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Update to Data Science Software Popularity

I’ve updated The Popularity of Data Science Software‘s market share estimates based on scholarly articles. I posted it below, so you don’t have to sift through the main article to read the new section.

Scholarly Articles

Scholarly articles provide a rich source of information about data science tools. Because publishing requires significant effort, analyzing the type of data science tools used in scholarly articles provides a better picture of their popularity than a simple survey of tool usage. The more popular a software package is, the more likely it will appear in scholarly publications as an analysis tool or even as an object of study.

Since scholarly articles tend to use cutting-edge methods, the software used in them can be a leading indicator of where the overall market of data science software is 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 Data Science Articles.

Figure 2a shows the number of articles found for the more popular software packages and languages (those with at least 4,500 articles) in the most recent complete year, 2022.

Figure 2a. The number of scholarly articles found on Google Scholar for data science software. Only those with more than 4,500 citations are shown.

SPSS is the most popular package, as it has been for over 20 years. This may be due to its balance between power and its graphical user interface’s (GUI) ease of use. R is in second place with around two-thirds as many articles. It offers extreme power, but as with all languages, it requires memorizing and typing code. GraphPad Prism, another GUI-driven package, is in third place. The packages from MATLAB through TensorFlow are roughly at the same level. Next comes Python and Scikit Learn. The latter is a library for Python, so there is likely much overlap between those two. Note that the general-purpose languages: C, C++, C#, FORTRAN, Java, MATLAB, and Python are included only when found in combination with data science terms, so view those counts as more of an approximation than the rest. Old stalwart FORTRAN appears last in this plot. While its count seems close to zero, that’s due to the wide range of this scale, and its count is just over the 4,500-article cutoff for this plot.

Continuing on this scale would make the remaining packages appear too close to the y-axis to read, so Figure 2b shows the remaining software on a much smaller scale, with the y-axis going to only 4,500 rather than the 110,000 used in Figure 2a. I chose that cutoff value because it allows us to see two related sets of tools on the same plot: workflow tools and GUIs for the R language that make it work much like SPSS.

Figure 2b. Number of scholarly articles using each data science software found using Google Scholar. Only those with fewer than 4,500 citations are shown.

JASP and jamovi are both front-ends to the R language and are way out front in this category. The next R GUI is R Commander, with half as many citations. Still, that’s far more than the rest of the R GUIs: BlueSky Statistics, Rattle, RKWard, R-Instat, and R AnalyticFlow. While many of these have low counts, we’ll soon see that the use of nearly all is rapidly growing.

Workflow tools are controlled by drawing 2-dimensional flowcharts that direct the flow of data and models through the analysis process. That approach is slightly more complex to learn than SPSS’ simple menus and dialog boxes, but it gets closer to the complete flexibility of code. In order of citation count, these include RapidMiner, KNIME, Orange Data Mining, IBM SPSS Modeler, SAS Enterprise Miner, Alteryx, and R AnalyticFlow. From RapidMiner to KNIME, to SPSS Modeler, the citation rate approximately cuts in half each time. Orange Data Mining comes next, at around 30% less. KNIME, Orange, and R Analytic Flow are all free and open-source.

While Figures 2a and 2b help study market share now, they don’t show how things are changing. It would be ideal to have long-term growth trend graphs for each software, but collecting that much data is too time-consuming. Instead, I’ve collected data only for the years 2019 and 2022. This provides the data needed to study growth over that period.

Figure 2c shows the percent change across those years, with the growing “hot” packages shown in red (right side) and the declining or “cooling” ones shown in blue (left side).

Figure 2c. Change in Google Scholar citation rate from 2019 to the most recent complete year, 2022. BlueSky (2,960%) and jamovi (452%) growth figures were shrunk to make the plot more legible.

Seven of the 14 fastest-growing packages are GUI front-ends that make R easy to use. BlueSky’s actual percent growth was 2,960%, which I recoded as 220% as the original value made the rest of the plot unreadable. In 2022 the company released a Mac version, and the Mayo Clinic announced its migration from JMP to BlueSky; both likely had an impact. Similarly, jamovi’s actual growth was 452%, which I recoded to 200. One of the reasons the R GUIs were able to obtain such high percentages of change is that they were all starting from low numbers compared to most of the other software. So be sure to look at the raw counts in Figure 2b to see the raw counts for all the R GUIs.

The most impressive point on this plot is the one for PyTorch. Back on 2a we see that PyTorch was the fifth most popular tool for data science. Here we see it’s also the third fastest growing. Being big and growing fast is quite an achievement!

Of the workflow-based tools, Orange Data Mining is growing the fastest. There is a good chance that the next time I collect this data Orange will surpass SPSS Modeler.

The big losers in Figure 2c are the expensive proprietary tools: SPSS, GraphPad Prism, SAS, BMDP, Stata, Statistica, and Systat. However, open-source R is also declining, perhaps a victim of Python’s rising popularity.

I’m particularly interested in the long-term trends of the classic statistics packages. So in Figure 2d, I have plotted the same scholarly-use data for 1995 through 2016.

