Monthly Archives: March, 2016

Machine Learning for Newbies

Machine Learning has slowly become a part and parcel of our lives without most of knowing anything about it. It is the element that drives the smartness of smart devices that have become an integral part of our lives. If this is topic that arouses your curiosity then you simply must watch this video.

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Packages That make Life Easier for R Users

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When you use R, at first the going is slow. The syntax is not all that intuitive and is quite tricky too and it takes time for person to feel settled within the environment and get accustomed to the finer nuances of the language. If one is new to R, he or she might miss out on the vibrant community that revolves around R and the available packages available that go towards adding to the diverse uses of the program.

R, sometimes, tends to be a bit obscure and prickly when compared to other languages like Java or Python. But the boon of availability of loads of packages that add to its functionality and even create a familiar and simple interface lying on top of Base R. Today we take a look at ten packages that make life easier for R Programmers.

  • sqldf

The syntax R is perhaps the hardest part of the R learning curve and it takes a while to get used to <- over = and other nuances of the R Programming language. R excels at munching data but mastering it has a steep learning curve. What sqldf lets you do is to perform SQL queries on the data frames of R. It is familiar to users migrating from SAS and should present no trouble to anyone with basic skills in SQL. Sqldf makes use of the SQLite syntax.

  • forecast

forecast is the library r users most often turn to while making a time series analysis. With forecast it is very easy to fit time series models like ARMA, ARIMA, AR, Exponential Smoothing amongst others. The forecast plot is a long standing feature endeared by forecast users.

  • plyr

The plyr feature of R lets you perform data manipulation, the smart way. When you want to call a particular function on each of the elements of a vector or list you want to turn to the apply function family. The plyr package is a good substitute for the functionality resulting from the combination of split, combine and apply functions in Base R.

You get a whole set of functions namely daply,ddply, adply, dlply and ldply which share a common blueprint- Split the structure of data into groups, apply them to each group and finally return the results in a proper data structure.

  • stringr

Many users complain the string functionality of R to be tedious and highly difficult to use. Here also stringr, a package written by Hadley Wickham provides an R string operator that was long overdue. In stark contrast to Base R, stringr is really easy to use. All functions have the prefix of ‘str’ and remembering them is really easy.

  • ggplot2

Yet another package from Hadley Wickham and probably the one that is most well known, ggplot2 is one of the most favorite packages in R. It is characterized by its ease of use and outputs some stunning plots. ggplot2 provides you with the best way with which you want to present your work.

These are just some of the packages that make it easy to work with R. You will surely find more with the progression of time and your continued involvement with the R World.

And if you are serious about making R the passion that fast forward your career then R Analytics Certification is highly recommended.

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For More Info On R Programming

The Hottest Job Locations for Data Analysis Personnel are:

The current salary trends of analytics personnel

In India the hottest job locations for a data analyst position according to our pay-scale and job scenario survey are – Gurgaon, Mumbai and Bangalore. For more details on payment packages on offer for various data analysis positions view our infographic with numbers based on industry-based survey.

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Why R Holds Sway over Excel in Big Data

Though the uses of MS Excel are far varied than that of R Programming when it comes to the world of Big Data, R outperforms Excel by leaps and bounds. Handling data as well as manipulating it, is done far more effectively when the tool of R Programming is used. Watch this presentation if you wish to know the exact reasons that give R Programming a competitive edge.

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MS Excel Makes Data Analysis Simple

After watching this video you would agree that data analysis might not be that hard after all. Your humble spreadsheet program, and we are talking about MS Excel, might be all it takes to complete your rudimentary tasks in data analysis. It also worthwhile to mention here that Excel has advanced capabilities in Big Data Analytics which are primarily meant for power users.

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