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Top Funny Charts

I have recently become a Quora addict, and you can see why it is such a great site. If possible say hello to me there at

http://www.quora.com/Ajay-Ohri

My latest favorite question-

What are the most hilarious pie charts?

https://www.quora.com/Pie-Charts/What-are-the-most-hilarious-pie-charts

I am only showing you some of the answers, you can see the rest yourself.

 

 

WordPress.com Analytics

The Analytics (or stats) dashboard at WordPress.com continues to disappoint, and is a major reason for people to move out of WordPress.com hosting (since they need better analytics like that by Google Analytics which cant be enabled on the default mode)

Its not really beautiful unlike the rest of WordPress Universe!

It can be made better if people try harder! Analytics matters

Here are some points

1) Bar charts and Histograms are not really the best way to visualize trends across time

2) Location Analytics is limited to just country level analysis and the heatmap (?) is aweful in terms of distinguishing gradients 

3) Referrers Tab needs to do a better job on distinguishing between mobile and non mobile traffic, social and non social traffic (and there are better ways to visualize than just a simple list)!

4)  I cant even export my traffic stats (and forget an api !) so I am stuck with the bad data viz here

Google Visualization Tools Can Help You Build a Personal Dashboard

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.

  1. 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)
  2. There are 12 kinds of charts available in the Chart Gallery (https://developers.google.com/chart/interactive/docs/gallery) .
  3. 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.

Book Review- Machine Learning for Hackers

This is review of the fashionably named book Machine Learning for Hackers by Drew Conway and John Myles White (O’Reilly ). The book is about hacking code in R.

 

The preface introduces the reader to the authors conception of what machine learning and hacking is all about. If the name of the book was machine learning for business analytsts or data miners, I am sure the content would have been unchanged though the popularity (and ambiguity) of the word hacker can often substitute for its usefulness. Indeed the many wise and learned Professors of statistics departments through out the civilized world would be mildly surprised and bemused by their day to day activities as hacking or teaching hackers. The book follows a case study and example based approach and uses the GGPLOT2 package within R programming almost to the point of ignoring any other native graphics system based in R. It can be quite useful for the aspiring reader who wishes to understand and join the booming market for skilled talent in statistical computing.

Chapter 1 has a very useful set of functions for data cleansing and formatting. It walks you through the basics of formatting based on dates and conditions, missing value and outlier treatment and using ggplot package in R for graphical analysis. The case study used is an Infochimps dataset with 60,000 recordings of UFO sightings. The case study is lucid, and done at a extremely helpful pace illustrating the powerful and flexible nature of R functions that can be used for data cleansing.The chapter mentions text editors and IDEs but fails to list them in a tabular format, while listing several other tables like Packages used in the book. It also jumps straight from installation instructions to functions in R without getting into the various kinds of data types within R or specifying where these can be referenced from. It thus assumes a higher level of basic programming understanding for the reader than the average R book.

Chapter 2 discusses data exploration, and has a very clear set of diagrams that explain the various data summary operations that are performed routinely. This is an innovative approach and will help students or newcomers to the field of data analysis. It introduces the reader to type determination functions, as well different kinds of encoding. The introduction to creating functions is quite elegant and simple , and numerical summary methods are explained adequately. While the chapter explains data exploration with the help of various histogram options in ggplot2 , it fails to create a more generic framework for data exploration or rules to assist the reader in visual data exploration in non standard data situations. While the examples are very helpful for a reader , there needs to be slightly more depth to step out of the example and into a framework for visual data exploration (or references for the same). A couple of case studies however elaborately explained cannot do justice to the vast field of data exploration and especially visual data exploration.

Chapter 3 discussed binary classification for the specific purpose for spam filtering using a dataset from SpamAssassin. It introduces the reader to the naïve Bayes classifier and the principles of text mining suing the tm package in R. Some of the example codes could have been better commented for easier readability in the book. Overall it is quite a easy tutorial for creating a naïve Bayes classifier even for beginners.

Chapter 4 discusses the issues in importance ranking and creating recommendation systems specifically in the case of ordering email messages into important and not important. It introduces the useful grepl, gsub, strsplit, strptime ,difftime and strtrim functions for parsing data. The chapter further introduces the reader to the concept of log (and affine) transformations in a lucid and clear way that can help even beginners learn this powerful transformation concept. Again the coding within this chapter is sparsely commented which can cause difficulties to people not used to learn reams of code. ( it may have been part of the code attached with the book, but I am reading an electronic book and I did not find an easy way to go back and forth between the code and the book). The readability of the chapters would be further enhanced by the use of flow charts explaining the path and process followed than overtly verbose textual descriptions running into multiple pages. The chapters are quite clearly written, but a helpful visual summary can help in both revising the concepts and elucidate the approach taken further.A suggestion for the authors could be to compile the list of useful functions they introduce in this book as a sort of reference card (or Ref Card) for R Hackers or atleast have a chapter wise summary of functions, datasets and packages used.

Chapter 5 discusses linear regression , and it is a surprising and not very good explanation of regression theory in the introduction to regression. However the chapter makes up in practical example what it oversimplifies in theory. The chapter on regression is not the finest chapter written in this otherwise excellent book. Part of this is because of relative lack of organization- correlation is explained after linear regression is explained. Once again the lack of a function summary and a process flow diagram hinders readability and a separate section on regression metrics that help make a regression result good or not so good could be a welcome addition. Functions introduced include lm.

