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The Google Visualization API is a great way for people to make dashboards with slick graphics based on data without getting into the fine print of the scripting language itself. It utilizes the same tools as Google itself does, and makes visualizing data using API calls to the Visualization API. Thus a real-time customizable dashboard that is publishable to the internet can be created within minutes, and more importantly insights can be much more easily drawn from graphs than from looking at rows of tables and numbers.
- There are 41 gadgets (including made by both Google and third-party developers ) available in the Gadget Gallery ( https://developers.google.com/chart/interactive/docs/gadgetgallery)
- There are 12 kinds of charts available in the Chart Gallery (https://developers.google.com/chart/interactive/docs/gallery) .
- However there 26 additional charts in the charts page at https://developers.google.com/chart/interactive/docs/more_charts )
Building and embedding charts is simplified to a few steps
- Load the AJAX API
- Load the Visualization API and the appropriate package (like piechart or barchart from the kinds of chart)
- Set a callback to run when the Google Visualization API is loaded
- Within the Callback – It creates and populates a data table, instantiates the particular chart type chosen, passes in the data and draws it.
- Create the data table with appropriately named columns and data rows.
- Set chart options with Title, Width and Height
- Instantiate and draw the chart, passing in some options including the name and id
- Finally write the HTML/ Div that will hold the chart
You can simply copy and paste the code directly from https://developers.google.com/chart/interactive/docs/quick_start without getting into any details, and tweak them according to your data, chart preference and voila your web dashboard is ready!
That is the beauty of working with API- you can create and display genius ideas without messing with the scripting languages and code (too much). If you like to dive deeper into the API, you can look at the various objects at https://developers.google.com/chart/interactive/docs/reference
First launched in Mar 2008, Google Visualization API has indeed come a long way in making dashboards easier to build for people wanting to utilize advanced data visualization . It came about directly as a result of Google’s 2007 acquisition of GapMinder (of Hans Rosling fame).
As invariably and inevitably computing shifts to the cloud, visualization APIs will be very useful. Tableau Software has been a pioneer in selling data visualizing to the lucrative business intelligence and business dashboards community (you can see the Tableau Software API at http://onlinehelp.tableausoftware.com/v7.0/server/en-us/embed_api.htm ), and Google Visualization can do the same and capture business dashboard and visualization market , if there is more focus on integrating it from Google in it’s multiple and often confusing API offerings.
However as of now, this is quite simply the easiest way to create a web dashboard for your personal needs. Google guarantees 3 years of backward compatibility with this API and it is completely free.
My favorite GUI (or one of them) R Commander has a relatively new plugin called KMGGplot2. Until now Deducer was the only GUI with ggplot features , but the much lighter and more popular R Commander has been a long champion in people wanting to pick up R quickly.
RcmdrPlugin.KMggplot2: Rcmdr Plug-In for Kaplan-Meier Plot and Other Plots by Using the ggplot2 Package
As you can see by the screenshot- it makes ggplot even easier for people (like R newbies and experienced folks alike)
This package is an R Commander plug-in for Kaplan-Meier plot and other plots by using the ggplot2 package.
