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