BlueSky Statistics is an easy-to-use menu system that uses the R language to do all its work. My detailed review of BlueSky is available here, and a brief comparison of the various menu systems for R is here. I’ve just released the BlueSky Statistics 7.1 User Guide in printed form on the world’s largest independent bookstore, Lulu.com. A description and detailed table of contents are available here.
I’ve also released the BlueSky Statistics 7.1 Intro Guide. It is a complete subset of the User Guide, and you can download it for free here (if you have trouble downloading it, your company may have security blocking Microsoft OneDrive; try it at home). Its description and table of contents are here, and soon you will also be able to purchase a printed copy of it from Lulu.com.
I’m enthusiastic about getting feedback on these books. If you have comments or suggestions, please send them to me at muenchen.bob at gmail dot com.
Publishing with Lulu.com has been a very pleasant experience. They put the author in complete control, making one responsible for every detail of the contents, obtaining reviewers, creating a cover file that includes the front, back, and spine of the book to match the dimensions of the book (e.g. more pages means wider spine, etc.) Advertising is left up to the writer as well, hence this blog post! If you are thinking about writing a book, I highly recommend both Lulu.com and getting a cover design from 99designs.com. The latter let me run a contest in which a dozen artists submitted several ideas each. Their built-in survey system let me ask many colleagues for their opinions to help me decide. Altogether, it was a very interesting experience.
To follow the progress of these and other R related books, subscribe to my blog, or follow me on Twitter.
The BlueSky Statistics graphical user interface (GUI) for the R language has added quite a few new features (described below). I’m also working on a BlueSky User Guide, a draft of which you can read about and download here. [Update: don’t download that, get the full Intro Guide download instead.] Although I’m spending a lot of time on BlueSky, I still plan to be as obsessive as ever about reviewing all (or nearly all) of the R GUIs, which is summarized here.
The new data management features in BlueSky are:
Date Order Check — this lets you quickly check across the dates stored in many variables, and it reports if it finds any rows whose dates are not always increasing from left to right.
Find Duplicates – generates a report of duplicates and saves a copy of the data set from which the duplicates are removed. Duplicates can be based on all variables, or a set of just ID variables.
Select First/Last Observation per Group – finding the first or last observation in a group can create new datasets from the “best” or “worst” case in each group, find the most current record, and so on.
Model Fitting / Tuning
One of the more interesting features in BlueSky is its offering of what they call Model Fitting and Model Tuning. Model Fitting gives you direct control over the R function that does the work. That provides precise control over every setting, and it can teach you the code that the menus create, but it also means that model tuning is up to you to do. However, it does standardize scoring so that you do not have to keep up with the wide range of parameters that each of those functions need for scoring. Model Tuning controls models through the caret package, which lets you do things like K-fold cross-validation and model tuning. However, it does not allow control over every model setting.
New Model Fitting menu items are:
Cox Proportional Hazards Model: Cox Single Model
Cox Multiple Models
Cox with Formula
Cox Stratified Model
Extreme Gradient Boosting
Neural Nets: Multi-layer Perceptron
NeuralNets (i.e. the package of that name)
There are so many Model Tuning entries that it’s easier to just paste in the list I updated on the main BlueSkly review that I updated earlier this morning:
Model Tuning: Adaboost Classification Trees
Model Tuning: Bagged Logic Regression
Model Tuning: Bayesian Ridge Regression
Model Tuning: Boosted trees: gbm
Model Tuning: Boosted trees: xgbtree
Model Tuning: Boosted trees: C5.0
Model Tuning: Bootstrap Resample
Model Tuning: Decision trees: C5.0tree
Model Tuning: Decision trees: ctree
Model Tuning: Decision trees: rpart (CART)
Model Tuning: K-fold Cross-Validation
Model Tuning: K Nearest Neighbors
Model Tuning: Leave One Out Cross-Validation
Model Tuning: Linear Regression: lm
Model Tuning: Linear Regression: lmStepAIC
Model Tuning: Logistic Regression: glm
Model Tuning: Logistic Regression: glmnet
Model Tuning: Multi-variate Adaptive Regression Splines (MARS via earth package)
Model Tuning: Naive Bayes
Model Tuning: Neural Network: nnet
Model Tuning: Neural Network: neuralnet
Model Tuning: Neural Network: dnn (Deep Neural Net)
Model Tuning: Neural Network: rbf
Model Tuning: Neural Network: mlp
Model Tuning: Random Forest: rf
Model Tuning: Random Forest: cforest (uses ctree algorithm)
Graphical User Interfaces (GUIs) for the R language help beginners get started learning R, help non-programmers get their work done, and help teams of programmers and non-programmers work together by turning code into menus and dialog boxes. There has been quite a lot of progress on R GUIs since my last post on this topic. Below I describe some of the features added to several R GUIs.
BlueSky Statistics has added mixed-effects linear models. Its dialog shows an improved model builder that will be rolled out to the other modeling dialogs in future releases. Other new statistical methods include quantile regression, survival analysis using both Kaplan-Meier and Cox Proportional Hazards models, Bland-Altman plots, Cohen’s Kappa, Intraclass Correlation, odds ratios and relative risk for M by 2 tables, and sixteen diagnostic measures such as sensitivity, specificity, PPV, NPV, Youden’s Index, and the like. The ability to create complex tables of statistics was added via the powerful arsenal package. Some examples of the types of tables you can create with it are shown here.
