The Popularity of Data Analysis Software

by Robert A. Muenchen


This article presents various ways of measuring the popularity or market share of software for advanced analytics software. Such software is also referred to as tools for data science, statistical analysis, machine learning, artificial intelligence, predictive analytics, business analytics, and is also a subset of business intelligence.

Updates: The latest data for Language Popularity Measures was updated 10/17/2015.
I announce the updates to this article on Twitter:


When choosing a tool for data analysis, now more commonly referred to as analytics or data science, there are many factors to consider:

  • Does it run natively on your computer?
  • Does the software provide all the methods you need? If not, how extensible is it?
  • Does its extensibility use its own unique language, or an external one (e.g. Python, R) that is commonly accessible from many packages?
  • Does it fully support the style (programming, or menus and dialog boxes, or workflow diagrams) that you like?
  • Are its visualization options (e.g. static vs. interactive) adequate for your problems?
  • Does it provide output in the form you prefer (e.g. cut & paste into a word processor vs. LaTeX integration)?
  • Does it handle large enough data sets?
  • Do your colleagues use it so you can easily share data and programs?
  • Can you afford it?

The software I track currently includes: Alpine, Alteryx, Angoss, C / C++ / C#, BMDPIBM SPSS Statistics, IBM SPSS ModelerInfoCentricity Xeno, Java, JMP, KNIME, Lavastorm, Mathworks’ MATLAB, Megaputer’s PolyAnalyst, Minitab, NCSS, Python, R, RapidMiner, SAS, SAS Enterprise Miner, Salford Predictive Modeler (SPM) etc., SAP’S KXEN, Stata, Statistica, SystatWEKA / Pentaho.

I don’t attempt to differentiate among variants of languages such as R vs. Revolution R Enterprise or TIBCO Enterprise Runtime for R; or SAS vs. the World Programming System (WPS) or Carolina.

Excluded from the list are products that focus on report writing (e.g. Cognos), or are tied to a specific database (e.g. Microsoft, Oracle, SAP), specific hardware (e.g. Teradata, IBM PureData) or a specific application field. I also exclude packages devoted more to visualization, such as Tableau, Spotfire, Origin, and SigmaPlot. These packages do occasionally appear in plots borrowed from other sites.

There are many ways to measure popularity or market share and each has its advantages and disadvantages. These include:

  • Job Advertisements
  • Scholarly Articles
  • Books
  • Blogs
  • Surveys of Use
  • IT Research Firm Reports
  • Discussion Forum Activity
  • Programming Popularity Measures
  • Sales & Downloads
  • Competition Use
  • Growth in Capability

Let’s examine each of them in turn.

Job Advertisements

One of the best ways to measure the popularity or market share of software for analytics is to count the number of job advertisements for each. Job advertisements are rich in information and are backed by money so they are perhaps the best measure of how popular each software is now. Plots of job trends give us a good idea of what is likely to become more popular in the future. is the biggest job site in the U.S. making its sample the best around. As their  CEO and co-founder Paul Forster stated, includes “all the jobs from over 1,000 unique sources, comprising the major job boards – Monster, Careerbuilder, Hotjobs, Craigslist – as well as hundreds of newspapers, associations, and company websites.” also has superb search capabilities and it includes a tool for tracking long-term trends.

Searching for analytics jobs using can be easy, but it can also be very tricky. For a package that has a unique name, all that is required is a simple search on that name. However, for software that’s hard to locate (e.g. R) or that is general purpose (e.g. Java) it required complex searches and/or some rather tricky calculations which are described here. All of the graphs in this section use those procedures to make the required queries.

Figure 1a shows that Java is in the lead followed by SAS. Python or C, C++/C# are roughly tied for third place. The tie between C and Python is not surprising as many advertisements for analytics jobs that use programming mention both together. (The C variants are combined in a single search since job advertisements usually seek any of them).


Figure 1a. The number of analytics jobs for the more popular software (250 jobs or more, 2/2014).

R resides in an interestingly large gap between the other domain-specific languages, SAS and SPSS. R has not only caught up with SPSS, but surpassed it with around 50% more job postings. MATLAB has many similarities to R, so it’s interesting to see that it has only around half the job postings. Note that these are specific to analtyics and MATLAB has many engineering jobs that are not counted in this total.

Much of the software had fewer than 250 jobs. When displayed on the same graph as the industry leaders, their job counts appeared to be zero. Therefore I have plotted them separately in Figure 1b. FICO comes out the leader of this group, followed by Enterprise Miner. Statistica and Alteryx are close to tied at around 55 jobs. From RapidMiner on down, the decline in jobs is fairly smooth. Megaputer’s Polyanalyst job count is actually zero.

Figure 1b. The number of analytics jobs for the less popular software (under 250 jobs) (2/2014).

Figure 1b. The number of analytics jobs for the less popular software (under 250 jobs, 2/2014).

It’s important to note that the values shown in Figures 1a and 1b are single points in time. The number of jobs for the more popular software do not change much from day to day. Therefore the relative rankings of the software shown in Figure 1a is unlikely to change much over the coming year. The less popular packages shown in Figure 1b have such low job counts that their ranking is likely to shift from month to month.

Each software has an overall trend that shows how the demand for jobs changes across the years. You can plot these trends using’s Job Trends tool. However, as before, focusing just on analytics jobs requires carefully constructed queries, and when comparing two trends at a time means they both have to fit in the same query limit allowed by Those details are described here.

I’m particularly interested in trends involving R, so let’s look at a couple of comparisons. Figure 1c compares the number of analytics jobs available for R and SPSS across time. Analytics jobs for SPSS have not changed much over the years, while those for R have been steadily increasing. The jobs for R finally crossed over and exceeded those for SPSS toward the middle of 2012.

Figure 1c. Analytics job trends for R and SPSS. Note that the legend labels at the top of the graph are truncated due to the very long size of the query.

We know from Figure 1a that SAS is still far ahead of R in analytics job postings. How far does R have to go to catch up with SAS? Figure 1d provides one perspective. It would be nice to have the data to forecast when R’s growth curve will catch up with SAS’s, but does not provide the raw data. However, we can use the approximate slope of each line to get a rough estimate. If jobs for SAS stay level and those for R continue to grow linearly as they have since January 2010, then R will catch up in 3.35 years. If instead the demand for SAS jobs that started in January of 2012 continues, then R will catch up in 1.87 years.

Figure 1c. The trend in analytics jobs for R and SAS.
Figure 1d. Analytics job trends for R and SAS. Legend labels are truncated due to long query length.

A debate has been taking place on the Internet regarding the relative place of Python and R. Ironically, this debate about software to do data analytics has involved very little actual data. However it is possible now to at least study the job trends. Figure 1a showed us that Python is well out in front of R, at least on that single day the searches were run. What has the data looked like over time? The answer is shown in Figure 1e.

Figure 1c. Jobs trends for R and Python (2/22/14).
Figure 1e. Jobs trends for R and Python. Legend labels are truncated due to long query length.

