In wammonj/genetics: Explore Big Data in Genetics

knitr::opts_chunk$set(echo = TRUE)

Checking the clinic data to make sure it makes sense

This document is only trying to look at the clinic data and check the data for inconsistencies or effects that we don't want

Clinic Data

This clinic data has several fields which are explained by the R package "genetics"

library(genetics)
?clinicData

Here is a summary of the data so we can get to know the ranges and values of the data.

library(genetics)
clinicData = data.frame(clinicData)
numericsInds = c(1,2,6)
factorInds = c(3,4,5)
for (i in 1:3){
  clinicData[,numericsInds[i]] = as.numeric(clinicData[,numericsInds[i]])
   clinicData[,factorInds[i]] = as.factor(clinicData[,factorInds[i]])
}
clinicData[,c(1,2,6)] = as.numeric(clinicData[,c(1,2,6)])
clinicData[,c(3,4,5)] = as.factor(clinicData[,c(3,4,5)])
summary(clinicData)

Lets check the data. Notice that we should have either just days to death or days to last followup. In the case we have both, sometimes they are the same and sometimes they are different. Notice that some had days to death smaller than days to last followup.

It also appears that age doesn't help us know how long they will

pairs( ~ days_to_death + days_to_last_followup + age_at_diagnosis,data = clinicData)

It would be good to see if sex is important in this study.

```r compare = c("days_to_death","days_to_last_followup","pathologic_grade","pathologic_stage","age_at_diagnosis") total = length(compare) par(mfrow = c(1,total)) for (i in compare){ plot(clinicData[,"sex"],clinicData[,i],ylab = i) }

wammonj/genetics documentation built on May 3, 2019, 11:51 p.m.