Exercise 1: Summarize the backpain data
load data file
## Loading required package: tools
## 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
divide the status variable into two mutate columns and summarize the results
## ─ Attaching packages ────────────────────────── tidyverse 1.3.0 ─
## ✓ ggplot2 3.2.1 ✓ purrr 0.3.3
## ✓ tibble 2.1.3 ✓ dplyr 0.8.4
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.4.0
## ─ Conflicts ─────────────────────────── tidyverse_conflicts() ─
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## driver suburban case control total
## 1 no no 26 47 73
## 2 no yes 6 7 13
## 3 yes no 64 63 127
## 4 yes yes 121 100 221
Exercise 2: City Murder
load data file from state.x77
## Population Income Illiteracy Life Exp Murder HS Grad Frost Area
## Alabama 3615 3624 2.1 69.05 15.1 41.3 20 50708
## Alaska 365 6315 1.5 69.31 11.3 66.7 152 566432
## Arizona 2212 4530 1.8 70.55 7.8 58.1 15 113417
## Arkansas 2110 3378 1.9 70.66 10.1 39.9 65 51945
## California 21198 5114 1.1 71.71 10.3 62.6 20 156361
## Colorado 2541 4884 0.7 72.06 6.8 63.9 166 103766
load data file from USArrests
## Murder Assault UrbanPop Rape
## Alabama 13.2 236 58 21.2
## Alaska 10.0 263 48 44.5
## Arizona 8.1 294 80 31.0
## Arkansas 8.8 190 50 19.5
## California 9.0 276 91 40.6
## Colorado 7.9 204 78 38.7
The both datasets contain the same city data and variable (murder), but the murder values of two data aree different.
merge two data depends on the same city name.
## 'data.frame': 50 obs. of 4 variables:
## $ 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 ...
Compute the pair-wise correlation
## Population Income Illiteracy Life Exp Murder.x
## 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.x 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.y 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.y 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.x -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.y -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.x 0.01638255 0.5799613
## HS Grad 0.35868123 0.2707250
## Frost -0.24618618 -0.2792054
## Area -0.06154747 0.5249551
## Murder.y 0.06957262 0.5635788
## Assault 0.25887170 0.6652412
## UrbanPop 1.00000000 0.4113412
## Rape 0.41134124 1.0000000
Exercise 4: Education and Vocabulary
Load data file
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
##
## recode
## The following object is masked from 'package:purrr':
##
## some
## year sex education vocabulary
## 19740001 1974 Male 14 9
## 19740002 1974 Male 16 9
## 19740003 1974 Female 10 9
## 19740004 1974 Female 10 5
## 19740005 1974 Female 12 8
## 19740006 1974 Male 16 8
Summarize the relationship between education and vocabulary over the years by gender.
## function (x, data, ...)
## UseMethod("xyplot")
## <bytecode: 0x7f84a1b16008>
## <environment: namespace:lattice>
Exercise 5: Body and Brain in Animals
Load data file
##
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
##
## select
## body brain
## Mountain beaver 1.35 8.1
## Cow 465.00 423.0
## Grey wolf 36.33 119.5
## Goat 27.66 115.0
## Guinea pig 1.04 5.5
## Dipliodocus 11700.00 50.0
## body brain
## Arctic fox 3.385 44.5
## Owl monkey 0.480 15.5
## Mountain beaver 1.350 8.1
## Cow 465.000 423.0
## Grey wolf 36.330 119.5
## Goat 27.660 115.0
Metge two datastes and delete duplicate data.
## body brain
## Mountain beaver1 1.350 8.1
## Cow1 465.000 423.0
## Grey wolf1 36.330 119.5
## Goat1 27.660 115.0
## Guinea pig1 1.040 5.5
## Asian elephant1 2547.000 4603.0
## Donkey1 187.100 419.0
## Horse1 521.000 655.0
## Patas monkey 10.000 115.0
## Cat1 3.300 25.6
## Giraffe1 529.000 680.0
## Gorilla1 207.000 406.0
## Human1 62.000 1320.0
## African elephant1 6654.000 5712.0
## Rhesus monkey1 6.800 179.0
## Kangaroo1 35.000 56.0
## Golden hamster1 0.120 1.0
## Mouse1 0.023 0.4
## Rabbit1 2.500 12.1
## Sheep1 55.500 175.0
## Jaguar1 100.000 157.0
## Chimpanzee1 52.160 440.0
## Rat1 0.280 1.9
## Mole rat 0.122 3.0
## Pig1 192.000 180.0
## 'data.frame': 90 obs. of 2 variables:
## $ body : num 1.35 465 36.33 27.66 1.04 ...
## $ brain: num 8.1 423 119.5 115 5.5 ...
Exercise 6: Body and Brain in Animals
Sorry, I found that the link have broken and I can’t download the data file.