Note that in this graph, Python appears to have less of advantage in Figure 1e than it had in Figure 1a. The final point on the trend graph was done only a few days after the queries used in Figure 1a, and that data changed very little in the meantime. The difference is due to the fact that has a limit on query length. Here is the query used for Figure 1e, and the analytic terms it contains were fewer than the one used for Figure 1a.

and ("big data"
or "statistical analysis"
or "data mining"
or "data analytics"
or "machine learning"
or "quantitative analysis"
or "business analytics"
or "statistical software"
or "predictive modeling")
!"R D" !"A R" !"H R" !"R N" 
!toys !kids !" R Walgreen" !walmart
!"HVAC R" !"R Bard" 
and ("big data"
or "statistical analysis"
or "data mining"
or "data analytics"
or "machine learning"
or "quantitative analysis"
or "business analytics"
or "statistical software"
or "predictive modeling")

One last trend I considered was for Megaputer’s PolyAnalyst. Using the string “Megaputer PolyAnalyst” (“or” is implied) the trend line was completely flat at zero. I only include it here because the IT advisory firm Gartner, Inc. considered Megaputer worth including in their Magic Quadrant for Advanced Analytics Platforms report of February 19, 2014.

The detailed description regarding the construction of all the queries used in Figures 1a through 1e is located here.

Scholarly Articles

Scholarly articles are also rich in information and backed by significant amounts of effort. The more popular a software package is, the more likely it will appear in scholarly publications as an analysis tool or even an object of study. 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; each 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, perhaps due to its balance between power and ease-of-use. SAS has around half as many, followed by a tight grouping of R, Stata and MATLAB. 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. Number of scholarly articles found in the most recent complete year (2014) for each software package.

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.)

Figures 2a and 2b are useful for studying market share as it is now, but they don’t show how things are changing. It would be ideal to have long-term growth trend graphs for each of the analytics packages, 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 2c shows the percent change across those years, 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 2c. 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.

Python is the fastest growing. Note that the Python figures are strictly for analytics use as defined here. The open-source KNIME and SPSS Modeler are the second and third fastest growing. Both of those use the easy yet powerful workflow approach to analytics. At the other end of the scale are SPSS and SAS, both of which declined in use by more than 25%. Recall that Fig. 2a shows that despite recent years of decline, SPSS is still extremely dominant for scholarly use.

It will be particularly interesting to see what the future holds for KNIME and RapidMiner. The companies were two of only four chosen by IT advisory firm Gartner, Inc. 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 rate of growth will 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.

I’m particularly interested in the long-term trends of the classic statistics packages. So in Figure 2d 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 2d. 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 2d 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 2e. Freeing up so much space in the plot now allows us to see that the use of R and Stata are both experiencing very rapid growth and are pulling away from the pack, solidifying their position in third and fourth place, respectively. If the current trends continue, the use of R and Stata may pass that of SPSS and SAS by the end of 2016. Note that trends have shifted before as discussed here. 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, Stata and Statistica were under-counted.]


Figure 2e. 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.

Using a logarithmic y-axis scales down the more popular packages, allowing us to see the full picture in a single image (Figure 2f.)  This view makes it more clear that R and Stata are closing in on SAS. However, even when one studies the y-axis values carefully, it can be hard to grasp how much the logarithmic transformation has changed the values. For example, in 2014 value for SPSS is well over twice the value for SAS. The original scale shown in Figure 2d makes that quite clear.


Figure 2f. A logarithmic view of the number of scholarly articles found in each year by Google Scholar. This combines the previous two figures into one by compressing the y-axis with a base 10 logarithm.


The number of books that include a software’s name in its title is a particularly useful information since it requires a significant effort to write one and publishers do their own study of market share before taking the risk of publishing. However, it can be difficult to do searches to find books that use general-purpose languages which also focus only on analytics. 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 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 the table immediately below, 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 Books 
SAS                  576
SPSS Statistics      339
R                    240    [Corrected from blog post: 172]
JMP                   97
Hadoop                89
Stata                 62
Minitab               33
Enterprise Miner      32

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


On Internet blogs, people write about software that interests them, showing how to solve problems and interpreting events in the field. Blog posts contain a great deal of information about their topic, and although it’s not as time consuming as a book to write, maintaining a blog certainly requires effort. Therefore, the number of bloggers writing about analytics software has potential as a measure of popularity or market share. Unfortunately, counting the number of relevant blogs is often a difficult task. General purpose software such as Java, Python, the C language variants and MATLAB have many more bloggers writing about general programming topics than just analytics. But separating them out isn’t easy. The name of a blog and the title of its latest post may not give you a clue that it routinely includes articles on analytics.

Another problem arises from the fact that what some companies would write up as a newsletter, others would do as a set of blogs, where several people in the company each contribute their own blog. Those individual blogs may also combined into a single company blog inflating the count further still. Statsoft and Minitab offer examples of this. So what’s really interesting is not company employees who are assigned to write blogs, but rather those written by outside volunteers. In a few lucky cases, lists of such blogs are maintained, usually by blog consolidators, who combine many blogs into a large “metablog.” All I have to do is find such lists and count the blogs. I don’t attempt to extract the few vendor employees that I know are blended into such lists. I only skip those lists that are exclusively employee-based (or very close to it). The results are shown here:

Software of Blogs Source
R         550
Python     60
SAS        40, Planet
Stata      11

Number of blogs devoted to each software package on April 7, 2014,
and the source of the data.

R’s 550 blogs is quite an impressive number. For Python, I could only find that list of 60 that were devoted to the SciPy subroutine library. Some of those are likely cover topics besides analytics, but to determine which never cover the topic would be quite time consuming. The 40 blogs about SAS is still an impressive figure given that Stata was the only other company that even garnered a list anywhere. That list is at the vendor itself, Statacorp, but it consists of non-employees except for one.

While searching for lists of blogs on other software, I did find individual blogs that at least occasionally covered a particular topic. However, keeping this list up to date is far too time consuming given the relative ease with which other popularity measures are collected.

If you know of other lists of relevant blogs, please let me know and I’ll add them. If you’re a software vendor employee reading this, and your company does not build a metablog or at least maintain a list of your bloggers, I recommend taking advantage of this important source of free publicity.


Discussion Forum Activity

Another way to measure software popularity is to see how many people are helping one another use each package or language. While such data is readily available, it too has its problems. Menu-driven software like SPSS or workflow-driven software such as KNIME are quite easy to use and tend to generate fewer questions. Software controlled by programming requires the memorization of many commands and requiring more support. Even within languages, some are harder to use than others, generating more questions (see Why R is Hard to Learn).

Another problem with this type of data is that there are many places to ask questions and each has its own focus. Some are interested in a classical statistics perspective while others have a broad view of software as general-purpose programming languages. In recent years, companies have set up support sites within their main corporate web site, further splintering the places you can go to get help. Usage data for such sites is not readily available.

Another problem is that it’s not as easy to use logic to focus in on specific types of questions as it was with the data from job advertisements and scholarly articles discussed earlier. It’s also not easy to get the data across time to allow us to study trends.  Finally, the things such sites measure include: software group members (a.k.a. followers), individual topics (a.k.a. questions or threads), and total comments across all topics (a.k.a. total posts). This makes combining counts across sites problematic.

Two of the biggest sites used to discuss software are LinkedIn and Quora. They both display the number of people who follow each software topic, so combining their figures makes sense. However, since the sites lack any focus on analytics, I have not collected their data on general purpose languages like Java, MATLAB, Python or variants of C. The results of data collected on 10/17/2015 are shown here:


Number of people who follow each software on LinkedIn and Quara.