|Depends:||R (≥ 2.15.0), stats, methods, grid, Rcmdr (≥ 1.8-4), ggplot2 (≥ 0.9.1)|
|Imports:||tcltk2 (≥ 1.2-3), RColorBrewer (≥ 1.0-5), scales (≥ 0.2.1), survival (≥ 2.36-14)|
|Author:||Triad sou. and Kengo NAGASHIMA|
|Maintainer:||Triad sou. <triadsou at gmail.com>|
|CRAN checks:||RcmdrPlugin.KMggplot2 results|
---------------------------------------------------------------- NEWS file for the RcmdrPlugin.KMggplot2 package ---------------------------------------------------------------- ---------------------------------------------------------------- Changes in version 0.1-0 (2012-05-18) o Restructuring implementation approach for efficient maintenance. o Added options() for storing package specific options (e.g., font size, font family, ...). o Added a theme: theme_simple(). o Added a theme element: theme_rect2(). o Added a list box for facet_xx() functions in some menus (Thanks to Professor Murtaza Haider). o Kaplan-Meier plot: added confidence intervals. o Box plot: added violin plots. o Bar chart for discrete variables: deleted dynamite plots. o Bar chart for discrete variables: added stacked bar charts. o Scatter plot matrix: added univariate plots at diagonal positions (ggplot2::plotmatrix). o Deleted the dummy data for histograms, which is large in size. ---------------------------------------------------------------- Changes in version 0.0-4 (2011-07-28) o Fixed "scale_y_continuous(formatter = "percent")" to "scale_y_continuous(labels = percent)" for ggplot2 (>= 0.9.0). o Fixed "legend = FALSE" to "show_guide = FALSE" for ggplot2 (>= 0.9.0). o Fixed the DESCRIPTION file for ggplot2 (>= 0.9.0) dependency. ---------------------------------------------------------------- Changes in version 0.0-3 (2011-07-28; FIRST RELEASE VERSION) o Kaplan-Meier plot: Show no. at risk table on outside. o Histogram: Color coding. o Histogram: Density estimation. o Q-Q plot: Create plots based on a maximum likelihood estimate for the parameters of the selected theoretical distribution. o Q-Q plot: Create plots based on a user-specified theoretical distribution. o Box plot / Errorbar plot: Box plot. o Box plot / Errorbar plot: Mean plus/minus S.D. o Box plot / Errorbar plot: Mean plus/minus S.D. (Bar plot). o Box plot / Errorbar plot: 95 percent Confidence interval (t distribution). o Box plot / Errorbar plot: 95 percent Confidence interval (bootstrap). o Scatter plot: Fitting a linear regression. o Scatter plot: Smoothing with LOESS for small datasets or GAM with a cubic regression basis for large data. o Scatter plot matrix: Fitting a linear regression. o Scatter plot matrix: Smoothing with LOESS for small datasets or GAM with a cubic regression basis for large data. o Line chart: Normal line chart. o Line chart: Line char with a step function. o Line chart: Area plot. o Pie chart: Pie chart. o Bar chart for discrete variables: Bar chart for discrete variables. o Contour plot: Color coding. o Contour plot: Heat map. o Distribution plot: Normal distribution. o Distribution plot: t distribution. o Distribution plot: Chi-square distribution. o Distribution plot: F distribution. o Distribution plot: Exponential distribution. o Distribution plot: Uniform distribution. o Distribution plot: Beta distribution. o Distribution plot: Cauchy distribution. o Distribution plot: Logistic distribution. o Distribution plot: Log-normal distribution. o Distribution plot: Gamma distribution. o Distribution plot: Weibull distribution. o Distribution plot: Binomial distribution. o Distribution plot: Poisson distribution. o Distribution plot: Geometric distribution. o Distribution plot: Hypergeometric distribution. o Distribution plot: Negative binomial distribution.
JMP , the visual data exploration, statistical quality control software from SAS Institute launched version 10 of its software today.
JMP 10 includes:
Numerous enhancements to the drag-and-drop Graph Builder, including a new iPad application.
A cutting-edge Control Chart Builder to create process control charts with drag-and-drop ease.
New reliability capabilities, including growth and forecast models.
Additions and improvements for sorting and filtering data, design of experiments, statistical modeling, scripting, add-in and application development, script debugging and more.
From JohnSall’s blog post at http://blogs.sas.com/content/jmp/2012/03/20/discover-more-with-jmp-10/
Much of the development centered on four focus areas:
1. Graph Builder everywhere. The Graph Builder platform itself has new features like Heatmap and Treemap, an elements palette and properties panel, making the choices more visible. But Graph Builder also has some descendents now, including the new Control Chart Builder, which makes creating control charts an interactive process. In addition, some of the drag-and-drop features that are used to change columns in Graph Builder are also available in Distribution, Fit Y by X, and a few other places. Finally, Graph Builder has been ported to the iPad. For the first time, you can use JMP for exploration and presentation on a mobile device for free. So just think of Graph Builder as gradually taking over in lots of places.
2. Expert-driven design.reliability, measurement systems, and partial least squares analyses.
3. Performance. this release has the most new multithreading so far
4. Application development
You can read more here -http://jmp.com/about/events/webcasts/jmpwebcast_detail.shtml?reglink=70130000001r9IP
Here is an interview with Kelci Miclaus, a researcher working with the JMP division of the SAS Institute, in which she demonstrates examples of how the R programming language is a great hit with JMP customers who like to be flexible.
Ajay- How has JMP been using integration with R? What has been the feedback from customers so far? Is there a single case study you can point out where the combination of JMP and R was better than any one of them alone?
