Tag: Data Science

R GUI Reviews Updated

I have just finished updating my reviews of graphical user interfaces for the R language. These include BlueSky Statistics, jamovi, JASP, R AnalyticFlow, R Commander, R-Instat, Rattle, and RKward. The permanent link to the article that summarizes it all is https://r4stats.com/articles/software-reviews/r-gui-comparison/.
I list the highlights below as this post to reach all the blog aggregators. If you have suggestions for improving any of the reviews, please let me know at muenchen.bob@gmail.com.

With so many detailed reviews of Graphical User Interfaces (GUIs) for R available, which should you choose? It’s not too difficult to rank them based on the number of features they offer, so I’ll start there. Then, I’ll follow with a brief overview of each.

I’m basing the counts on the number of dialog boxes in each category of the following categories:

  • Ease of Use
  • General Usability
  • Graphics
  • Analytics
  • Reproducibility

This data is trickier to collect than you might think. Some software has fewer menu choices, depending on more detailed dialog boxes instead. Studying every menu and dialog box is very time-consuming, but that is what I’ve tried to do to keep this comparison trustworthy. Each development team has had a chance to look the data over and correct errors.

Perhaps the biggest flaw in this methodology is that every feature adds only one point to each GUI’s total score. I encourage you to download the full dataset to consider which features are most important to you. If you decide to make your own graphs with a different weighting system, I’d love to hear from you in the comments below.

Ease of Use

For ease of use, I’ve defined it primarily by how well each GUI meets its primary goal: avoiding code. They get one point for each of the following abilities, which include being able to install, start, and use the GUI to its maximum effect, including publication-quality output, without knowing anything about the R language itself. Figure one shows the result. R Commander is abbreviated Rcmdr, and R AnalyticFlow is abbreviated RAF. The commercial BlueSky Pro comes out on top by a slim margin, followed closely by JASP and RKWard. None of the GUIs achieved the highest possible score of 14, so there is room for improvement.

  • Installs without the use of R
  • Starts without the use of R
  • Remembers recent files
  • Hides R code by default
  • Use its full capability without using R
  • Data editor included
  • Pub-quality tables w/out R code steps
  • Simple menus that grow as needed
  • Table of Contents to ease navigation
  • Variable labels ease identification in the output
  • Easy to move blocks of output
  • Ease reading columns by freezing headers of long tables
  • Accepts data pasted from the clipboard
  • Easy to move header row of pasted data into the variable name field
Figure 1. The number of ease of use features offered by each R GUI.

General Usability

This category is dominated by data-wrangling capabilities, where data scientists and statisticians spend most of their time. It also includes various types of data input and output. We see in Figure 2 that both BlueSky versions and R-Instat come out on top not just due to their excellent selection of data-wrangling features but also for their use of the rio package for importing and exporting files. The rio package combines the import/export capabilities of many other packages, and it is easy to use. I expect the other GUIs will eventually adopt it, raising their scores by around 20 points.

  • Operating systems (how many)
  • Import data file types (how many)
  • Import from databases (how many)
  • Export data file types (how many)
  • Languages displayable in UI (how many, besides English)
  • Easy to repeat any step by groups (split-file)
  • Multiple data files open at once
  • Multiple output windows
  • Multiple code windows
  • Variable metadata view
  • Variable types (how many)
  • Variable search/filter in dialogs
  • Variable sort by name
  • Variable sort by type
  • Variable move manually
  • Model Builder (how many effect types)
  • Magnify GUI for teaching
  • R code editor
  • Comment/uncomment blocks of code
  • Package management (comes with R and all packages)
  • Output: word processing features
  • Output: R Markdown
  • Output: LaTeX
  • Data wrangling (how many)
  • Transform across many variables at once (e.g., row mean)
  • Transform down many variables at once (e.g., log, sqrt)
  • Assign factor labels across many variables at once
  • Project saves/loads data, dialogs, and notes in one file
Figure 2. The number of general usability features in each R GUI.

Graphics

This category consists mainly of the number of graphics each software offers. However, the other items can be very important to completing your work. They should add more than one point to the graphics score, but I scored them one point since some will view them as very important while others might not need them at all. Be sure to see the full reviews or download the Excel file if those features are important to you. Figure 3 shows the total graphics score for each GUI. R-Instat has a solid lead in this category. In fact, this underestimates R-Instat’s ability if you include its options to layer any “geom” on top of another graph. However, that requires knowing the geoms and how to use them. That’s knowledge of R code, of course.

