The results of the latest KDnuggets Poll on software for Analytics, Big Data and Data Mining are out, and R has moved into the #1 position by a wide margin. I’ve updated the Surveys of Use section of The Popularity of Data Analysis Software to include a subset of those results, which I include here:
…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.
Figure 6b. Percent of respondents that used each software in KDnuggets’ 2015 poll. Only software with 5% market share are shown. The % alone is the percent of tool voters that used only that tool alone. For example, only 3.6% of R users have used only R, while 13.7% of RapidMiner users indicated they used that tool alone. Years are color coded, with 2015, 2014, 2013 from top to bottom.
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.
Figure 9. Number of R packages available on its main distribution site for the last version released in each year.
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.
It was only a few years ago when the N.Y. Times declared my job “sexy”. My old job title of statistician had sounded dull and stodgy, but then it became filled with exciting jargon: I’m a data scientist doing predictive analytics with (occasionally) big data. Three hot buzzwords in a single job description! However, in recent years, the powerful technology that has made my job so buzzworthy has me contemplating the future of the field. Computer programs that automatically generate complex models are becoming commonplace. Rob Hyndman’s forecast package for R, SAS Institite’s Forecast Studio, and IBM’s SPSS Forecasting offer the ability to generate forecasts that used to require years of training to develop. Similar tools are now available for other types of models as well.
Countless other careers have been eliminated due to new technology. The United States previously had over 70% of the population employed in farming and fewer than 2% are farmers today. Things change, people move on to other careers. The KDnuggests web site recently asked its readers, “When will most expert-level Predictive Analytics/Data Science tasks – currently done by human Data Scientists – be automated?” Fifty-one percent of the respondents – most of them data scientists themselves – estimated that this would happen within 10 years. Not all the respondents had such a dismal view though; 19% said that this would never happen.
My brain being analyzed by the machine that replaced my brain! (Photograpy by Mike O’Neil)
If you had asked me in 1980 what would be the very last part of my job to be eliminated through automation, I probably would have said: brain wave analysis. It had far more steps involved than any other type of work I did. We were measuring the electrical activity of many parts of the brain, at many frequencies, thousands of times per second. An analysis that simply compared two groups would take many weeks of full-time work. Surprisingly, this was the first part of my job to be eliminated. However, our statistical consulting team supports many different departments, so I didn’t really notice when work stopped arriving from the EEG Lab. Years later I got a call from the new lab director offering to introduce me to my replacement: a “robot” named LORETA.
When I visited the lab, I was outfitted with the usual “bathing cap” full of electrodes. EEG paste (essentially K-Y jelly) was squirted into a hole in each electrode to ensure a good contact and the machine began recording my brain waves. I used bio-feedback to generate alpha waves which made a car go around a track in a simple video game. Your brain creates alpha waves when you get into a very relaxed, meditative state. Moments after I finished, LORETA had already analyzed my brain waves. “She” had done several weeks of analysis in just a few moments.
So that part of my career ended years ago, but I didn’t really notice it at the time. I was too busy using the time LORETA freed up to learn image analysis using ImageJ, text mining using WordStat and SAS Text Miner, and an endless variety of tasks using the amazing R language. I’ve never had a moment when there wasn’t plenty of interesting new work to do.
There’s another aspect to my field that’s easy to overlook. When I began my career, 90% of the time was spent “battling” computers. They were incredibly difficult to operate. Today someone may send you a data file and you’ll be able to see the data moments after receiving it. In 1980 data arrived on tapes, and every computer manufacturer used a different tape format, each in numerous incompatible variations. Unless you had a copy of the program that created a tape, it might take days of tedious programming just to get the data off of it. Even asking the computer to run a program required error-prone Job Control Language. So from that perspective, easier-to-use computing technology has already eliminated 90% of what my job used to be. It wasn’t the interesting part of the job, so it was a change for the better.
Will the burgeoning field of data science eventually put itself out of business by developing a LORETA for every problem that needs to be solved? Will we just be letting our Star-Trek-class computers and robots do our work for us while we lounge around self-actualizing? Perhaps some day, but I doubt it will happen any time soon!
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.
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.
Figure 2e. Change in the number of scholarly articles using each software in the most recent two complete years (2013 to 2014). Packages shown in red are “hot” and growing, while those shown in blue are “cooling down” or declining.
