The recently-created R Consortium consists of companies that are deeply involved in R such as RStudio, Microsoft/Revolution Analytics, Tibco, and others. The Consortium’s goals include advancing R’s worldwide promotion and support, encouraging user adoption, and improving documentation and tools. Those are admirable goals and below I suggest a few specific examples that the consortium might consider tackling.

As I work with various organizations to help them consider migrating to R, common concerns are often raised. With thousands of packages to choose from, where do I start? Do packages go through any reliability testing? What if I start using a package and its developer abandons it? These, and others, are valid concerns that the R Consortium could address.

**Choosing Packages**

New R users face a daunting selection of thousands of packages. Some guidance is provided by CRAN’s Task Views. In R’s early years, this area was quite helpful in narrowing down a package search. However, R’s success has decreased the usefulness of Task Views. For example, say a professor asks a grad student to look into doing a cluster analysis. In SAS, she’ll have to choose among seven procedures. When considering the Task View on the subject, she’ll be presented with 105 choices in six categories! The greater selection is one of R’s strengths, but to encourage the adoption of R by a wider community it would be helpful to list the popularity of each package. The more popular packages are likely to be the most useful.

R functions are integrated into other software such as Alteryx, IBM SPSS Statistics, KNIME, and RapidMiner. Some are also called from R user interfaces such as Deducer, R Commander, and RATTLE. Within R, some packages depend on others, adding another vote of confidence. The R Consortium could help R users by documenting these various measures of popularity, perhaps creating an overall composite score.

**Accuracy**

People often ask how they can trust the accuracy (or reliability) of software that is written by a loosely knit group of volunteers, when there have even been notable lapses in the accuracy of commercial software developed by corporate teams [1]. Base R and its “recommended packages” are very well tested, and the details of the procedures are documented in The R Software Development Life Cycle. That set of software is substantial, the equivalent of Base SAS + GRAPH + STAT + ETS + IML + Enterprise Miner (excluding GUIs, Structural Equation Modeling, and Multiple Imputation, which are in add-on packages). Compared to SPSS, it’s the rough equivalent to IBM SPSS Base + Statistics + Advanced Stat. + Regression + Forecasting + Decision Trees + Neural Networks + Bootstrapping.

While that set is very capable, it still leaves one wondering about all the add-on packages. Performing accuracy tests is very time consuming work [2-5] and even changing the options on the same routine can affect accuracy [6]. Increasing the confidence that potential users have in R’s accuracy would help to increase the use of the software, one of the Consortium’s goals. So I suggest that they consider ways to increase the reliability testing of functions that are outside the main R packages.

Given the vast number of R packages available, it would be impossible for the Consortium to perform such testing on all packages. However, for widely used packages, it might behoove the Consortium to use its resources to develop such tests themselves. A web page that referenced Consortium testing, as well as testing from any source, would be helpful.

**Package Longevity**

If enough of a package’s developers got bored and moved on or, more dramatically, were hit by the proverbial bus, development would halt. *Base R plus recommended packages* has the whole R Development Core Team backing them up. Other packages are written by employees of companies. In such cases, it is unclear whether the packages are supported by the company or by the individual developer(s).

Using the citation function will list a package’s developers. The more there are, the better chance there is of someone taking over if the lead developer moves on. The Consortium could develop a rating system that would provide guidance along these lines. Nothing lasts forever, but knowing the support level a package has would be of great help when choosing which to use.

**Encourage Support and Use of Key Generic Functions**

Some fairly new generic functions play a key role in making R easier to use. For example, David Robinson’s broom package contains functions that translate the output of modeling functions from list form into data frames, making output management much easier. Other packages, including David Dahl’s xtable and Philip Leifeld’s texreg, do a similar translation to present the output in nicely formatted forms for publishing. Those developers have made major contributions to R by writing all the methods themselves. The R Consortium could develop a list of such functions and encourage other developers to add methods to them, when appropriate. Such widely applicable functions could also benefit from having the R Consortium support their development, assuring longer package longevity and wider use.

**Output to Microsoft Word**

R has the ability to create beautiful output in almost any format you would like, but it takes additional work. Its competition, notably SAS and SPSS, let you choose the font and full formatting of your output tables at installation. From then on, any time you want to save output to a word processor, it’s a simple cut & paste operation. SPSS even formats R output to fully formatted tables, unlike any current R IDEs. Perhaps the R Consortium could pool the resources needed to develop this kind of output. If so, it would be a key aspect of their goal of speeding R’s adoption. (I do appreciate the greater power of LaTeX and the ease of use of knitr and Rmarkdown, but they’ll never match the widespread use of Word.)