Figure 2d. The number of Google Scholar citations for each classic statistics package per year from 1995 through 2016.

SPSS has a clear lead overall, but now you can see that its dominance peaked in 2009, and its use is in sharp decline. SAS never came close to SPSS’s level of dominance, and its usage peaked around 2010. GraphPad Prism followed a similar pattern, though it peaked a bit later, around 2013.

In Figure 2d, the extreme dominance of SPSS makes it hard to see long-term trends in the other software. To address this problem, I have removed SPSS and all the data from SAS except for 2014 and 2015. The result is shown in Figure 2e.

Figure 2e. The number of Google Scholar citations for each classic statistics package from 1995 through 2016, with SPSS removed and SAS included only in 2014 and 2015. The removal of SPSS and SAS expanded scale makes it easier to see the rapid growth of the less popular packages.

Figure 2e shows that most of the remaining packages grew steadily across the time period shown. R and Stata grew especially fast, as did Prism until 2012. The decline in the number of articles that used SPSS, SAS, or Prism is not balanced by the increase in the other software shown in this graph.

These results apply to scholarly articles in general. The results in specific fields or journals are likely to differ.

You can read the entire Popularity of Data Science Software here; the above discussion is just one section.

Data Science Software Popularity Update

I have recently updated my extensive analysis of the popularity of data science software. This update covers perhaps the most important section, the one that measures popularity based on the number of job advertisements. I repeat it here as a blog post, so you don’t have to read the entire article.

Job Advertisements

One of the best ways to measure the popularity or market share of software for data science is to count the number of job advertisements that highlight knowledge of each as a requirement. Job ads 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 change in job demand give us a good idea of what will become more popular in the future.

Indeed.com is the biggest job site in the U.S., making its collection of job ads the best around. As their co-founder and former CEO Paul Forster stated, Indeed.com 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.” Indeed.com also has superb search capabilities.

Searching for jobs using Indeed.com is easy, but searching for software in a way that ensures fair comparisons across packages is challenging. Some software is used only for data science (e.g., scikit-learn, Apache Spark), while others are used in data science jobs and, more broadly, in report-writing jobs (e.g., SAS, Tableau). General-purpose languages (e.g., Python, C, Java) are heavily used in data science jobs, but the vast majority of jobs that require them have nothing to do with data science. To level the playing field, I developed a protocol to focus the search for each software within only jobs for data scientists. The details of this protocol are described in a separate article, How to Search for Data Science Jobs. All of the results in this section use those procedures to make the required queries.

I collected the job counts discussed in this section on October 5, 2022. To measure percent change, I compare that to data collected on May 27, 2019. One might think that a sample on a single day might not be very stable, but they are. Data collected in 2017 and 2014 using the same protocol correlated r=.94, p=.002. I occasionally double-check some counts a month or so later and always get similar figures.

The number of jobs covers a very wide range from zero to 164,996, with a mean of 11,653.9 and a median of 845.0. The distribution is so skewed that placing them all on the same graph makes reading values difficult. Therefore, I split the graph into three, each with a different scale. A single plot with a logarithmic scale would be an alternative, but when I asked some mathematically astute people how various packages compared on such a plot, they were so far off that I dropped that approach.

Figure 1a shows the most popular tools, those with at least 10,000 jobs. SQL is in the lead with 164,996 jobs, followed by Python with 150,992 and Java with 113,944. Next comes a set from C++/C# at 48,555, slowly declining to Microsoft’s Power BI at 38,125. Tableau, one of Power BI’s major competitors, is in that set. Next comes R and SAS, both around 24K jobs, with R slightly in the lead. Finally, we see a set slowly declining from MATLAB at 17,736 to Scala at 11,473.

Figure 1a. Number of data science jobs for the more popular software (>= 10,000 jobs).

Figure 1b covers tools for which there are between 250 and 10,000 jobs. Alteryx and Apache Hive are at the top, both with around 8,400 jobs. There is quite a jump down to Databricks at 6,117 then much smaller drops from there to Minitab at 3,874. Then we see another big drop down to JMP at 2,693 after which things slowly decline until MLlib at 274.

Figure 1b. Number of jobs for less popular data science software tools, those with between 250 and 10,000 jobs.

The least popular set of software, those with fewer than 250 jobs, are displayed in Figure 1c. It begins with DataRobot and SAS’ Enterprise Miner, both near 182. That’s followed by Apache Mahout with 160, WEKA with 131, and Theano at 110. From RapidMiner on down, there is a slow decline until we finally hit zero at WPS Analytics. The latter is a version of the SAS language, so advertisements are likely to always list SAS as the required skill.

Figure 1c. Number of jobs for software having fewer than 250 advertisements.

Several tools use the powerful yet easy workflow interface: Alteryx, KNIME, Enterprise Miner, RapidMiner, and SPSS Modeler. The scale of their counts is too broad to make a decent graph, so I have compiled those values in Table 1. There we see Alteryx is extremely dominant, with 30 times as many jobs as its closest competitor, KNIME. The latter is around 50% greater than Enterprise Miner, while RapidMiner and SPSS Modeler are tiny by comparison.

