I previously reported on the initial results of Rexer Analytics’ 2015 survey of data science tools here. More results are now available, and the comprehensive report should be released soon. One of the more interesting questions on the survey was, “Please rate your overall satisfaction with [your previously chosen software].” Most of the measures I report in my regularly-updated article, The Popularity of Data Analysis Software are raw measures of usage, so it’s nice to have data that goes beyond usage and into satisfaction. The results are show in the figure below for the more popular software (other software had very small sample sizes and so are not shown).
Results from the question, “Please rate your overall satisfaction with [your previously chosen software].” Only software with substantial number of responses shown.People reported being somewhat satisfied with their chosen tool, which doesn’t come a much of a surprise. If they weren’t at least somewhat satisfied, they would be likely to move on to another tool. What really differentiated the tools was the percent of people who reported being extremely satisfied. The free and open source KNIME program came out #1 with 69% of its users being extremely satisfied. (KNIME is also the 2nd fastest growing data science package among scholarly researchers). IBM SPSS Modeler came in second with 60%, followed closely by R with 57%.
Both of the top two packages use the workflow user interface which has many advantages that I’ve written about here and here. However, RapidMiner and SAS Enterprise Miner also use the workflow interface, and their percent of extremely satisfied customers were less than half at 32% and 29%, respectively. We might wonder if people are more satisfied with KNIME because they’re using the free desktop version, but RapidMiner also has a free version, so cost isn’t a factor on that comparison.
Although both R and SAS have menu-based interfaces, they are predominantly programming languages. R has almost triple the number of extremely satisfied users, which may be the result of its being generally viewed as the more powerful language, albeit somewhat harder to learn. The fact that R is free while SAS is not may also be a factor in that difference.
I’ll be presenting two workshops back-to-back at Nicholls State University in Thibodaux Louisiana June 14-16. The first workshop will cover a broad range of R topics. Each topic will include a brief comparison to how R differs from the popular commercial data science packages, SAS, SPSS, and Stata. If you have a background in any of those packages, you’ll know the things that are likely to trip you up as you learn R. If not, you’ll just come away with a solid intro to R along with how it compares to other software.
The second workshop will focus on the broad range of data management tasks that are usually needed to prepare your data for analysis. Both workshops will be done using the very latest R packages to make things as easy as possible. These packages include tibble, dplyr, magrittr, stringr, lubridate, broom, and more.
Seats are still available at no charge with the Nicholls’ State community and other academics getting priority seating. To register for the workshop, please contact professor Allyse Ferrara, 985/448-4736, or allyse.ferrara@nicholls.edu.
Each year I update the growth in R’s capability on The Popularity of Data Analysis Software. And each year, I think R’s incredible rate of growth will finally slow down. Below is a graph of the latest data, and as you can see, R’s growth continues to accelerate.
Since I’ve added coverage for many more software packages, I have restructured the main article to reflect the value of each type of data. They now appear in this order:
Job Advertisements
Scholarly Articles
IT Research Firm Reports
Surveys of Use
Books
Blogs
Discussion Forum Activity
Programming Popularity Measures
Sales & Downloads
Competition Use
Growth in Capability
Growth in Capability remains last because I only have complete data for R. To save you from having to dig through all 40+ pages of the article, the updated section is below. I’ll be updating several other sections in the coming weeks. If you’re interested, you can follow this blog, or follow me on Twitter as @BobMuenchen.
The capability of analytics software has grown significantly over the years. It would be helpful to be able to plot the growth of each software package’s capabilities, but such data are hard to obtain. John Fox (2009) acquired them for R’s main distribution site http://cran.r-project.org/ for each version of R. To simplify ongoing data collection, I kept only the values for the last version of R released each year (usually in November or December), and collected data through the most recent complete year.
These data are displayed in Figure 10. The right-most point is for version 3.2.3, released 12/10/2015. The growth curve follows a rapid parabolic arc (quadratic fit with R-squared=.995).
Figure 10. 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, and QC). In 2015, R added 1,357 packages, counting only CRAN, or approximately 27,642 functions. During 2015 alone, R added more functions/procs than SAS Institute has written in its entire history.
Of course while 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 Fig. 10. A program run on 4/19/2016 counted 11,531 R packages at all major repositories, 8,239 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 40% higher on the y-axis than the one shown in Figure 10.
As with any analysis software, individuals also maintain their own separate collections 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 19.78 functions per package. Given the package count of 11,531, as of 4/19/2016 there were approximately 228,103 total functions in R. In total, R has approximately 190 times as many commands as its main commercial competitor, SAS.