We see that R is the dominant software and that moving down through SAS, SPSS, and Stata results in a loss of roughly half the number of people in each step. Lavastorm follows Stata, but I find it odd that there was absolutely zero discussion of Lavastorm on Quora. The last bar that you can even see on this plot is the 62 people who follow Minitab. All the ones below that have tiny audiences of fewer than 10.

Next let’s examine two sites that focus only on statistical questions: Talk Stats and Cross Validated. They both report the number of questions (a.k.a. threads) for a given piece of software, allowing me to total their counts:


Number of questions and for each software on Talk Stats and Cross Validated. Those not shown had no questions.

We see that R has a 4-to-1 lead over the next most popular package, SPSS. Stata comes in at 3rd place, followed by SAS. The fact that SAS is in fourth place here may be due to the fact that it is strong in data management and report writing, which are not the types of questions that these two sites focus on. Although MATLAB and Python are general purpose languages, I include them here because the questions on this site are within the realm of analytics. Note that I collected data on as many packages as were shown in the previous graph, but those not shown have a count of zero. Julia appears to have a count of zero due to the scale of the graph, but it actually had 5 questions on Cross Validated.


Surveys of Use

Survey data adds additional information regarding software popularity, but they are commonly done using “snowball sampling” in which the survey provider tries to widely distribute the link and then vendors vie to see who can get the most of their users to participate. So long as they all do so with equal effect, the results can be useful. However, the information is often limited, because the questions are short and precise (e.g. “tools data mining” or “program languages for data mining”) and responding requires just a few mouse clicks, rather than the commitment required to place a job advertisement or publish a scholarly article, book or blog post. As a result, it’s not unusual to see market share jump 100% or drop 50% in a single year, which is very unlikely to reflect changes in actual use.

Rexer Analytics conducts a survey of data scientists every other year, asking a wide range of questions regarding data science (previously referred to as data mining by the survey itself.) Figure 6a shows the tools that the 1,220 respondents reported using in 2015.

Figure 6a. Analytics tools used.

Figure 6a. Analytics tools used by respondents to the 2015 Rexer Analytics Survey. In this view, each respondent was free to check multiple tools.

We see that R has a more than 2-to-1 lead over the next most popular packages, SPSS Statistics and SAS. Microsoft’s Excel Data Mining software is slightly less popular, but note that it is rarely used as the primary tool. Tableau comes next, also rarely used as the primary tool. That’s to be expected as Tableau is principally a visualization tool with minimal capabilities for advanced analytics.

The next batch of software appears at first to be all in the 15% to 20% range, but KNIME and RapidMiner are listed both in their free versions and, much further down, in their commercial versions. These data come from a “check all that apply” type of question, so if we add the two amounts, we may be over counting. However, the survey also asked,  “What one (my emphasis) data mining / analytic software package did you use most frequently in the past year?”   Using these data, I combined the free and commercial versions and plotted the top 10 packages again in figure 6b. Since other software combinations are likely, e.g. SAS and Enterprise Miner; SPSS Statistics and SPSS Modeler; etc. I combined a few others as well.

Figure 6b. The percent of survey respondents who checked each package as their primary tool.

Figure 6b. The percent of survey respondents who checked each package as their primary tool in 2015. Note that free and commercial versions of KNIME and RapidMiner are combined. Multiple tools from the same company are also combined. Only the top 10 are shown.

In this view we see R even more dominant, with a 3-to-1 advantage compared to the software from IBM SPSS and SAS Institute. However, the overall ranking of the top three didn’t change. KNIME however rises from 9th place to 4th. RapidMiner rises as well, from 10th place to 6th. KNIME has roughly a 2-to-1 lead over RapidMiner, even though these two packages have similar capabilities and both use a workflow user interface. This may be due to RapidMiner’s move to a more commercially oriented licensing approach. For free, you can still get an older version of RapidMiner or a version of the latest release that is quite limited in the types of data files it can read. Even the academic license for RapidMiner is constrained by the fact that the company views “funded activity” (e.g. research done on government grants) the same as commercial work. The KNIME license is much more generous as the company makes its money from add-ons that increase productivity, collaboration and performance, rather than limiting analytic features or access to popular data formats.

The results of a similar poll done by the web site in May of 2015 are shown in Figure 6c. 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 that these survey results reverse the order in the previous one, showing RapidMiner as being more popular than KNIME. Both are still the top two “point-and-click” type packages generally used by non-programmers.

KDnuggests 2015

Figure 6c. 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.

O’Reilly Media conducts an annual Data Science Salary Survey which also asks questions about analytics tools. Although the full report of results As their report notes,”O’Reilly content—in books, online, and at conferences—is focused on technology, in particular new technology, so it makes sense that our audience would tend to be early adopters of some of the newer tools.”  The results from their “over 600” respondents are shown in figures 6d and 6e.


Figure 6d. Tools used by 2015 respondents to O’Reilly 2015 salary survey. The less popular tools among this audience are shown in the following figure.


Figure 6e. The less popular tools used by the respondents of O’Reilly’s 2015 salary survey.

The O’Reilly results have SQL in first place with 70% of users reporting it, followed closely by Excel. Python and R follow seemingly tied for third place with 55%. However, Python also appears in 6th place with its subroutine libraries numpy, etc., and R’s popular ggplot package appears in 7th place, with around 38% market share. The first commercial package with deep analytic capabilities is SAS in 23rd place!  This emphasizes that the O’Reilly sample is heavily weighted towards their usual open source audience. Hopefully in the future they will advertise the survey to a wide audience and do so as more than just a salary survey. Tool surveys gain additional respondents since they are advertised by advocates of the various tools (vendors, fans, etc.)

Lavastorm, Inc. conducted a survey of analytic communities including LinkedIn’s Lavastorm Analytics Community Group, Data Science Central and KDnuggets. The results were published in March, 2013, and the bar chart of “self-service analytic tool” usage among their respondents is shown in Figure 6f. Excel comes out as the top tool, with 75.6% of respondents reporting its use.

R comes out as the top advanced analytics tool with 35.3% of respondents, followed closely by SAS. MS Access’ position in 4th place is a bit of an outlier as no other surveys include it at all. Lavastorm comes out with 3.4%, while other surveys don’t show them at all. That’s hardly a surprise given than the survey was aimed at the Lavastorm’s LinkedIn community group.

Figure 7e. Lavastorm survey of analytics tools.

Figure 6f. Lavastorm survey of analytics tools.

IT Research Firms

IT research firms study software products and corporate strategies, they survey customers regarding their satisfaction with the products and service,  and then provide their opinions on each in reports they sell to their clients. Each company has its own criteria for rating companies, so they don’t always agree. However, I find the reports extremely interesting reading. While the reports are expensive, you can usually find free copies by searching the internet for the report names in combination with the name of one of the companies that received a good review.

Gartner, Inc. is one of the companies that provides such reports.  The KDnuggets web site combined two years of “Magic Quadrant” plots from from Gartner’s report, Advanced Analytics for Business Analysts, and the result is shown in Figure 7a. Gray circles show where Gartner rated each company in 2014 and blue ones show their 2015 rating. If the company improved, a green arrow points to their new position. If the company received a worse rating, a red arrow shows where they moved to. Black arrows show changes that are relatively neutral. Since Gartner rates companies, strictly open source software such as R, Python and Java are not shown.