**Graphical User Interface**

Programming offers the greatest control over an analysis, but many researchers don’t analyze data often enough to become good programmers; many simply don’t like programming. Graphical User Interfaces (GUIs) help such people get their work done more easily. The traditional menu-based systems, such as R Commander or Deducer, make one-time work easy, but they don’t offer a way to do repetitive projects without relying on the code that non-programmers wish to avoid.

Workflow-based GUIs are also easy to use and, more importantly, they save all the steps as a flowchart. This allows you to check your work and repeat it on another data set simply by updating the data import node(s) and clicking “execute.” To take advantage of this approach, Microsoft’s Revolution R Enterprise integrates into Alteryx and KNIME, and Tibco’s Enterprise Runtime for R integrates into KNIME as well. Alteryx is a commercial package, and KNIME is free and open source on the desktop. While both have commercial partners, each can work with the standard community version of R as well.

Both packages contain many R functions that you can control with a dialog box. Both also allow R programmers to add a programming node in the middle of the workflow. Those nodes can be shared, enabling an organization to get the most out of both their programming and non-programming analysts. Both systems need to add more R nodes to be considered general-purpose R GUIs, but they’re making fairly rapid progress on that front. In each system, it takes less than an hour to add a node to control a typical R function.

The R Consortium could develop a list of recommended steps for developers to consider. One of these steps could be adding nodes to such GUIs. Given the open source nature of R, encouraging the use of the open source version of KNIME would make the most sense. That would not just speed the adoption of R, it would enable its adoption by the large proportion of analysts who prefer not to program. For the more popular packages, the Consortium could consider using their own resources to write such nodes.

**Conclusion**

The creation of the R Consortium offers an intriguing opportunity to expand the use of R around the world. I’ve suggested several potential goals for the Consortium, including ways to help people choose packages, increase reliability testing, rating package support levels, increasing visibility of key generic functions, adding support for Word, and making R more accessible through stronger GUI support. What else should the R Consortium consider? Let’s hear your ideas in the comments section below.

Is your organization still learning R? I’d be happy to stop by and help. I also have a workshop, *R for SAS, SPSS and Stata Users,* on DataCamp.com. If you found this post useful, I invite you to follow me on Twitter.

**Acknowledgements**

Thanks to Drew Schmidt and Michael Berthold for their suggestions that improved this post.

**References**

- Micah Altman (2002), A Review of JMP 4.03 With Special Attention to its Numerical Accuracy, The American Statistician, 56:1, 72-75, DOI: 10.1198/000313002753631402
- D. McCullough (1998), Assessing the Reliability of Statistical Software: Part I, The American Statistician, 52:4, 358-366
- D. McCullough (1999), Assessing the Reliability of Statistical Software: Part II, The American Statistician, 53:2, 149-159
- Kellie B. Keeling, Robert J. Pavur (2007), A comparative study of the reliability of nine statistical software packages, Computational Statistics & Data Analysis, Vol. 51, Issue 8, pp. 3811-3831
- Oluwartotimi O. Odeh, Allen M. Featherstone and Jason S. Bergtold (2010), Reliability of Statistical Software, American Journal of Agricultural Economics,doi: 1093/ajae/aaq068
- Jason S. Bergtold, Krishna Pokharel and Allen Featherstone (2015), Selected Paper prepared for presentation at the 2015 Agricultural & Applied Economics Association and Western Agricultural Economics Association Annual Meeting, San Francisco, CA, July 26-28

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webinar will take place on July 15, 2015 at 10 am California time. Everyone is welcome

to attend.

Agenda:

– What is SparkR?

– Recent improvements to SparkR

– SparkR Roadmap

– Live Demo

– Q & A

Speaker:

Shivaram Venkataraman, Co-author of SparkR

Duration: 45-60 minutes

Cost: $0

Location: Internet

Registration:

https://attendee.gotowebinar.com/register/4761879673365920770

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**Books**

The number of books published on each software package or language reflects its relative popularity. Amazon.com offers an advanced search method which works well for all the software except R and the general-purpose languages such as Java, C, and MATLAB. I did not find a way to easily search for books on analytics that used such general purpose languages, so I’ve excluded them in this section.