Software	Jobs
Alteryx	8,566
KNIME	281
Enterprise Miner	181
RapidMiner	69
SPSS Modeler	17

Table 1. Job counts for workflow tools.

Let’s take a similar look at packages whose traditional focus was on statistical analysis. They have all added machine learning and artificial intelligence methods, but their reputation still lies mainly in statistics. We saw previously that when we consider the entire range of data science jobs, R was slightly ahead of SAS. Table 2 shows jobs with only the term “statistician” in their description. There we see that SAS comes out on top, though with such a tiny margin over R that you might see the reverse depending on the day you gather new data. Both are over five times as popular as Stata or SPSS, and ten times as popular as JMP. Minitab seems to be the only remaining contender in this arena.

Software	Jobs only for “Statistician”
SAS	1040
R	1012
Stata	176
SPSS	146
JMP	93
Minitab	55
Statistica	2
BMDP	3
Systat	0
NCSS	0

Table 2. Number of jobs for the search term “statistician” and each software.

Next, let’s look at the change in jobs from the 2019 data to now (October 2022), focusing on software that had at least 50 job listings back in 2019. Without such a limitation, software that increased from 1 job in 2019 to 5 jobs in 2022 would have a 500% increase but still would be of little interest. Percent change ranged from -64.0% to 2,479.9%, with a mean of 306.3 and a median of 213.6. There were two extreme outliers, IBM Watson, with apparent job growth of 2,479.9%, and Databricks, at 1,323%. Those two were so much greater than the rest that I left them off of Figure 1d to keep them from compressing the remaining values beyond legibility. The rapid growth of Databricks has been noted elsewhere. However, I would take IBM Watson’s figure with a grain of salt as its growth in revenue seems nowhere near what the Indeed.com’s job figure seems to indicate.

The remaining software is shown in Figure 1d, where those whose job market is “heating up” or growing are shown in red, while those that are cooling down are shown in blue. The main takeaway from this figure is that nearly the entire data science software market has grown over the last 3.5 years. At the top, we see Alteryx, with a growth of 850.7%. Splunk (702.6%) and Julia (686.2%) follow. To my surprise, FORTRAN follows, having gone from 195 jobs to 1,318, yielding growth of 575.9%! My supercomputing colleagues assure me that FORTRAN is still important in their area, but HPC is certainly not growing at that rate. If any readers have ideas on why this could occur, please leave your thoughts in the comments section below.

Figure 1d. Percent change in job listings from March 2019 to October 2022. Only software that had at least 50 jobs in 2019 is shown. **IBM (2,480%) and Databricks (1,323%) are excluded to maintain the legibility of the remaining values.**

SQL and Java are both growing at around 537%. From Dataiku on down, the rate of growth slows steadily until we reach MLlib, which saw almost no change. Only two packages declined in job advertisements, with WEKA at -29.9%, Theano at -64.1%.

This wraps up my analysis of software popularity based on jobs. You can read my ten other approaches to this task at https://r4stats.com/articles/popularity/. Many of those are based on older data, but I plan to update them in the first quarter of 2023, when much of the needed data will become available. To receive notice of such updates, subscribe to this blog, or follow me on Twitter: https://twitter.com/BobMuenchen.

Data Science Jobs Report 2019: Python Way Up, Tensorflow Growing Rapidly, R Use Double SAS

In my ongoing quest to track The Popularity of Data Science Software, I’ve just updated my analysis of the job market. To save you from reading the entire tome, I’m reproducing that section here.

Job Advertisements

Searching for jobs using Indeed.com is easy, but searching for software in a way that ensures fair comparisons across packages is challenging. Some software is used only for data science (e.g. SPSS, Apache Spark) while others are used in data science jobs and more broadly in report-writing jobs (e.g. SAS, Tableau). General-purpose languages (e.g. Python, C, Java) are heavily used in data science jobs, but the vast majority of jobs that use them have nothing to do with data science. To level the playing field, I developed a protocol to focus the search for each software within only jobs for data scientists. The details of this protocol are described in a separate article, How to Search for Data Science Jobs. All of the graphs in this section use those procedures to make the required queries.

I collected the job counts discussed in this section on May 27, 2019 and February 24, 2017. One might think that a sample of on a single day might not be very stable, but the large number of job sources makes the counts in Indeed.com’s collection of jobs quite consistent. Data collected in 2017 and 2014 using the same protocol correlated r=.94, p=.002.

Figure 1a shows that Python is in the lead with 27,374 jobs, followed by SQL with 25,877. Java and Amazon’s Machine Learning (ML) tools are roughly 25% further below, with jobs in the 17,000s. R and the C variants come next with around 13,000. People frequently compare R and Python, but when it comes to getting a data science job, there are only half as many for R as for Python. That doesn’t mean they’re the same sort of job, of course. I still see more statisticians using R and machine learning people preferring Python, but Python is definitely on a roll! From Hadoop on down, there is a slow decline in jobs. R is also frequently compared to SAS, which has only 8,123 compared to R’s 13,800.

The scale of Figure 1a is so wide that the bottom package, H20 appears to be zero, when in fact there are 257 jobs for it.

