R-CRAN

Guide to getting started with R-CRAN software using web-based resources, tutorials and books. If you are looking for software for statistics computing you are welcome to try this guide. If you still have doubts whether you should make an effort and spend your time learning R check Rationale for using R.

R-CRAN biomedical minicourse
Comprehensive R Archive Network reference cards
Tutorials
R Graphical User Interfaces (GUIs)
Courses
Books

R-CRAN biomedical minicourse

Navigation in R
- getwd()
- setwd("c:/")
- use arrows to select previous commands
- list.files()
Importing and managing data in the R-CRAN environment
- read.csv()
- how to prepare csv data?
Datatypes
Summary statistics
Graphical representation of data (part 1)
Graphical representation of data (part 2)
Simple statistical tests
General linear model
Saving and running the code in R-CRAN

R reference cards

short

Jonathan Baron refcard

Ricci regression refcard

Tom Short R Reference card

Tutorials

Revolution analytics R resources

Learning R basic

K. Broman@ Wisconsin R intro

R cheatsheet

Kickstarting R by Jim Lemon

Statistics with R by Vincent Zoonekynd

Fitting distributions with R - Ricci

Geographic data processing in R

R Tips

R tutorial 2004

R tutorial - MAT 356 R Tutorial, Spring 2004

http://linuxlearningsurveyresults.pbworks.com/w/page/34317936/FrontPage

Medical imaging in R

R CRAN medical imaging hub

Perfusion analysis package - dcemri

R GUIs

R Commander

R commander an Introduction by Natasha Karp

Rattle

JGR rforge

Java Gui for R – cross-platform stand-alone R terminal and editor based on Java (also known as JGR)
Deducer - GUI for menu driven data analysis (similar to SPSS/JMP/Minitab). It has a to do common data manipulation and analysis tasks, and an excel- designed to be used with JGR but also RGUI.
Rattle GUI – cross-platform GUI based on RGtk2 and specifically designed for data mining. Video tutorials
R Commander – cross-platform menu-driven GUI based on tcltk (several plug-ins to Rcmdr are also available)
RExcel – using R and Rcmdr from within Microsoft Excel
Sage – web browser interface as well as rpy support
Sim.DiffProcGUI – Graphical User Interface for Simulation of Diffusion Processes based on tcltk
Cantor (software) – KDE worksheet interface to several mathematical applications, including R
Red-R – visual analysis interface that uses R for statistics
Tinn-R – GUI for R Language and Environment
RKWard – extensible GUI and IDE for R
R AnalyticFlow - analysis flowcharts with R (freeware)
RStudio- cross platform open source IDE able to be run on a remote linux server *recommended*
RapidMiner and the RapidMiner R extension - extensible open source GUI and IDE for R, Weka, and RapidMiner data mining processes and their seamless integration

Recommended R books (since listing all of them is almost impossible)

Introductory Statistics with R - Dalgaard

Data mining with R - Luis Torgo book companion web site

Handbook of Statistical Analyses Using R

Quick-R - R in Action by Quick-R author

R books official listing

R manuals

R Contributed documentation - you will definitely find something interesting here

Using R for Data Analysis and Graphics - 3rd edition; files; ch1exercise; DM apps

List of books for learning R

P. Biecek - Przewodnik po pakiecie R

Courses

David Mease video lectures - Statistics 202: Statistical Aspects of Data Mining

UIC Statistics course using R

The R Journal - used to be R News

Contributed documents

Primer of Biostatistics - Glantz

Chapter 2 - How to summarize data

Key concepts

Mean

Measures of variability: variance and standard deviation

The normal distribution

Percentiles

Random sampling

Bias

Experimental and observational studies

Randomized controlled trials

Central limit theorem

Problems

example 2-1

Data derived from

Quinn TC, Wawer MJ, Sewankambo N, Serwadda D, Li C, Wabwire-Mangen F, Meehan MO, Lutalo T, Gray RH. Viral load and heterosexual transmission of humanimmunodeficiency virus type 1. Rakai Project Study Group. N Engl J Med. 2000 Mar 30;342(13):921-9. PubMed PMID: 10738050.