Several new dialogs have been added to the Data menu. The Compute Dummy Variables dialog creates dummy (aka indicator) variables from factors for use in modeling. That approach offers greater control over how the dummies are created than you would have when including factors directly in models.
A new Factor Levels menu item leads to many of the functions from the forcats package. They allow you to reorder factor levels by count, by occurrence in the dataset, by functions of another variable, allow you to lump low-frequency levels into a single “Other” category, and so on. These are all helpful in setting the order and nature of, for example, bars in a plot or entries in a table.
The BlueSky Data Grid now has icons that show the type of variable i.e. factor, ordered factor, string, numeric, date or logical. The Output Viewer adds icons to let you add notes to the output (not full R Markdown yet), and a trash can icon lets you delete blocks of output.
A comprehensive list of the changes to this release is located here and my updated review of it is here.
New modules expand jamovi’s capabilities to include time-based survival analysis, Bland-Altman analysis & plots, behavioral change analysis, advanced mediation analysis, differential item analysis, and quantiles & probabilities from various continuous distributions.
jamovi’s new Flexplot module greatly expands the types of graphs it can create, letting you take a single graph type and repeat it in rows and/or columns making it easy to visualize how the data is changing across groups (called facet, panel, or lattice plots).
You can read more about Flexplot here, and my recently-updated review of jamovi is here.
The JASP package has added two major modules, machine learning, and network analysis. The machine learning module includes boosting, K-nearest neighbors, and random forests for both regression and classification problems. For regression, it also adds regularized linear regression. For clustering, it covers hierarchical, K-means, random forest, density-based, and fuzzy C-means methods. It can generate models and add predictions to your dataset, but it still cannot save models for future use. The main method it is missing is a single decision tree model. While less accurate predictors, a simple tree model can often provide insight that is lacking from other methods.
Another major addition to JASP is Network Analysis. It helps you to study the strengths of interactions among people, cell phones, etc. With so many people working from home during the Coronavirus pandemic, it would be interesting to see what this would reveal about how our patterns of working together have changed.
A really useful feature in JASP is its Data Library. It greatly speeds your ability to try out a new feature by offering a completely worked-out example including data. When trying out the network analysis feature, all I had to do was open the prepared example to see what type of data it would use. With most other data science software, you’re left to dig about in a collection of datasets looking for a good one to test a particular analysis. Nicely done!
I’ve updated my full review of JASP, which you can read here.
The main improvement to the RKWard GUI for R is adding support for R Markdown. That makes it the second GUI to support R Markdown after R Commander. Both the jamovi and BlueSky teams are headed that way. RKWard’s new live preview feature lets you see text, graphics, and markdown as you work. A comprehensive list of new features is available here, and my full review of it is here.
R GUIs are gaining features at a rapid pace, quickly closing in on the capabilities of commercial data science packages such as SAS, SPSS, and Stata. I encourage R GUI users to contribute their own additions to the menus and dialog boxes of their favorite(s). The development teams are always happy to help with such contributions. To follow the progress of these and other R GUIs, subscribe to my blog, or follow me on twitter.
Data science is being used in many ways to improve healthcare and reduce costs. We have written a textbook, Introduction to Biomedical Data Science, to help healthcare professionals understand the topic and to work more effectively with data scientists. The textbook content and data exercises do not require programming skills or higher math. We introduce open source tools such as R and Python, as well as easy-to-use interfaces to them such as BlueSky Statistics, jamovi, R Commander, and Orange. Chapter exercises are based on healthcare data, and supplemental YouTube videos are available in most chapters.
For instructors, we provide PowerPoint slides for each chapter, exercises, quiz questions, and solutions. Instructors can download an electronic copy of the book, the Instructor Manual, and PowerPoints after first registering on the instructor page.
The book is available in print
and various electronic formats. Because it is self-published, we plan to update it more rapidly than would be
possible through traditional publishers.
Below you will find a detailed table of contents and a list
of the textbook authors.
