Download Graphical user interfaces

Advanced Review
Graphical user interfaces
Wendy L. Martinez∗
This article provides a brief introduction to graphical user interfaces or GUIs. The
first section defines graphical user interfaces, describes interface components, and
the different types of GUIs. This is followed by a short discussion of GUI design
principles and descriptions of some tools for easily creating GUIs. The article
concludes with some examples of GUIs that would be of interest to researchers
and statisticians.  2011 John Wiley & Sons, Inc. WIREs Comp Stat 2011 3 119–133 DOI:
10.1002/wics.150
Keywords:
MATLAB; CRAN R; GUI design; software development
INTRODUCTION
G
raphical user interfaces or GUIs are everywhere
today, even on our smart phones. In fact,
the major personal computer operating systems like
Microsoft Windows and the one used on the
Apple Macintosh are GUI based. Thus, it is a
good idea to learn more about them and to take
advantage of this human–computer interface. The
result will be a wider audience for one’s research
accomplishments. This article first provides some
background information on graphical interfaces, to
include definitions, the historical development of
GUIs, user interface components, and different types
of graphical interfaces.
Most readers of this article are probably
researchers and statisticians and are not GUI
developers. However, it is possible that some might
want to implement their latest algorithms and methods
in a graphical interface to make it easier for a wider
audience to use them. Thus, the background section
is followed by some design principles and guidance in
developing GUIs.
Next is a description of some tools for creating
GUIs, with a focus on software that many data
analysts use. This includes the MATLAB and R
computing environments. The article concludes with
some GUI examples that work with these two analytic
packages.
BACKGROUND
This section includes a definition of GUIs, followed
by a discussion of the historical development of GUIbased mechanisms for human–computer interactions.
∗
Correspondence to: [email protected]
Department of Defense, Fredericksburg, VA 22405, USA
DOI: 10.1002/wics.150
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This is followed by a description of the major types of
GUIs, with an emphasis on those used for data analysis. This section is concluded with a list of individual
components that can be included in a GUI application.
Definition of GUIs
A GUI (pronounced ‘gooey’) provides an easy
way for humans and computers to interact and
communicate.1,2 GUIs take advantage of the graphics
capabilities of computers to make this communication easier by hiding the details of the programming
language from the user. This type of interface uses
windows, icons, menus, buttons, drop-down lists, dialog boxes, and more, as a means of communication
between humans and computers. These graphical widgets are usually activated when the user manipulates
them with a mouse or other pointing device.
GUIs are typically event-driven, which means
that some task is executed whenever an event is
detected, such as the user clicking on a button or
a menu item.3 For example, computer programs can
be opened and executed by clicking on their icons,
or dialog boxes can be invoked when a menu item is
selected.
It might help to compare a GUI with the command line interface. With this type of interface, the
user is presented with a blank screen and a prompt. All
communications with the computer are accomplished
via the keyboard, where commands are entered by
the user. The command line interface assumes that
the user knows the language required to communicate
with the computer and to accomplish the tasks. Some
examples of command line interfaces are the MS-DOS
and Linux (in console mode) operating systems.
History of GUIs
Some4 trace the beginning of GUI concepts to
Vannevar Bush who worked at the Massachusetts
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Institute of Technology (MIT). In 1945, he published
an article5 in The Atlantic Monthly, where he
described a tool called memex. This article was based
on ideas he had starting in the early 1930s. He
envisioned a tool that combined technologies such as
high resolution microfilm, multiple graphical screen
viewers, cameras, and keyboards, with the goal of
storing data in such a way that it is accessible and
linkable. This memex laid out ideas that have become
reality today with the advent of personal computers
and the World Wide Web.
Another early pioneer in developing GUI
concepts was Ivan Sutherland. He was a graduate
student at MIT and wrote his dissertation6–8
in 1963 on something he called Sketchpad,
a man–machine graphical communication system.
Sutherland’s Sketchpad system allowed the human
and computer to communicate using line drawings
created on a CRT screen with a light pen. He also
included the ability to zoom in and out on the display
and to store objects in memory for later recall.