Figure 7a. Gartner

Figure 7a. Gartner “Magic Quadrant” plot of companies that sell advanced analtyics software.

For both years, IBM (SPSS), SAS, RapidMiner and KNIME are the companies in the Leaders quadrant, indicating that they have both “completeness of vision” and the ability to execute that vision. Of these, KNIME is the only one whose core desktop product is free and open source. RapidMiner offers an older version of their software for free.

Microsoft improved the most, especially in its completeness of vision. This may be due to the release of their Azure Machine Learning platform. Alpine, Alteryx and SAS also improved their positions. Angoss dropped quite a bit and a few companies were removed altogether for various reasons described in the report. The display shows Statsoft (Statistica) being bought by Dell and barely moving, but Microsoft’s acquisition of Revolution Analytics is not reflected in this plot.

Forrester Research, Inc. is another company that provides similar reports. The KDnuggets site overlaid the “Wave” plots from their two most recent reports, The Forrester Wave: Big Data Predictive Analytics Solutions, shown in Figure 7b. The lighter circles represent each company’s position in the previous report (2013) and the more solid white circles represent their position in 2015.

Figure 7b. Forrester Wave plot of big data predictive analytics solutions, Q1 2013.

Figure 7b. Forrester Wave plot of big data predictive analytics solutions, Q1 2015 vs. Q1 2013.

Again, IBM (SPSS) and SAS are the strongest companies, but that seems to be all that the two reports seem to agree on! The reports emphasize different aspects of the companies being rated, which accounts for the radically different plots. While the Gartner report showed Angoss losing strength, the Forrester analysts see it gaining strength! RapidMiner is not in the Leaders area here, but at least it’s close. KNIME on the other hand is barely inside the Strong Performer area.

Hurwitz & Associates released their Advanced Analytics: The Hurwitz Victory Index Report in mid 2014. Figure 7c shows their plot of strength of company strategy vs. viability. This is similar to the measures plotted in the Gartner Group’s Magic Quadrant plot (Fig. 7a). These two plots both show IBM and SAS in the best position, but after that there’s not much similarity. Gartner sees RapidMiner in the same ballpark as IBM and SAS, while Hurwitz shows it towards the opposite end. KNIME is also toward the top of Gartner’s plot and not covered by Hurwitz at all.

Figure 7c. Hurwitz & Associates plot of

Figure 7c. Hurwitz & Associates plot of corporate vision vs. viability.

Programming Popularity Measures

Several web sites rank the popularity of programming languages. Unfortunately, they don’t differentiate between general-purpose languages and application-specific ones used for analytics. However, it’s easy to choose the few analytics languages their results.

The most comprehensive of these sites is the IEEE Spectrum Ranking. This site combines 12 metrics from 10 different sites. These include some of the measures discussed above, such as popularity on job sites and search engines. They also include fascinating and useful measures such as how much new programming code was added to the popular GitHub repository in the last year. This table shows their top 10 languages for 2015:


IEEE Spectrum language popularity rankings. The left column (orange) shows the 2015 ranking, while the left (yellow) one shows the 2014 ranking.

We see that R is in 6th place and that it has increased from 9th place in 2014. Not shown on this is SAS in 26th place. Python is ranked in 4th place, but that’s for all purposes, while the use of R is more focused on analytics. No other analytics-specific language makes it in their rankings at all. This ranking is based on a weighted composite score and the site is interactive, allowing you to generate a ranking more suited to your needs.

The next most comprehensive analysis is provded by RedMonk. Their analysis is simple and objective. They plot the number of lines of code written using each language on the popular Github repository against the number of tagged comments on the discussion forum Here is the result:


RedMonkpProgramming language popularity as measured by number of projects on GitHub and the amount of discussion on StackOverflow.

Moving from the upper right corner downward the lower left, we can see that Redmonk’s approach shows R as a very popular language, around 12th place. Although a substantial amount of the metrics for Python, MATLAB, and Julia may be due to analytics use, we have no way of knowing how much.

The TIOBE Community Programming Index also ranks the popularity of programming languages. It extracts measurements from the 25 most popular search engines including Google, YouTube, Wikipedia,, and combines them into a single index. In their October 2015 rankings, they place R in 20th place and SAS in 23rd. Stata is in a bundle they call “the next 50” languages, whose popularity among general-purpose languages is so sparse that their relative rankings are too unstable to bother giving individual ranks. SPSS is a language they monitor, but it doesn’t make it into their top 100. This brings us to an important limitation of the Tiobe index: it searches for one single string: “X programming.” So if it didn’t find “SPSS programming” then it doesn’t count. The complex searches that I used for jobs and scholarly articles was far more useful in estimating each package’s popularity. Another limitation to the Tiobe index is that it measures what is on the Internet now, so it’s a lagging indicator. There’s no way to plot trends without purchasing their data, which is quite expensive.

A very similar popularity index is PYPL PopularitY of Programming Language. It only tracks the top 15 languages and, in October of 2015 it placed R in 11th place. It searches on the single string, “X tutorial” making it a leading indicator of what’s likely to be more popular in the future.

The Transparent Language Popularity Index is very similar to the TIOBE Index with except that its ranking software, algorithm and data are published for all to see. Work on this index ceased as of July, 2013.


Sales & Downloads

Sales figures reported by some commercial vendors include products that have little to do with analysis. Many vendors don’t release sales figures, or they release them in a form that combines many different products, making the examination of a particular product impossible. For open source software such as R (Ihaka and Gentleman 1996) you could count downloads, but one confused person can download many copies, inflating the total. Conversely, many people can use a single download on a server, deflating it.

Download counts for the R-based Bioconductor project are located at Similar figures for downloads of Stata add-ons (not Stata itself) are available at  A list of Stata repositories is available at The many sources of downloads both in repositories and individuals’ web sites makes counting downloads a very difficult task.


Comptetition Use is a web site that sponsors data analysis contests. People post data analysis problems there along the amount of money they are willing pay the person or team who solves their problem the best. Figure 8 shows the software used by the data analysts working on the problems. R is in the lead by a wide margin. R’s dominance is even greater among the contest winners, over 50% of whom used R. A potential source of bias in these figures is that the licenses of most proprietary software prohibits its use for the benefit of outside organizations (universities can help federal grant-providing agencies such as NSF and NIH, but cannot even solve problems for government agencies in general or nonprofits). However, I manage the research software site licenses at the University of Tennessee, and I can attest to the fact that people are often unaware of this limitation. (Note that as of 4/11/2014 this graph of 2011 data is still Kaggle’s most current graph.)

Figure 8. Software used in data analysis competitions in 2011.

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, 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.


What’s Missing?

I previously included on Google Trends. That site tracks not what’s actually on the Internet via searches, but rather the keywords and phrases that people are entering into their Google searches. That ended up being so variable as to be essentially worthless. For an interesting discussion of this topic, see this article by Rick Wicklin.

Website Popularity – in previous editions I have included measures of this. However, as the corporate landscape has consolidated, we end up comparing huge companies with interests far outside the field of analytics (e.g. IBM) with relatively small focused ones, which no longer makes sense.