Table of Contents
OVERVIEW OF BIOMEDICAL DATA SCIENCE
Background and history
the statistician’s perspective
the machine learner’s perspective
the database administrator’s perspective
the data visualizer’s perspective
Data analytical processes
exploratory data analysis (EDA)
predictive modeling approaches
types of models
types of software
Major types of analytics
predictive analytics (modeling)
putting it all together
Biomedical data science tools
Biomedical data science education
Biomedical data science careers
Importance of soft skills in data science
Biomedical data science resources
Biomedical data science challenges
SPREADSHEET TOOLS AND TIPS
basic spreadsheet functions
download the sample spreadsheet
Navigating the worksheet
Clinical application of spreadsheets
formulas and functions
Tips and tricks
Microsoft Excel shortcuts – windows users
Google sheets tips and tricks
Measures of central tendency & dispersion
the normal and log-normal distributions
Descriptive and inferential statistics
Categorical data analysis
Types of research studies
Comparing two groups
the independent-samples t-test
the wilcoxon-mann-whitney test
Comparing more than two groups
Other types of tests
exact or permutation tests
bootstrap or resampling tests
Stats packages and online calculators
non-commercial or open source packages
historical data visualizations
Data visualization software
R programming language
other visualization programs
visualizing categorical data
visualizing continuous data
INTRODUCTION TO DATABASES
A brief history of database models
Relational database structure
Clinical data warehouses (CDWs)
Structured query language (SQL)
The seven v’s of big data related to health care data
BIOINFORMATICS and PRECISION MEDICINE
Biological data analysis – from data to discovery
Biological data types
bioinformatics data in public repositories
biomedical cancer data portals
Tools for analyzing bioinformatics data
command line tools
Genomic data analysis
Genomic data analysis workflow
variant calling pipeline for whole exome sequencing data
variant filtering and annotation
reporting and visualization
Precision medicine – from big data to patient care
Examples of precision medicine
PROGRAMMING LANGUAGES FOR DATA ANALYSIS
installing R & rstudio
an example R program
getting help in R
user interfaces for R
R’s default user interface: rgui
menu & dialog guis
some popular R guis
R graphical user interface comparison
an example Python program
getting help in Python
user interfaces for Python
R vs. Python
training vs test data
bias and variance
supervised and unsupervised learning
Common machine learning algorithms
Evaluation of predictive analytical performance
classification model evaluation
regression model evaluation
Machine learning software
Programming languages and machine learning
Machine learning challenges
Machine learning examples
example 1 classification
example 2 regression
example 3 clustering
example 4 association rules
Image analysis (computer vision)
Image libraries and packages
Natural language processing
NLP libraries and packages
Text mining and medicine
Electronic health record data and AI
deep learning platforms and programs
Artificial intelligence challenges
Socio economic and legal
Adverse unintended consequences
Need for more ML and AI education
Brenda Griffith Technical Writer Data.World Austin, TX
Robert Hoyt MD, FACP, ABPM-CI, FAMIA Associate Clinical Professor Department of Internal Medicine Virginia Commonwealth University Richmond, VA
David Hurwitz MD, FACP, ABPM-CI Associate CMIO Allscripts Healthcare Solutions Chicago, IL
Madhurima Kaushal MS Bioinformatics Washington University at St. Louis, School of Medicine St. Louis, MO
Robert Leviton MD, MPH, FACEP, ABPM-CI, FAMIA Assistant Professor New York Medical College Department of Emergency Medicine Valhalla, NY
Karen A. Monsen PhD, RN, FAMIA, FAAN Professor School of Nursing University of Minnesota Minneapolis, MN
Robert Muenchen MS, PSTAT Manager, Research Computing Support University of Tennessee Knoxville, TN
Dallas Snider PhD Chair, Department of Information Technology University of West Florida Pensacola, FL
A special thanks to Ann Yoshihashi MD for her help with the publication of this textbook.
The WPS Analytics’ version of the SAS language is now available in a Community Edition. This edition allows you to run SAS code on datasets of any size for free. Purchasing a commercial license will get you tech support and the ability to run it from the command line, instead of just interactively. The software license details are listed in this table.
While the WPS version of the SAS language doesn’t do everything the version from SAS Institute offers, it does do quite a lot. The complete list of features is available here.
Back in 2009, the SAS Institute filed a lawsuit against the creators of WPS Analytics,World Programming Limited (WPL), in the High Court of England and Wales. SAS Institute lost the case on the grounds that copyright law applies to software source code, not to its functionality. WPL never had access to SAS Institute’s source code, but they did use a SAS educational license to study how it works. SAS Institute lost another software copyright battle in North Carolina courts, but won over the use of their educational license. SAS Institute is suing a third time, hoping to do better by carefully choosing a pro-patent court in East Texas.
Although I prefer using R, I’m a big fan of the SAS language, as well as SAS Institute, which offers superb technical support. However, I agree with the first two court findings. Copyright law should not apply to a computer language, only to a particular set of source code that creates the language.
R AnalyticFlow (RAF) is a free and open source graphical user interface (GUI) for the R language that focuses on beginners looking to point-and-click their way through analyses. What sets it apart from the other half-dozen GUIs for R is that it uses a flowchart-like workflow diagram to control the analysis instead of only menus. In my first programming class back in the Pleistocene Era, my professor told us to never begin a program without doing a flowchart of what you were trying to accomplish. With workflow tools, you get the benefit of the diagram outlining the big picture, while the dialog box settings in each node control what happens at each step. In Figure 1 you can get a good idea of what is happening without any further information.
Another advantage you get with most workflow tools is the ability to reuse workflows very easily because the dataset is read in only once at the beginning. Unfortunately, most of that advantage is missing from R AnalyticFlow (hereafter, “RAF”) since you must specify which dataset is used in every node. The downside to workflow tools is that they’re slightly harder to learn than menu-based systems. This involves learning how to draw a diagram, what flows through it (e.g. datasets, models), and how to generate a single comprehensive reports for the entire analysis.
This post is one of a series of comparative reviews which aim to help non-programmers choose the GUI that is best for them. The reviews all follow a standard template to make comparisons across products easier. These reviews also include a cursory description of the programming support that each GUI offers.
There are various definitions of user interface types, so here’s how I’ll be using these terms:
GUI = Graphical User Interface using menus and dialog boxes to avoid having to type programming code. I do not include any assistance for programming in this definition. So, GUI users are people who prefer using a GUI to perform their analyses. They don’t have the time or inclination to become good programmers.
IDE = Integrated Development Environment which helps programmers write code. I do not include point-and-click style menus and dialog boxes when using this term. IDE users are people who prefer to write R code to perform their analyses.