When studying these graphs, it’s important to consider the difference between the relative and absolute performance. For example, relatively speaking, R Commander is not doing well here, but it does offer over 25 types of plots! That absolute figure might be fine for your needs.

Continued…

BlueSky Statistics Version 10 is Not Open Source

BlueSky Statistics is a graphical user interface for the powerful R language. On July 10, 2024, the BlueskyStatistics.com website said:

“…As the BlueSky Statistics version 10 product evolves, we will continue to work on orchestrating the necessary logistics to make the BlueSky Statistics version 10.x application available as an open-source project. This will be done in phases, as we did for the BlueSky Statistics 7.x version. We are currently rearchitecting its key components to allow the broader community to make effective contributions. When this work is complete, we will open-source the components for broader community participation…”

In the current statement (September 5, 2024), the sentence regarding version 10.x becoming open source is gone. This line was added:

“…Revenue from the commercial (Pro) version plays a vital role in funding the R&D needed to continue to develop and support the open-source (BlueSky Statistics 7.x) version and the free version (BlueSky Statistics 10.x Base Edition)…”

I have verified with the founders that they no longer plan to release version 10 with an open-source license. I’m disappointed by this change as I have advocated for and written about open source for many years.

There are many advantages of open-source licensing over proprietary. If the company decides to stop making version 10 free, current users will still have the right to run the currently installed version, but they will only be able to get the next version if they pay. If it were open source, its users could move the code to another repository and base new versions on that. That scenario has certainly happened before, most notably with OpenOffice. BlueSky LLC has announced no plans to charge for future versions of BlueSky Base Edition, but they could.

I have already updated the references on my website to reflect that BlueSky v10 is not open source. I wish I had been notified of this change before telling many people at the JSM 2024 conference that I was demonstrating open-source software. I apologize to them.

BlueSky Statistics Enhancements

BlueSky Statistics is a free and open-source graphical user interface for the powerful R language. There is also a commercial “Pro” version that offers tech support, priority feature requests, and many powerful additional features. The Pro version has been beefed up considerably with the new features below. These features apply to quality control, general statistics, team collaboration, project management, and scripting. Many are focused on quality control and Six Sigma as a result of requests from organizations migrating from Minitab and JMP. However, both versions of BlueSky Statistics offer a wide range of statistical, graphical, and machine-learning methods.

The free version saves every step of the analysis for full reproducibility. However, repeating the analysis is a step-by-step process. The Pro version can now rerun the entire set at once, substituting other datasets when needed.

You can obtain either version at https://BlueSkyStatistics.com. A detailed review is available at https://r4stats.com/articles/software-reviews/bluesky/. If you plan to attend the Joint Statistical Meetings (JSM) in Portland next week, stop by Booth 406 to get a demonstration. We hope to see you there!

Copy and Paste data from Excel

      Copy data from Excel and paste it into the BlueSky Statistics data grid. This is in addition to the existing mechanism of bringing data through file import for various file formats into BlueSky Statistics to perform data analysis.

      Undo/Redo data grid edits

      Single-item and muti-items data element edits can be discarded by undo and restored by redo operations.

      Project save/open to save/open all work (all open datasets and output analysis)

      Analysis performed can be saved into one or more projects. Each project contains all the datasets along with all the analyses and any R code from the editor. The projects can be exported and shared (sent as .bsp, which is a zip file; “bsp” is an abbreviation of BlueSky Statistics Project) with other BlueSky Statistics users. The users can import projects, see all the datasets and analyses stored in the projects, and subsequently add/modify/rerun all the analyses.

      Enhanced cleaning/adjustment of copied/imported Excel/CSV data on the Datagrid

      Dataset > Excel Cleanup

      There are a few enhancements made to offer additional data cleanup/adjustment options to the existing Excel Cleanup dialog to clean/adjust (i.e., rows. Columns, data type, etc.) data on the BlueSky Statistics data grid, irrespective of how the data was loaded into the data grid with the file open option or by copying and pasting from Excel/CSV file.

      Renaming output tabs

      Double-clicking on the output tab will open a dialog box asking for the new name. The user can type in a name to rename the output tab.

      Enhanced Pie Chart and Bar Chart

      Graphics > Pie Charts > Pie Chart
      Graphics > Bar Chart

                    The pie chart and bar chart have been enhanced to show % and counts on the plot.

      Scatterplot Matrix

      Graphics > Scatterplot Matrix

      The Scatter Plot Matrix dialog has been added.