In the 1950s, Douglas Englebart was inspired
by Vannevar Bush’s ideas, and he started thinking
about how a machine could be used to enhance the
human’s ability to exploit information and turn it
into knowledge.9,10 As a radar operator during the
war, Englebart had some experience with processing
information graphically, and he saw the computer
as a tool for augmenting the human intellect rather
than replacing the human. Englebart and his staff at
the Augmentation Research Center at the Stanford
Research Institute worked on many innovative ideas
for human–computer interaction and demonstrated
them at a 1968 computer conference in San Francisco.
One of the interfaces presented at the conference was
the first mouse, and it became accepted as the best and
most natural way to maneuver a cursor on a viewing
screen. The group also demonstrated a system called
oN-Line System (or NLS) that included collaboration
between multiple networked computers.
The first usable GUI was developed by
researchers at the Xerox Palo Alto Research Center
(PARC) in 1977.4 This was called the Xerox Star.
The developers were very careful to design the
human–computer interface before they worked on the
actual application. However, the GUI turned out to be
too slow and was not a commercial success—perhaps
because of its high cost.
Steve Jobs of Apple Computer learned of
these developments at PARC, including the Xerox
Star. He ended up hiring several of the PARC
researchers, and this inspired him to eventually
use GUIs in Apple computers. A predecessor to
the Macintosh (successfully launched in 1984) was
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the Lisa computer.4 This computer initially had a
command line interface, but was soon converted
to a GUI interface—mostly because of the newlyhired PARC people. The Apple Computer company
extended the original GUI ideas at PARC to include a
menu bar, drop-down menus, overlapping windows,
icons, and more. In a broad sense, one might say that
the methods for human–computer interaction that
were eventually incorporated into this original Apple
Macintosh provided the basis for the GUIs we have
today.
There were other companies working on creating
GUIs for personal computers and workstations in the
1980s. Some examples of the GUI applications include
VisiOn (made by VisiCorp), Interface Manager (the
original Microsoft Windows), GEM (a GUI for
DOS and the Atari computer), Workbench for the
Commodore Amiga, and the X Window System for
UNIX. See Reimer4 for an excellent description and
screenshots of these applications and more.
Types of GUIs
This section includes a list and description of the
general types of GUIs in use on personal computers
today.3 One of the most common types is based on
menus and dialog boxes, and it is one we are familiar
with in operating systems like Microsoft Windows. It
is also commonly found in most commercial statistical
data analysis packages, such as SPSS (http://www.
spss.com/), Minitab (http://www.minitab.com/enUS/default.aspx), Statistica (http://www.statsoft.
com/), JMP (http://www.jmp.com/), Systat (http://
www.systat.com/), S-Plus (http://spotfire.tibco.com/
products/s-plus/statistical-analysis-software.aspx),
and others.
Another type of GUI is based on a spreadsheet
or tabular interface.3 It might seem strange to think of
this as a GUI, but a little thought should convince
the reader that it is a form of human–computer
interface for data analysis. The major part of this
type of interface consists of a table of cells, where
each cell can contain numbers, text, or formulas. The
analysis is accomplished by performing computations
on the rows or columns using either internal
functions or formulas. Most spreadsheet programs
(or interfaces) include other GUI components, such
as menus and dialog boxes. Examples of this type
of interface are Microsoft Excel, Gnumeric (http://
projects.gnome.org/gnumeric/) (part of the GNOME
(http://www.gnome.org/) desktop environment), and
Open Office (Calc module).
Some data analysis packages have a notebook interface. This can be considered as a
type of word processor that also includes the
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ability to perform computations. The document
itself can have text and code that is executed
by the underlying computer algebra engine. Some
examples of software that have notebook interfaces are Mathematica (http://www.wolfram.com/),
Mathcad (http://www.ptc.com/products/mathcad/),
Scientific WorkPlace (http://www.mackichan.com/),
and Maple (http://www.maplesoft.com/). Most of
these can be thought of as a type of note pad, where
one types in algebraic or mathematical expressions,
as if one where writing them on the board or typing them using an equation editor (like MathType)
(http://www.dessci.com/en/products/mathtype/). The
computational engine then interprets the expressions
and provides answers in a similar fashion or traditional mathematical notation. Most also include the
ability to produce images and graphics based on the
code.
With the advent of the World Wide Web, we
also have web-based interfaces. These consist of web
pages or applications that contain forms and possibly
other types of interface components. The forms are
filled in and then some data analysis is performed
on another server or computer. This type of application has some advantages, such as platform portability, client/server architecture, and availability of
browsers.