Kelci- Feedback from customers has been very positive. Some customers are using JMP to foster collaboration between SAS and R modelers within their organizations. Many are using JMP’s interactive visualization to complement their use of R. Many SAS and JMP users are using JMP’s integration with R to experiment with more bleeding-edge methods not yet available in commercial software. It can be used simply to smooth the transition with regard to sending data between the two tools, or used to build complete custom applications that take advantage of both JMP and R.
One customer has been using JMP and R together for Bayesian analysis. He uses R to create MCMC chains and has found that JMP is a great tool for preparing the data for analysis, as well as displaying the results of the MCMC simulation. For example, the Control Chart platform and the Bubble Plot platform in JMP can be used to quickly verify convergence of the algorithm. The use of both tools together can increase productivity since the results of an analysis can be achieved faster than through scripting and static graphics alone.
I, along with a few other JMP developers, have written applications that use JMP scripting to call out to R packages and perform analyses like multidimensional scaling, bootstrapping, support vector machines, and modern variable selection methods. These really show the benefit of interactive visual analysis of coupled with modern statistical algorithms. We’ve packaged these scripts as JMP add-ins and made them freely available on our JMP User Community file exchange. Customers can download them and now employ these methods as they would a regular JMP platform. We hope that our customers familiar with scripting will also begin to contribute their own add-ins so a wider audience can take advantage of these new tools.
Ajay- Are there plans to extend JMP integration with other languages like Python?
Kelci- We do have plans to integrate with other languages and are considering integrating with more based on customer requests. Python has certainly come up and we are looking into possibilities there.
Ajay- How is R a complimentary fit to JMP’s technical capabilities?
Kelci- R has an incredible breadth of capabilities. JMP has extensive interactive, dynamic visualization intrinsic to its largely visual analysis paradigm, in addition to a strong core of statistical platforms. Since our brains are designed to visually process pictures and animated graphs more efficiently than numbers and text, this environment is all about supporting faster discovery. Of course, JMP also has a scripting language (JSL) allowing you to incorporate SAS code, R code, build analytical applications for others to leverage SAS, R and other applications for users who don’t code or who don’t want to code.
JSL is a powerful scripting language on its own. It can be used for dialog creation, automation of JMP statistical platforms, and custom graphic scripting. In other ways, JSL is very similar to the R language. It can also be used for data and matrix manipulation and to create new analysis functions. With the scripting capabilities of JMP, you can create custom applications that provide both a user interface and an interactive visual back-end to R functionality. Alternatively, you could create a dashboard using statistical and/or graphical platforms in JMP to explore the data and with the click of a button, send a portion of the data to R for further analysis.
Another JMP feature that complements R is the add-in architecture, which is similar to how R packages work. If you’ve written a cool script or analysis workflow, you can package it into a JMP add-in file and send it to your colleagues so they can easily use it.
Ajay- What is the official view on R from your organization? Do you think it is a threat, or a complimentary product or another statistical platform that coexists with your offerings?
Kelci- Most definitely, we view R as complimentary. R contributors are providing a tremendous service to practitioners, allowing them to try a wide variety of methods in the pursuit of more insight and better results. The R community as a whole is providing a valued role to the greater analytical community by focusing attention on newer methods that hold the most promise in so many application areas. Data analysts should be encouraged to use the tools available to them in order to drive discovery and JMP can help with that by providing an analytic hub that supports both SAS and R integration.
Ajay- While you do use R, are there any plans to give back something to the R community in terms of your involvement and participation (say at useR events) or sponsoring contests.
Kelci- We are certainly open to participating in useR groups. At Predictive Analytics World in NY last October, they didn’t have a local useR group, but they did have a Predictive Analytics Meet-up group comprised of many R users. We were happy to sponsor this. Some of us within the JMP division have joined local R user groups, myself included. Given that some local R user groups have entertained topics like Excel and R, Python and R, databases and R, we would be happy to participate more fully here. I also hope to attend the useR! annual meeting later this year to gain more insight on how we can continue to provide tools to help both the JMP and R communities with their work.
We are also exploring options to sponsor contests and would invite participants to use their favorite tools, languages, etc. in pursuit of the best model. Statistics is about learning from data and this is how we make the world a better place.