Figure 1a. Number of data science jobs for the more popular software.

To let us compare the less popular software, I plotted them separately in Figure 1b. Mathematica and Julia are the leaders of this set, with around 219 jobs each. The ancient FORTRAN language is still hanging on to life with 195 jobs. The open source WEKA software and IBM’s Watson are next, with around 185 each. From XGBOOST on down, there is a fairly steady slow decline.

There are several tools that use a workflow interface: Enterprise Miner, KNIME, RapidMiner, and SPSS Modeler. They’re all around the same area between 50 and 100 jobs. In many of the other measures of popularity, RapidMiner beats the very similar KNIME tool, but here there are 50% more jobs for the latter. Alteryx is also a workflow-based tool, however, it has pulled away from the pack, appearing back on Figure 1a with 901 jobs.

Figure 1b. Number of jobs for less popular data science software tools, those with fewer than 250 advertisements.

When interpreting the scale on Figure 1b, what looks like zero is indeed zero. From Systat on down, none of the packages have more than 10 job listings.

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 or two. The less popular packages shown in Figure 1b have such low job counts that their ranking is more likely to shift from month to month, though their position relative to the major packages should remain more stable.

Next, let’s look at the change in jobs from the 2017 data to now (2019). Figure 1c shows the percent change for those packages that had at least 100 job listings back in 2017. Without such a limitation, software that goes from 1 job in 2017 to 5 jobs in 2019 would have a 500% increase, but still would be of little interest. Software whose job market is heating up, or growing, is shown in red, while those that are cooling down are shown in blue.

Figure 1c. Percent change in job listings from 2017 to 2019. Only software that had at least 100 jobs in 2017 is shown.

Tensorflow, the deep learning software from Google, is the fastest growing at 523%. Next is Apache Flink, a tool that analyzes streaming data, at 289%. H2O is next, with 150% growth. Caffe is another deep learning framework and its 123% growth reflects the popularity of artificial intelligence algorithms.

Python shows “only” 97% growth, but its popularity was already so high that the 13,471 jobs that it added surpasses the total jobs of many of the other packages!

Tableau is showing a similar rate of growth, though it was a comparably small number of additional jobs, at 4,784.

From the Julia language on down, we see a slowing decrease in growth. I’m surprised to see that jobs for SAS and SPSS are still growing, though barely at 6% and 1%, respectively.

If you enjoyed reading this article, you might be interested in my recent series of reviews on point-and-click front-ends for the R language. I invite you to subscribe to this blog, or follow me on Twitter.

Data Science Software Used in Journals: Stat Packages Declining (including R), AI/ML Software Growing

In my neverending quest to track The Popularity of Data Science Software, it’s time to update the section on Scholarly Articles. The rapid growth of R could not go on forever and, as you’ll see below, its use actually declined over the last year.

Scholarly Articles

Scholarly articles provide a rich source of information about data science tools. Because publishing requires significant amounts of effort, analyzing the type of data science tools used in scholarly articles provides a better picture of their popularity than a simple survey of tool usage. The more popular a software package is, the more likely it will appear in scholarly publications as an analysis tool, or even as an object of study.

Figure 2a shows the number of articles found for the more popular software packages and languages (those with at least 1,700 articles) in the most recent complete year, 2018. To allow ample time for publication, insertion into online databases, and indexing, the was data collected on 3/28/2019.

Figure 2a. The number of scholarly articles found on Google Scholar, for data science software. Only those with more than 1,700 citations are shown.

SPSS is by far the most dominant package, as it has been for over 20 years. This may be due to its balance between power and ease-of-use. R is in second place with around half as many articles. It offers extreme power, though with less ease of use. SAS is in third place, with a slight lead over Stata, MATLAB, and GraphPad Prism, which are nearly tied.

Note that the general-purpose languages: C, C++, C#, FORTRAN, Java, MATLAB, and Python are included only when found in combination with data science terms, so view those counts as more of an approximation than the rest.

The next group of packages goes from Python through C, with usage declining slowly. The next set starts at Caffe, dropping nearly 50%, and continuing to IBM Watson with a slow decline.

The last two packages in Fig 2a are Weka and Theano, which are quite a drop from IBM Watson, though it’s getting harder to see as the lines shrink.

To continue on this scale would make the remaining packages all appear too close to the y-axis to read, so Figure 2b shows the remaining software on a much smaller scale, with the y-axis going to only 1,700 rather than the 80,000 used on Figure 2a.

Figure 2b. Number of scholarly articles using each data science software found using Google Scholar. Only those with *fewer than* 1,700 citations are shown.

I chose to begin Figure 2b with software that has fewer than 1,700 articles because it allows us to see RapidMiner and KNIME on the same scale. They are both workflow-driven tools with very similar capabilities. This plot shows RapidMiner with 49% greater usage than KNIME. RapidMiner uses more marketing, while KNIME depends more on word-of-mouth recommendations and a more open source model. The IT advisory firms Gartner and Forrester rate them as tools able to hold their own against the commercial titans, IBM’s SPSS and SAS. Given that SPSS has roughly 50 times the usage in academia, that seems like quite a stretch. However, as we will soon see, usage of these newer packages are growing, while the use of the older ones is shrinking quite rapidly.