GUI Components
The actual appearance of a GUI depends on the
operating system and platform, as well as the purpose
of the GUI. However, most of them have similar
components. Some of the main elements or UIs (user
interfaces) of GUIs are described below.
• Windows: This is one of the main graphical
mechanisms used to group and isolate functionality in GUI-based software. It divides the screen
into separate areas, where the user can execute
a different program, display directories, show a
different plot, etc.
• Desktop: The desktop is the main area (i.e., the
computer screen) in most operating systems. One
could think of this as the main window where
the icons are usually displayed.
• Icons: Icons are small graphics or pictures
that represent tasks, programs, folders, files,
windows, and more. These are usually organized
on the desktop, toolbars, ribbons, directories,
and menus. The main purpose of icons is to
convey some helpful information to the user as
to the task that will be invoked by clicking on
the icon.
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• Pointing device: This is typically a mouse,
trackball, or touch pad. The pointing device
moves the cursor on the screen. It allows the
user to select objects on the screen, click to
activate user interface components (e.g., buttons,
menus), drag files, or objects, resize windows,
and more. Some GUIs include help information
that is tied to the cursor or pointing device. These
are sometimes called tool tips. When the user puts
the cursor on some icon or UI, then a little text
area shows up—attached to the cursor—with
information about that particular interface.
• Menus: Menus are used in the great majority of
programs that have a graphical interface. These
are similar to menus one finds in restaurants—a
list of available options from which the user can
choose.
• Shortcut Menus: This type of menu is very
useful. They are activated by right-clicking with
the mouse when the cursor is in some area of
interest on the GUI. Different menus and options
come up, depending on the area where the click
happens. Thus, they are tied to the context and
are sometimes called context menus.
• Drop-down Menus: When there is not enough
GUI real estate, one can use drop-down or popup menus. These typically show the current value
for the UI and have an arrow pointing down,
indicating more choices are available. The user
can click on the arrow to get a menu or list of
other options. Examples of a drop-down menu
are the font type and font size UIs in Microsoft
Word.
• Toolbars: Toolbars are horizontal strips that
typically run across the top of a GUI interface.
They are sometimes used to provide interface
options similar to menus. The toolbars contain
small icons (or buttons) that are grouped together
according to their functionality. One could
think of the toolbar as a toolkit and the icons
representing the tools. In many applications, the
user can select what toolbars are visible, where
they appear on the GUI, and separate them from
the main GUI window.
• Ribbons: Programs in the Microsoft Office
2007 suite have a new type of user interface
called a ribbon. A set of toolbars are placed
on tabs and are organized across the top
of the window. Each tab contains icons and
control elements that pertain to the functionality
designated by the tab. This eliminates the need
for different toolbars and icons that clutter the
window.
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untitled1.fig
File Edit View Layout Tools Help
Tag: figure1
Current Point: [5, 364]
Position: [520, 380, 560, 420]
FIGURE 1 | Example of a blank layout for MATLAB’s GUIDE tool for creating GUIs.
• Dialog Boxes: There are many different types of
dialog boxes or pop-up windows—from those
that just provide information to those that ask
the user to enter something or to make choices.
One example of a dialog box is the one that pops
up and asks you if you want to overwrite the
existing file and another is the open file dialog
box in Microsoft Windows.
• Buttons: Push buttons are used when the
developer wants to present the user with discrete
choices, where some action will take place
immediately once the button is pushed. There
might be several buttons offering a range of
choices, and the default button is highlighted.
The user can hit the return key instead of clicking
on a button; in which case, the highlighted option
is executed.
• Radio Buttons and Check Boxes: These UIs are
similar to the push buttons in that they offer
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ways for the user to make selections from a short
list of options. Radio buttons and check boxes
are typically grouped together and written in a
way that makes the choices mutually exclusive.
In other words, the user can only make one
selection.
• Text Boxes: GUIs can include interfaces or small
areas that allow the user to type in some text.
These are useful when the user is allowed to have
an infinite number of choices. An example of this
type of interface is the URL address box in most
browsers.