The various user interfaces available for R differ quite a lot in how they’re installed. Some, such as BlueSky Statistics, jamovi, and RKWard, install in a single step. Others, such as Deducer, install in multiple steps (up to seven steps, depending on your needs). Advanced computer users often don’t appreciate how lost beginners can become while attempting even a simple installation. The Help Desks at most universities are flooded with such calls at the beginning of each semester!
RAF is available for Mac, and Linux. Its installation takes four steps:
Install Java, if you don’t already have it installed. This can be tricky as you must match the type of Java to the type of R you use. Most computers these days have 64-bit operating systems. Whether 32-bit or 64-bit, you must use the same “bitness” on all of these steps, or it will not work.
Next, install R if you haven’t already (available here).
Install RAF itself after downloading it from here.
Start RAF. It will prompt you to install some R packages, notably rJava. This step requires Internet access. To install if you don’t have such access, see the RAF website’s About R Packages section for important details on how to proceed (from another machine that does have Internet access, of course).
When choosing a GUI, one of the most fundamental questions is: what can it do for you? What the initial software installation of each GUI gets you is covered in the Graphics, Analysis, and Modeling sections of this series of articles. Regardless of what comes built-in, it’s good to know how active the development community is. They contribute “plug-ins” which add new menus and dialog boxes to the GUI. This level of activity ranges from very low (RKWard, Deducer) through moderate (jamovi) to very active (R Commander).
RAF does not offer any plug-in modules, though its developers do provide instruction on how you can create your own.
Some user interfaces for R, such as BlueSky and jamovi, start by double-clicking on a single icon, which is great for people who prefer to not write code. Others, such as R Commander and JGR, have you start R, then load a package from your library, and then call a function. That’s better for people looking to learn R, as those are among the first tasks they’ll have to learn anyway.
You start RAF directly by double-clicking its icon from your desktop or choosing it from your Start Menu (i.e. not from within R itself). On my system, I had to right-click the icon and choose, “Run as Administrator” or I would get the message, “Failed to Launch R. Confirm Settings?” If I responded “Yes”, it showed the path to my installation of R, which was already correct. I tried a second computer and it did start, but when it tried to install the JavaGD and rJava packages, it said, “Warning in install.packages (c(“JavaGD”,”rJava”)) : ‘lib = “C:/Program Files/R/R-3.6.1/library” ‘ is not writable. Would you like to use a personal library instead?”
Upon startup, it displays its startup screen, shown in Figure 2. Quick Start puts you into the software with a new Flow window open. New Project starts a new workflow, and Bookmarks give you quick access to existing workflows.
A data editor is a fundamental feature in data analysis software. It puts you in touch with your data and lets you get a feel for it, if only in a rough way. A data editor is such a simple concept that you might think there would be hardly any differences in how they work in different GUIs. While there are technical differences, to a beginner what matters the most are the differences in simplicity. Some GUIs, including jamovi, let you create only what R calls a data frame. They use more common terminology and call it a data set: you create one, you save one, later you open one, then you use one. Others, such as RKWard trade this simplicity for the full R language perspective: a data set is stored in a workspace. So the process goes: you create a data set, you save a workspace, you open a workspace, and choose a data set from within it.
To start entering data, choose “Input> Enter Data” and drag the selection onto the workflow editor window. An empty spreadsheet will appear (Figure 3). You can enter variable names on the first line if you check the “Header: Use 1st Row” box at the bottom of the window. This is the first hint you’ll see that RAF leans on R terminology that can be somewhat esoteric. RAF’s developers could have labeled this choice as “Column Names” but went with the R terminology of “Header” instead. This approach may be confusing for beginners, but if their goal is to learn R, it will help in the long run.
To enter factors (R’s categorical variables), choose the “Options” tab and check, “Convert Characters to Factors”, then RAF will convert the character string variables you enter to factors. Otherwise, it will leave them as characters. Dates remain stored as characters; you have to use “Processing> Set Data Type” node to change them, and they must be entered in the form yyyy-mm-dd.
There is no limit to the number of rows and columns you can enter initially. However, once you choose “Run”, the data frame is created and can no longer be edited!
Saving the workflow is done with the standard “File > Save As” menu. You must save each one to its own file. To save the flow and the various objects that it uses such as data frames and models, use “Project > Export”. When receiving a project from a colleague, use “Project> Import” to begin using it.
To analyze data, you must first read it. While many R GUIs can import a wide range of data formats such as files created by other statistics programs and databases, RAF can import only text and R objects.
RAF’s text import feature is well done. Once you select an Input File, it quickly scans the file and figures out if variable names are present, the delimiters it uses to separate the columns, and so on. It then displays a “preview” (Figure 4, bottom). It does this quickly since its preview is only on the first 100 rows of data. If the preview displays errors, you then manually change the settings and check the preview until it’s correct. When the preview looks good, you click, “Run”, it will then read all the data.
The ability to export data to a wide range of file types helps when you, or other members of your research team, have to use multiple tools to complete a task. Unfortunately, this is a very weak area for R GUIs. Deducer offers no data export at all, and R Commander, and rattle can export only delimited text files (an earlier version of this listed jamovi as having very limited data export; that has now been expanded). Only BlueSky offers a fairly comprehensive set of export options. Unfortunately, RAF falls into the former group, being able only to export data in text and R object files.