      Scatterplot with mean and confidence interval bar

      Graphics > Scatterplot > Scatter Plot with Intervals

      A Scatter Plot dialog with mean and confidence interval bar has been made available with an unlimited number of grouping variables for the X-axis to group a numeric variable for the Y-axis.

      Enhanced Scatterplot with both horizontal and vertical reference lines

      Graphics > Scatterplot > Scatter Plot Ref Lines

      The Scatterplot dialog has been enhanced so that users can add an unlimited number of reference lines (horizontal and vertical axis) to the plot. 

      Enhancements to BlueSky Statistics R Editor and Output Syntax/Code Editor

      For R-programmers many enhancements have been made to the BlueSky R Editor and the output syntax/code editor to improve ease of use and productivity with tooltips, find and replace, undo/redo, comment/uncomment blocks, etc.

      Enhanced Normal Distribution Plot

        Distribution > Normal > Normal Distribution Plot with Labels 

        • The normal distribution plot will show the computed probability and x values on the plot for the shaded area for x value and quantiles, respectively
        • Plot one tail (left and right), two tails, and other ranges

        Automatic randomization of generating normal sample distribution

          Distribution > Normal > Sample from Normal Distribution

          In addition to setting a seed value for reproducibility, the default option has been set to randomize automatically the sample data generation every time.

          Automatic randomization of design creations of all DoE designs

            DOE > Create Design > …. 

            In addition to setting a seed value for reproducibility, the default option has been set to randomize the creation of any DoE design every time automatically.

            Enhanced Distribution Fit analysis

              Analysis > Distribution Analysis > Distribution Fit P-value

              The distribution fit analysis has been enhanced to compute AD, KS, and CVM tests and show test statistics, as well as corresponding p-values. These assist users in determining the best fit in addition to the existing AIC and BIC values.

              Moreover, an option has been introduced for users to see only the comparison of distributions and skip displaying the analysis of the individual distribution fit analysis.

              Tolerance Intervals

                Six Sigma > Tolerance Intervals

                A new Tolerance Intervals analysis has been introduced. The tolerance interval describes the range of values for a distribution with confidence limits calculated to a particular percentile of the distribution. These tolerance limits, taken from the estimated interval, are limits within which a stated proportion of the population is expected to occur.

                Equivalence (and Minimal Effect) test

                  Analysis > Means > Equivalence test

                  This new feature tests for mean equivalence and minimal effects.

                  Nonlinear Least Square – all-purpose Non-Linear Regression modeling

                    Model Fitting > Nonlinear Least Square

                    Performs non-linear regression with flexibility and many user options to model, test, and plot.

                    Polynomial Models with different degrees

                      Model Fitting > Polynomial

                      Computes and fits an orthogonal polynomial model with a specified degree. Also, optionally compares multiple Polynomial models of different degrees side by side. 

                      Enhanced Pareto Chart

                        Six Sigma > Pareto Chart > Pareto Chart

                        A new option has been added for data that does not have a count column but only has the raw data. Automatically computes cumulative frequency from Raw Data for plotting.

                        Frequency analysis with an option to draw a Pareto chart

                          Analysis > Summary > Frequency Plot

                          A new dialog has been introduced to plot (optionally) the Pareto Chart from the frequency table and, if desired, display the frequency table on the Datagrid.

                          MSA (Measurement System Analysis) Enhancements

                          Gage Study Design Table

                          Six Sigma > MSA > Design MSA Study

                          Users can generate a randomized design experiment table for any combination of the number of operators, parts, and replications to set up a Gage study table to perform experiments and collect the results to analyze the accuracy of the Gage under study with analysis like Gage R&R, Gage Bias, etc.

                          Enhanced Gage R&R

                          Six Sigma > MSA > Gage R&R

                          Many enhancements and options have been introduced to the Gage of R&R dialog and the underlying analysis

                          • Report header table
                          • Enlarged graphs
                          • Nested gage data analysis, in addition to crossed
                          • Usage of historical process std dev to estimate Gage Evaluation values (%StudyVar table)
                          • Show %Process

                          Enhanced Gage Attribute Analysis

                          Six Sigma > MSA > Attribute Analysis

                          Many enhancements and options have been introduced to the Attribute Analysis dialog and the underlying analysis

                          • Report header table
                          • Accuracy and classification rate calculations, in addition to agreement and disagreement
                          • Optional Cohen’s Kappa stats (between each pair of raters) in addition to Fleiss Kappa (multi-raters)