GUI DESIGN PRINCIPLES
This section includes several general GUI design
principles. While most readers of this journal are
likely data analysts and not software developers, some
might be interested in creating GUIs that implement
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Basic Fitting - 1
Select data:
data 1
Center and scale x data
Numerical results
Plot fits
Check to diaplay fits on figure
spline interpolant
shape-preserving interpolant
linear
quadratic
cubic
4th degree polynomial
5th degree polynomial
6th degree polynomial
7th degree polynomial
8th degree polynomial
9th degree polynomial
10th degree polynomial
quadratic
Coefficients and norm of residuals
y = p1*x^2 + p2*x +
p3
Find y = f(x)
Enter value(s) or a valid MATLAB
expression such as x, 1:2:10 or
[10 15]
Evaluate
Coefficients:
p1 = –0.023216
p2 = 0.91838
x
f(x)
p3 = 28.215
Norm of residuals =
30.054
Show equations
Significant digits:
Fit:
2
Plot residuals
Save to workspace...
Scatter plot
Plot evaluated results
Subplot
Show norm of residuals
Help
Save to workspace...
Close
FIGURE 2 | The top figure shows the Basic Fitting GUI, which is part of the main MATLAB package. This can be invoked via the Tools menu in the
figure window. The second figure shows the data, the fit, and the residuals.
the methodologies they develop for computational
statistics. We discuss some of the tools that GUIs can
be developed in the next section.
There are several good references and websites
that provide guidelines for designing useful GUI11 . The
following principles were taken and adapted from Jeff
Johnson’s book12 describing the do’s and don’ts of
developing GUIs.
Principle 1. ‘Focus on the users and their tasks,
not the technology’.
This principle is first, because it is the most
important. It will also become apparent that several of
the following principles are corollaries to this first one.
It means that one should start the GUI development
process by answering the following questions:
• Who are the intended customers or users of the
GUI?
• What activities should it support?
• What problems does it solve?
• What are the skills and knowledge of the typical
user?
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• What are the usual processes and tasks that the
users need to accomplish?
In the ideal case, GUI developers should meet
with the intended users to get answers to these
questions, but statisticians and researchers do not
usually have specific users or customers in mind when
developing a GUI to implement some methodology
they created. However, the researcher can and should
think about these questions from a user viewpoint,
because it will save time in coding and redesign.
Principle 2. ‘Consider function first, presentation
later’.
When creating a GUI, one is often tempted to
start sketching the look of the GUI using paper or
some sort of GUI layout tools. This is still something
one must do eventually, but a developer should start
with the answers to some of the questions from
above. Developers must consider the purpose and
role of the GUI first before any coding. Johnson12
recommends that one build a conceptual model, which
is a model of the analysis (or product) the users need to
understand.
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Figure 1
File
Edit
View
Insert
Tools
Desktop
Window
Help
60
data 1
quadratic
40
20
0
−20
0
10
20
30
Residuals
40
50
60
30
40
50
60
15
Quadratic: norm of residuals = 30.054
10
5
0
−5
−10
−15
0
10
20
FIGURE 2 | Continued.
Principle 3. ‘Conform to the user’s view of the
task, and do not make it more complicated’.
Too often software developers want to make
the application what they think it should be, and
they tend to include lots of flashy animations and
colorful graphics, as well as interface controls that are
unnecessary. The whole idea of creating a GUI should
be to make to make things easier for the user—not
make them more complicated. So, go back to the
first principle and make sure that the focus is on the
user.
Principle 4. ‘Promote learning and deliver
information, not just data’.
As stated before, one of the objectives for
creating a GUI instead of a command line interface
is to eliminate the task of learning a complicated
programming language or text-based commands.
Making GUIs complicated defeats this purpose, and
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they should not be more complicated than they have
to be. It is also a good idea to use the layout of the
GUI to promote ease of use and learning—say by
grouping UI controls together by their functionality
or by providing helpful information (titles, tool tips,
etc.) on the GUI itself.
Once these principles are addressed—especially
the first one—it is time to create a sketch of the GUI,
along with task flows and processes. It is also a good
idea to allow potential users to take a look at the
design and offer ideas for improvement. At this point,
it is all right to start writing code to create the GUI,
possibly using one of the tools described in the next
section.
TOOLS FOR CREATING GUIs
This section briefly describes several tools for developing GUIs in MATLAB, R, and other platforms. These
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Distribution Fitting Tool
File View Tools Window Help
Distribution: Normal
Display type: Density (PDF)
Density (PDF)
Cumulative probability (CDF)
Evaluate...
Manage Fits...
Exclude...
