home1_2.R

example(ToothGrowth)

## 
## TthGrw> require(graphics)
## 
## TthGrw> coplot(len ~ dose | supp, data = ToothGrowth, panel = panel.smooth,
## TthGrw+        xlab = "ToothGrowth data: length vs dose, given type of supplement")

dta <- ToothGrowth
str(ToothGrowth)

## 'data.frame':    60 obs. of  3 variables:
##  $ len : num  4.2 11.5 7.3 5.8 6.4 10 11.2 11.2 5.2 7 ...
##  $ supp: Factor w/ 2 levels "OJ","VC": 2 2 2 2 2 2 2 2 2 2 ...
##  $ dose: num  0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...

aggregate(len ~ supp, dat=ToothGrowth, FUN=length)

##   supp len
## 1   OJ  30
## 2   VC  30

lattice::bwplot(len ~ supp, data=dta,
                main="boxplot of length at different supplements", xlab="Sup(OJ & VC)", ylab="Len")

t.test(len ~ supp, data=dta)

## 
##  Welch Two Sample t-test
## 
## data:  len by supp
## t = 1.9153, df = 55.309, p-value = 0.06063
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.1710156  7.5710156
## sample estimates:
## mean in group OJ mean in group VC 
##         20.66333         16.96333

multicon::diffPlot(len ~ supp, data=dta, grp.names=c("VC","OJ"),xlab="Treatment", ylab="Len")

dice <- set.seed("1234")
dice <- sample(3:18, size=9999, replace=T,)
head(dice, 100)

##   [1] 14 18 12  8  7 14 17 11  7  8 18  6  4  9  8 12  8 17 16  6 16 16 10 16  6
##  [26]  6  7 10  6 10  5  6 17 17 15 12  7  4 16 17 10 13  6 18 14  5  9 11  5  8
##  [51]  6 10 18 12 13  4  7 17  8  3  8 10  5  8  3 15 18  3 11 10 12  3 17 13 10
##  [76] 12 18 17  8  5 11 18  5  8 11 12  9  8 10 15 11  5  5  4  7 10 12  9  8 14

dice_count <- table(dice)
dice_count

## dice
##   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18 
## 590 639 628 586 658 641 597 645 623 643 630 630 608 612 628 641

hist(dice,freq=T)