The Amazon.com advanced search configuration that I used was (using SAS as an example):

Title: SAS -excerpt -chapter -changes -articles Subject: Computers & Technology Condition: New Format: All formats Publication Date: After January, 2000

The “title” parameter allowed me to focus the search on books that included the software names in their titles. Other books may use a particular software in their examples, but they’re impossible to search for easily. SAS has many manuals for sale as individual chapters or excerpts. They contain “chapter” or “excerpt” in their title so I excluded them using the minus sign, e.g. “-excerpt”. SAS also has short “changes and enhancements” booklets that the developers of other packages release only in the form of flyers and/or web pages, so I excluded “changes” as well. Some software listed brief “articles” which I also excluded. I did the search on June 1, 2015, and I excluded excerpts, chapters, changes, and articles from all searches.

“R” is a difficult term to search for since it’s used in book titles to indicate Registered Trademark as in “SAS(R)”. Therefore I verified all the R books manually.

The results are shown in Table 1, where it’s clear that a very small number of analytics software packages dominate the world of book publishing. SAS has a huge lead with 576 titles, followed by SPSS with 339 and R with 240. SAS and SPSS both have many versions of the same book or manual still for sale, so their numbers are both inflated as a result. JMP and Hadoop both had fewer than half of R’s count and then Minitab and Enterprise Miner had fewer then half again as many. Although I obtained counts on all 27 of the domain-specific (i.e. not general-purpose) analytics software packages or languages shown in Figure 2a, I cut the table off at software that had 8 or fewer books to save space.

Software Number of BooksSAS 576 SPSS Statistics 339 R 240 [Corrected from: 172] JMP 97 Hadoop 89 Stata 62 Minitab 33 Enterprise Miner 32

Table 1. The number of books whose titles contain the name of each software package.

[Correction: Thanks to encouragement from Bernhard Lehnert (see comments below) the count for R has been corrected from 172 to the more accurate 240.]

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…The results of a similar poll done by the KDnuggets.com web site in May of 2015 are shown in Figure 6b. This one shows R in first place with 46.9% of users reporting having used it for a real project. RapidMiner, SQL, and Python follow quite a bit lower with around 30% of users. Then at around 20% are Excel, KNIME and HADOOP. It’s interesting to see what has happened to two very similar tools, RapidMiner and KNIME. Both used to be free and open source. RapidMiner then adopted a commercial model, with an older version still free. KNIME kept its desktop version free and, likely as a result, its use has more than tripled over the last three years. SAS Enterprise Miner uses a very similar workflow interface, and its reported use, while low, has almost doubled over the last three years. Figure 6b only shows those packages that have at least 5% market share. KDnuggets’ original graph and detailed analysis are here.

I invite you to follow me here or at http://twitter.com/BobMuenchen. If you’re interested in learning R, DataCamp.com offers my 16-hour interactive workshop, R for SAS, SPSS and Stata Users for $25. That’s a monthly fee, but it definitely won’t take you a month to take it! For students & academics, it’s $9. I also do R training on-site.

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In my ongoing quest to *analyze the world of analytics*, I’ve updated the Growth in Capability section of The Popularity of Data Analysis Software. To save you the trouble of foraging through that tome, I’ve pasted it below.

**Growth in Capability**

The capability of analytics software has grown significantly over the years. It would be helpful to be able to plot the growth of each software package’s capabilities, but such data are hard to obtain. John Fox (2009) acquired them for R’s main distribution site http://cran.r-project.org/, and I collected the data for later versions following his method.

Figure 9 shows the number of R packages on CRAN for the last version released in each year. The growth curve follows a rapid parabolic arc (quadratic fit with R-squared=.995). The right-most point is for version 3.1.2, the last version released in late 2014.

To put this astonishing growth in perspective, let us compare it to the most dominant commercial package, SAS. In version, 9.3, SAS contained around 1,200 commands that are roughly equivalent to R functions (procs, functions etc. in Base, Stat, ETS, HP Forecasting, Graph, IML, Macro, OR, QC). In 2014, R added 1,357 packages, counting only CRAN, or approximately 27,642 functions. During 2014 alone, R added more functions/procs than SAS Institute has written in its entire history.