Although the ranking of each package varies depending on the criteria used, we can still see major trends. Among the software that tends to be used as a collection of pre-written methods, R, SAS, SPSS and Stata tend to always be in the top, with R and SAS occasionally swapping places depending on the criteria used. I don’t include Python in this group as I rarely see someone using it exclusively to call pre-written routines.

Among software that tends to be used as a language for analytics, C/C#/C++, Java, MATLAB, Python, R and SAS are always towards the top. I list those in alphabetical order since many of the measures cover not only use for analytics but for other uses as well. Among my colleagues, those who are more towards the computer science side of the data science field tend to prefer Python, while those who are more towards the statistics send tend to prefer R. A language worth mentioning is Julia, whose goal is to have syntax as clean as Pythons while maintaining the top speed reached by the C/C#/C++ group.

A trend that I find very interesting is the rise of software that uses the workflow (or flowchart) style of control. While menu-driven software is easy to learn, it’s not easy to re-use the work. Workflow-driven software is almost as easy — the dialog boxes that control each node are almost identical to menu-driven software — but you also get to save and re-use the work. Software that uses this approach includes: KNIME, Microsoft Azure Machine Learning, RapidMiner, SPSS Modeler (the first to popularize this approach), SAS Enterprise Miner, SAS Studio, and even two cloud-based system that I have not been tracking, Dotplot Designer and Microsoft Azure Machine Learning.  The wide use of this interface is allowing non-programmers to make use of advanced analytics.

I’m interested in other ways to measure software popularity.  If  you have any ideas on the subject, please contact me at

If you are a SAS or SPSS user interested in learning more about R, you might consider my book, R for SAS and SPSS Users. Stata users might want to consider reading R for Stata Users, which I wrote with Stata guru Joe Hilbe. I also teach workshops on these topics both online and with site visits.



I am grateful to the following people for their suggestions that improved this article: John Fox (2009) provided the data on R package growth; Marc Schwartz (2009) suggested plotting the amount of activity on e-mail discussion lists; Duncan Murdoch clarified the pitfalls of counting downloads; Martin Weiss pointed out both how to query Statlist for its number of subscribers; Christopher Baum provided information regarding counting Stata downloads; John (Jiangtang) HU suggeseted I add more detail from the TIOBE index;  Andre Wielki suggested the addition of SAS Institute’s support forums; Kjetil Halvorsen provided the location of the expanded list of Internet R discussions; Dario Solari and Joris Meys suggested how to improve Google Insight searches; Keo Ormsby provded useful suggestions regarding Google Scholar; Karl Rexer provided his data mining survey data; Gregory Piatetsky-Shapiro provided his KDnuggets data mining poll; Tal Galili provided advice on blogs and consolidation, as well as Stack Exchange and Stack Overflow; Patrick Burns provided general advice; Nick Cox clarified the role of Stata’s software repositories and of popularity itself; Stas Kolenikov provided the link of known Stata repositories; Rick Wicklin convinced me to stop trying to get anything useful out of Google Insights; Drew Schmidt automated some of the collection; Francois Briatte provided the link that creates Figure 1c; Rasmus Bååth provided the median number of functions in an R package; Peter Hedström greatly improved my search string for Stata.



J. Fox. Aspects of the Social Organization and Trajectory of the R Project. R Journal,

R. Ihaka and R. Gentleman. R: A language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5:299–314, 1996.

R. Muenchen, R for SAS and SPSS Users, Springer, 2009

R. Muenchen, J. Hilbe, R for Stata Users, Springer, 2010

M. Schwartz, 1/7/2009,



Alpine, Alteryx, Angoss, Microsoft C#, BMDPIBM SPSS Statistics, IBM SPSS ModelerInfoCentricity Xeno, Oracle’s Java, SAS Institute’s JMP, KNIME, Lavastorm, Mathworks’ MATLAB, Megaputer’s PolyAnalyst, Minitab, NCSS, Python, R, RapidMiner, SAS, SAS Enterprise Miner, Salford Predictive Modeler (SPM) etc., SAP’S KXEN, Stata, Statistica, SystatWEKA / Pentaho are are registered trademarks of their respective companies.

Copyright 2010-2015 Robert A. Muenchen, all rights reserved.

81 Responses to The Popularity of Data Analysis Software

  1. bob mcconnaughey says:

    i’m not surprised that R, in particular, has done spectacularly well with respect to analytic use – it has, as best i can tell, virtually all the analytic tools one might need. I’ve been worried for decades now about the ever increasing use of excel for both data mgt and analysis. So many projects/”data sets”/ analyses have come our way in excel spreadsheets only for major problems in data integrity, tracing flow of data changes that led to errors, even analyses that were later found to be completely hosed because a user had done something as simple, and as deadly, as sorting a column instead of the records/rows.

    What SAS has and,really should concentrate on, is its data handling, manipulation, organization, data validation features..that are all built into Base SAS. I have, and appreciate, your R for SAS/SpSS Users – and i can’t help but think that organizations that rely on both “data integrity” which, really, is SAS’ great strength and analysis could profitably use SAS for complex data manipulations and then write out files in one of the many formats R takes, do the analytics in R and pull the results back into Base SAS. A few months ago i helped out a friend who was analyzing generational data drawn from 80 + yrs from the complete medical birth registry of Norway. SPSS is the data manipulation software they use..and the task of linking families, sibs, half sibs with flags/subsets for individuals/families that had various birth defects over multiple generations was seemingly intractable in SPSS, whereas while it was a non-trivial exercise in SAS, it was certainly conceptually straight forward. And the resulting files could be analyzed in either R or SPSS, of course.(or SAS – which isn’t a package that they licence because of its increasingly pricey )

    • Bob Muenchen says:

      I’ve done quite a lot of complex data management in SAS, SPSS and R. To me they seem quite similar in capability except that R must fit the data into the computer’s main memory (unless you’re using Revolution Analytic’s version). Where SAS may have the edge is reading unusual files where you have to read some data and, based upon that data, decide what other data to continue reading. I see that type of data rarely and I’ve only read it in SAS. The others may be able to do it but I haven’t taken the time to see if they can or not.

      • Christian says:

        “To me they seem quite similar in capability except that R must fit the data into the computer’s main memory”

        I’ve been thinking about this lately, and I wonder if this might be a blessing in disguise? Every time our group hits memory constraints, we buy more RAM. It’s cheap, and it grows exponentially cheaper/larger over time. Of course, that doesn’t work for “very large problems”. But, on the other hand, there’s the MapReduce paradigm of divide-and-conquer. I don’t often encounter datasets that I can’t subdivide and process in chunks. Working with on-disc data is orders of magnitude slower (though SSD seems to help quite a bit), and so the dataset-in-RAM paradigm strikes me, after some thought, as a “good idea in disguise”.

  2. Jeremie says:

    Excellent summary, thank you very much. The exponential growth of R packages is impressive.

    I am trying to catch how you measured the statistical softwares on the job market.

    Indeed a research with just “R” leads of course to nothing meaningful. I would search for expressions like theses :
    “STATA (statistic OR statistical)” = 627
    “MINITAB (statistic OR statistical)” =1277
    “SPSS (statistic OR statistical)” = 2488
    “R (statistic OR statistical)” = 2957
    “SAS (statistic OR statistical)” = 7053

    which shows the prevalence of SAS, but to a less degree.

