Paul Johnson's Collection of Rtips and Guidance

At the very bottom of the page, you should see the "raw" list of files and directories. Sometimes I upload things and forget to create "links" for them in this README file. If I forget, you can still find what you need at the bottom.


The official home of the new and improved Rtips is here, in pdf and "html! Rtips was my first effort to accumulate user-guide material for R, mostly because I could not remember details while translating from SAS to R. That effort started before the turn of the century, well before the world became awash in introductory guides, tip-sheets, and primer books for R.

As a joke, I used to call this "StatsRus", but I stopped because it seemed presumptuous to make "me" part of "us". But I still think it was a funny title.

WorkingExamples of R Code

I maintain small, runable working examples in the "WorkingExamples" folder. I do that because I believe the best way to learn R is to solve specific problems. It is necessary to challenges, develop solutions, and make them re-producible. You can run these; get smart. The one on barplots is so incredibly awesome, you won't believe it when you see it. As my skills in R programming have improved, I notice these WorkingExamples are becoming a little more elaborate. And you will notice that, when I'm troubled about something, the examples proliferate. Look at plot-plotmath-* for example.

This is also helpful for newcomers who need to learn how to ask questions correctly in email support lists and Stack Overflow. In order to ask for help, it is necessary to provide a self contained, focused example. (This is emphasized in the r-help email list Posting Guide). I thought that was a good idea, we did the same with the Swarm support project in the early part of this century.

My "WorkingExamples" now have HTML output as companion files. Perhaps this makes it easier for students to understand what the results might be.

R Packages

I am now primary developer on several R packages. You can get the latest and greatest from my package server. These things do go into CRAN now and then, but CRAN is for occasional releases, not rapid development. If you want access, the following stanza can be used to integrate our KRAN updates with the standard CRAN offerings. As evidence of my modesty, I also include a command that installs all of my fine packages for you (smiley face!).
CRAN <-""
KRAN <-""
options(repos =c(KRAN,CRAN))
install.packages(c("stationery", "rockchalk", "kutils", "semTable",
All of these packages have "vignettes," essays about their functions and results. They are provided with the packages. The vignettes can be accessed from the online listings for the packages on CRAN, of course, but I'm copying some highlights into this directory for your inspection.


In late 2011, I created an R package called "rockchalk". That was created because the graduate students in the stats course had trouble keeping up with all of the R details needed to make nice looking graphs and regression tables. The rockchalk package is a complete, end-to-end regression analysis and reporting framework.

That's available on CRAN now. CRAN is for major releases, testing version is available on my server,

The vignettes from rockchalk (see the file list below) are

  1. outreg: A power tour of the regression table making software
  2. rockchalk: A survey of fuctions in rockchalk.
  3. RStyle: A style guide for R aimed at people who have basic R user skills and need to become R programmers
  4. Rchaeology: That's a pithy name, don't you think? It includes "deep insights" and programming advice that reflects the customs and mannerisms of the R leaders.
In rockchalk, I also stashed some material I use for teaching in my graduate R class. Distributed with rockchalk, there is an examples folder. Students should look for files "nowords-*.R" for some R programming insights.


Templates for R markdown and LaTeX files with integrated, consistent themes. The 8 document types are
  1. HTML webpages prepared with Rmarkdown
  2. PDF guide documents prepared with Rmarkdown
  3. PDF report documents prepared with Rmarkdown
  4. PDF guide documents prepared with LaTeX/NoWeb using the knitr code chunk engine
  5. PDF guide documents prepared with LaTeX/NoWeb using the Sweave code chunk engine
  6. PDF report documents prepared with LaTeX/NoWeb using the knitr code chunk engine
  7. PDF report documents prepared with LaTeX/NoWeb using the Sweave code chunk engine
  8. PDF slides prepared with a KU customized Beamer slide template and the Sweave code chunk engine
The stationery package includes 4 vignettes, but I've only uploaded "stationery.pdf" in the file set below.


This includes functions that we use in the Center for Research Methods and Data Analysis. There are some super handy utilities, like "file.backup()". Avoid accidental erasures! If you are ever saving a graph or table, run file.backup() first to keep a copy of the previous version. The main feature in kutils is the "Variable Key" data management framework. That's described in a vignette included with the package, The Variable Key Framework for Project Management.