Figure 2b also lets us see IBM’s SPSS Modeler, SAS Enterprise Miner, and Alteryx on the same plot. These three are also workflow-driven tools which are quite expensive. None are doing as well here as RapidMiner or KNIME, tools that much less expensive – or free – depending on how you use them (KNIME desktop is free but server is not; RapidMiner is free for analyzing fewer than 10,000 cases).

Another interesting comparison on Figure 2b is JASP and jamovi. Both are open-source tools that focus on statistics rather than machine learning or artificial intelligence. They both use graphical user interfaces (GUIs) in a style that is similar to SPSS. Both also use R behind the scenes to do their calculations. JASP emphasizes Bayesian Analysis and hides its R code; jamovi has a more frequentist orientation, it lets you see its R code, and it lets you execute your own R code directly from within it. JASP currently has nine times as many citations here, though jamovi’s use is growing much more rapidly.

Even newer on the GUI for R scene is BlueSky Statistics, which doesn’t appear on the plot at all since it has zero scholarly articles so far. It was created by a new company and only adopted an open source model a few months ago.

While Figures 2a and 2b are useful for studying market share as it stands now, 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 that much data annually is too time-consuming. What I’ve done instead is collect data only for the past two complete years, 2017 and 2018. This provides the data needed to study year-over-year changes.

Figure 2c shows the percent change across those years, with the growing “hot” packages shown in red (right side); the declining or “cooling” are shown in blue (left side). Since the number of articles tends to be in the thousands or tens of thousands, I have removed any software that had fewer than 1,000 articles in 2015. A package that grows from 1 article to 5 may demonstrate 500% growth but is still of little interest.

Figure 2c. Change in Google Scholar citation rate in the most recent complete two years, 2017 and 2018.

The recent changes in data science software can be summarized succinctly: AI/ML up; statistics down. The software that is growing contains none of the packages that are associated more with statistical analysis. The software in decline is dominated by the classic packages of statistics: SPSS Statistics, SAS, GraphPad Prism, Stata, Statgraphics, R, Statistica, Systat, and Minitab. JMP is the only traditional statistics package whose scholarly usage is growing. Of the machine learning software that’s declining in usage, there are rough equivalents that are growing (e.g. Mahout down, Spark up).

Of course another summary is: cheap (or free) up; expensive down. Of the growing packages, 13 out of 17 are available in open source. Of those in decline, only 5 out of 13 are open source.

Statistics software has been around much longer than AI/ML software, started back in the days before open source. Stat vendors have been adding AI/ML methods to their software, making them the more comprehensive solutions. The AI/ML vendors or projects are missing an opportunity to add more comprehensive statistics capabilities. Some, such as RapidMiner and KNIME, are indeed expanding in this direction, but very slowly indeed.

At the top of Figure 2c, we see that the deep learning packages Keras and TensorFlow are the fastest growing at nearly 150%. PyTorch is not shown here because it did not have enough usage in the previous year. However, its citation rate went from 616 to 4,670, a substantial 658% growth rate! There are other packages that are not shown here, including JASP with 223% growth, and jamovi with 720% growth. Despite such high growth, the latter still only has 108 citations in 2018. The rapid growth of JASP and jamovi lend credence to the perspective that the overall pattern of change shown in Figure 2c may be more of a result of free vs. expensive software. Neither of them offers any AI/ML features.

Scikit Learn, the Python machine learning library, was a fast grower with a 60% increase.

I was surprised to see IBM Watson growing a healthy 34% as much of the news about it has not been good. It’s awesome at Jeopardy though!

In the RapidMiner vs. KNIME contest, we saw previously that RapidMiner was ahead. From this plot, we that KNIME growing slightly (5.7%) while RapidMiner is declining slightly (1.8%).

The biggest losers in Figure 2c are SPSS, down 39%, and SAS, Prism, and Mahout, all down 24%. Even R is down 13%. Recall that Figure 2a shows that despite recent years of decline, SPSS is still extremely dominant for scholarly use, and R and SAS are still the #2 and #3 most widely used packages in this arena.

I’m particularly interested in the long-term trends of the classic statistics packages. So in Figure 2d I have plotted the same scholarly-use data for 1995 through 2016.

SPSS has a clear lead overall, but now you can see that its dominance peaked in 2009 and its use is in sharp decline. SAS never came close to SPSS’ level of dominance, and its use peaked around 2010. GraphPAD Prism followed a similar pattern, though it peaked a bit later, around 2013.

Figure 2e. The number of Google Scholar citations for each classic statistics package from 1995 through 2016, this time with SPSS removed and SAS included only in 2014 and 2015. The removal of SPSS and SAS expanded scale makes it easier to see the rapid growth of the less popular packages.

Figure 2e makes it easy to see that most of the remaining packages grew steadily across the time period shown. R and Stata grew especially fast, as did Prism until 2012. Note that the decline in the number of articles that used SPSS, SAS, or Prism is not balanced by the increase in the other software shown in this particular graph. Even adding up all the other software shown in Figures 2a and 2b doesn’t account for the overall decline. However, I’m looking at only 58 out of over 100 data science tools.