    • Bob Muenchen says:

      Many of the strings are easy:

      JMP, BDMP Minitab, SPSS, Stata, Statistica, Systat

      And SAS isn’t too bad but but you have to exclude any hard drive interface references for which SAS has another meaning:

      SAS (excluding SATA, storage, firmware)

      R is devilishly difficult to get. Since you found more jobs for R than for SPSS I’m pretty sure you’re getting mostly bad hits. You have to study a lot of the job descriptions to see what’s actually being found. Plain old “R” is found in many irrelevant situations. I use a Linux shell script that searches for:

      (“SAS or R” or “R or SAS”) and it repeats that pattern for the above packages and MATLAB, SQL, Java, Python, Perl

      After much study that is the only way I have found to locate “R” that is relevant. If you find another way, I’d love to hear it!

      The whole thing is a Linux shell script written by a former research assistant. A variation of it which I used for figures 7a and 7b is described in detail at:

      • Jakob says:

        Another option to exclude lists is to manually inspect N samples of each query and estimate the chance of a query to be relevant. For example, you may get 5000 hits on an R query and estimate 1/20 to be referring to the statistical software -> approx 250 hits.

      • Bob Muenchen says:

        Hi Jakob,

        I ended up doing something similar as described in How to Search for Analytics Jobs. I’ll update the post to reflect this new perspective 2/25/14.


  3. omar says:

    thank you for this stats article just what i needed

  4. rjrich says:

    It would be interesting to include popular scientific plotting and statistics packages such as Origin Pro, SigmaPlot, and GraphPad Prism.

  5. Ken says:

    Where you say, “No other data analysis languages covered by this article even make their top 100.”, is not true. If you look at the portion that says the next 50, covering 51-100 you will see S, S-PLUS, and SPSS which are all data analysis languages. It is also debatable that MATLAB, PL/SQL and Transact-SQL could be considered data analysis languages.

  6. Bob Muenchen says:

    Ken, thanks very much for pointing that out. One of the hardest things about tracking so many sources of information is noticing all the changes that are relevant! I’ll deleted that sentence.

  7. Karup Pekar says:

    This is a very good article. I especially admire the way you have tried to quantify various measures. It’s worth reading just to learn that you can use “not” operators on google and amazon. Most illustrative of trends in stats packages and languages. Thank you!

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  9. Ken says:

    One interesting thing to look at could be comparing trends from the kdnuggets polls. You have the current year but there is also links to some of the prior years. For instance the following show two very different perspectives from two different points in time.

    I am not sure what all could be done with this but it would be interesting.

  10. Monica Lewis says:

    I’m curious about a review of tools used by non-statisticians for analysis in business. Do you know what products that help smooth some of most basic data related tasks that the masses are currently doing in Excel — such as pivot tables, commenting and collaboration? I’ve been building one to try to answer this, and am curious about others!

    Thanks for all of the details on tool functionalities and preferences for true big data analysts!

  11. inundata says:

    Fantastic! Is this published somewhere peer-reviewed that I can cite? I’m working on a journal article (which strongly discourages citing webpages) and would love to cite this as a source.

  12. seo ranking tools says:

    Hello! Do you use Twitter? I’d like to follow you if that would be okay. I’m absolutely enjoying
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    • Bob Muenchen says:

      I’m @BobMuenchen on Twitter and I do tweet when each new post or article is finished. It’s certainly OK to follow me. I don’t tweet a lot, so you won’t be bombarded with crazy messages about where I’m eating lunch!

  13. says:

    SAS just doesn’t seem affordable except for corporations. Do they even have a single user academic perpetual license?

    • Bob Muenchen says:

      SAS Institute never does perpetual licenses. A single user academic license is very expensive but they do make it very cheap per copy when you get an unlimited-copies license.

  14. Bob McConnaughey says:

    I have quite a wonderful “ANCIENT” book that has a comparison of Stats/database packages circa 1980 back in my office. I DO remember that back in the day….the yearly license for the “Statistical Analysis Software” package was $1000.00 for a university. If I could attach a pdf I actually scanned the chapter on “General Statistical Packages.” The book was basically the result of a survey of users…My favorite line: “More importantly, SAS’s users think almost as highly of this program as its developer does”

  15. Dr.Az says:

    lovely post.
    one tiny error– there are two captions titled the same serial “7a”.
    maybe you mean 7b in the latter one.

  16. Sue Briggs says:

    “quiet” under Fig. 1d should be “quite”

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    If you might be interested feel free to shoot me an e-mail.
    I look forward to hearing from you! Superb blog by the way!

  18. gawbul says:

    I’d love to see how Julia ( fairs over the coming years 🙂

  19. Rosaria says:

    Do you think you can include more of KNIME in some of your graphs? I am curious to see how it compares. I use KNIME and I have seen it cited only in figure 3 and figure 4.

    • Bob Muenchen says:

      Hi Rosaria,

      I started out studying just classic statistics packages while the data mining software came from data collected by others. However I do hope to expand the graphs next year to include them. There’s little real difference between the two types of software other than the user interface, which is better on most data mining packages.


  20. Fred says:

    This is absolutely amazing. Given the passion that most scientists have towards their software packages and that you are a self-proclaimed Stata user, I’m amazed that you can have such an unbiased and rational approach to answering this question.

    1) There seem to be way too many stats packages.
    2) I was happy to see Number Cruncher Statistical Analysis in there. The copy I have is 10 years old, but I still use it for 3d graphing capabilities.
    3) I conducted a web search of “SAS vs Stata” because a coworker uses Stata and won’t shut up about it. I use SAS/Excel…and won’t shut up about it. My hypothesis was that my coworker is using an outdated stats package and he is stubbornly set in his outdated ways. This article mostly disproves that hypothesis, but does give me some ammo on the comparison. Thanks!

    • Bob Muenchen says:

      Hi Fred,

      I actually use Stata only occasionally, and then usually just to study how it does a particular thing. My co-author Joe Hilbe is the Stata guru. It is a beautiful system though. You can tell that a tiny number of people cared about making its structure consistent. SAS, SPSS and especially R were at the mercy of too many developers so their syntax is less consistent. All four are wonderful packages though, and each has an audience that thinks it’s the best by far. I like ’em all!


      • Wayne says:


        I’ve used R for years, and just bought Stata/IC 13 yesterday for several reasons. First, the company has a great attitude/culture and it’s always good to deal with a company where you like the people. Second, it seems to me to be the best option among SAS, SPSS, Minitab, et al, and it’s also a better deal for an individual purchaser. Third, it implements some algorithms that are more advanced than the R equivalents. And fourth, Stata 13 was just released and has a lot of nice new features.

        My first thought is that it reminds me a lot of Igor Pro, by Wavemetrics, which I used to use. They both have a great bunch of people (developers and users), a great culture, an interface that you can drive via commands or a GUI (though the GUI generates the command line equivalents so you can learn it or reuse it), and a consistent flavor. The difference being that Stata is statistics-oriented, while Igor Pro is scientific/experimental-oriented.