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.
Only last August I wrote that among scholars, the use of R had probably exceeded that of SPSS to become their most widely used software for analytics. That forecast was based on Google Scholar searches focused on one year at a time, from 1995 through 2014. Each year from 2010 through 2014, I re-collected that entire data set just in case Google changed the search algorithm enough to affect the overall pattern. The data stayed roughly the same for those four years, but Google Scholar now finds almost twice as many articles for SPSS (at its peak year of 2008) than it found last year and 12% more articles for SAS. Changes in search results for articles that used R varied slightly with fewer in the early years and more in the latter ones. So R did not become the most widely used analytics software among academics in 2014. It’s unlikely to become so for another two years, unless present trends change.
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 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 Data Science 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.
Figure 2a. The number of scholarly articles that use each software package during the most recent complete year, 2014.
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.
Figure 2b. The number of scholarly articles for software that was used by fewer than 825 scholarly articles (i.e. the bottom part of Fig. 2a, rescaled.)
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).
Figure 2c. The number of scholarly articles found in each year by Google Scholar. Only the top six “classic” statistics packages are shown.
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.”]
Figure 2d. The number of scholarly articles found in each year by Google Scholar for classic statistics packages after market leaders SPSS and SAS have been removed.
I’ll announce future update on Twitter, where you can follow me as @BobMuenchen.
Every other year Rexer Analytics surveys Data Analysts, Predictive Modelers, Data Scientists, Data Miners, and all other types of analytic professionals, students, and academics regarding the software they use. I then update the main results in The Popularity of Data Analysis Software. Here’s where you can take the survey:
Survey results will be unveiled at the Fall-2015 Boston Predictive Analytics World event.
Rexer Analytics has been conducting the Data Miner Survey since 2007. Each survey explores the analytic behaviors, views and preferences of data miners and analytic professionals. Over 1,200 people from around the globe participated in the 2013 survey. Summary reports (40 page PDFs) from previous surveys are available FREE to everyone who requests them by emailing DataMinerSurvey@RexerAnalytics.com. Also, highlights of earlier Data Miner Surveys are available at www.rexeranalytics.com/Data-Miner-Survey-Results-2013.html, including best practices shared by respondents on analytic success measurement, overcoming data mining challenges, and other topics. The FREE Summary Report for this 2015 Data Miner Survey will be available to everyone Fall-2015.
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Rexer Analytics is a consulting firm focused on providing data mining and analytic CRM solutions. Recent solutions include customer loyalty analyses, customer segmentation, predictive modeling to predict customer attrition and to target direct marketing, fraud detection, sales forecasting, market basket analyses, and complex survey research. More information is available at www.RexerAnalytics.com or by calling +1 617-233-8185.
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: my own on site training, Training and Consulting Partners – RStudio and here: R for SAS, SPSS and STATA Users | DataCamp. Books that help with the conversion process include:
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!
The WordPress.com stats helper monkeys prepared a 2014 annual report for this blog.
Here’s an excerpt:
The Louvre Museum has 8.5 million visitors per year. This blog was viewed about 260,000 times in 2014. If it were an exhibit at the Louvre Museum, it would take about 11 days for that many people to see it.
I’ve been tracking The Popularity of Data Analysis Software for many years now, and a clear trend is the decline of the market share of the bigger analytics firms, notably SAS and SPSS. Many people have interpreted my comments as implying the decline in the revenue of those companies. But the fields involved in analytics (statistics, data mining, analytics, data science, etc.) have been exploding in popularity, so having a smaller slice of a much bigger pie still leaves billions in revenue for the big players.
Each year, the Gartner Group, “the world’s leading information technology research and advisory company”, collects data in a survey of the customers of 42 business intelligence firms. They recently released the data on the customers’ plans to discontinue use of their current software in one to three years. The results are shown in the figure below. Over 16% of the SAS Institute customers surveyed reported considering discontinuing their use of the software, the highest of any of the vendors shown. It will be interesting to see if this will actually lead to an eventual decline in revenue. Although I have helped quite a few organizations migrate from SAS to R, I would be surprised to see SAS Institute’s revenue decline. They offer excellent software and service which I still use, though not anywhere near as much as R.