It’s often said that 80% of data analysis time is spent preparing the data. Variables need to be transformed, recoded, or created; strings and dates need to be manipulated; missing values need to be handled; datasets need to be stacked or merged, aggregated, transposed, or reshaped (e.g. from wide to long and back). A critically important aspect of data management is the ability to transform many variables at once. For example, social scientists need to recode many survey items, biologists need to take the logarithms of many variables. Doing these types of tasks one variable at a time can be tedious. Some GUIs, such as jamovi and RKWard handle only a few of these functions. Others, such as BlueSky and the R Commander, can handle many, but not all, of them.
RAF handles a fairly basic set of data management tools:
Rename – Variables in a data frame)
Set Data Type
Missing Values – Sets values as missing, no imputation)
Merge – Various joins
Merge – Adds rows
Manage Objects (copies, deletes, renames)
Workflows, Menus & Dialog Boxes
The goal of pointing & clicking your way through an analysis is to save time by recognizing dialog box settings rather than performing the more difficult task of recalling programming commands. Some GUIs, such as BlueSky and jamovi, make this easy by sticking to menu standards and using simpler dialog boxes; others, such as RKWard, use non-standard menus that are unique to it and hence require more learning.
RAF uses a unique interface. There are two ways to add build a workflow that guides your analysis. First, you can click on a toolbar icon, which drops down a menu. Click on a selection, and – without releasing the mouse button – drag your selection onto the flow window. In that case, the dialog box with its options opens below the flow area (Figure 3, bottom right).
The second way to use it is to click on a toolbar icon, drop down its menu, click on a selection and immediately release the mouse button. This causes the dialog box to appear floating in the middle of the screen (not shown). When you finish choosing your settings, there is a “Drag to Add” button at the top of the dialog. Clicking that button causes the dialog box to collapse into an icon which you can then drag onto the workflow surface.
Regardless of which method you choose, if you drop the new icon onto the top of one that is already in the workflow, it will move the new icon to the right and draw an arrow (called an “edge”) connecting the older one to the new. If you don’t drop it onto an icon that’s already in your workflow, you can add a connecting arrow later by clicking on the first icon, then choose “Draw Edge” and an arrow will appear aimed to the right (workflows go mostly left to right). The arrow will float around as you move your mouse, until you click on the second icon. A third way to connect the nodes in a flow is to click one icon, hold the Alt key down, then drag to the second icon.
Figure 3 shows the entire RAF window. On the top right is the workflow. Here are the steps I followed to create it:
I chose “Input> Read Text File” and dragged it onto the workflow. The icon’s settings appeared in the bottom right window.
I filled in the dialog box’s settings, then clicked “Run”. It named the icon after the file mydata.csv and a spreadsheet appeared in the upper-right.
I chose “Statistics> Cross Tabulation”, and dragged its icon onto the data icon.
I clicked the downward-facing arrow in the “Group By” box, and chose the variables. The first one I chose (workshop) formed the rows and the second (gender) formed the columns. Unlike most GUIs, there’s no indication of row and column roles.
I clicked “Run Node” at the top of the cross tabulation dialog box. The cross tabulation output appeared in the upper left window (right half). The code that RAF wrote to perform the task appears in the R Console window in the lower left.
You can run an entire flow by clicking “Run Flow” at the top left of the Flow window. While describing the process of building a workflow is tedious, learning to build one is quite easy to learn.
The goal of using a GUI is to make analysis easy, so GUI dialog boxes are usually quite simple to use and include everything that’s relevant within a single box. I looked at all the options in this dialog but could not find one to do a very common test for such a cross-tabulation table: the chi-squared test. RAF uses an aspect of R objects that ends up essentially creating two different types of dialog boxes in separate parts of its interface. R objects contain multiple bits of output. You can display them using generic R functions such as summary() and print(). The output window has radio buttons for those functions (Figure 3, right above the cross-tabulation table). Clicking the “summary” button will call R’s summary() function to display the chi-squared results where the table is currently shown. To study the pattern in the table and the chi-squared results requires clicking back and forth on Table and summary; you can’t get them to both appear on your screen at the same time.
Correlations provide another example. The statistics are shown, but their p-values are not shown until you click on the “summary” button. This approach is confusing for beginners, but good for people wishing to learn R.
A common data analysis task is repeating the same analysis across many variables. For example, you might want to repeat the above cross tabulation (or t-tests, etc.) on many variables at once. This is usually quite easy to accomplish in most GUIs, but not in RAF. Since R’s functions may not offer that ability without using R’s “apply” family of functions (or loops), and RAF does not support such functions, such simple tasks become quite a lot of work when using RAF. You need to add an node to your flow for each and every variable!
Each dialog box has an “Advanced” tab which allows you to enter the name of any R argument(s) in one column, and any value(s) you would like to pass to that argument in another. That’s a nice way to offer graphical control over common tasks, while assuring that every task a function is capable of is still available.
In a complex analysis, workflows can become quite complex and hard to read. A solution to this problem is the concept of a “metanode”. Metanodes allow you t take an entire section of your workflow and collapse it into what appears to be a single node. For example, you might commonly use eight nodes to prepare a dataset for analysis. You could combine all eight into a new node you call “Data Prep”, greatly simplifying the workflow. Unfortunately, RAF does not offer metanodes, as do other workflow-driven data science tools such as KNIME and RapidMiner.
One of the most surprising aspects of RAF’s workflow style is that every node specifies its input and output objects. That means that you can run any analysis with no connecting arrows in your diagram! Rather than be a required feature as with many workflow-based tools, in RAF they offer only the convenience of re-running an entire flow at once.