        I like Stata a lot, but it won’t replace R. I’d say that it’s much more elegant than SAS, et al. (SAS was developed for punched cards and influenced by IBM’s punched-card JCL, and has all kinds of obvious seams between its various parts. It’s definitely a Frankenstein.) I’d disagree with you that Stata’ syntax is more consistent than R’s though. I believe you’re talking about how functions in R have been written by various people, so the function calls may have some inconsistent argument names or perhaps result formats. On the other hand, Stata suffers from the data (essentially a spreadsheet) versus free-form variable (r(), e(), _b, _se, etc) distinction, which itself sets up various inconsistencies and makes me feel claustrophobic.

        So I still think that R’s the best option, but have definitely added Stata to my toolbox and it will be there long-term. I’d definitely recommend it to others.

        To some degree, I think it makes a difference what direction you come to statistics from. If you’re used to programming and like having the full machinery and flexibility of a programming language, R makes a lot of sense. If you don’t really program — you just want to give commands and get results — though you want the option of automating some things or using programs that others have written, Stata makes a lot of sense.

      • Bob Muenchen says:

        Hi Wayne,

        Thanks for your interesting comments. I’ve talked to a couple of other people recently make the point that R is better as a programming language, while Stata is easier to use as a way to control pre-written procedures. SAS certainly has some odd inconsistencies, but I’ve used it for so many years that they seem second nature to me.


      • Fred says:

        Hi Bob,

        As I mentioned before, my coworker uses Stata and I use SAS/Excel. Unfortunately, my coworker has retired and I have no way to validate my SAS/Excel code with her Stata output. If I were to provide the code that my coworker used, datasets, and any other information required, could you reference somebody to me who can run a Stata program? I can’t seem to find anybody who runs Stata!

        Any information is helpful.

  21. Karl Rexer says:

    Great analysis, as always. This is a great resource for the entire analytics community. Thanks!

  22. Kamal says:

    Hi Bob,
    Thanks for providing an overall big picture of statistical packages. I am using SAS from past couple of years and is preparing for its certification too. As a beginner I always used to wonder about the differences among different statistical packages but your article has answered a lot of my questions.

  23. Ajay Ohri says:

    so why does SAS Institute still make 2.5 $ billion every year. Your data is overwhelmingly conclusive- but the SAS revenue is what makes me a hold out believer

    • Bob Muenchen says:

      Hi Ajay,

      As far as I know, SAS Institute is still the largest privately held software company in the world and I don’t see that changing anytime soon. They continue to innovate, especially by offering complete solutions to problems rather than just offering tools that let you come up with your own solutions. I think the whole analytics pie is getting much larger. While SAS gets a smaller slice of this pie each year, it still adds up to more revenue.


  24. Nice data on use of different packages. A couple of comments. It would be interesting to know who is using what software and for what purposes. As an experimental psychologist, for example, I very much like SPSS for its handling of analysis of variance (both GLM and the older Manova). When I was generating course evaluations on my campus for a number of years, I liked to use SAS because of its powerful relational database functions (SQL). The same things could be done in SPSS but not nearly as “elegantly.” Is it perhaps the case that different classes of users are finding the features they need in particularly packages? Finally, we might like to believe that the “best” product wins out, but that is not always the case with respect to software (e.g., Word vs Wordperfect?) and should perhaps warrant some caution with respect to usage statistics. Nice job!

    • Bob Muenchen says:

      Hi Jim,

      You make some good points. Different packages definitely dominate in different market segments. Our campus (University of Tennessee) has a large social science presence and SPSS dominates by far overall. However, among economists Stata is dominant, the agriculturalists and business analytics folks use SAS, and while R use is in the minority, it seems like every department has someone on the cutting edge of their field using R.

      I like all these packages for their various strengths and agree that it makes little sense to say which is “best” for everyone.


  25. Hypersphere says:

    Extremely engaging. Although Sage is much more than a statistical package, it encompasses statistics, and it would be interesting to include it in the mix.

  26. john painter says:

    Should the caption for figure 1a say, “MORE popular”? The caption appears same as the one for Figure 1b

    “Figure 1a. The number of analytics jobs for the less popular software”
    “Figure 1b. The number of analytics jobs for the less popular software”

    Your descriptions of the challenges faced when compiling data from readily available but harder to interpret data shows how much work you have put into this site. Thanks!

    • Bob Muenchen says:

      Hi John,

      Thanks for catching that! It’s fixed. Regarding the amount of work, I wish I had tracked it. I do know that the job search section alone took over 100 hours. Now that I understand the problem better, I can update the figures in about an hour, but determining the optimal searches was really difficult.


  27. Joseph Hilbe says:

    The primary reason I show either both Stata and R code or just R code for the examples in my books now is due to the fact that the far majority of statistics journal manuscripts that I referee or edit use R for examples. SAS and Stata seem to come in as the second most used stat packages. However, I realize that this may in part be due to the type of manuscripts I referee. I’m on the editorial board of six journals, and am asked to referee by a number of others. But these are generally related to biostatistics, econometrics, ecology, and recently astrostatistics (where Python and R are most common). It also seemed to me that most of the books I read or referenced when researching for my books also used R for examples, followed by Stata and SAS.

    The second reason is due to the students I teach with I teach 5 courses (9 classes a year) with the company. These are month long courses over the web with discussion pages which I use to interact with those enrolled in the courses. A good 95% (seriously) of enrollees are active researchers working in government, research institutions, hospitals, large corporations, and so forth, as well as university professors wanting to update their knowledge of the area, or learn about it if they knew little before. Students come from literally everywhere — the US, UK, Italy, Australia/NZ, Brazil, China, Japan, South Africa, Near Eastern nations, Nigeria, and even Mongolia. I always ask for their software preference, and have on average 15-30 students. Logistic Regression is the most popular course followed by Modeling Count Data. R is by far the most used software package. I started teaching with their first year (2003) , using Stata. I would accept submissions using SAS and SPSS, but the course text and handouts I used were in Stata. It is a very easy package to learn and it has a very large range of statistical capabilities. But I increasingly had more and more students wanting to use R. So I started to become more proficient, co-authored R for Stata Users with Bob Muenchen, (2010) which really spiked my knowledge of the software and now used the two package equally. My “Methods of Statistical Model Estimation” book with Andrew Robinson (2013) is a book for R programmers, and “A Beginners Guide to GLM and GLMM using R” (2013) with Alain Zuur and Elena Ieno uses only R and JAGS – I am ever more becoming a Bayesian as well. Other book, like my “Modeling Count Data” (Cambridge Univ Press) which comes out in May uses both R and Stata in the text, with SAS code for the examples in the Appendix. R, JAGS, and SAS is used for the Bayesian chapter.

    Look through the new books that are being authored and the journal articles being published by the major statistics journals. Its mostly R, Stata, and SAS, with SPSS also used in books/journal articles specifically devoted to the social sciences. Minitab occasionally as well. Python and R almost exclusively for the physical sciences. For the many new books on Bayesian modeling, most use WinBUGS/OpenBUGS and R (and R with JAGS), and some SAS. I see Python becoming more popular though.