                          Enhanced Gage Bias Analysis

                          Six Sigma > MSA > Gage Bias Analysis

                          Many enhancements and options have been introduced to the Gage Bias Analysis dialog and the underlying analysis

                          • Efficient single dialog with options for linearity and type-1 tests for one or more References
                          • A new option – “Method to use for estimating repeatability std dev”
                          • Cg and Cgk – calculated for different Reference values in one go
                          • Run charts for every reference value and an overall run chart for all reference values
                          • Usage of historical std dev to calculate RF (Reference Figure)
                          • %RE, %EV are introduced, and all tables show how the computed values compared to the required/cut-off values specified by users on the dialog

                          PCA (Process Capability Analysis) Enhancements

                          Enhanced Process Capability Analysis (for normal data)

                          Six Sigma > Process Capability > Process Capability

                          •  pp_l = pp_k and ppU = ppk is shown when a one-sided tolerance is used
                          • Removed underscores to only show Ppl, Ppk, Ppu, Cp, Cpk, .. etc
                          • A new option – “Do not use unbiasing constant to estimate std dev for overall process capability indices” to compute overall Ppk (Ppl)
                          • Underlying charts (xbar.one) renamed to MR or I Chart based on SD or MR
                          • Handling of missing values
                          • Customizable number of decimals to show on the plot
                          • Standard Deviation label on the plot marked as “Overall StdDev” and “Within StdDev”

                          Process Capability Analysis for non-normal data

                          Six Sigma > Process Capability > Process Capability (Non-Normal)

                          A new dialog has been introduced to perform process capability analysis for non-normal data.

                          Multi-Vari graph

                          Six Sigma > Multi-Vari Chart

                          A new option has been added to adjust horizontal and vertical position offset to place/move the values for the data points on the plot.

                          Enhanced Shewhart Charts

                          Six Sigma > Shewhart Charts > …….

                          A new option has been added to all Shewhart Charts dialogs: the ability to add any number of spec/reference lines to the chart specified by the user.

                          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.

                          Updated Comparison of R Graphical User Interfaces

                          I have just updated my detailed reviews of Graphical User Interfaces (GUIs) for R, so let’s compare them again. It’s not too difficult to rank them based on the number of features they offer, so let’s start there. I’m basing the counts on the number of dialog boxes in each category of four categories:

                          • Ease of Use
                          • General Usability
                          • Graphics
                          • Analytics

                          This is trickier data to collect than you might think. Some software has fewer menu choices, depending instead on more detailed dialog boxes. Studying every menu and dialog box is very time-consuming, but that is what I’ve tried to do. I’m putting the details of each measure in the appendix so you can adjust the figures and create your own categories. If you decide to make your own graphs, I’d love to hear from you in the comments below.

                          Figure 1 shows how the various GUIs compare on the average rank of the four categories. R Commander is abbreviated Rcmdr, and R AnalyticFlow is abbreviated RAF. We see that BlueSky is in the lead with R-Instat close behind. As my detailed reviews of those two point out, they are extremely different pieces of software! Rather than spend more time on this summary plot, let’s examine the four categories separately.

                          Figure 1. Mean of each R GUI’s ranking of the four categories. To make this plot consistent with the others below, the larger the rank, the better.

                          For the category of ease-of-use, I’ve defined it mostly by how well each GUI does what GUI users are looking for: avoiding code. They get one point each for being able to install, start, and use the GUI to its maximum effect, including publication-quality output, without knowing anything about the R language itself. Figure two shows the result. JASP comes out on top here, with jamovi and BlueSky right behind.

                          Figure 2. The number of ease-of-use features that each GUI has.

                          Figure 3 shows the general usability features each GUI offers. This category is dominated by data-wrangling capabilities, where data scientists and statisticians spend most of their time. This category also includes various types of data input and output. BlueSky and R-Instat come out on top not just due to their excellent selection of data wrangling features but also due to their use of the rio package for importing and exporting files. The rio package combines the import/export capabilities of many other packages, and it is easy to use. I expect the other GUIs will eventually adopt it, raising their scores by around 40 points. JASP shows up at the bottom of this plot due to its philosophy of encouraging users to prepare the data elsewhere before importing it into JASP.

                          Figure 3. Number of general usability features for each GUI.