Of course SAS and R commands solve many of the same problems, they are certainly not perfectly equivalent. Some SAS procedures have many more options to control their output than R functions do, so one SAS procedure may be equivalent to many R functions. On the other hand, R functions can nest inside one another, creating nearly infinite combinations. SAS is now out with version 9.4 and I have not repeated the arduous task of recounting its commands. If SAS Institute would provide the figure, I would include it here. While the comparison is far from perfect, it does provide an interesting perspective on the size and growth rate of R.

As rapid as R’s growth has been, these data represent only the main CRAN repository. R has eight other software repositories, such as Bioconductor, that are not included in

Figure 9. A program run on 5/22/2015 counted 8,954 R packages at all major repositories, 6,663 of which were at CRAN. (I excluded the GitHub repository since it contains duplicates to CRAN that I could not easily remove.) So the growth curve for the software at all repositories would be approximately 34.4% higher on the y-axis than the one shown in Figure 9. Therefore, the estimated total growth in R *functions* for 2014 was 28,260 * 1.344 or 37981.

As with any analysis software, individuals also maintain their own separate collections typically available on their web sites. However, those are not easily counted.

What’s the total number of R functions? The Rdocumentation site shows the latest counts of both packages and functions on CRAN, Bioconductor and GitHub. They indicate that there is an average of 20.37 functions per package. Since a program run on 5/22/2015 counted 8,954 R packages at all major repositories except GitHub, on that date there were approximately 182,393 total functions in R. In total, R has *over 150 times* as many commands as SAS.

I invite you to follow me here or at http://twitter.com/BobMuenchen. If you’re interested in learning R, DataCamp.com offers my 16-hour interactive workshop, R for SAS, SPSS and Stata Users for $25. That’s a monthly fee, but it definitely won’t take you a month to take it! For students & academics, it’s $9. I also do R training on-site.

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Analytics tools take significant effort to master, so once learned people tend to stick with them for much of their careers. This makes the tools used in academia of particular interest in the study of future trends of market share. I’ve been tracking The Popularity of Data Analysis Software regularly since 2010, and thanks to an astute reader, I now have a greatly improved estimate of Stata’s academic growth. Peter Hedström, Director of the Institute for Analytical Sociology at Linköping University, wrote to me convinced that I was underestimating Stata’s role by a wide margin, and he was right.

Two things make Stata’s popularity difficult to guage: 1) Stata means “been” in Italian, and 2) it’s a common name for the authors of scholarly papers and those they cite. Peter came up with the simple, but very effective, idea of adding Statacorp’s headquarter, College Station, Texas, to the search. That helped us find far more Stata articles while blocking the irrelevant ones. Here’s the search string we came up with:

("Stata" "College Station") OR "StataCorp" OR "Stata Corp" OR "Stata Journal" OR "Stata Press" OR "Stata command" OR "Stata module"

The blank between Stata and College Station is an implied logical “and”. This string found 20% more articles than my previous one. This success motivated me to try and improve some of my other search strings. R and SAS are both difficult to search for due to how often those letters stand for other things. I was able to improve my R search string by 15% using this:

"r-project.org" OR "R development core team" OR "lme4" OR "bioconductor" OR "RColorBrewer" OR "the R software" OR "the R project" OR "ggplot2" OR "Hmisc" OR "rcpp" OR "plyr" OR "knitr" OR "RODBC" OR "stringr" OR "mass package"

Despite hours of effort, I was unable to improve on the simple SAS search string of “SAS Institute.” Google Scholar’s logic seems to fall apart since “SAS Institute” OR “SAS procedure” finds fewer articles! If anyone can figure that out, please let me know in the comments section below. As usual, the steps I use to document all searches are detailed here.

The improved search strings have affected all the graphs in the Scholarly Articles section of The Popularity of Data Analysis Software. At the request of numerous readers, I’ve also added a log-scale plot there which shows the six most popular classic statistics packages:

If you’re interested in learning R, DataCamp.com offers my 16-hour interactive workshop,

R for SAS, SPSS and Stata Users for $25. That’s a monthly fee, but it definitely won’t take you a month to take it! For students & academics, it’s $9. I also do training on-site but I’m often booked about 8 weeks out.

I invite you to follow me on this blog and on Twitter.

