Tag Archives: R experience

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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How You May Import Your Data into R Programming

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The kind of data that you want to import in to R may come in formats of various sorts like those of statistical software, flat files, web data and databases.

In R, it is often found that the various data types require varied approaches. Through this post we list how the more common file types may be imported into R programming.

Typically flat files may said to be simply text files containing table data. R has through its standard distribution the ability to import such a file in to the R environment through the aid of functions like read.table() as well as read.csv() from the package referred to as utils. Also, you may import files like these through readr which is a package famed for its ease of use and swiftness.

If you want to import excel files in to R, one need to carefully examine the readxl package. As an alternative you may also use the gdata package which includes in its functionality importing Excel data and also the XLConnect package. The XLConnect package is more of a real bridge between R and Excel. This basically means that any action that might be done with Excel might very well be done from R.

Other packages of software like SPSS, SAS and STATA are used to produce their own formats of file. This is best handled with the Haven package created by Hadley Wickham. Besides its ability to import such files it is also characterized by its ease of use. As an alternative there is packages like foreign which has the ability to import more esoteric formats like Weka and Systat. It comes with the added functionality to export data to a large number of formats as well.

The database type that you wish to connect to determines the package which is to be used to import from and connect to a relational database. MySQL databases may be connected to through the means of the RMySQL package. Other examples are RpostgreSQL and ROracle. Then you must make use of another R package like DBI in order to manipulate and access the required database.

One may also harvest web data using the R Programming language. This may be done by connecting online resources to R through the use of API or scrape with the help of packages like rvest.

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Got A Breakthrough Idea For The R Language?

R-Language-01In the June of 2015 the R Consortium was first announced. The aim of the R Consortium is to provide support to the ecosystem as well as the community surrounding the R programming language and facilitate the adaptation of R as a Big Data tool. The Infrastructure Steering Committee or ISC of The R Consortium seeks to direct the technical focus of the language along with overseeing projects that are meant to improve the, shall we say, R experience.

It is to be noted that all this lacks a central vision for R beyond simple support. And the reason behind that lies in the fact that the aims of the R Consortium is to provide support for projects that the R community thinks will aid it in its growth and thrive at the same time.

The R Consortium will grant awards to project proposals that according to them will be of maximum benefit to the community in a biennial basis. It is the ISC that is responsible for the reception, evaluation and selection of projects.

R-Hub happens to be the first proposal to be selected under the program. It provides an essential build and check process in the pre-CRAN period. This gives the developers of packages the ability conduct tests on the builds proposed by them before it is sent to the CRAN. This makes the whole process easier and swifter for CRAN volunteers.

The proposals that will be accepted next has a deadline of the 10th of January, 2016 and the final announcement regarding the grant will only be made in the middle of February next year. The process has been described and guidelines provided by the ISC on the website of the R Consortium.

Steph Locke from Mango has drafted a proposal template that you are free to customize for your very own proposal. Though the template is indeed exhaustive but it needs to be customized in terms of both sections and topics.

The movement demands that even ordinary folks using R are given the chance to come up with solutions to the shortcomings presently found in R.

Also there is need to raise the number of institutes and courses both off and online that impart R language training.

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