Quantile (inverse CDF)
Probability plot
Survivor function
Cumulative hazard
geyser data
0.035
0.03
Density
0.025
0.02
0.015
0.01
0.005
0
40
50
60
70
80
90
100
110
Data
FIGURE 3 | This shows the distribution fitting GUI from the MATLAB statistics toolbox.
two computing environments—MATLAB and R—are
emphasized here because researchers and statisticians
often implement their methods and algorithms in one
of these languages. There are several ways to create
GUIs in MATLAB, and there are many GUI creation
packages for R. Thus, what follows is just a partial
list of what is available.
Creating GUIs in MATLAB
MATLAB is a commercial computing environment
developed by The MathWorks, Inc. for engineering, simulation, modeling, statistical data analysis,
and much more (http://www.mathworks.com/). It is
used by many statisticians and researchers to prototype their methods, and a lot of user-written
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MATLAB programs for state-of-the-art research are
available.13,14 Also, MATLAB has excellent visualization and GUI-development capabilities.15
All of MATLAB graphics are based on lowlevel functionality called Handle Graphics. One can
write GUIs directly using these low-level functions.
However, MATLAB also includes something called
GUIDE—Graphical User Interface Development
Environment. One could think of this as a
GUI for building GUIs. The low-level handprogramming approach for creating GUIs will
not be discussed in this article. The interested
reader can refer to the MATLAB documentation
or the book Graphics and GUIs in MATLAB by
Marchand and Holland for more information.16
There is also some online documentation for
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Random Number Generation Tool
File
Edit
View
Insert
Tools Desktop
Window
Help
Normal
Distribution
500
Samples
70
60
50
Counts
40
30
20
10
0
−8
−6
−4
−2
0
Values
Upper
bound
2
Upper
bound
2
Mu
0
Sigma
1
Lower
bound
−2
Lower
bound
0.5
2
4
6
8
Upper
Upper
bound
bound
Lower
Lower
bound
bound
Resample
Export ...
FIGURE 4 | This is a screen shot of the random number generation GUI in the MATLAB Statistics Toolbox.
creating GUIs (with GUIDE and programmatically)
(http://www.mathworks.com/help/techdoc/), as well
as some helpful video tutorials at MATLAB Central
(http://www.mathworks.com/).
Like any good GUI, GUIDE alleviates the
learning and programming skills required to build
useful GUIs in MATLAB. GUIDE is actually a suite
of tools for creating GUIs. GUIDE can be invoked
in several ways, but the easiest is to type in the
word guide at the prompt. At this point, the GUIDE
Layout Editor is opened, along with a blank GUI
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figure, as shown in Figure 1. This allows the user to
add UI controls (buttons, axes, sliders, drop-down
menus, list boxes, etc.) to the GUI. Simply select the
component and drag it to the figure area. The Layout
Editor also allows one to create toolbars, change the
appearance of the controls, set the tab order, and
modify other GUI options.
Once the GUI layout is saved, MATLAB
creates a template that contains MATLAB code for
controlling the way the GUI works, so the right
things happen when a UI component is activated.
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Exploratory Data Analysis GUI
Exploratory Data Analysis
CLOSE
Set UP Data: These options can be used with all sections below.
LOAD DATA
TRANSFORM
Load the data and labels for class, cases, and variables.
With this GUI, you can sphere the data and apply other common transforms.
Graphical EDA: Use these functions to visually explore your data set - both variables and observations.
GRAPHICS
SHAPES-UNIVARIATE
SHAPES-BIVARIATE
DATA TOURS
PROJECTION PURSUIT
Visualize using 2D/3D scatterplots, parallel coordiante plots, Andrews' curves, scatterplot
Construct boxplots, histrograms, q-q plots to understand the distribution of single variables.
Explore two variables at a time with hexagonal binning, bivariate histograms, and polar
Search for structure using the grand tour and the permutation tour.
Search for structure in 2-D using projection pursuit.
Dimensionality Reduction: Use these functions to reduce the number of variables in your data set.
MULTIDIMENSIONAL SCALING
Reduce the dimensionality of your data using classical, metric, and nonmetric MDS.
DIMENSIONALITY REDUCTION
Reduce the dimenstionality of your data using PCA, ISOMAP, and LLE.
Search for Groups: Use these functions to search for groups or clusters.
K-MEANS CLUSTERING
AGGLOMERATIVE CLUSTERING
MODEL-BASED CLUSTERING
Partition the data into a specified number of groups.
