Data Science for Doctors – Part 1 : Data Display

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Data science enhances people’s decision making. Doctors and researchers are making critical decisions every day, so it is obvious that those people must have a substantial knowledge of data science. This series aims to help people that are around medical field to enhance their data science skills.

We will work with a health related database the famous “Pima Indians Diabetes Database”. It was generously donated by Vincent Sigillito from Johns Hopkins University. Please find further information regarding the dataset here.

This is the first part of the series, it is going to be about data display.

Before proceeding, it might be helpful to look over the help pages for the table, pie, geom_bar , coord_polar, barplot, stripchart, geom_jitter, density, geom_density, hist, geom_histogram, boxplot, geom_boxplot, qqnorm, qqline, geom_point, plot, qqline, geom_point .


Please run the code below in order to load the data set and make it into a proper data frame format:

url <- ""
data <- read.table(url, fileEncoding="UTF-8", sep=",")
names <- c('preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class')
colnames(data) <- names

Answers to the exercises are available here.

If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page.

Exercise 1

Create a frequency table of the class variable.

Exercise 2

class.fac <- factor(data[['class']],levels=c(0,1), labels= c("Negative","Positive"))
Create a pie chart of the class.fac variable.

Exercise 3

Create a bar plot for the age variable.

Exercise 4

Create a strip chart for the mass against class.fac.

Exercise 5

Create a density plot for the preg variable.

Exercise 6

Create a histogram for the preg variable.

Exercise 7

Create a boxplot for the age against class.fac.

Exercise 8

Create a normal QQ plot and a line which passes through the first and third quartiles.

Exercise 9

Create a scatter plot for the variables age against the mass variable .

Exercise 10

Create scatter plots for every variable of the data set against every variable of the data set on a single window.
hint: it is quite simple, don’t overthink about it.

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