Chapter 6 showcases Generalized Additive Model (GAM) and Polynomial Regression, including an introduction to singularity and of over-fitting. Functions included in this chapter are transform, and poly while the package glmnet is also used here. The chapter also introduces the reader formally to the concept of cross validation (though examples of cross validation had been introduced in earlier chapters) and regularization. Logistic regression is also introduced at the end in this chapter.

Chapter 7 is about optimization. It describes error metric in a very easy to understand way. It creates a grid by using nested loops for various values of intercept and slope of a regression equation and computing the sum of square of errors. It then describes the optim function in detail including how it works and it’s various parameters. It introduces the curve function. The chapter then describes ridge regression including definition and hyperparameter lamda. The use of optim function to optimize the error in regression is useful learning for the aspiring hacker. Lastly it describes a case study of breaking codes using the simplistic Caesar cipher, a lexical database and the Metropolis method. Functions introduced in this chapter include .Machine$double.eps .

Chapter 8 deals with Principal Component Analysis and unsupervised learning. It uses the ymd function from lubridate package to convert string to date objects, and the cast function from reshape package to further manipulate the structure of data. Using the princomp functions enables PCA in R.The case study creates a stock market index and compares the results with the Dow Jones index.

Chapter 9 deals with Multidimensional Scaling as well as clustering US senators on the basis of similarity in voting records on legislation .It showcases matrix multiplication using %*% and also the dist function to compute distance matrix.

Chapter 10 has the subject of K Nearest Neighbors for recommendation systems. Packages used include class ,reshape and and functions used include cor, function and log. It also demonstrates creating a custom kNN function for calculating Euclidean distance between center of centroids and data. The case study used is the R package recommendation contest on Kaggle. Overall a simplistic introduction to creating a recommendation system using K nearest neighbors, without getting into any of the prepackaged packages within R that deal with association analysis , clustering or recommendation systems.

Chapter 11 introduces the reader to social network analysis (and elements of graph theory) using the example of Erdos Number as an interesting example of social networks of mathematicians. The example of Social Graph API by Google for hacking are quite new and intriguing (though a bit obsolete by changes, and should be rectified in either the errata or next edition) . However there exists packages within R that should be atleast referenced or used within this chapter (like TwitteR package that use the Twitter API and ROauth package for other social networks). Packages used within this chapter include Rcurl, RJSONIO, and igraph packages of R and functions used include rbind and ifelse. It also introduces the reader to the advanced software Gephi. The last example is to build a recommendation engine for whom to follow in Twitter using R.

Chapter 12 is about model comparison and introduces the concept of Support Vector Machines. It uses the package e1071 and shows the svm function. It also introduces the concept of tuning hyper parameters within default algorithms . A small problem in understanding the concepts is the misalignment of diagram pages with the relevant code. It lastly concludes with using mean square error as a method for comparing models built with different algorithms.

 

Overall the book is a welcome addition in the library of books based on R programming language, and the refreshing nature of the flow of material and the practicality of it’s case studies make this a recommended addition to both academic and corporate business analysts trying to derive insights by hacking lots of heterogeneous data.

Have a look for yourself at-
http://shop.oreilly.com/product/0636920018483.do

JMP 10 released

JMP , the visual data exploration, statistical quality control software from SAS Institute launched version 10 of its software today.

Source-http://jmp.com/about/events/webcasts/jmp_webcast.shtml?name=jmp10

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

Top 5 XKCD on Data Visualization

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

http://xkcd.com/657/ or the Money Chart at http://xkcd.com/980/ because I dont believe in data visualization to show off, but to keep it simple simply :)

Now I gotta find me a software that can write my blog for me :)

R Concerto- Computer Adaptive Tests

A really nice use for R is education

http://www.psychometrics.cam.ac.uk/page/300/concerto-testing-platform.htm

Concerto: R-Based Online Adaptive Testing Platform

Concerto is a web based, adaptive testing platform for creating and running rich, dynamic tests. It combines the flexibility of HTML presentation with the computing power of the R language, and the safety and performance of the MySQL database. It’s totally free for commercial and academic use, and it’s open source. If you have any questions, you feel like generously supporting the project, or you want to develop a commerical test on the platform, feel free to email Michal Kosinski.

We rely as much as possible on popular open source packages in order to maximize the safety and reliability of the system, and to ensure that its elements are kept up-to-date.

Why choose Concerto?

  • Simple to use: Check our Step-by-Step tutorial to see how to create a test in minutes.
  • Flexibility: You can use the R engine to apply virtually any IRT or CAT models.
  • Scalability: Modular design, MySQL tables, and low system requirements allow the testing of thousands for pennies.
  • Reliability: Concerto relies on popular, constantly updated, and reliable elements used by millions of users world-wide.
  • Elegant feedback and items: The flexibility of the HTML layer and the power of R allow you to use (or generate on the fly!) polished multi-media items, as well as feedback full of graphs and charts generated by R for each test taker.
  • Low costs: It’s free and open-source!

Demonstration tests:

 Concerto explained:

Get Concerto:

Before installing concerto you may prefer to test it using a demo account on our server.Email Michal Kosinski in order to get demo account.

Training in Concerto:

Next session 9th Dec 2011: book early!

Commercial tests and Concerto:

Concerto is an open-source project so anyone can use it free of charge, even for commercial purposes. However, it might be faster and less expensive to hire our experienced team to develop your test, provide support and maintenance, and take responsibility for its smooth and reliable operation. Contact us!

 

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