Construct a hierarchy of nested partitions.
Cluster your data based on probability density estimation (Gaussian finite mixtures).
FIGURE 5 | Entrance GUI (optional) for the EDA GUI Toolbox.
In MATLAB, GUI actions are governed by callbacks,
which are functions that execute in response to GUI
events (e.g., clicking a button or a menu option). The
MATLAB programming editor can be used to fill in
the rest of the template with the required code.
Tcl/Tk
R is an open-source environment and high-level
language for statistical computing and graphics. It has
been greatly extended by user-written packages that
implement state-of-the-art analysis methodologies. See
the Comprehensive R Archive Network (CRAN) to
download the software, packages, documentation,
and more (http://cran.r-project.org/). Some of the
examples described in the next section are based on R.
This section will provide information on a tool that
can be used for building GUIs in R, as well as in many
other computing environments.
Tcl (http://www.tcl.tk/)17 is a scripting or
dynamic language that is extensible and has been used
in a wide variety of applications. This type of language
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is sometimes called ‘high-level’, and it is similar to
other languages like Perl,18 Python,19 MATLAB, and
R. These can be contrasted to lower-level system
programming languages like C++ and Java.
Tk is a graphical toolkit that works with Tcl,
and allows one to easily create GUI applications that
work with a variety of operating systems, such as
Microsoft Windows, Linux, and the Macintosh OS.
Tk can also be used with other languages, like C,
Perl, Python, and more. There is a package in R called
tcltk that provides access to the Tk toolkit.20,21 In
addition, there are other packages that are built on
the tcltk package, such as rpanel (builds simple
UI controls for functions), traitr (builds GUIs using
Python functionality), and gWidgetstcltk (toolkit
for tcltk).
wxWidgets
The wxWidgets is a free cross-platform library that
enables the rapid development of GUI applications for
desktop computing platforms and more.22 wxWidgets
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Data Tours GUI
Data Tours
LOAD DATA
TRANSFORM
Grand Tour
1. Enter step size:
2. Number of
dimensions to
display:
Pseudo
Grand Tour
CLOSE
Use these capabilities to explore projections of the data. All plots/graphics are animated and
will be shown in a plot window.
Implements Asimov's torus grand tour. Cand display 2 to p dimensions.
0.01
2
3. Enter max
interations:
1000
5. Push to:
OUTPUT DATA
START
4. Select diaplay type: Scatterplot
OUTPUT PROJECTION
Implements Wegman's psuedo grand tour. Only does 2D scatterplots.
OUTPUT DATA
1. Enter step size:
0.01
2. Enter max iterations:
1000
3. Push to:
START
Permutation
Tour
Permutes the order of the variables and displays as
either Andrews' curves or in parallel coordinates.
1. Select display: Andrews
2. Select type of tour: All permutations
OUTPUT PROJECTION
Use group colors
3. Push to:
START
FIGURE 6 | This is the Data Tours GUI in the EDA GUI Toolbox. Note that this GUI can be started from the command prompt, as well as the GUI in
Figure 5.
is written in C++, and it can be used with other
languages, such as Python and Perl. For the most part,
the wxWidgets toolkit uses native UI widgets. So, the
applications built using this library have the look users
expect for each particular platform.
The R-wxPython package23 is an R interface
for the wxWidgets GUI library of tools. This
is achieved by using Python as an intermediary
between R and the wxWidgets. The process is made
simpler through the use of the Boa Constructor
cross-platform Python development environment
(http://boa-constructor.sourceforge.net/). This GUI
development process was described by Wettenhall.23
EXAMPLES OF GUIs IN
COMPUTATIONAL STATISTICS
There are many examples of GUIs that might be
of interest to statisticians and researchers. These
include ones developed commercially and by the user
community. A very short list of GUI examples in
128
MATLAB and R is provided below, the purpose of
which is to show the user what one can do with GUIs
and statistical data analysis.
MATLAB GUIs
The main MATLAB package has very powerful
visualization and graphics capabilities. One of the
basic plots available to analysts is the 2-D scatterplot.
If such a plot is created, then the user can choose a
menu option in the figure window called Basic Fitting.
This brings up a GUI as shown in Figure 2. The user
can explore various fits to the data, as well as residuals
to assess the goodness of the fit.
