Some of the most important technology programs that keep Washington accountable are in danger of being eliminated. Data.gov, USASpending.gov, the IT Dashboard and other federal data transparency and government accountability programs are facing a massive budget cut, despite only being a tiny fraction of the national budget. Help save the data and make sure that Congress doesn’t leave the American people in the dark.
I wonder why the federal government/ non profit agencies can help create a SPARQL database, and in days of cloud computing, why a tech major cannot donate storage space to it, after all despite US corporate tax rate being high, US technological companies do end up paying a lower rate thanks to tax breaks/routing overseas revenue.
In the new age data is power, and the US has led in its mission to use technology to further its own values even especially in Middle East. The datasets should be made public and transitioned to the private sector/academia for research and re designing for data augmentation with out straining the massive deficit /borrowing/ fighting 3 wars. Of particular interest would be datasets of campaign finances and donors especially given large number of retail/small donors/internet marketing in elections as it will also help serve as an example of democracy and change. Even countries like China can create a corruption/expense efficiency tracking internal dashboard with restricted rights to help with rural and urban governance.
I have not been really posting or writing worthwhile on the website for some time, as I am still busy writing ” R for Business Analytics” which I hope to get out before year end. However while doing research for that, I came across many types of graphs and what struck me is the actual usage of some kinds of graphs is very different in business analytics as compared to statistical computing.
The criterion of top ten graphs is as follows-
1) Usage-The order in which they appear is not strictly in terms of desirability but actual frequency of usage. So a frequently used graph like box plot would be recommended above say a violin plot.
2) Adequacy- Data Visualization paradigms change over time- but the need for accurate conveying of maximum information in a minium space without overwhelming reader or misleading data perceptions.
3) Ease of creation- A simpler graph created by a single function is more preferrable to writing 4-5 lines of code to create an elaborate graph.
4) Aesthetics– Aesthetics is relative and in addition studies have shown visual perception varies across cultures and geographies. However , beauty is universally appreciated and a pretty graph is sometimes and often preferred over a not so pretty graph. Here being pretty is in both visual appeal without compromising perceptual inference from graphical analysis.
so When do we use a bar chart versus a line graph versus a pie chart? When is a mosaic plot more handy and when should histograms be used with density plots? The list tries to capture most of these practicalities.
Let me elaborate on some specific graphs-
1) Pie Chart- While Pie Chart is not really used much in stats computing, and indeed it is considered a misleading example of data visualization especially the skewed or two dimensional charts. However when it comes to evaluating market share at a particular instance, a pie chart is simple to understand. At the most two pie charts are needed for comparing two different snapshots, but three or more pie charts on same data at different points of time is definitely a bad case.
In R you can create piechart, by just using pie(dataset$variable)
As per official documentation, pie charts are not recommended at all.
Pie charts are a very bad way of displaying information. The eye is good at judging linear measures and bad at judging relative areas. A bar chart or dot chart is a preferable way of displaying this type of data.
Cleveland (1985), page 264: “Data that can be shown by pie charts always can be shown by a dot chart. This means that judgements of position along a common scale can be made instead of the less accurate angle judgements.” This statement is based on the empirical investigations of Cleveland and McGill as well as investigations by perceptual psychologists.
Despite this, pie charts are frequently used as an important metric they inevitably convey is market share. Market share remains an important analytical metric for business.
The pie3D( ) function in the plotrix package provides 3D exploded pie charts.An exploded pie chart remains a very commonly used (or misused) chart.
pie(rep(1,24), col=rainbow(24), radius=0.9)
title(main="Color Wheel", cex.main=1.4, font.main=3)
title(xlab="(test)", cex.lab=0.8, font.lab=3)
(Note adding a grey background is quite easy in the basic graphics device as well without using an advanced graphical package)
Some common analytical tasks from the diary of the glamorous life of a business analyst-
1) removing duplicates from a dataset based on certain key values/variables
2) merging two datasets based on a common key/variable/s
3) creating a subset based on a conditional value of a variable
4) creating a subset based on a conditional value of a time-date variable
5) changing format from one date time variable to another
6) doing a means grouped or classified at a level of aggregation
7) creating a new variable based on if then condition
8) creating a macro to run same program with different parameters
9) creating a logistic regression model, scoring dataset,
10) transforming variables
11) checking roc curves of model
12) splitting a dataset for a random sample (repeatable with random seed)
13) creating a cross tab of all variables in a dataset with one response variable
14) creating bins or ranks from a certain variable value
15) graphically examine cross tabs
18)creating a pie chart
19) creating a line graph, creating a bar graph
20) creating a bubbles chart
21) running a goal seek kind of simulation/optimization
22) creating a tabular report for multiple metrics grouped for one time/variable
23) creating a basic time series forecast
and some case studies I could think of-
As the Director, Analytics you have to examine current marketing efficiency as well as help optimize sales force efficiency across various channels. In addition you have to examine multiple sales channels including inbound telephone, outgoing direct mail, internet email campaigns. The datawarehouse is an RDBMS but it has multiple data quality issues to be checked for. In addition you need to submit your budget estimates for next year’s annual marketing budget to maximize sales return on investment.