    For what its worth, I’ve seen a lot of software over the years, From 1997 to 2009 I was Software Reviews Editor for The American Statistician, and received free stat software to review and use for 12 years and pretty much still ongoing. I turn 70 this year, so have watched the development of statistics and statistical software for quite awhile. I would not purchase stock in SPSS, nor in SAS for that matter. SAS is ingrained in the pharmaceutical and healthcare industry, and in much of “big” business, folks have jobs as SAS programmers, or SAS analysts. Too much is invested by business to simply drop it. But that’s not the case as much with SPSS. With more Revolution-like businesses developing in the next decade, I believe R will predominate as the Franca Lingua statistical software. Stata will become ever more popular, but needs to develop a strong Bayesian component. Its not difficult to do given Stata’s excellent programming and matrix languages. Python, OpenBUGS (WinBUGS is not being developed any more), JAGSs and perhaps some other Bayesian software will grow fast in use as well. The Predictive Analytics movement is having an influence as well, and together with academia is focused on employing more Bayesian, basic sampling, and enlightened machine learning into the analysis community.

    • Bob Muenchen says:

      Hi Joe,

      It’s good to hear from you! I, too, have noticed the rapid growth of R used as code examples in journals and books. I only measure books that use the software name in their titles since they’re easy to find. However, I do think it would be much more indicative of R’s dominance to somehow count the books that used R in examples. I see some that use R and Stata, or R and SAS, etc. so R may already be the dominant software used across all stat books.


  28. Partha says:

    I am getting addicted by your writings. I am a student of statistics and want to learn as much as possible from your writings.

  29. David says:

    Excellent article, very detailed presentation of data. Good to follow the analytics trend. Thank you for this article.

  30. Simon says:

    Nice article, and impressive thinking too!

    Just one query: In fig. 1a (2/2014), don’t you mean over 250 jobs, not under?

    • Bob Muenchen says:

      Hi Simon,

      Thanks for catching that typo! It’s fixed.


      • Isaac says:

        hello all,

        I am a graduate of statistics. i want to focus my career in customer insight analysis, building predictive models. i have. learnt sql and SAS programming for data extraction and manipulation. I am confused on which stat package to really learn for data mining. I know SAS EM but feel coys wont employ based on point and click. What about SPSS? I would love to learn SAS programming for data minin on BASE SAS. Pls any recommendations as well as. books to read? thanks a lot.

  31. Jonathan Gezos says:

    This is great, but you’re missing out on a lot by only looking at tools that are 10+ years old. There is a lot of innovation in the industry right now with new players like Tableau and Looker in the mix.

    • Bob Muenchen says:

      Hi Jonathan,

      Tableau is shown in figures 2a, 6b, 6d and 6e. However, most of those are from people who collected their own data. I’m focusing on advanced analytics or predictive analytics. Tableau is more of a visualization package. It does a nice job with a small number of variables but you can only see perhaps 8 at a time on a graph (x, y, z, color, size, shape, small multiples, time in animation). Even with that many it’s hard to absorb. The other software can find patterns in hundreds or even thousands of variables. I just edited the paper to make the focus more clear.


  32. REmi says:

    I have found another rather popular data-analysis software called SCaVis ( It looks like it has about 150 weekly dowloads since 2005. Doyou have any opinion about it?

  33. Arlin Stoltzfus says:

    That was fascinating. Thanks for all of the effort.

    I’m curious about the enormous decline in Fig 2b in scholarly articles citing SAS and SPSS, from a peak ca. 2007. Fig 5A also shows a dramatic reduction in references to SAS at around the same time. At one point in the article you mention competition from R, suggesting that free software edged out pricey packages. But this hypothesis isn’t really borne out by Fig 2b. The numbers for R are so much smaller that they don’t make up the loss– instead, the total number of google scholar hits citing analytics packages decreases dramatically. It appears to suggest a net loss of productivity. What is going on? I was thinking it might be a major drop in biomedical research funding from NIH, but that happened ca. 2000 which seems too early. When the grant money dries up, the license won’t get renewed. The lag time for that effect to show up in publications might be several years.

    • Bob Muenchen says:

      Hi Arlin,

      Good point. I’ve been asked this so many times that I should have modified the text by now. Here’s what I’ve added: “Note that the decline in the number of SPSS and SAS articles 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 I have time to track.”


  34. Hypersphere says:

    It could be argued that programs such as JMP emphasize data visualization or visual data exploration, but it is not excluded from the comparisons. With this in mind I would urge inclusion of GraphPad Prism and OriginPro — these programs certainly have excellent visualization and graphing capabilities, but they also offer considerable strength in data analysis and statistics. In any event, thank you for a most interesting and thorough comparison of other data analysis and statistics software.

    • Bob Muenchen says:

      Hi Hypersphere,

      That’s a good point. Just as the classic stats packages have added much better visualization, the viz packages have added statistics blurring the difference. However, I’m barely keeping this document up to date as it is. It’s a lot of work!


      • Hypersphere says:

        Hi Bob,

        Thanks for taking the time to reply. If you should decide to add a software package or two to your list, please keep Origin Pro and GraphPad Prism in mind. These packages are used by quite a few scientists and engineers.

        Best wishes,


  35. dragosh says:

    Thank you for the useful, empirically backed findings, and the practical R search query.

  36. Dear Bob,

    thank you for this informative article! I referenced it in my own blog (in german), also showing your figure 1a (with appropriate credits). Hope that’s ok.


  37. David says:

    Dear Bob,

    I really appreciate your scholarly approach to this question.

    I have just started introducing R to spanish speakers in Ecuador, many of whom regard it with great suspicion and resent it doubly for being code-based and in english. I am looking for an analysis of the popularity of R amongst its peer programs, considering users with first-languages, or work contexts other than english.

    My question is, are you aware of an analysis of this type that adjusts for the language “problem”?


    • Bob Muenchen says:

      Hi David,

      I’ve thought quite a bit about the “language problem” and I think it helps explain the dominance of SPSS. While its language is only usable in English, SPSS uses a graphical user interface that is available in many different languages. So it has the advantage of being easy to use in any popular language. All the programming-based packages like R, SAS & Stata face the same language barrier.

      Unfortunately, I’m unaware of any analysis that attempts to quantify this effect, so it’s all just guesswork on my part.


  38. krexer says:

    Earlier in August I remember seeing that Ajay Ohri posted some introductions to R, Python and SAS — these were in Spanish. In case they are helpful for you, here is the link:

  39. Jake W. says:

    Bob, awesome article. Thank you so much for the time/effort it took to produce such an in-depth study.

    I came across this article as I’ve slowly slipped into a mild depression (kidding. . . kind of) after I recently graduated with a Masters in Statistics and found that virtually no job advertisement requires STATA, my statistical package of choice. There is some hope, however, as it looks like SAS may be on the decline relative to STATA. *fingers crossed*

    • Bob Muenchen says:

      Hi Jake,

      Stata is really interesting. The first chapter in my book, “R for Stata Users” talks about all of the similarities between R and Stata. If you’ve been programming in Stata – rather than pointing & clicking on the menus – you should be able to transition to R more easily than a SAS programmer would. As you can see, the use of Stata in scholarly work is growing as rapidly as R, but you’re right, in the corporate world, it’s rare to find a job looking for it. One of the attributes it shares with R is having to store its data in the computer’s main memory. There are a few ways to break that limit in R, but I don’t know offhand if there are similar ways to do that in Stata.


  40. Jazz says:

    This is great, as a librarian, I’m trying to figure out which software packages our University should be investing in for our computer labs. I’m going to use this as reasoning for our choices. If you know of anyone doing similar things with qualitative data software, let me know! (nvivo etc)

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