The MathWorks, Inc. sells a Statistics Toolbox
that includes some useful GUIs. One is the Distribution
Fitting Tool, which is invoked at the prompt using
the command dfittool. This GUI allows one to
fit various distributions to the data and to evaluate
the results. A screen shot of the GUI is shown in
Figure 3.
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WIREs Computational Statistics
Graphical user interfaces
Rattle: Effective Data Mining with R
Project
Execute
Data
Edit
Tools Settings
New
Open
Rattle Version 2.3.6 togaware.com
Help
Save
Export
Quit
Two Class
Unsupervised
Time Series
Multi Class
Regression
Text Miner
Select Explore Transform Model Evaluate Log
Type:
File name:
CSV Rle
ARFF
(None)
ODBC
RData File
Separator:
R Dataset
Header
Library
View Data
Data Entry
Edit Data
Welcome to Rattle,
Rattle is and Open Source project to develop a GNOME Data Mining suite of applications built on top of the
statistical and data mining package R, licensed under the GNU General Public License, and Copyright (c)
Graham j Williams.
See the Help menu for extensive support in using Rattle.
Togaware's Desktop Data Mining Survival Guide (under development) includes extensive documentation on
using Rattle. It is available from
http://rattle.togaware.com
Rattle is Copyright (c) 2007-2008, Graham [email protected].
Rattle is licensed under the GNU General Public License, Version 2.
See Help.>About for details
FIGURE 7 | Screen shot of the opening Rattle GUI (http://rattle.togaware.com).
A rather fun GUI that comes with the Statistics
Toolbox is the Random Number Generation GUI
(type randtool at the prompt). This allows one to
generate random numbers from many distributions,
visualize them in a histogram, and export them to
the workspace. This GUI is shown in Figure 4, where
we have a random sample from the standard normal
distribution.
The above examples are commercial products,
but the next one—called the EDA GUI Toolbox—is
freely available (http://pi-sigma.info/).24 It implements
many of the tasks that one might want for exploratory
data analysis and data mining, including clustering,
graphical displays, dimensionality reduction, exploring distributions, data tours, and more.
Figure 5 shows a screen shot of the (optional)
opening GUI that provides a menu of buttons—along
with descriptions—to other GUIs in the toolbox.
Pressing a button brings up the associated GUI, which
Vo lu me 3, March/April 2011
can also be opened individually. For example, the
Data Tours GUI is displayed in Figure 6.
Rattle—A Data Mining GUI in R
Rattle (R Analytical Tool to Learn Easily) is an open
source GUI data mining tool (http://rattle.togaware.
com/).25 It connects to several R packages (described
below) that can be used for data mining applications.
Like any good GUI, it removes the burden of
understanding the R language and using the command
line interface. Rattle is based on the Gnome interface
and the RGtk2 package in R. Additionally, the GUI
itself was developed using Glade.18
Some of the R packages that Rattle utilizes
include ada (stochastic boosting), arules (mining
association rules), gplots (R plotting functions),
randomForest, rggobi (interface with GGobi),
and much more. These packages can be installed,
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Advanced Review
R Commander
File Edit Data Statistics Graphs Models Distributions Tools Help
Data set: <No active dataset>
Edit data set View data set
Model: <No active model>
Script Window
Submit
Output Window
Messages
[2] WARNING: The Windows version of the R Commander works best under RGui
with the single-document interface (SDI) ; see ?Commander.
FIGURE 8 | Screen shot of R Commander GUI for basic statistics.
but Rattle will tell the user—via a graphical pop-up
window—that it needs to be installed.
Rattle has a UI that is based on tabs, which
is similar to the ribbon-tab interface described
previously. It is an attempt to provide information
to the user about the task flow in a typical data
mining process (design Principle 4). There are tabs for
the following tasks (and more):
1. Data—This allows one to load the data and
select variables for analysis.
130
2. Explore—Exploring one’s data is an important
task in data mining, since it informs the rest of
the analysis and enables better understanding of
the results.
3. Test—This area provides tools for parametric
and nonparametric statistical tests of distributions, e.g., Kolmogorov–Smirnov and Wilcoxon
signed rank tests.
