James Oguta R-Practice-Latest

My current R practicals

Upload the heart dataset-Ensure it is saved in the same repository

Heart <- read.csv ("Heart(3).csv", header=TRUE)

Step 4: Use the 'structure' command to see and overview of the data

str(Heart)

Step 5: Use the 'summary' command to see statistical summary of different data

The summarize command provides a statistical summary of your data. It includes the mean, median, minimum and maximum values and centiles of your data. summary(Heart$Leg) summary(Heart$Heart) ###Step 6: There has been a data entry error for patient ID 2, blood alcohol level from leg.
#See the value: Heart[Heart$PatientID==2,"Leg"] #Correct the value: change it from 3000 to 265: Heart[Heart$PatientID==2,"Leg"] <- 265

#Step 7: Create a new variable to record the difference between blood alcohol levels from leg and from heart. We want to investigate whether we think the blood alcohol levels are similar between levels taken in the leg and heart by looking at the difference in the levels, if levels are similar you would expect the difference to be zero. Heart$diff <- Heart$Leg - Heart$Heart summary(Heart$diff) #Step 8: See the number of males and females in the dataset table(Heart$Gender) #Recode the value to make it more readable Heart$Sex <- ifelse(Heart$Gender == 1, "Male", "Female") table(Heart$Sex) #Step 9: Summarise blood alcohol separately for males and females summary(Heart$Leg[Heart$Sex =="Male"]) summary(Heart$Leg[Heart$Sex =="Female"]) summary(Heart$Heart[Heart$Sex =="Male"]) summary(Heart$Heart[Heart$Sex =="Female"])

Do you notice a difference in levels for males and females?

#Step 10: plot a histogram of blood alcohol levels taken from the leg hist(Heart$Leg) #Do you think the data is normally distributed (symmetrical)? #Step 11: do a log transformation of blood alcohol levels taken from the leg Heart$lleg10 <- log10(Heart$Leg) hist(Heart$lleg10) #Does the log transformed data appear to be more normally distributed?