Now in the second portion of the project, we’re going to analyze the ToothGrowth data in the R datasets package.
1.Load the ToothGrowth data and perform some basic exploratory data analysis
library(datasets)
data(ToothGrowth)
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.6.3
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 ...
head(ToothGrowth)
summary(ToothGrowth)
## len supp dose
## Min. : 4.20 OJ:30 Min. :0.500
## 1st Qu.:13.07 VC:30 1st Qu.:0.500
## Median :19.25 Median :1.000
## Mean :18.81 Mean :1.167
## 3rd Qu.:25.27 3rd Qu.:2.000
## Max. :33.90 Max. :2.000
ggplot(data=ToothGrowth, aes(x=as.factor(dose), y=len, fill=supp)) +
geom_bar(stat="identity") +
facet_grid(. ~ supp) +
xlab("Dose(mg)") +
ylab("Tooth length")
hypothesis tests to compare tooth growth by supp and dose. (Only use the techniques from class, even if there’s other approaches worth considering)
hypoth1 <- t.test(len ~ supp, data = ToothGrowth)
hypoth1$conf.int
## [1] -0.1710156 7.5710156
## attr(,"conf.level")
## [1] 0.95
hypoth1$p.value
## [1] 0.06063451
hypoth2<-t.test(len ~ supp, data = subset(ToothGrowth, dose == 0.5))
hypoth2$conf.int
## [1] 1.719057 8.780943
## attr(,"conf.level")
## [1] 0.95
hypoth2$p.value
## [1] 0.006358607
hypoth3<-t.test(len ~ supp, data = subset(ToothGrowth, dose == 1))
hypoth3$conf.int
## [1] 2.802148 9.057852
## attr(,"conf.level")
## [1] 0.95
hypoth3$p.value
## [1] 0.001038376
hypoth4<-t.test(len ~ supp, data = subset(ToothGrowth, dose == 2))
hypoth4$conf.int
## [1] -3.79807 3.63807
## attr(,"conf.level")
## [1] 0.95
hypoth4$p.value
## [1] 0.9638516
OJ ensures more tooth growth than VC for dosages 0.5 & 1.0. OJ and VC givesthe same amount of tooth growth for dose amount 2.0 mg/day. For the entire trail we cannot conclude OJ is more effective that VC for all scenarios.