4. Transform—This tab has functions for rescaling, imputation, handling outliers, data cleaning, and more.
 2011 Jo h n Wiley & So n s, In c.
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WIREs Computational Statistics
Graphical user interfaces
GGobi
File
Display
flea.csv. Scatterplot (current)
View
XY Plot
Interaction
X
Plot cycling
X
Cycle
X
No fixed axes
X
X
Change direction
Tools
Help
File
Options
tars2
Variable Manipulation
Variable Transformation
Sphering (PCA)
1
5
0
Variable Jittering
1
4
0
Color Schemes
Automatic Brushing
X
Color & Glyph Groups
X
Case Subsetting and Sampling
Missing Values
1
3
0
Data Viewer
Save Display description
Graph Layout
1
2
0
Graph Operations
Variogram Cloud
1
1
0
ggvis (MDS)
tars1
1
100
150
200
250
FIGURE 10 | This is the scatterplot that is being manipulated and
explored using the GGobi interface.
flea.csv: 74 x 7(C:\Program Files\ggobi\data\flea.csv)
FIGURE 9 | This shows the GUI that appears and some of the
options that are available to explore a data set using GGobi.
5. Model—Several options for building models are
available, such as decision trees, support vector
machines, generalized linear models, neural
networks, etc.
6. Evaluate—In this tab, one can evaluate models
using the confusion table, ROC curves, and life
charts.
A screen shot of the opening GUI window for
Rattle is shown in Figure 7.
R Commander—A Basic Statistics GUI for R
Some time was spent on GUI design principles in a
previous section. The developer of the R Commander
GUI had these specific design objectives in mind: (1) to
provide an easy-to-use interface that supports the
functionality used in a basic-statistics course, (2) to
protect the user from doing unreasonable things, and
(3) to convey information about the R commands
generated by UI events.26 It is clear from these
objectives, that the developer spent some time thinking
about the users of this GUI application (design
Principles 1 and 3). The R Commander GUI is based
on the tcltk package in R that provides an interface
to Tcl/Tk described in a previous section.
Vo lu me 3, March/April 2011
The R Commander GUI is a window that has
menus, buttons, and information fields. The window
also has some areas within it to display R commands
generated by the user’s choices (Script Window) and
for outputs (Output Window). Text in the Script
Window is editable, which means that commands
can be executed or typed in directly. Note that these
windows promote learning (design Principle 4).
Some of the main menus include the usual ones
like File and Edit. There are also ones pertaining to
statistics, such as Data (reading and manipulating
data), Statistics (basic analyses), Graphs, Models
(hypothesis tests, confidence intervals, diagnostics,
and more), and Distributions (probabilities, quantiles,
graphs). A screen shot of the opening R Commander
window is shown in Figure 8.
GGobi
GGobi is a stand-alone open source GUI application
for dynamic visualization and exploration of highdimensional data (http://www.ggobi.org/).27 The
GGobi environment provides access to many useful
tools to interactively visualize and explore data sets.
Some of the options include data tours, scatter plots,
parallel coordinate plots, brushing, transformations,
and more.
GGobi will also work with the R computational
environment via the rggobi package. This allows
the user to exploit the computational power of R
to analyze any interesting data structures discovered
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using GGobi. Examples of the GGobi interface are
given in Figures 9 and 10. Figure 9 shows some of the
options under the Tools menu for the data in the 2-D
scatterplot in Figure 10.
Other R GUIs
This section concludes with a very brief list of some
other interesting GUIs for R. They are included here
because they are example of the types of GUIs mentioned previously in this article.
• Red-R: This is an open source GUI interface
that provides a visual programming capability
for R. The main goal is to make the programming power of R available to users with little
to no experience in programming (http://www.
red-r.org/).
• RExcel: This is a package that works with
Microsoft Windows and Excel. It enables transferring data between R and Excel, running R
code from Excel, and more. Essentially, RExcel
provides a spreadsheet GUI for R.28
• Tinn-R: This is a GUI that replaces the
basic code editor that comes with installations of R (i.e., Rgui). It has some nice features that promote easier programming in R,
such as color coding of reserved words, etc
(http://www.sciviews.org/Tinn-R/).
• Rpad: This R package provides an interactive workbook-style interface through a web
browser.
CONCLUSION
This article provides a very brief introduction to GUIs
and shows some examples of how GUIs can be used
in statistical data analysis. Of course, there are many
more examples of GUIs in R, MATLAB, and other
software applications or platforms. The hope is that
this introduction will motivate readers to take advantage of the development tools and to create GUIs,
making it easier for the user community to better
understand and use their methods.
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