As the Director, Risk you have to examine the overdue mortgages book that your predecessor left you. You need to optimize collections and minimize fraud and write-offs, and your efforts would be measured in maximizing profits from your department.
As a social media consultant you have been asked to maximize social media analytics and social media exposure to your client. You need to create a mechanism to report particular brand keywords, as well as automated triggers between unusual web activity, and statistical analysis of the website analytics metrics. Above all it needs to be set up in an automated reporting dashboard .
As a consultant to a telecommunication company you are asked to monitor churn and review the existing churn models. Also you need to maximize advertising spend on various channels. The problem is there are a large number of promotions always going on, some of the data is either incorrectly coded or there are interaction effects between the various promotions.
As a modeller you need to do the following-
1) Check ROC and H-L curves for existing model
2) Divide dataset in random splits of 40:60
3) Create multiple aggregated variables from the basic variables
4) run regression again and again
5) evaluate statistical robustness and fit of model
6) display results graphically
All these steps can be broken down in little little pieces of code- something which i am putting down a list of.
Are there any common data analysis tasks that you think I am missing out- any common case studies ? let me know.
Additional features in R over other analytical packages-
1) Source Code is given to ensure complete custom solution and embedding for a particular application. Open source code has an advantage that is extensively peer- reviewed in Journals and Scientific Literature. This means bugs will found, shared and corrected transparently.
2) Wide literature of training material in the form of books is available for the R analytical platform.
3) Extensively the best data visualization tools in analytical software (apart from Tableau Software ‘s latest version). The extensive data visualization available in R is of the form a variety of customizable graphs, as well as animation. The principal reason third-party software initially started creating interfaces to R is because the graphical library of packages in R is more advanced as well as rapidly getting more features by the day.
4) Free in upfront license cost for academics and thus budget friendly for small and large analytical teams.
5) Flexible programming for your data environment. This includes having packages that ensure compatibility with Java, Python and C++.
6) Easy migration from other analytical platforms to R Platform. It is relatively easy for a non R platform user to migrate to R platform and there is no danger of vendor lock-in due to the GPL nature of source code and open community.
Statistics are numbers that tell (descriptive), advise ( prescriptive) or forecast (predictive). Analytics is a decision-making help tool. Analytics on which no decision is to be made or is being considered can be classified as purely statistical and non analytical. Thus ease of making a correct decision separates a good analytical platform from a not so good analytical platform. The distinction is likely to be disputed by people of either background- and business analysis requires more emphasis on how practical or actionable the results are and less emphasis on the statistical metrics in a particular data analysis task. I believe one clear reason between business analytics is different from statistical analysis is the cost of perfect information (data costs in real world) and the opportunity cost of delayed and distorted decision-making.
Specific to the following domains R has the following costs and benefits
R is free per license and for download
It is one of the few analytical platforms that work on Mac OS
It’s results are credibly established in both journals like Journal of Statistical Software and in the work at LinkedIn, Google and Facebook’s analytical teams.
It has open source code for customization as per GPL
It also has a flexible option for commercial vendors like Revolution Analytics (who support 64 bit windows) as well as bigger datasets
It has interfaces from almost all other analytical software including SAS,SPSS, JMP, Oracle Data Mining, Rapid Miner. Existing license holders can thus invoke and use R from within these software
Huge library of packages for regression, time series, finance and modeling
High quality data visualization packages
R as a computing platform is better suited to the needs of data mining as it has a vast array of packages covering standard regression, decision trees, association rules, cluster analysis, machine learning, neural networks as well as exotic specialized algorithms like those based on chaos models.
Flexibility in tweaking a standard algorithm by seeing the source code
The RATTLE GUI remains the standard GUI for Data Miners using R. It was created and developed in Australia.
Business Dashboards and Reporting
Business Dashboards and Reporting are an essential piece of Business Intelligence and Decision making systems in organizations. R offers data visualization through GGPLOT, and GUI like Deducer and Red-R can help even non R users create a metrics dashboard
For online Dashboards- R has packages like RWeb, RServe and R Apache- which in combination with data visualization packages offer powerful dashboard capabilities.
R can be combined with MS Excel using the R Excel package – to enable R capabilities to be imported within Excel. Thus a MS Excel user with no knowledge of R can use the GUI within the R Excel plug-in to use powerful graphical and statistical capabilities.
Additional factors to consider in your R installation-
There are some more choices awaiting you now-
1) Licensing Choices-Academic Version or Free Version or Enterprise Version of R
2) Operating System Choices-Which Operating System to choose from? Unix, Windows or Mac OS.
3) Operating system sub choice- 32- bit or 64 bit.