HIV1RNAload <- c(79725,12862,18022,76712256440,14013,46083,6808,

+ 85781,1251,6081,50397,11020,13633,1064,496433,25308,6616,11210,13900)

Provide mean, median, Q1, Q3 for this set using R-CRAN functions.

example 2-2

Perform logaritmic transformation of the data in 2-1 and evaluate the distribution.

example 2-3

#######################################################

Selected methods and sample plots and solutions in R CRAN

###################

ANOVA

Two-way anova

Repeated measures anova

R statistical test implementation

Chi^2 and Fisher exact test

Fisher exact test

###############################

# Practical linear regression training using 2012 London Olympics data

100m Butterfly swimming

Forecasting the Olympics

###################################

# install new packages in R CRAN on linux

install.packages("outliers", dependencies = TRUE)

#################################

Normality testing

Normality testing in R

Lilliefors test wiki

Detection and treatment of outliers

Engineering statistic textbook

Grubbs test for outliers

Grubbs implementation in graphpad

R CRAN outliers package Grubbs implementation

#####################################

Survival analysis

KM plots 1 - antrophological demography course

Superpc - survival plot

Enhanced Kaplan-Meier plot original

#Consultant plot

# Variable labels

R CRAN plotting

Cyclismo R plotting

###############################################

########### R Graphics training ##################

###############################################

####################

#R Graphics exercise 1

####################

# Creating vertical stripchart with

#R Code

#load the dataset from Oasis Brains project website - data is strored in csv file

oasis1 <- read.csv("http://www.oasis-brains.org/pdf/oasis_cross-sectional.csv", header=T, sep=",", dec=".")

#check whether the file loaded properly and check the names of the variables

names(oasis1)

# attach the dataset to be able to access the variables just using their names

attach(oasis1)

#in this exercise we are interested in one continuous variable (Age) and one factor - gender (M.F)

summary(M.F)

summary(Age)

#the aim is to produce publication ready quality plot of stripchart with jitter using those two variables

#we want file format png, however some journals want tiff files - check the tiff function in R using ?tiff

png(filename = "Oasis_age_gender_stripchart.png", width = 960, height = 960, units = "px", pointsize = 16, bg = "white", res = NA)

stripchart(Age ~ M.F, method = "jitter", vertical=TRUE, jitter = .1, pch=20, main="Distribution of age as a function of gender")

dev.off()

#0. Check the newly created file using image viewer. Check the directory where the image was created using getwd().

#1. Check the distribution of other variables like MMSE and eTIV (total intracranial volume) and create a new plot.

#2. Create a similar plot in different image file format like jpeg and tiff. Why are they different?

#3. Change the font size and jitter value and evaluate the results.

#4. Check stripchart function description

# the end of R Graphics exercise 1

####################

#R Graphics exercise 2

####################

# Creating a scatterplot with overlapping datasets and transparent colors

jpeg(filename = "overlapping_transparent_plots.jpeg", width = 960, height = 960, units = "px", pointsize = 17, quality = 100, bg = "white")

# create two variables x and y

x <- rnorm(100)+2

y <- rnorm(100)+1

#define color and transparency for each set of points

plotcolor <- rgb(red=255, green=100, blue=0, alpha=150, max=255)

plotcolor2 <- rgb(red=100, green=255, blue=100, alpha=200, max=255)

# plot the points(x,y) using created colors

plot(x,y, pch=21, lwd=3, col=plotcolor)

# plot the points(y,x) using created colors

points(y,x, pch=24, lwd=2, col=plotcolor2)

#close the device

dev.off()

#end of plot

#0. Check the visibility of various graphical elements in plots

#Controling points visibility in plots

pch=19: solid circle,

pch=20: bullet (smaller circle),

pch=21: circle,

pch=22: square,

pch=23: diamond,

pch=24: triangle point-up,

pch=25: triangle point down.

#1. Examine changing the transparency settings and color selection

R>col2rgb('blue', alpha=T)

[,1]

red 0

green 0

blue 255

alpha 255

R> rgb(red=0, green=0, blue=255, alpha=255, max=255)

[1] "#0000FFFF"

Play with different values of alpha (0 < alpha <= 255) in the above call to get different levels of opacity for your points.

R> rgb(red=0, green=0, blue=255, alpha=10, max=255)

[1] "#0000FF0A"

#end of exercise 2

####################

#R Graphics exercise 3

####################

Create scatterplot with third dimension defined as size of the points and control for the transparency of oveerlapping points

#define variables

x <- rnorm(10,5,3)

y <- rnorm(10,7,3)

#define variable representing pointsize

z <- seq(1:10)

#define color and transparency

plotcolor <- rgb(red=000, green=255, blue=000, alpha=100, max=255)

#plot

plot(x,y, pch=20, cex=z, col=plotcolor)

#define a vector of colors to set variable colors to each point size (in this example every third point)

colors <- c(rgb(red=100, green=100, blue=250, alpha=150, max=255), rgb(red=000, green=255, blue=000, alpha=50, max=255), rgb(red=000, green=000, blue=000, alpha=100, max=255))

plot(x,y, pch=20, cex=z, col=colors)

#tasks

# use sort() function to create plots with incremental point size

# use pch parameter to define representation of points

# use more advanced color and fill representation

# how to create dynamic plot in the google data style with time as fourth variable?

#end of exercise 3

External R Graphics resources

Creating plots in R

Histograms and density plots

Boxplots

Dotplots