Demo entry 6660292

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Submitted by anonymous on Nov 14, 2017 at 22:29
Language: S. Code size: 1.9 kB.

#Q1(a)
lung<-read.csv("C:/Users/zs2a17/Desktop/lung.csv",header=T)#read data
#calculate the lower quartile, median and upper quartile for time,age,meal.cal,wt.loss
summary(lung$time)
summary(lung$age)
summary(lung$meal.cal)
summary(lung$wt.loss)
#summarry the distribution of other vairables
table(lung$inst)
table(lung$status)
table(lung$sex)
table(lung$ph.ecog)
table(lung$ph.karno)
table(lung$pat.karno)
#Q1(b)
#calculate the mean and standard deviation of survival time for those patients who died
mean(lung[which(lung$status==2),]$time)
sd(lung[which(lung$status==2),]$time)
#Q1(c)
#the total includes sample individuals with missing data
library(dplyr)
#greater than
p1<-filter(lung,lung$ph.karno>lung$pat.karno)
round(length(p1$ph.karno)/length(lung$ph.karno),2)#Keep two decimal places
library(dplyr)
#equal to
p2<-filter(lung,lung$ph.karno==lung$pat.karno)
round(length(p2$ph.karno)/length(lung$ph.karno),2)#Keep two decimal places
library(dplyr)
#better than
p3<-filter(lung,lung$ph.karno<lung$pat.karno)
round(length(p3$ph.karno)/length(lung$ph.karno),2)#Keep two decimal places
#Q1(d)
#create newage to devide age to 3 categories
lung$newage<-cut(lung$age,3,labels=F)
#remove empty value
p4<-lung[complete.cases(lung[,10]),]
#calculate the mean weight loss for each category
aggregate(p4$wt.loss,by=list(newage=p4$newage),FUN=mean)
#Q1(e)
#remove empty value
p5<-lung[complete.cases(lung[,9]),]
#calculate range of mean calories consumed across institutions
p6<-aggregate(p5$meal.cal,by=list(inst=p5$inst),FUN=mean)
max(p6$x)-min(p6$x)
#Q1(f)
#take two variables as x/y axis to draw the distribution
plot(lung$meal.cal,lung$time)
#try a linear fit
install.packages("ggplot2")
library(ggplot2)
ggplot(lung)+
  geom_point(aes(x=lung$meal.cal,y=lung$time))+
  theme_bw()+
  geom_smooth(aes(x=lung$meal.cal,y=lung$time,method="lm")

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