While Figures 2d and 2e show the historical trend that ended in 2016, Figure 2f shows a fresh set of data collected in March, 2019. Since Google’s algorithm changes, preventing the new data from matching exactly with the old, this new data starts at 2015 so the two sets overlap. SPSS is not shown on this graph because its dominance would compress the y-axis, making trends in the others harder to see. However, keep in mind that despite SPSS’ 39% drop from 2017 to 2018, its use is still 66% higher than R’s in 2018! Apparently people are willing to pay for ease of use.

Figure 2f. The number of Google Scholar citations for each classic statistics package per year from 2015 through 2018.

In Figure 2f we can see that the downward trends of SAS, Prism, and Statistica are continuing. We also see that the long and rapid growth of R and Stata has come to an end. Growth that rapid can’t go on forever. It will be interesting to see next year to see if this is merely a flattening of usage or the beginning of a declining trend. As I pointed out in my book, R for Stata Users, there are many commonalities between R and Stata. As a result of this, and the fact that R is open source, I expect R use to stabilize at this level while use of Stata continues to slowly decline.

SPSS’ long-term rapid decline has to level out at some point. They have been chipped away at by many competitors. However, until recently these competitors have either been free and code-based such as R, or menu-based and proprietary, such as Prism. With the fairly recent arrival of JASP, jamovi, and BlueSky Statistics, SPSS now faces software that is both free and menu-based. Previous projects to add menus to R, such as the R Commander and Deducer, were also free and open source, but they required installing R separately and then using R code to activate the menus.

These results apply to scholarly articles in general. The results in specific fields or journals are very likely to be different.

To see many other ways to estimate the market share of this type of software, see my ongoing article, The Popularity of Data Science Software. My next post will update the job advertisements that list science software. You may also be interested in my in-depth reviews of point-and-click user interfaces to R. I invite you to subscribe to my blog or follow me on twitter where I announce new posts. Happy computing!

Data Science Tool Market Share Leading Indicator: Scholarly Articles

Below is the latest update to The Popularity of Data Science Software. It contains an analysis of the tools used in the most recent complete year of scholarly articles. The section is also integrated into the main paper itself.

New software covered includes: Amazon Machine Learning, Apache Mahout, Apache MXNet, Caffe, Dataiku, DataRobot, Domino Data Labs, GraphPad Prism, IBM Watson, Pentaho, and Google’s TensorFlow.

Software dropped includes: Infocentricity (acquired by FICO), SAP KXEN (tiny usage), Tableau, and Tibco. The latter two didn’t fit in with the others due to their limited selection of advanced analytic methods.

Scholarly Articles

Scholarly articles provide a rich source of information about data science tools. Their creation requires significant amounts of effort, much more than is required to respond to a survey of tool usage. 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.

Since graduate students do the great majority of analysis in such articles, the software used can be 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. Searching through concise job requirements (see previous section) is easier than searching through scholarly articles; however only software that has advanced analytical capabilities can be studied using this approach. 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 the previous years.

Figure 2a shows the number of articles found for the more popular software packages (those with at least 750 articles) in the most recent complete year, 2016. To allow ample time for publication, insertion into online databases, and indexing, the was data collected on 6/8/2017.

SPSS is by far the most dominant package, as it has been for over 15 years. This may be due to its balance between power and ease-of-use. R is in second place with around half as many articles. SAS is in third place, still maintaining a substantial lead over Stata, MATLAB, and GraphPad Prism, which are nearly tied. This is the first year that I’ve tracked Prism, a package that emphasizes graphics but also includes statistical analysis capabilities. It is particularly popular in the medical research community where it is appreciated for its ease of use. However, it offers far fewer analytic methods than the other software at this level of popularity.

Note that the general-purpose languages: C, C++, C#, FORTRAN, MATLAB, Java, and Python are included only when found in combination with data science terms, so view those counts as more of an approximation than the rest.

Figure 2a. Number of scholarly articles found in the most recent complete year (2016) for the more popular data science software. To be included, software must be used in at least 750 scholarly articles.

The next group of packages goes from Apache Hadoop through Python, Statistica, Java, and Minitab, slowly declining as they go.

Both Systat and JMP are packages that have been on the market for many years, but which have never made it into the “big leagues.”

From C through KNIME, the counts appear to be near zero, but keep in mind that each are used in at least 750 journal articles. However, compared to the 86,500 that used SPSS, they’re a drop in the bucket.

Toward the bottom of Fig. 2a are two similar packages, the open source Caffe and Google’s Tensorflow. These two focus on “deep learning” algorithms, an area that is fairly new (at least the term is) and growing rapidly.

The last two packages in Fig 2a are RapidMiner and KNIME. It has been quite interesting to watch the competition between them unfold for the past several years. They are both workflow-driven tools with very similar capabilities. The IT advisory firms Gartner and Forester rate them as tools able to hold their own against the commercial titans, SPSS and SAS. Given that SPSS has roughly 75 times the usage in academia, that seems like quite a stretch. However, as we will soon see, usage of these newcomers are growing, while use of the older packages is shrinking quite rapidly. This plot shows RapidMiner with nearly twice the usage of KNIME, despite the fact that KNIME has a much more open source model.