During GUI-driven analysis, the fact that R is doing the work is quite obvious as the code and any resulting messages appear in the Console window.
R GUIs provide simple task-by-task dialog boxes that generate much more complex code. So for a particular task, you might want to get help on 1) the dialog box’s settings, 2) the custom functions it uses (if any), and 3) the R functions that the custom functions use. Nearly all R GUIs provide all three levels of help when needed. The notable exception is the R Commander, which lacks help on the dialog boxes themselves.
The level of help that RAF offers is only the built-in R help file for the particular function you’re using. However, I had problems with the help getting stuck and showing me the help file from previous tasks rather than the one I was currently using.
The various GUIs available for R handle graphics in several ways. Some, such as R Commander and RKWard, focus on R’s built-in graphics. Others, such as BlueSky Statistics use the popular ggplot2 package. Still others, such as jamovi, use their own functions and integrate them into analysis steps.
GUIs also differ quite a lot in how they control the style of the graphs they generate. Ideally, you could set the style once, and then all graphs would follow it. That’s how BlueSky and jamovi work.
RAF uses the very flexible lattice package for all of its graphics. That makes it particularly easy to display “small multiples” of the same plot repeated by levels of another variable or two. There does not appear to be any way to control the style of the plots.
One of us (Muenchen) has been tracking The Popularity of Data Science Software using a variety of different approaches. One approach is to use Google Scholar to count the number of scholarly articles found each year for each software. He chose Google Scholar since it searches “across many disciplines and sources: articles, theses, books, abstracts, and court opinions, from academic publishers, professional societies, online repositories, universities, and other web sites.” Figure 1 shows the results from 1995 through 2016. Data collected in 2018 showed that while SPSS use dropped 39% drop from 2017 to 2018, its use was still 66% higher than R in 2018.
We see in the plot that SPSS was extremely dominant for most of that time period. Even after its precipitous decline, it still beats the rest by more than a 2 to 1 margin. Over the years, several people questioned the accuracy of Figure 1. In a time when scholarly publications are proliferating, how could SPSS use be in such decline?
One hypothesis that has often been suggested revolves around one of the most bizarre product name changes in the history of marketing. As a result of a legal battle for control of the name “SPSS”, the SPSS company changed the name of the product to “PASW”, an acronym for Predictive Analytics Software. The change made about as much sense as Coke people renaming Coke to “BSW”, for Bubbly Sugar Water. The battle was settled and in 2011 and the product name reverted back to SPSS.
Could that name change account for the apparent
decline in its use? A search on Google Scholar from 2009 to 2012 on the string:
yielded 12,000 hits. That sounds like quite a few, but when “SPSS” was substituted for “PASW” in that search, we found 701,000 references. At first glance, it seems that the scholarly use of SPSS was undercounted by 1.7%. However, when searching a vast volume of documents, each string may have problems with over-counting. For example, PASW stands for “Plant Available Soil Water” which accounts for 138 of those 12,000 articles. There may be many other such abbreviations. That’s the type of analysis Muenchen did several years ago, before concluding that PASW was more trouble than it was worth (details are here). In 2018 that search yields only 361 hits, and the title of the very first article begins with, “Projections Analysis of Surface Waves (PASW)…”
Muenchen’s hypothesis regarding the apparent decline of SPSS is that it was caused by competition. Back in 2002, SPSS shared the statistical software market with SAS and a couple of others. Its momentum carried it upward for a few more years, then the competition started chipping away at it. GraphPad Prism improved significantly with the release of its version 5 in 2007 and medical users of SPSS found an alternative that was as easy to use while focusing more on their needs. R added enough useful packages around the same time to become competitive. By now there are probably hundreds of packages that people can use to analyze data, only a few of which are shown in Figure 1.
Mackinnon remained skeptical of this hypothesis because the overall graph appears to show decreases in statistical software citation over time. This would seem to contradict evidence that the number of journal articles published has been increasing at about 3% per year over the last 3 centuries, and about 3.9% per year in the past decade (2018 STM Report, pg. 25). Thus, the total number of citations to statistical software as a collective group should be increasing concurrently with this overall increase.
Mackinnon gathered data from a different source: Scopus. According to Wikipedia, “Scopus covers nearly 36,377 titles from approximately 11,678 publishers, of which 34,346 are peer-reviewed journals in top-level subject fields: life sciences, social sciences, physical sciences, and health sciences.” Mackinnon limited the search to reference lists, reasoning that such citations are likely an indicator of using the software in the paper. Two search strings were used:
REF(“the R software” OR “the R
project” OR “r-project.org” OR “R development core”)
These searches are being a bit generous to SPSS by including Modeler and AMOS, and very conservative for R by not including citations to common packages (e.g., ggplot2). The resulting data are plotted in Figure 2.
we see that the citations of R in scholarly journals exceeded that of SPSS back
in 2012. However, the scale of Figure 2 tops out at 30,000 while Figure 1’s
scale peaks at 300,000. Google is finding a lot more documents! So, which of
these software packages is used the most in scholarly work? Good question! We would like to hear your comments below,
especially from readers who collect data from other sources.
It has been only two months since I summarized my reviews of point-and-click front ends for R, and it’s already out of date! I have converted that post into a regularly-updated article and added a plot of total features, which I repeat below. It shows the total number of features in each package, including the latest versions of BlueSky Statistics, JASP, and jamovi. The reviews which initially appeared as blog posts are now regularly-updated pages.
