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Using R on a Mac

Below are notes covering how to set-up R and its related software on a Windows machine.

· Installing R:

· Installing Tcl-Tk: A tool that lets R draw a point-and-click interface

· Installing RStudio: A program that makes R easier to manage

· Installing R Commander

The PowerPoint 2008 slides showing how to installing R and related stuff to make it easier to work with on a Mac can be found here.

Using R on a Windows Machine

Below are notes covering how to set-up R and its related software on a Windows machine.

·
Installing R*: Don’t just push NEXT*

· Installing R Commander

· Installing RStudio: A program that makes R easier to manage

· (optional) Installing Tinn-R: A code editor for R

· Installing Perl: Helps import Excel files into R

· Installing LaTeX: Allows you to do Sweave (to make pretty reports)

· Showing file extensions

· Configuring Office 2007/2010 to show file paths

The PowerPoint 2007/2010 slides showing how to installing R and related stuff to make it easier to work with on a Windows PC can be found here.

Using R for Statistics and
Graphics: An Introduction for Busy Non-programmers

R is a free software package that calculates statistics and
generates publication quality graphics.
It has a difficult learning curve if you do not have the correct
tools. This class will teach the
essentials of R to people who have little time to learn and especially those
busy people who have no computer programming experience. People who attend will learn how to set up
and use R to generate descriptive statistics, as well as common graphics and
statistical tests like t-tests on means and χ^{2 }tests on
frequency counts. Attendees should be
comfortable working with either Windows or Mac OS X and ideally be familiar
with Microsoft Excel. This is both a
lecture and hands on lab so students should ideally have a laptop.

Students must have completed the instructions listed at the top of this page for installing R on a Windows Machine or a Mac before taking this class.

After completing the class attendees will know how to:

· recognize files that R can easily Understand

· make readable Excel files

· use Excel to get data ready for analysis

· add a point-and-click system to R

· load data into R with a point-and-click system or with code

· do descriptive statistics and graphics with code generated from a point-and-click system

· write an R program

· find additional help

Attendees will also understand:

· the common objects that R uses

· what a R function is

· what function arguments do

The PowerPoint 2007/2010 slides for Using R for Statistics and Graphics: An Intro for Non-programmers can be found here.

Data on Marriage can be found here in Excel format.

Data on Marriage can be found here in a not useful CSV format.

Data on Marriage can be found here in a good CSV format.

Data for Saint Croix USVI weather can be found here.

Using R for Graphics
Programming: An Introduction for Busy Non-programmers

R is a free software package that can generate publication ready statistical graphics with either a point and click system or using code that a programmer crafts. This class introduces people, without a programming background, to the core graphical methods that ship with the R programming language and introduces a few additional free R packages which allow novices to produce high quality scientific graphics. This is both a lecture and hands on lab so students should ideally have a laptop.

Students must have completed the instructions listed at the top of this page for installing R on a Windows Machine or a Mac before taking this class.

· How R functions work

· How to generate common univariate and multivariate graphics

o Pie charts

o Bar plots

o Dot charts

o Scatter plots

o Sunflower plots

o Jittered dot plots

o Stem and Leaf plots

o Histograms

o Boxplots

o Violin plots

o Line plots

· How to do analyses on subsets

· How to do multi-panel plots

· Commonly used Graphic Display Parameters

· Setting custom colors

· Exporting pretty graphics into PDFs and other graphics formats

· How to learn more

The PowerPoint 2007/2010 slides for Using R for Statistical Graphics can be found here.

The main dataset for the talk can be found here as an Excel file or here in CSV format. The dated file in CSV format is here: here.

The R code file producing all the graphics can be found here.

A spreadsheet with graphics parameters mentioned in this talk can be found here.

Using R with REDCap: An Introduction to Secure Data Collection and
Analysis for Busy Non-programmers

REDCap is a user-friendly online database tool that researchers can use to collect, store and describe data including Protected Health Information. REDCap 4.3.x allows researchers to load data from the Stanford’s Electronic Medical Record (EPIC or Cerner with help from IRT), enter data directly into the system by hand, or allow patients to enter data with web enabled devices like laptops, desktop or tablet computers like IPADs. While REDCap is an excellent tool to collect and store data its analysis tools are primitive. R is a free software package that can read data out of REDCap and then calculates statistics and generate publication quality graphics. This class introduces people, without a programming background, to REDCap and R. This is both a lecture and hands on lab so students should ideally have a laptop.

Students must have completed the instructions listed at the top of this page for installing R on a Windows Machine or a Mac before taking this class. Attendance of Using R for Statistics and Graphics: An Introduction for Busy Non-programmers is recommended but not required.

After completing the class attendees will know how to:

· access REDCap from on or off campus

· create an online survey for subject recruitment

· create a database to hold data from a research study

· do basic summary statistics in REDCap

· load REDCap data into R

· use R with a point-and-click interface to do basic statistics on data originating in REDCap

The PowerPoint 2007/2010 slides for Using R for Statistical Graphics can be found here. (Updated 2011-08-19)

The first data dictionary can be found here

The edited data dictionary can be found here

The long form data dictionary can be found here.