Figure 2b shows the results for software used in fewer than 750 articles in 2016. This change in scale allows room for the “bars” to spread out, letting us make comparisons more effectively. This plot contains some fairly new software whose use is low but growing rapidly, such as Alteryx, Azure Machine Learning, H2O, Apache MXNet, Amazon Machine Learning, Scala, and Julia. It also contains some software that is either has either declined from one-time greatness, such as BMDP, or which is stagnating at the bottom, such as Lavastorm, Megaputer, NCSS, SAS Enterprise Miner, and SPSS Modeler.

Figure 2b. The number of scholarly articles for the less popular data science (those used by fewer than 750 scholarly articles in 2016.

While Figures 2a and 2b are useful for studying market share as it stands now, 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 that much data annually is too time consuming. What I’ve done instead is collect data only for the past two complete years, 2015 and 2016. This provides the data needed to study year-over-year changes.

Figure 2c shows the percent change across those years, with the “hot” packages whose use is growing shown in red (right side); those whose use is declining or “cooling” are shown in blue (left side). Since the number of articles tends to be in the thousands or tens of thousands, I have removed any software that had fewer than 500 articles in 2015. A package that grows from 1 article to 5 may demonstrate 500% growth, but is still of little interest.

Figure 2c. Change in the number of scholarly articles using each software in the most recent two complete years (2015 to 2016). Packages shown in red are “hot” and growing, while those shown in blue are “cooling down” or declining.

Caffe is the data science tool with the fastest growth, at just over 150%. This reflects the rapid growth in the use of deep learning models in the past few years. The similar products Apache MXNet and H2O also grew rapidly, but they were starting from a mere 12 and 31 articles respectively, and so are not shown.

IBM Watson grew 91%, which came as a surprise to me as I’m not quite sure what it does or how it does it, despite having read several of IBM’s descriptions about it. It’s awesome at Jeopardy though!

While R’s growth was a “mere” 14.7%, it was already so widely used that the percent translates into a very substantial count of 5,300 additional articles.

In the RapidMiner vs. KNIME contest, we saw previously that RapidMiner was ahead. From this plot we also see that it’s continuing to pull away from KNIME with quicker growth.

From Minitab on down, the software is losing market share, at least in academia. The variants of C and Java are probably losing out a bit to competition from several different types of software at once.

In just the past few years, Statistica was sold by Statsoft to Dell, then Quest Software, then Francisco Partners, then Tibco! Did its declining usage drive those sales? Did the game of musical chairs scare off potential users? If you’ve got an opinion, please comment below or send me an email.

The biggest losers are SPSS and SAS, both of which declined in use by 25% or more. Recall that Fig. 2a shows that despite recent years of decline, SPSS is still extremely dominant for scholarly use.

I’m particularly interested in the long-term trends of the classic statistics packages. So in Figure 2d I have plotted the same scholarly-use data for 1995 through 2016.

Figure 2d. The number of scholarly articles found in each year by Google Scholar. Only the top six “classic” statistics packages are shown.

As in Figure 2a, SPSS has a clear lead overall, but now you can see that its dominance peaked in 2009 and its use is in sharp decline. SAS never came close to SPSS’ level of dominance, and its use peaked around 2010. GraphPAD Prism followed a similar pattern, though it peaked a bit later, around 2013.

Note that the decline in the number of articles that used SPSS, SAS, or Prism is not balanced by the increase in the other software shown in this particular graph. Even adding up all the other software shown in Figures 2a and 2b doesn’t account for the overall decline. However, I’m looking at only 46 out of over 100 data science tools. SQL and Microsoft Excel could be taking up some of the slack, but it is extremely difficult to focus Google Scholar’s search on articles that used either of those two specifically for data analysis.

Since SAS and SPSS dominate the vertical space in Figure 2d by such a wide margin, I removed those two curves, leaving only two points of SAS usage in 2015 and 2016. The result is shown in Figure 2e.

Figure 2e. The number of scholarly articles found in each year by Google Scholar for classic statistics packages after the curves for SPSS and SAS have been removed.

Freeing up so much space in the plot allows us to see that the growth in the use of R is quite rapid and is pulling away from the pack. If the current trends continue, R will overtake SPSS to become the #1 software for scholarly data science use by the end of 2018. Note however, that due to changes in Google’s search algorithm, the trend lines have shifted before as discussed here. Luckily, the overall trends on this plot have stayed fairly constant for many years.

The rapid growth in Stata use seems to be finally slowing down. Minitab’s growth has also seemed to stall in 2016, as has Systat’s. JMP appears to have had a bit of a dip in 2015, from which it is recovering.

The discussion above has covered but one of many views of software popularity or market share. You can read my analysis of several other perspectives here.