                          Figure 4 shows the number of graphics features offered by each GUI. R-Instat has a solid lead in this category. In fact, this underestimates R-Instat’s ability if you…

                          Continued…

                          Gartner’s 2019 Take on Data Science Software

                          I’ve just updated The Popularity of Data Science Software to reflect my take on Gartner’s 2019 report, Magic Quadrant for Data Science and Machine Learning Platforms. To save you the trouble of digging through all 40+ pages of my report, here’s just the updated section:

                          IT Research Firms

                          IT research firms study software products and corporate strategies. They survey customers regarding their satisfaction with the products and services and provide their analysis in reports that they sell to their clients. Each research firm has its own criteria for rating companies, so they don’t always agree. However, I find the detailed analysis that these reports contain extremely interesting reading. The reports exclude open source software that has no specific company backing, such as R, Python, or jamovi. Even open source projects that do have company backing, such as BlueSky Statistics, are excluded if they have yet to achieve sufficient market adoption. However, they do cover how company products integrate open source software into their proprietary ones.

                          While these reports are expensive, the companies that receive good ratings usually purchase copies to give away to potential customers. An Internet search of the report title will often reveal companies that are distributing them. On the date of this post, Datarobot is offering free copies.

                          Gartner, Inc. is one of the research firms that write such reports.  Out of the roughly 100 companies selling data science software, Gartner selected 17 which offered “cohesive software.” That software performs a wide range of tasks including data importation, preparation, exploration, visualization, modeling, and deployment.

                          Gartner analysts rated the companies on their “completeness of vision” and their “ability to execute” that vision. Figure 3a shows the resulting “Magic Quadrant” plot for 2019, and 3b shows the plot for the previous year. Here I provide some commentary on their choices, briefly summarize their take, and compare this year’s report to last year’s. The main reports from both years contain far more detail than I cover here.

                          Gartner-2019

                          Figure 3a. Gartner Magic Quadrant for Data Science and Machine Learning Platforms from their 2019 report (plot done in November 2018, report released in 2019).

                          The Leaders quadrant is the place for companies whose vision is aligned with their customer’s needs and who have the resources to execute that vision. The further toward the upper-right corner of the plot, the better the combined score.

                          • RapidMiner and KNIME reside in the best part of the Leaders quadrant this year and last. This year RapidMiner has the edge in ability to execute, while KNIME offers more vision. Both offer free and open source versions, but the companies differ quite a lot on how committed they are to the open source concept. KNIME’s desktop version is free and open source and the company says it will always be so. On the other hand, RapidMiner is limited by a cap on the amount of data that it can analyze (10,000 cases) and as they add new features, they usually come only via a commercial license with “difficult-to-navigate pricing conditions.” These two offer very similar workflow-style user interfaces and have the ability to integrate many open sources tools into their workflows, including R, Python, Spark, and H2O.
                          • Tibco moved from the Challengers quadrant last year to the Leaders this year. This is due to a number of factors, including the successful integration of all the tools they’ve purchased over the years, including Jaspersoft, Spotfire, Alpine Data, Streambase Systems, and Statistica.
                          • SAS declined from being solidly in the Leaders quadrant last year to barely being in it this year. This is due to a substantial decline in its ability to execute. Given SAS Institute’s billions in revenue, that certainly can’t be a financial limitation. It may be due to SAS’ more limited ability to integrate as wide a range of tools as other vendors have. The SAS language itself continues to be an important research tool among those doing complex mixed-effects linear models. Those models are among the very few that R often fails to solve.

                          The companies in the Visionaries Quadrant are those that have good future plans but which may not have the resources to execute that vision.

                          • Mathworks moved forward substantially in this quadrant due to MATLAB’s ability to handle unconventional data sources such as images, video, and the Internet of Things (IoT). It has also opened up more to open source deep learning projects.
                          • H2O.ai is also in the Visionaries quadrant. This is the company behind the open source  H2O software, which is callable from many other packages or languages including R, Python, KNIME, and RapidMiner. While its own menu-based interface is primitive, its integration into KNIME and RapidMiner makes it easy to use for non-coders. H2O’s strength is in modeling but it is lacking in data access and preparation, as well as model management.
                          • IBM dropped from the top of the Visionaries quadrant last year to the middle. The company has yet to fully integrate SPSS Statistics and SPSS Modeler into its Watson Studio. IBM has also had trouble getting Watson to deliver on its promises.
                          • Databricks improved both its vision and its ability to execute, but not enough to move out of the Visionaries quadrant. It has done well with its integration of open-source tools into its Apache Spark-based system. However, it scored poorly in the predictability of costs.
                          • Datarobot is new to the Gartner report this year. As its name indicates, its strength is in the automation of machine learning, which broadens its potential user base. The company’s policy of assigning a data scientist to each new client gets them up and running quickly.
                          • Google’s position could be clarified by adding more dimensions to the plot. Its complex collection of a dozen products that work together is clearly aimed at software developers rather than data scientists or casual users. Simply figuring out what they all do and how they work together is a non-trivial task. In addition, the complete set runs only on Google’s cloud platform. Performance on big data is its forte, especially problems involving image or speech analysis/translation.
                          • Microsoft offers several products, but only its cloud-only Azure Machine Learning (AML) was comprehensive enough to meet Gartner’s inclusion criteria. Gartner gives it high marks for ease-of-use, scalability, and strong partnerships. However, it is weak in automated modeling and AML’s relation to various other Microsoft components is overwhelming (same problem as Google’s toolset).