New Features in JASP
Let’s take a look at some of the new features, starting with the version of JASP that was released three hours ago:
Data panel, analysis input panel and results panel can be manipulated much more intuitively with sliders and show/hide buttons
Changed the analysis input panel to have an overview of all opened analyses and added the possibility to change titles, to show documentation, and remove analyses
Enhanced the navigation through the file menu; it is now possible to use arrow keys or simply hover over the buttons
Added possibility to scale the entire application with Ctrl +, Ctrl – and Ctrl 0
Added Confirmatory Factor Analysis
Added Bayesian Multinomial Test
Included additional menu preferences to customize JASP to your needs
Added/updated help files for most analyses
R engine updated from 3.4.4 to 3.5.2
Added Šidák correction for post-hoc tests (AN(C)OVA)
A complete list of fixes and features is available here. JASP is available for free from their download page. My comparative review of JASP is here.
New Features in jamovi
Two of the usability features added to jamovi recently are templates and multi-file input. Both are described in detail here.
Templates enable you to save all the steps in your work as a template file. Opening that file in jamovi then lets you open a new dataset and the template will recreate all the previous analyses and graphs using the new data. It provides reusability without having to depend on the R code that GUI users are trying to avoid using.
The multi-file input lets you select many CSV files at once and jamovi will open and stack them all (they must contain common variable names, of course).
Other new analytic features have been added with a set of modeling modules. They’re described in detail here, and a list of some of their capability is below. You can read my full review of jamovi here, and you can download it for free here.
OLS Regression (GLM)
OLS ANOVA (GLM)
OLS ANCOVA (GLM)
Random coefficients regression (Mixed)
Random coefficients ANOVA-ANCOVA (Mixed)
Logistic regression (GZLM)
Logistic ANOVA-like model (GZLM)
Probit regression (GZLM)
Probit ANOVA-like model (GZLM)
Multinomial regression (GZLM)
Multinomial ANOVA-like model (GZLM)
Poisson regression (GZLM)
Poisson ANOVA-like model (GZLM)
Overdispersed Poisson regression (GZLM)
Overdispersed Poisson ANOVA-like model (GZLM)
Negative binomial regression (GZLM)
Negative binomial ANOVA-like model (GZLM)
Continuous and categorical independent variables
Omnibus tests and parameter estimates
Simple slopes analysis
Plots for up to three-way interactions for both categorical and continuous independent variables.
Automatic selection of best estimation methods and degrees of freedom selection
Type III estimation
New Features in BlueSky Statistics
The BlueSky developers have been working on adding psychometric methods (for a book that is due out soon) and support for distributions. My full review is here and you can download BlueSky Statistics for free here.
Model Fitting: IRT: Simple Rasch Model
Model Fitting: IRT: Simple Rasch Model (Multi-Faceted)
Model Fitting: IRT: Partial Credit Model
Model Fitting: IRT: Partial Credit Model (Multi-Faceted)
Model Fitting: IRT: Rating Scale Model
Model Fitting: IRT: Rating Scale Model (Multi-Faceted)
Model Statistics: IRT: ICC Plots
Model Statistics: IRT: Item Fit
Model Statistics: IRT: Plot PI Map
Model Statistics: IRT: Item and Test Information
Model Statistics: IRT: Likelihood Ratio and Beta plots
Model Statistics: IRT: Personfit
Distributions: Continuous: BetaProbabilities
Distributions: Continuous: Beta Quantiles
Distributions: Continuous: Plot Beta Distribution
Distributions: Continuous: Sample from Beta Distribution
Distributions: Continuous: Cauchy Probabilities
Distributions: Continuous: Plot Cauchy Distribution
Distributions: Continuous: Cauchy Quantiles
Distributions: Continuous: Sample from Cauchy Distribution
Distributions: Continuous: Sample from Cauchy Distribution
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.
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 is likely to 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. It used to have a job trend plotter, but that tool has apparently been shut down.
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.
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.
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.
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.
Now that I’ve completed seven detailed reviews of Graphical User Interfaces (GUIs) for R, let’s compare them. It’s easy enough to count their features and plot them, so let’s start there. I’m basing the counts on the number of menu items in each category. That’s not too hard to get, but it’s far from perfect. Some software has fewer menu choices, depending instead on dialog box choices. Studying every menu and dialog box would be too time-consuming, so be aware of this limitation. I’m putting the details of each measure in the appendixso you can adjust the figures and create your own graphs. If you decide to make your own graphs, I’d love to hear from you in the comments below.
Figure 1 shows the number of analytic methods each software supports on the x-axis and the number of graphics methods on the y-axis. The analytic methods count combines statistical features, machine learning / artificial intelligence ones (ML/AI), and the ability to create R model objects. The graphics features count totals up the number of bar charts, scatterplots, etc. each package can create.
The ideal place to be in this graph is in the upper right corner. We see that BlueSky and R Commander offer quite a lot of both analytic and graphical features. Rattle stands out as having the second greatest number of graphics features. JASP is the lowest on graphics features and 3rd from the bottom on analytic ones.
Next, let’s swap out the y-axis for general usability features. These consist of a variety of features that make your work easier, including data management capabilities (see appendix for details).
Figure 2 shows that BlueSky and R Commander still in the top two positions overall, but now Deducer has nearly caught up with R Commander on the number of general features. That’s due to its reasonably strong set of data management tools, plus its output is in true word processing tables saving you the trouble of formatting it yourself. Rattle is much lower in this plot since, while its graphics capabilities are strong (at least in relation to ML/AI tasks), it has minimal data management capabilities.