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It would be useful to have growth trend graphs for each of the analytics packages I track, but collecting such data is too time consuming since it must be re-collected every year (since search algorithms change). What I’ve done instead is collect data only for the past two complete years, 2013 and 2014. Figure 2e shows the percent change from 2013 to 2014, with the “hot” packages whose use is growing shown in red. Those whose use is declining or “cooling” are shown in blue. Since the number of articles tends to be in the thousands or tens of thousands, I have removed any software that had fewer than 100 articles in 2013. Going from one to five articles may represent 500% growth, but it’s not of much interest.

The three fastest growing packages are all free and open source: Python, R and KNIME. All three saw more than 25% growth. Note that the Python figures are strictly for analytics use as defined here. At the other end of the scale are SPSS and SAS, both of which declined in use by around 25%. Recall that Fig. 2a shows that despite recent years of decline, SPSS is still extremely dominant for scholarly use.

Three of the packages whose use is growing implement the powerful and easy-to-use workflow or flowchart user interface: KNIME, RapidMiner and SPSS Modeler. As useful as that approach is, it’s not sufficient for success as we see with SAS Enterprise Miner, whose use declined nearly 15%.

It will be particularly interesting to see what the future holds for KNIME and RapidMiner. The companies were two of only four chosen by the Gartner Group as having both a complete vision of the future and the ability to execute that vision (Fig. 7a). Until recently, both were free and open source. RapidMiner then started charging for its current version, leaving its older version as the only free one. Recent offers to make it free for academic use don’t include use on projects with grant funding, so I expect KNIME’s higher rate of growth to remain faster than RapidMiner’s. However, in absolute terms, scholarly use of RapidMiner is currently almost twice that of KNIME, as shown in Fig. 2b.

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So what happened? We’re looking back across many years, so while it’s possible that SPSS suddenly became much more popular in 2014, that could not account for lifting the whole trend line. It’s possible Google Scholar improved its algorithm to find articles that existed previously. It’s also possible that new journal archives have opened themselves up to being indexed by Google. Why would it affect SPSS more than SAS or R? SPSS is menu-driven so it’s easy to install with its menus and dialog boxes translated into many languages. Since SAS and R are much more frequently used via their English-based languages, they may not be as popular in non-English speaking countries. Therefore, one might see a disproportionate impact on SPSS by new non-English archives becoming available. If you have an alternate hypothesis, please leave it in the comments below.

The remainder of this post is the complete updated section on this topic from The Popularity of Data Analysis Software:

**Scholarly Articles**

The more popular a software package is, the more likely it will appear in scholarly publications as a topic or as a tool of analysis. The software that is used in scholarly articles is what the next generation of analysts will graduate knowing, so it’s a leading indicator of where things are headed. Google Scholar offers a way to measure such activity. However, no search of this magnitude is perfect and will include some irrelevant articles and reject some relevant ones. The details of the search terms I used are complex enough to move to a companion article, How to Search For Analytics Articles. Since Google regularly improves its search algorithm, each year I re-collect the data for all years.

Figure 2a shows the number of articles found for each software package for the most recent complete year, 2014. SPSS is by far the most dominant package, likely due to its balance between power and ease-of-use. SAS has around half as many, followed by MATLAB and R. The software from Java through Statgraphics show a slow decline in usage from highest to lowest. Note that the general purpose software C, C++, C#, MATLAB, Java and Python are included only when found in combination with analytics terms, so view those as much rougher counts than the rest.

From RapidMiner on down, the counts appear to be zero. That’s not the case, the counts are just very low compared to the more popular packages, used in tens of thousands articles. Figure 2b shows the software only for those packages that have fewer than 825 articles (i.e. the bottom part of Fig. 2a), so we can see how they compare. RapidMiner, KNIME, SPSS Modeler and SAS Enterprise Miner are packages that all use the powerful and easy-to-use workflow interface, but their use has not yet caught on among scholars. BMDP is one of the oldest packages in existence. Its use has been declining for many years, but it’s still hanging in there. The software in the bottom half of this figure contain the newcomers, with the notable exception of Megaputer, whose Polyanalyst software has been around for many years now.

I’m particularly interested in the long-term trends of the classic statistics packages. So in Figure 2c I’ve plotted the same scholarly-use data for 1995 through 2014, the last complete year of data when this graph was made. As in Figure 2a, SPSS has a clear lead, but now you can see that its dominance peaked in 2008 and its use is in sharp decline. SAS never came close to SPSS’ level of dominance, and it also peaked around 2008. Note that the decline in the number of articles that used SPSS or SAS is not balanced by the increase in the other software shown. This is likely due to the fact that those two leaders faced increasing competition from many more software packages than can be shown in this type of graph (such as those shown in Figure 2a).

