 31478 9184 1144 282
## 4 5416 711 156 27215 5172 23044 5773 554 121
## White AfrAmer AsianPI Latino Multirace AmerInd
## 1 14881 6959 24541 14480 2851 165
## 2 105798 11111 48379 31605 3766 782
## 3 43694 7352 26404 8674 1220 354
## 4 20072 3329 17190 3428 450 76#10. Give labels.
bapop3 <- array(tmp, c(4,6,2,2))
dimnames(bapop3) <- list(agelabs, racen, sexlabs, ctylabs)
bapop3## , , Female, Alameda
##
## White AfrAmer AsianPI Latino Multirace AmerInd
## [0,20) 58160 31765 40653 49738 10120 839
## [20,45) 112326 44437 72923 58553 7658 1401
## [45,65) 82205 24948 33236 18534 2922 822
## [65,100] 49762 12834 16004 7548 1014 246
##
## , , Male, Alameda
##
## White AfrAmer AsianPI Latino Multirace AmerInd
## [0,20) 61446 32277 42922 53097 10102 828
## [20,45) 115745 36976 69053 69233 6795 1263
## [45,65) 81332 20737 29841 17402 2506 687
## [65,100] 33994 8087 11855 5416 711 156
##
## , , Female, San Francisco
##
## White AfrAmer AsianPI Latino Multirace AmerInd
## [0,20) 14355 6986 23265 13251 2940 173
## [20,45) 85766 10284 52479 23458 3656 526
## [45,65) 35617 6890 31478 9184 1144 282
## [65,100] 27215 5172 23044 5773 554 121
##
## , , Male, San Francisco
##
## White AfrAmer AsianPI Latino Multirace AmerInd
## [0,20) 14881 6959 24541 14480 2851 165
## [20,45) 105798 11111 48379 31605 3766 782
## [45,65) 43694 7352 26404 8674 1220 354
## [65,100] 20072 3329 17190 3428 450 76