One of my major complaints about structural equation modeling (SEM) has been the lack of a quick, standard method for generating tables that can be included in reports. The graduate assistants here have been accustomed to fitting models for a few days, and then spending weeks to prepare tables. The semTable package is a self contained solution for that problem. See semTable.


In Monte Carlo simulations, one of the practical problems is managing the streams of random numbers across experiments. portableParallelSeeds offers a workable method that has been employed in many CRMDA projects as well as dissertations in the departments of psychology and in the School of Education. See pps ("Portable Parallel Seeds").

Lectures & guides

Most of my course-oriented lectures on R and stats and computing are collected here:

Most of these are LaTeX, many are Sweaved to combine R with the document. I'm uploading everything here, the source code, anything necessary to reproduce the pdf lectures. I'm offering this as a free service, free for reuse to anybody as long as they acknowledge where they took the material.

  1. Rcourse. The sub-heading "Rcourse" includes the lectures that are primarily about R (apart from the statistical background)
  2. stat. Stats lectures, most of which use R and Sweave. The primary emphasis is on statistical applications and interpretation. The difference between the two is sometimes hard to describe (its mostly audience dependent).
  3. Computing-HOWTO.

If you are just beginning with R, I have prepared some very basic presentations for a group of undergraduate interns and I think they will help you get started. Please look at


In my job at the Center for Research Methods and Data Analysis at KU, I've made several screen casts in support of guide documents I've prepared. These can be accessed through the CRMDA guides index.

The highlights for R that I have prepared are intended to help Windows users get started with R in a productive way.

Windows Install Guides

I found myself walking through the same installs over and over with Windows users. So I made these screencasts to give people a head start. I put a couple of those on YouTube. Later, I put updated versions in the CRMDA Web page. Please visit, where I hope you find the "big picture" on system configuration and links to my Windows system guides.

If you check on YouTube, you'll find the older ones, StartR 01-Install R for Windows and ActivePerl and StartR 02-Editors for R in Windows: Emacs (ESS), RStudio, Notepad++

Rockchalk Webinar, hosted by Orange County R User Group (OCRUG)

On September 27, 2013, I was delighted/honored to participate in a Webinar on rockchalk, graciously hosted by Ray DiGiacomo, president of the OCRUG. The screen cast of the recording is in Quicktime format, in the file Webinar-OCRUG-20130927/ which you can download, decompress, and watch (over and over!). The slides themelves are in the same directory, in a file called rockchalk-slides.pdf.

There are 2 R files to note in the same directory.

  1. rockchalk-slides.R.

    The Sweave process that generates the slides also creates an R code file that one could use to replicate the analysis.

  2. curve-example-1.R. Code to "walk through" the demo discussed in the last 10 minutes of the Webinar.

Sweave Tutorial

During the Stats Camp at University of Kansas, a "camper" asked me for help in learning how to use Sweave. More recently, I've developed some notes on Sweave and Beamer presentations. That materials are now online LyX-sweave-tutorial, which shows the bare minimum steps needed to use Sweave.

I have a "LaTeX Get Started" document in the guides directory (read below). PJ, 2019-04-15

      Name                    Last modified      Size  Description
Parent Directory - variablekey.pdf 2019-04-19 08:22 224K 2019-04-19 08:26 50 statsRus.html 2014-03-24 16:08 233K stationery.pdf 2019-04-19 08:28 384K semtable.pdf 2019-04-19 08:22 221K rockchalk.pdf 2019-04-19 08:21 444K rmdonrmd/ 2015-02-27 21:11 - pps.pdf 2019-04-19 08:23 179K outreg.pdf 2019-04-19 08:21 387K or1-test.html 2013-05-15 15:26 1.0K gloating/ 2013-09-26 12:41 - WorkingExamples/ 2020-11-23 15:30 - Webinar-OCRUG-20130927/ 2013-10-01 13:33 - SystemAdmin/ 2016-06-16 10:09 - Rtips.pdf 2014-03-24 16:08 535K Rtips.html 2014-03-24 16:08 233K Rstyle.pdf 2019-04-19 08:21 230K Rchaeology.pdf 2019-04-19 08:21 325K RUReady.pdf 2010-03-24 11:56 1.1M HEADER.html-bak 2013-10-01 13:17 6.7K First-R/ 2020-08-12 08:04 -