Since SAS and SPSS dominate the vertical space in Figure 2c by such a wide margin, I removed those two packages and added the next two most popular statistics packages, Systat and JMP, with the result shown in Figure 2d. Freeing up so much space in the plot now allows us to see that the use of R is experiencing very rapid growth and is pulling away from the pack, solidifying its position in third place. If the current trends continue, the use of R may pass that of SPSS and SAS by the end of 2016. Note that current trends have shifted before as discussed here.

Stata has moved into fourth place, crossing above Statistica in 2014. The growth in the use of Stata is more rapid than all the classic statistics packages except for R. The use of Statistica, Minitab, Systat and JMP are next in popularity, respectively, with their growth roughly parallel to one another. [Note that in the plots from previous years, Statistica was displayed as a flat line at the very bottom of the graph. That turned out to be a search-related artifact. Many academics who use Statistica don’t mention the package by software name but rather say something like, “we used the statistics package by Statsoft.”]

I’ll announce future update on Twitter, where you can follow me as @BobMuenchen.

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Survey results will be unveiled at the Fall-2015 Boston Predictive Analytics World event.

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As the R programming environment has grown in capability and popularity, so have the number of organizations planning to migrate to it from proprietary tools. I’ve helped members of various organizations transition from SAS, SPSS and/or Stata to R (see Workshop Participants), and the process typically involves the following steps:

1) Begin with the most important question: who should you migrate to R? Learning R is not a trivial task (see Why R is Hard to Learn). However, once mastered by people who use it regularly, I think it’s easier to use than other software. But if you have some people who use something like SAS only occasionally and view it as hard to use, you might consider getting them something other than R. Menu-based solutions such as SPSS or

R Commander may be a better fit for them. If they want to continue using SAS while lowering your licensing costs, you might consider the SAS implementation used in WPS (see World Programming).

2) Motivate people to migrate. Discussing your current software budget may help. Showing your staff the growth of R’s capabilities and popularity may also help (see The Popularity of Data Analysis Software). Keep in mind that attempting to motivate people to change by criticizing their current choice is likely to backfire. People’s choice of software is very personal and criticizing it is like telling them they have the wrong religion.

3) Use training & documentation that leverages what they already know, that speaks their language. A trainer who knows both your existing environment and R can convert what the analysts currently know rather than simply starting from scratch (note that this self-serving advice!). There are two parts to this process: learning the new R code and learning to interpret the new R output. Choosing to use R packages that provide output similar to your current software choice will help smooth the transition. Good sources for training are listed here: Training and Consulting Partners – RStudio and here: R for SAS, SPSS and STATA Users | DataCamp. Books that help with the conversion process include:

R for SAS and SPSS Users, Muenchen

SAS and R, Kleinman & Horton

R for Stata Users, Muenchen & Hilbe

R Through Excel, Heiberger & Neuwirth (for those who use the SAS Excel plug-in)

4) Provide in-house tech support. Before training a whole team, get one in-house expert trained to act as a consultant to others. Make sure this person is well known by everyone and has time freed up to provide help.

5) Match your staff’s current work style, work flow and output. This is a particularly complex topic. Some examples: if your people are running SAS from the Excel plug-in, get the the R plug-in; if they’re using Enterprise Miner, consider a similar interface that controls R such as the KNIME Analytics Platform. If Microsoft Word is their main word processor, don’t complicate the conversion by switching them to LaTeX text processor at the same time (LaTeX is wonderful and very popular among R users, but it’s a mess to learn that and R at the same time). Instead, use an approach that generates Word output.

6) Migrate one step at a time if possible. For example, if you use SAS/ETS for forecasting, consider replacing just that one piece. When finished and successful, choose the next product, saving SAS/Base for last.

7) Convert your programs or use conversion services. If your programs are all in production, this could be a huge job. However, if you mostly use SAS for new research tasks, you may not need to convert old code from which you just needed a solution. Be careful to avoid line-by-line conversion; think like R (e.g. avoid for- and while-loops in R). When using external conversion services, make sure to involve your own staff in the process so you don’t end up with code that’s almost impossible to maintain.

I have found that following these steps helps during conversions to R. It’s a big job, though, so allocate plenty of time to it. Good luck!

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