About- Kelci Miclaus
Kelci is a research statistician developer for JMP Life Sciences at SAS Institute. She has a PhD in Statistics from North Carolina State University and has been using SAS products and R for several years. In addition to research interests in statistical genetics, clinical trials analysis, and multivariate analysis/visualization methods, Kelci works extensively with JMP, SAS, and R integration.
By request, an analysis of Top 5 XKCDs on data visualization. Statisticians and Data Scientists to note-
1) DOT PLOT
2) LINE PLOTS
3) FLOW CHARTS
4) PIE CHARTS and 5) BAR GRAPHS
I am not going into the big big graphs of course like the Star Wars Plot data visualization at
Now I gotta find me a software that can write my blog for me :)
After the Navy I worked as a Respiratory Therapist, a roofer, and I repaired print shop equipment. Basically whatever it took to make a buck or two. Eventually I started selling computers. That led me to running a multi-line dial-up BBS and I taught myself how to program. Eventually that led to a job with a small engineering company where we developed WiFi.
After the WiFi project I started consulting on my own. I used Rackspace to host my clients, and eventually they hired me. I’ve been here almost three years and have held several roles. I currently manage Social Media, building 43 and am involved in several other projects such as the Rackspace Startup Program.
Rob- Building43 is a web site devoted to telling the stories behind technology startups. Basically, after we hired Robert Scoble and Rocky Barbanica we were figuring out how best we could work with them to both highlight Rackspace and customers. That idea expanded beyond customers to highlighting anyone doing something incredible in the technology industry – mostly software startups. We’ve had interviews with people like Mark Zuckerberg, CEO and Founder of FaceBook. We’ve broken some news on the site, but it isn’t really a news site. It is a story telling site.
Rackspace has met some amazing new customers through the relationships that started with an interview.
Rob- Robert isn’t much different to manage than the rest of my employees. He is a person – no super powers. But he does establish a unique perspective on things because he gets to see so much new technology early. Often earlier than almost anyone else. It helps him to spot trends that others might not be seeing yet.
and we actually look for people focused on delivering amazing customer experiences during our interviewing and hiring practices. People that find a personal sense of pride and reward by helping others should apply at
Rackspace. We are hiring like crazy!
Brief Biography- (in his own words from http://www.lagesse.org/about/)
My technical background includes working on the development of WiFi, writing wireless applications for the Apple Newton, mentoring/managing several software-based start-ups, running software quality assurance teams and more. In 2008 I joined Rackspace as an employee – a “Racker”. I was previously a 7 year customer and the company impressed me. My initial role was as Director of Software Development for the Rackspace Cloud. It was soon evident that I was better suited to a customer facing role since I LOVE talking to customers. I am currently the Director of Customer Development Chief Disruption Officer. I manage building43 and enjoy working with Robert Scoble and Rocky Barbanica to make that happen. The org chart says they work for me. Reality tells me the opposite
Go take a look – I’m proud of what we are building there (pardon the pun!).
I do a lot of other stuff at Rackspace – mostly because they let me! I love a company that lets me try. Rackspace does that.Going further back, I have been a Mayor (in Hawaii). I have written successful shareware software. I have managed employees all over the world. I have been all over the world. I have also done roofing, repaired high end print-shop equipment, been a Neonatal Respiratory Therapist, done CPR on a boat, in a plane, and in a hardware store (and of course in hospitals).
I have treated jumpers from the Golden Gate Bridge – and helped save a few. I have lived in Illinois (Kankakee), California (San Diego, San Francisco and Novato), Texas (Corpus Christi and San Antonio), Florida (Pensacola and Palm Bay), Hawaii (Honolulu/Fort Shafter) and several other places for shorter durations.
For the last 8+ years I have been a single parent – and have done an amazing job (yes, I am a proud papa) thanks to having great kids. They are both in College now – something I did NOT manage to accomplish. I love doing anything someone thinks I am not qualified to do.
I can be contacted at rob (at) lagesse (dot) org
you can follow Rob at http://twitter.com/kr8tr
- Rackspace offers hosted virtual desktops from Citrix (infoworld.com)
- Citrix Taps Open Stack For Building Private Clouds (informationweek.com)
- A2SM Podcast #22 – Special Report: Robert Scoble on the Rackspace Cloud Going Open Source (a2sm.com)
- Support from Rackspace Customers Helped me Finish the Ironman (rackspace.com)