heights=c(177,174,173,164,165,154,170,157,180,206,171,169,180,168,170,160,160)
gender=c("M","M","M",'F','M',"F","M",'F','M',"M",'F','F','M','F',"M",'F','F')
range(heights)
## [1] 154 206
class2=NA
class2$ht=heights
## Warning in class2$ht = heights: Coercing LHS to a list
class2$gen=gender
library(Hmisc)
## Loading required package: lattice
## Loading required package: survival
## Loading required package: Formula
## Loading required package: ggplot2
##
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:base':
##
## format.pval, round.POSIXt, trunc.POSIXt, units
summarize(class2$ht,class2$gen,mean)
## class2$gen class2$ht
## 1 F 162.8750
## 2 M 177.2222
mean(heights)
## [1] 170.4706
median(heights)
## [1] 170
#?mean
#?median
smht=c(177,168,189,156,189)
mean(smht)
## [1] 175.8
sort(smht)
## [1] 156 168 177 189 189
median(smht)
## [1] 177
mean(heights)
## [1] 170.4706
median(heights)
## [1] 170
hist(heights)
plot(density(heights))
range(heights)
## [1] 154 206
quantile(heights)
## 0% 25% 50% 75% 100%
## 154 164 170 174 206
quantile(heights, prob = seq(0, 1, length = 11), type = 5)
## 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
## 154.0 157.6 160.0 164.6 168.3 170.0 170.7 173.4 177.3 180.0 206.0
data(mtcars)
mtcars
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
summarize(mtcars$mpg,mtcars$cyl,mean)
## mtcars$cyl mtcars$mpg
## 1 4 26.66364
## 2 6 19.74286
## 3 8 15.10000
table(mtcars$cyl,mtcars$gear)
##
## 3 4 5
## 4 1 8 2
## 6 2 4 1
## 8 12 0 2