These plots help show us three main overall feature sets, but each package offers things that the others don’t. Let’s look at a brief overview of each. Remember that each of these has a detailed review that follows my standard template. I’ll start with the two that have come out on top, then follow in alphabetical order.
The R Commander – This is the oldest GUI, having been around since at least 2005. There are an impressive 41 plug-ins developed for it. It is currently the only R GUI that saves R Markdown files, but it does not create word processing tables by default, as some of the others do. The R code it writes is classic, rarely using the newer tidyverse functions. It works as a partner to R; you install R separately, then use it to install and start R Commander. It makes it easy to blend menu-based analysis with coding. If your goal is to learn to code in classic R, this is an excellent choice.
BlueSky Statistics – This software was created by former SPSS employees and it shares many of SPSS’ features. BlueSky is only a few years old, and it converted from commercial to open source just a few months ago. Although BlueSky and R Commander offer many of the same features, they do them in different ways. When using BlueSky, it’s not initially apparent that R is involved at all. Unless you click the “Syntax” button that every dialog box has, you’ll never see the R code or the code editor. Its output is in publication-quality tables which follow the popular style of the American Psychological Association.
Deducer – This has a very nice-looking interface, and it’s probably the first to offer true word processing tables by default. Being able to just cut and paste a table into your word processor saves a lot of time and it’s a feature that has been copied by several others. Deducer was released in 2008, and when I first saw it, I thought it would quickly gain developers. It got a few, but development seems to have halted. Deducer’s installation is quite complex, and it depends on the troublesome Java software. It also used JGR, which never became as popular as the similar RStudio. The main developer, Ian Fellows, has moved on to another very interesting GUI project called Vivid.
jamovi– The developers who form the core of the jamovi project used to be part of the JASP team. Despite the fact that they started a couple of years later, they’re ahead of JASP in several ways at the moment. Its developers decided that the R code it used should be visible and any R code should be executable, something that differentiated it from JASP. jamovi has an extremely interactive interface that shows you the result of every selection in each dialog box. It also saves the settings in every dialog box, and lets you re-use every step on a new dataset by saving a “template.” That’s extremely useful since GUI users often don’t want to learn R code. jamovi’s biggest weakness its dearth of data management tasks, though there are plans to address that.
JASP– The biggest advantage JASP offers is its emphasis on Bayesian analysis. If that’s your preference, this might be the one for you. At the moment JASP is very different from all the other GUIs reviewed here because it won’t show you the R code it’s writing, and you can’t execute your own R code from within it. Plus the software has not been open to outside developers. The development team plans to address those issues, and their deep pockets should give them an edge.
Rattle– If your work involves ML/AI (a.k.a. data mining) instead of standard statistical methods, Rattle may be the best GUI for you. It’s focused on ML/AI, and its tabbed-based interface makes quick work of it. However, it’s the weakest of them all when it comes to statistical analysis. It also lacks many standard data management features. The only other GUI that offers many ML/AI features is BlueSky.
RKWard– This GUI blends a nice point-and-click interface with an integrated development environment that is the most advanced of all the other GUIs reviewed here. It’s easy to install and start, and it saves all your dialog box settings, allowing you to rerun them. However, that’s done step-by-step, not all at once as jamovi’s templates allow. The code RKWard creates is classic R, with no tidyverse at all.
I hope this brief comparison will help you choose the R GUI that is right for you. Each offers unique features that can make life easier for non-programmers. If one catches your eye, don’t forget to read the full review of it here.
Writing this set of reviews has been a monumental undertaking. It would not have been possible without the assistance of Bruno Boutin, Anil Dabral, Ian Fellows, John Fox, Thomas Friedrichsmeier, Rachel Ladd, Jonathan Love, Ruben Ortiz, Christina Peterson, Josh Price, Eric-Jan Wagenmakers, and Graham Williams.
Appendix: Guide to Scoring
In figures 1 and 2, Analytic Features adds up: statistics, machine learning / artificial intelligence, the ability to create R model objects, and the ability to validate models using techniques such as k-fold cross-validation. The Graphics Features is the sum of two rows, the number of graphs the software can create plus one point for small multiples, or facets, if it can do them. Usability is everything else, with each row worth 1 point, except where noted.
Is it done in one step?
Does it start on its own without starting R, loading packages, etc.?
Import Data Files
How many files types can it import?
How many databases can it read from?
Export Data Files
How many file formats can it write to?
Does it have a data editor?
Can work on >1 file
Can it work on more than one file at a time?
Does it show metadata in a variable view, allowing for many fast edits to metadata?
How many data management tasks can it do?
Can it transform many variables at once?
How many graph types does it have?
Can it show small multiples (facets)?
Can it create R model objects?
How many statistical methods does it have?
How many ML / AI methods does it have?
Does it offer model validation (k-fold, etc.)?
R Code IDE
Can you edit and execute R code?
Does it let you re-use work without code?
Does it let you rerun all using code?
Does it manage packages for you?
Table of Contents
Does output have a table of contents?
Can you re-order output?
Is output in publication quality by default?
Can it create R Markdown?
Can you add comments to output?
Does it do group-by repetition of any other task?
Output as Input
Does it save equivalent to broom’s tidy, glance, augment? (They earn 1 point for each)