R Passes SAS in Scholarly Use (finally)

Way back in 2012 I published a forecast that showed that the use of R for scholarly publications would likely pass the use of SAS in 2015. But I didn’t believe the forecast since I expected the sharp decline in SAS and SPSS use to level off. In 2013, the trend accelerated and I expected R to pass SAS in the middle of 2014. As luck would have it, Google changed their algorithm, somehow finding vast additional quantities of SAS and SPSS articles. I just collected data on the most recent complete year of scholarly publications, and it turns out that 2015 was indeed the year that R passed SAS to garner the #2 position. Once again, models do better than “expert” opinion! I’ve updated The Popularity of Data Analysis Software to reflect this new data and include it here to save you the trouble of reading the whole 45 pages of it.

If you’re interested in learning R, you might consider reading my books R for SAS and SPSS Users, or R for Stata Users. I also teach workshops on R, but I’m currently booked through mid October, so please plan ahead.

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 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, 2015. SPSS is by far the most dominant package, as it has been for over 15 years. This may be due to its balance between power and ease-of-use. For the first time ever, R is in second place with around half as many articles. Although now in third place, SAS is nearly tied with R. Stata and MATLAB are essentially tied for fourth and fifth place. Starting with Java, usage slowly tapers off. Note that the general-purpose software C, C++, C#, MATLAB, Java, and Python are included only when found in combination with data science terms, so view those as much rougher counts than the rest. Since Scala and Julia have a heavy data science angle to them, I cut them some slack by not adding any data science terms to the search, not that it helped them much!

From Spark 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 1,200 articles (i.e. the bottom part of Fig. 2a), so we can see how they compare. Spark and RapidMiner top out the list of these packages, followed by KNIME and BMDP. There’s a slow decline in the group that goes from Enterprise Miner to Salford Systems. Then comes a group of mostly relative new arrivals beginning with Microsoft’s Azure Machine Learning. A package that’s not a new arrival is from Megaputer, whose Polyanalyst software has been around for many years now, with little progress to show for it. Dead last is Lavastorm, which to my knowledge is the only commercial package that includes Tibco’s internally written version of R, TERR.

Fig_2b_ScholarlyImpact2015 — Figure 2b. The number of scholarly articles for software that was used by fewer than 1,200 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 Google’s search algorithms change). What I’ve done instead is collect data only for the past two complete years, 2014 and 2015. 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 500 articles in 2014.

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. — Figure 2c. Change in the number of scholarly articles using each software in the most recent two complete years (2014 to 2015). 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 data science use as defined here. The open-source KNIME and RapidMiner are the second and third fastest growing, respectively. Both use the easy yet powerful workflow approach to data science. Figure 2b showed that RapidMiner has almost twice the marketshare of KNIME, but here we see use of KNIME is growing faster. That may be due to KNIME’s greater customer satisfaction, as shown in the Rexer Analytics Data Science Survey. The companies are 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. 3a).

R is in fourth place in growth, and given its second place in overall marketshare, it is in an enviable position.

At the other end of the scale are SPSS and SAS, both of which declined in use by 25% or more. Recall that Fig. 2a shows that despite recent years of decline, SPSS is still extremely dominant for scholarly use. Hadoop use declined slightly, perhaps as people turned to alternatives Spark and H2O.

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 2015, 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 in this particular graph. However, if you add up all the other software shown in Figure 2a, you come close. There still seems to be a slight decline in people reporting the particular software tool they used.

Fig_2d_ScholarlyImpact — 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 curves, leaving only a single point of SAS usage in 2015. The the result is shown in Figure 2e. Freeing up so much space in the plot now allows us to see that the growth in the use of R is quite rapid and is pulling away from the pack (recall that the curve for SAS has a steep downward slope). If the current trends continue, R will cross SPSS to become the #1 software for scholarly data science use by the end of 2017. Stata use is also growing more quickly than the rest. 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.

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. — Figure 2e. The number of scholarly articles found in each year by Google Scholar for classic statistics packages after the curves for 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 use has passed that of SAS, and that Stata use is closing in on it. 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 2015 value for SPSS is well over twice the value for R. The original scale shown in Figure 2d makes that quite clear.

Fig_2f_ScholarlyImpactLogs — 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.

Using Discussion Forum Activity to Estimate Analytics Software Market Share

I’m finally getting around to overhauling the Discussion Forum Activity section of The Popularity of Data Analysis Software. To save you the trouble of reading all 43 pages, I’m posting just this section below.

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:

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:

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.

If you found this interesting, you can read about the results of other surveys and several other ways to measure software popularity here.

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

Estimating Analytics Software Market Share by Counting Books

Below is the latest update to The Popularity of Data Analysis Software.

Books

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

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

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

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

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

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

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

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

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

Stata’s Academic Growth Nearly as Fast as R’s

by Bob Muenchen

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.

Fig_2e_ScholarlyImpactBig6

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.

Google Scholar Finds Far More SPSS Articles; Analytics Forecast Updated

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

Scholarly Articles

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

Fig_2a_ScholarlyImpact2014 — 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.

Fig_2b_ScholarlyImpact2014 — 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).

Fig_2a_ScholarlyImpactBig6 — 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.”]

Fig_2a_ScholarlyImpactLittle6 — 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.