                          Figure 3b. Last year’s Gartner Magic Quadrant for Data Science and Machine Learning Platforms (January, 2018)

                          Those in the Challenger’s Quadrant have ample resources but less customer confidence in their future plans, or vision.

                          • Alteryx dropped slightly in vision from last year, just enough to drop it out of the Leaders quadrant. Its workflow-based user interface is very similar to that of KNIME and RapidMiner, and it too gets top marks in ease-of-use. It also offers very strong data management capabilities, especially those that involve geographic data, spatial modeling, and mapping. It comes with geo-coded datasets, saving its customers from having to buy it elsewhere and figuring out how to import it. However, it has fallen behind in cutting edge modeling methods such as deep learning, auto-modeling, and the Internet of Things.
                          • Dataiku strengthed its ability to execute significantly from last year. It added better scalability to its ease-of-use and teamwork collaboration. However, it is also perceived as expensive with a “cumbersome pricing structure.”

                          Members of the Niche Players quadrant offer tools that are not as broadly applicable. These include Anaconda, Datawatch (includes the former Angoss), Domino, and SAP.

                          • Anaconda provides a useful distribution of Python and various data science libraries. They provide support and model management tools. The vast army of Python developers is its strength, but lack of stability in such a rapidly improving world can be frustrating to production-oriented organizations. This is a tool exclusively for experts in both programming and data science.
                          • Datawatch offers the tools it acquired recently by purchasing Angoss, and its set of “Knowledge” tools continues to get high marks on ease-of-use and customer support. However, it’s weak in advanced methods and has yet to integrate the data management tools that Datawatch had before buying Angoss.
                          • Domino Data Labs offers tools aimed only at expert programmers and data scientists. It gets high marks for openness and ability to integrate open source and proprietary tools, but low marks for data access and prep, integrating models into day-to-day operations, and customer support.
                          • SAP’s machine learning tools integrate into its main SAP Enterprise Resource Planning system, but its fragmented toolset is weak, and its customer satisfaction ratings are low.

                          To see many other ways to rate this type of software, see my ongoing article, The Popularity of Data 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!

                          Business Intelligence and Data Science Groups in East Tennessee

                          The Knoxville area has four groups that help people learn about business intelligence and data science.

                          The Knoxville R Users Group (KRUG) focuses on the free and open source R language. Each meeting begins with a bit of socializing followed by a series of talks given by its members or guests. The talks range from brief five-minute demos of an R function to 45-minute in-depth coverage of some method of analysis. Beginning tutorials on R are occasionally offered as well. Membership is free of charge, but donations are accepted to defray the cost of snacks and web site maintenance. You can join at the KRUG web site.

                          Data Science KNX is a group of people interested in the broad field of data science. Members range from beginners to experts. As their web site states, their “…aim is to maintain a forum for connecting people around data science specific topics such as tutorials and their applications, local success stories, discussions of new technologies, and best practices. All are welcome to attend, network, and present!” You can go here to join at the Data Science KNX web site. Membership is free, though the group gladly accepts donations to help defray the costs of the pizza and beer provided at their meetings.

                          The East Tennessee Business Intelligence Users Group is “committed to learning, sharing, and advancing the field of Business Intelligence in the East Tennessee region.” They meet several times each year featuring speakers who demonstrate business intelligence software such as IBM’s Watson and Microsoft’s PowerBI. Meetings are at lunch and a meal is provided by sponsoring companies. Membership is free, and so is the lunch! You can join the group at their web site.

                          Each spring and fall, The University of Tennessee’s Department of Business Analytics and Statistics offers a Business Analytics Forum that features speakers from both industry and academia. The group consists of non-competing companies for whom business analytics is an important part of their operation. Forum members work together to share best practices and to develop more effective strategies. The forum is open to paid members only and you can join on their registration page.