4) Hardware choices-Cost -benefit trade-offs for additional hardware for R. Choices between local ,cluster and cloud computing.
5) Interface choices-Command Line versus GUI? Which GUI to choose as the default start-up option?
6) Software component choice- Which packages to install? There are almost 3000 packages, some of them are complimentary, some are dependent on each other, and almost all are free.
7) Additional Software choices- Which additional software do you need to achieve maximum accuracy, robustness and speed of computing- and how to use existing legacy software and hardware for best complementary results with R.
1) Licensing Choices-
You can choose between two kinds of R installations – one is free and open source from http://r-project.org The other R installation is commercial and is offered by many vendors including Revolution Analytics. However there are other commercial vendors too.
Windows remains the most widely used operating system on this planet. If you are experienced in Windows based computing and are active on analytical projects- it would not make sense for you to move to other operating systems. This is also based on the fact that compatibility problems are minimum for Microsoft Windows and the help is extensively documented. However there may be some R packages that would not function well under Windows- if that happens a multiple operating system is your next option.
Enterprise R from Revolution Analytics- Enterprise R from Revolution Analytics has a complete R Development environment for Windows including the use of code snippets to make programming faster. Revolution is also expected to make a GUI available by 2011. Revolution Analytics claims several enhancements for it’s version of R including the use of optimized libraries for faster performance.
Reasons for choosing MacOS remains its considerable appeal in aesthetically designed software- but MacOS is not a standard Operating system for enterprise systems as well as statistical computing. However open source R claims to be quite optimized and it can be used for existing Mac users. However there seem to be no commercially available versions of R available as of now for this operating system.
Red Hat Enterprise Linux
Other versions of Linux
Linux is considered a preferred operating system by R users due to it having the same open source credentials-much better fit for all R packages and it’s customizability for big data analytics.
Ubuntu Linux is recommended for people making the transition to Linux for the first time. Ubuntu Linux had an marketing agreement with revolution Analytics for an earlier version of Ubuntu- and many R packages can installed in a straightforward way as Ubuntu/Debian packages are available. Red Hat Enterprise Linux is officially supported by Revolution Analytics for it’s enterprise module. Other versions of Linux popular are Open SUSE.
Multiple operating systems-
Virtualization vs Dual Boot-
You can also choose between having a VMware VM Player for a virtual partition on your computers that is dedicated to R based computing or having operating system choice at the startup or booting of your computer. A software program called wubi helps with the dual installation of Linux and Windows.
64 bit vs 32 bit – Given a choice between 32 bit versus 64 bit versions of the same operating system like Linux Ubuntu, the 64 bit version would speed up processing by an approximate factor of 2. However you need to check whether your current hardware can support 64 bit operating systems and if so- you may want to ask your Information Technology manager to upgrade atleast some operating systems in your analytics work environment to 64 bit operating systems.
Hardware choices- At the time of writing this book, the dominant computing paradigm is workstation computing followed by server-client computing. However with the introduction of cloud computing, netbooks, tablet PCs, hardware choices are much more flexible in 2011 than just a couple of years back.
Hardware costs are a significant cost to an analytics environment and are also remarkably depreciated over a short period of time. You may thus examine your legacy hardware, and your future analytical computing needs- and accordingly decide between the various hardware options available for R.
Unlike other analytical software which can charge by number of processors, or server pricing being higher than workstation pricing and grid computing pricing extremely high if available- R is well suited for all kinds of hardware environment with flexible costs. Given the fact that R is memory intensive (it limits the size of data analyzed to the RAM size of the machine unless special formats and /or chunking is used)- it depends on size of datasets used and number of concurrent users analyzing the dataset. Thus the defining issue is not R but size of the data being analyzed.
Local Computing- This is meant to denote when the software is installed locally. For big data the data to be analyzed would be stored in the form of databases.
Server version- Revolution Analytics has differential pricing for server -client versions but for the open source version it is free and the same for Server or Workstation versions.
Cloud Computing- Cloud computing is defined as the delivery of data, processing, systems via remote computers. It is similar to server-client computing but the remote server (also called cloud) has flexible computing in terms of number of processors, memory, and data storage. Cloud computing in the form of public cloud enables people to do analytical tasks on massive datasets without investing in permanent hardware or software as most public clouds are priced on pay per usage. The biggest cloud computing provider is Amazon and many other vendors provide services on top of it. Google is also coming for data storage in the form of clouds (Google Storage), as well as using machine learning in the form of API (Google Prediction API)
Cluster-Grid Computing/Parallel processing- In order to build a cluster, you would need the RMpi and the SNOW packages, among other packages that help with parallel processing.
How much resources
RAM-Hard Disk-Processors- for workstation computing
Instances or API calls for cloud computing
Software Component Choices
Packages to install
Additional software choices
Additional legacy software
Optimizing your R based computing
Libraries to speed up R
citation- R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.