dieudo/607project2

project2ofCUNYMSDA

607PROJECT2

This Data is from the Minnesota Breast Cancer Family Study. This contains extended

#pedigrees from 426 families, each identified by a single proband in 1945-52, with #follow up for incident breast cancer. #there are men and women in the data even the title infer only women(breast)

for men instead of breast cancer ,there is prostate cancer

#you can find this data in the package (kinship2) #I will try in the following codes belows to get useful informations about this data

by applying what i have learn so far in this course "607"

install.packages("kinship2") require(kingship2) data(minnbreast) View(minnbreast) install.packages("dplyr") library(dplyr) library(plyr) sdata<-tbl_df(minnbreast) #this allow me to see dataframe locally sdata #selecting 2 columns Educated <-select(minnbreast,education,yob) sEducated<-tbl_df(Educated) sEducated #adding a column for death year# #from a 15 variables(columns ,I get 16 variables) newminnbreast<-mutate(minnbreast,yod=(yob+endage)) #getting the population of people with age less than 50 AgeLess50<-subset(minnbreast,endage<50,na.rm=True) #i got 2915 obs,this just the population of the study less than 50 sAgeless50<-tbl_df(AgeLess50) sAgeless50 #the number of people less than 50 age dead with cancer is : ## cancer==1 : DeadCancerLess50<-subset(minnbreast,endage<50 & cancer==1,na.rm=True) #number of people dead with cancer with age less than 50 #i got 372 obs this represent men and women,men(prostate cancer,women breast)

to find the number of female actually dead with cancer at age less than 50 ,we do:

FemaleCancerless50<-subset(minnbreast,endage<50 & cancer==1 & sex=="F")

I found 361 obs

is there a relationship between education and life,we compute and add a new col to the data

EdperLife<-transform(minnbreast,Educatio_age=education/endage) head(EdperLife) EdperLifeValue<-select(EdperLife,Educatio_age) EdperLifeValue #some may argue that the number of years in school is related to life expectency plot(EdperLifeValue,main="Ratio of years of education to years lived ") genderGroup<-group_by(minnbreast,sex) #average age of female dying with cancer AvFemaleCancer<-subset(minnbreast,sex=="F" & cancer==1,na.rm=True) FemaleAgewithCancer<-mean(AvFemaleCancer$endage) #I found the average age of female with cancer at death to be 58.3674 #average age of male dying with cancer is below AvMaleCancer<-subset(minnbreast,sex=="M"& cancer==1,na.rm=True) AvMaleCancer summary(AvMaleCancer) #I get 69.33 average age of male at death with cancer.

#the average education level of female dying with cancer is 3.499 year based on this summary summary(AvFemaleCancer)

the average number of breast affected on a female dying with cancer is 1

Female<-select(minnbreast,sex=="F") plot(Female$endage~Female$education) plot(Female$endage,Female$education,main="females with education",xlab="Female age",ylab="education",pch=15) #from this picture I can see that there is no relationship between education and females Male<-select(minnbreast,sex=="M")%>% filter(!is.na(education)) #this tell us the number of male which education has a value,I found 6195 cases summary(Male)

for those male that we know the level a education they have ,there average age is

#63.43,there education average 3.683,and there is 6.5 cases of cancer between them

#set.seed(5689) #qplot(Male$endage, binwidth=.5) this should work fine but #on my machine ploting with ggplot2 seems not to work #ggplot(Female, aes(x=endage)) + geom_histogram(binwidth=.5, colour="black", fill="white")

i got this error :cannot open file 'C:/Users/tt/Documents/R/win-library/3.1/proto/R/proto.rdb':

#In GeomHistogram$new : restarting interrupted promise evaluation hist(Male$endage,freq=FALSE,main="Men ages") hist(AvFemaleCancer$education,freq=FALSE,main="education of female with cancer") #This project allowed me to understand how data can be analysed using R ,

from basic R codes to packages codes many tools can be used to have

an understanding and a visualization of the data .

#Through what I have learned in this course I am able to manipulate

data and give conclusions by using various tools