exercise1
## Loading required package: tools
## 'data.frame': 434 obs. of 4 variables:
## $ ID : Factor w/ 217 levels "1","2","3","4",..: 1 1 2 2 3 3 4 4 5 5 ...
## $ status : Factor w/ 2 levels "case","control": 1 2 1 2 1 2 1 2 1 2 ...
## $ driver : Factor w/ 2 levels "no","yes": 2 2 2 2 2 2 1 1 2 2 ...
## $ suburban: Factor w/ 2 levels "no","yes": 2 1 2 2 1 2 1 1 1 2 ...
## ID status driver suburban
## 1 1 case yes yes
## 2 1 control yes no
## 3 2 case yes yes
## 4 2 control yes yes
## 5 3 case yes no
## 6 3 control yes yes
## [1] 434 4
## ID status driver suburban
## 1 : 2 case :217 no : 86 no :200
## 2 : 2 control:217 yes:348 yes:234
## 3 : 2
## 4 : 2
## 5 : 2
## 6 : 2
## (Other):422
## driver suburban status frequency
## 1 no no case 26
## 2 yes no case 64
## 3 no yes case 6
## 4 yes yes case 121
## 5 no no control 47
## 6 yes no control 63
## 7 no yes control 7
## 8 yes yes control 100
drivers <- c("no", "no", "yes", "yes")
suburbans <- c("no", "yes", "no", "yes")
cases <- c(26, 6, 64, 121)
controls <- c(47, 7, 63, 100)
totals <- c(73, 13, 127, 221)
Summarize <- data.frame(driver = drivers, suburban =suburbans , case = cases, control = controls, total = totals)
exercise2
## 'data.frame': 50 obs. of 12 variables:
## $ Population: num 3615 365 2212 2110 21198 ...
## $ Income : num 3624 6315 4530 3378 5114 ...
## $ Illiteracy: num 2.1 1.5 1.8 1.9 1.1 0.7 1.1 0.9 1.3 2 ...
## $ Life Exp : num 69 69.3 70.5 70.7 71.7 ...
## $ Murder : num 15.1 11.3 7.8 10.1 10.3 6.8 3.1 6.2 10.7 13.9 ...
## $ HS Grad : num 41.3 66.7 58.1 39.9 62.6 63.9 56 54.6 52.6 40.6 ...
## $ Frost : num 20 152 15 65 20 166 139 103 11 60 ...
## $ Area : num 50708 566432 113417 51945 156361 ...
## $ Murder : num 13.2 10 8.1 8.8 9 7.9 3.3 5.9 15.4 17.4 ...
## $ Assault : int 236 263 294 190 276 204 110 238 335 211 ...
## $ UrbanPop : int 58 48 80 50 91 78 77 72 80 60 ...
## $ Rape : num 21.2 44.5 31 19.5 40.6 38.7 11.1 15.8 31.9 25.8 ...
## Population Income Illiteracy Life Exp
## Min. : 365 Min. :3098 Min. :0.500 Min. :67.96
## 1st Qu.: 1080 1st Qu.:3993 1st Qu.:0.625 1st Qu.:70.12
## Median : 2838 Median :4519 Median :0.950 Median :70.67
## Mean : 4246 Mean :4436 Mean :1.170 Mean :70.88
## 3rd Qu.: 4968 3rd Qu.:4814 3rd Qu.:1.575 3rd Qu.:71.89
## Max. :21198 Max. :6315 Max. :2.800 Max. :73.60
## Murder HS Grad Frost Area
## Min. : 1.400 Min. :37.80 Min. : 0.00 Min. : 1049
## 1st Qu.: 4.350 1st Qu.:48.05 1st Qu.: 66.25 1st Qu.: 36985
## Median : 6.850 Median :53.25 Median :114.50 Median : 54277
## Mean : 7.378 Mean :53.11 Mean :104.46 Mean : 70736
## 3rd Qu.:10.675 3rd Qu.:59.15 3rd Qu.:139.75 3rd Qu.: 81162
## Max. :15.100 Max. :67.30 Max. :188.00 Max. :566432
## Murder Assault UrbanPop Rape
## Min. : 0.800 Min. : 45.0 Min. :32.00 Min. : 7.30
## 1st Qu.: 4.075 1st Qu.:109.0 1st Qu.:54.50 1st Qu.:15.07
## Median : 7.250 Median :159.0 Median :66.00 Median :20.10
## Mean : 7.788 Mean :170.8 Mean :65.54 Mean :21.23
## 3rd Qu.:11.250 3rd Qu.:249.0 3rd Qu.:77.75 3rd Qu.:26.18
## Max. :17.400 Max. :337.0 Max. :91.00 Max. :46.00
## Population Income Illiteracy Life Exp Murder
## Population 1.00000000 0.20822756 0.10762237 -0.06805195 0.34364275
## Income 0.20822756 1.00000000 -0.43707519 0.34025534 -0.23007761
## Illiteracy 0.10762237 -0.43707519 1.00000000 -0.58847793 0.70297520
## Life Exp -0.06805195 0.34025534 -0.58847793 1.00000000 -0.78084575
## Murder 0.34364275 -0.23007761 0.70297520 -0.78084575 1.00000000
## HS Grad -0.09848975 0.61993232 -0.65718861 0.58221620 -0.48797102
## Frost -0.33215245 0.22628218 -0.67194697 0.26206801 -0.53888344
## Area 0.02254384 0.36331544 0.07726113 -0.10733194 0.22839021
## Murder 0.32024487 -0.21520501 0.70677564 -0.77849850 0.93369089
## Assault 0.31702281 0.04093255 0.51101299 -0.62665800 0.73976479
## UrbanPop 0.51260491 0.48053302 -0.06219936 0.27146824 0.01638255
## Rape 0.30523361 0.35738678 0.15459686 -0.26956828 0.57996132
## HS Grad Frost Area Murder Assault
## Population -0.09848975 -0.3321525 0.02254384 0.32024487 0.31702281
## Income 0.61993232 0.2262822 0.36331544 -0.21520501 0.04093255
## Illiteracy -0.65718861 -0.6719470 0.07726113 0.70677564 0.51101299
## Life Exp 0.58221620 0.2620680 -0.10733194 -0.77849850 -0.62665800
## Murder -0.48797102 -0.5388834 0.22839021 0.93369089 0.73976479
## HS Grad 1.00000000 0.3667797 0.33354187 -0.52159126 -0.23030510
## Frost 0.36677970 1.0000000 0.05922910 -0.54139702 -0.46823989
## Area 0.33354187 0.0592291 1.00000000 0.14808597 0.23120879
## Murder -0.52159126 -0.5413970 0.14808597 1.00000000 0.80187331
## Assault -0.23030510 -0.4682399 0.23120879 0.80187331 1.00000000
## UrbanPop 0.35868123 -0.2461862 -0.06154747 0.06957262 0.25887170
## Rape 0.27072504 -0.2792054 0.52495510 0.56357883 0.66524123
## UrbanPop Rape
## Population 0.51260491 0.3052336
## Income 0.48053302 0.3573868
## Illiteracy -0.06219936 0.1545969
## Life Exp 0.27146824 -0.2695683
## Murder 0.01638255 0.5799613
## HS Grad 0.35868123 0.2707250
## Frost -0.24618618 -0.2792054
## Area -0.06154747 0.5249551
## Murder 0.06957262 0.5635788
## Assault 0.25887170 0.6652412
## UrbanPop 1.00000000 0.4113412
## Rape 0.41134124 1.0000000
## corrplot 0.84 loaded
