The fun fact with R is that it first originated in academia, the creators of R Programming Ross Ihaka and Robert Gentlemen developed this programming language at the University of Auckland in New Zealand and it has been widely used in graduate programs ever since. In programs that require that include strong statistical analysis. This programming language has often been used in MOOCs i.e. Massive Open Online Courses. In fact this programming language is extensively used in graduate educational programs that involve crunching data and students of statistics will encounter R in their academic life. And like everything else that is exposed to students in schools, R will naturally also be widely adopted for industrial use as well. As R is widely used in higher education, thus it is evident that its demand will increase in business and this is the reason why people who miss the R train in college often seek, R Programming Online Training programs like the one from DexLab Analytics.
Why drive for adoption of technology?
While technology makes things easier for us and could be deemed as fun, but then again most us who use technology also do it for a living. To the advantage of R users it is not only a pleasure to use this software but also due to its high demand in business it is also hugely profitable with fat checks for those who are well-versed.
The survey conducted by Dice Technology Salary Survey suggested that R is the highest paying skill as of last year. In a recent survey conducted by O’Reilly Data Science Salary Survey also put R as one of the most used statistical tools by the highest paid data scientists.
R has a diverse community:
The professionals working with R come from a diverse range of backgrounds; the list consists of scientists, academics, business analysts, statisticians and professional programmers. The diversity can be well perceived in the packages maintained by the community CRAN (Comprehensive R Archive Network) which brings the colorful backgrounds of the community members to the forefront.
The packages available with R can take care of several types of tasks like – creating maps, stock market analysis, high throughput genomic analysis, usual language processing. Moreover, people can get access to all the latest R-based news, from R Bloggers, which is a blog aggregation site which serves as a hub for latest news and updates related to R.
R is easy to use:
Many people get drawn to R due to its ease of use. One can generate complex charts and maps in R with only a few lines of code. This is an advantage of using R as other languages will require several lines of codes to complete these tasks. Though the popular notion about this software is that it is quirky, but it has several powerful features especially geared towards Data Analysis.
For more news and updates on R programming and details about the best R Programming Online Training programs stay hooked to our daily posts.
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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.
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 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.
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.
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.
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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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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This post is targeted towards established R Programmers or those who are learning the basics of R Programming through proper R programming certification.
The top ten tips that R enthusiasts should follow while writing their code are as follows:
- You don’t have to tidy up things manually
Though it is indeed a best practice to keep your code neat and clean, you can prevent unnecessary waste of time by letting linters like formatR do the trick for you. After its job has been done you can just lay back and relax with a few minor tweaks here and there.
- Make Use of an IDE
Though you most certainly can write code on a text editor or even the R graphical user interface but Rstudio like Interactive Development Environments make the process of writing code hassle free and so much easier. The code completion hints is sure to save you much time.
- Get To Know the Hotkeys
IDEs quite akin to the OS comes with its share of hotkeys. This saves loads of time as you accomplish more without ever taking your hands off the keyboard.
- Plan Before Coding
If you are sure of the direction the practice of your coding may take you will find the task of coding far easier. And doing things like commenting makes things even more easier.
- If unsure about something make sure to just Google it
If you start from scratch you are sure to learn a lot of things but there is a better more sensible way. Just Google the problem in search of canonical solutions, some of the more common pitfalls or perhaps simply some things that you should take into consideration.
- Avoid repetition
In all probability this tip is one of those that you are pretty much sure to have heard befory but nonetheless is worthwhile to mention. R has the potential to create functions, split the codes that need to be repeated into a set of functions.
- Select the appropriate tool in your context
Avoid relying on R as the primary hammer tool of your choice. Make evaluations of project needs and make use of appropriate languages. If you learn a bit at the outset you are spared of whole lot of pain later.
- Write code that facilitates tests
Make it a second nature to test your code and make the whole procedure quick and one that may be conducted with ease. While writing code incorporate validation and avoid using functions that have negative effects.
- Make proper documentation
Make this a regular part of your code writing practice that is write documentation as you go along the process of writing your code. If you leave the task till the end not only will the task be harder to complete but the final documentation will rarely be completed.
- Make Proper Source Control
Make use of source control like Git or SVN that lets you regularly maintain code versions and lets you make your development more conducive to collaboration.
The value of a code lies in a great part on whether it is documented, tracked and may be tested easily. If you are undergoing a course in a proper R programming training institute these tips are sure to separate you from your peers.
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Many programmers suggest that while they have developed software professionally in a plethora of programming languages, but the hardest language they have come across was R. While this statement may be debatable dividing the software developers’ community in the middle, as many others say that language is fairly easy to cope with. While the language may seem somewhat unconventional to learn initially, it is due to these factors that one with experience in languages like Java, Perl and C++ etc find it easier to handle. It has been developed keeping their abilities in mind.
What truly makes R programming stand-out from every other language is the fact that it is not just a programming language but also an environment for carrying out statistical analysis. Many experts suggest that they like to think that R is more of an environment consisting of a programming language component within that it being a programming language.
Most job sites these days are teeming with vacancies for R programmers, so it is highly recommendable to aspiring professionals to board the R train with a well-recognized R programming certification course.
When speaking about R programming it is safe to say, that is more like a scripting language for the R environment on similar lines as VBA is for MS Excel. This way some of the unconventional aspects of R can be explained when viewed in this perspective.
Understanding ‘sequences’ in R programming:
The reason behind using the expression seq(a, b, n) is used is to create a closed interval that starts from ‘a’ ends at ‘b’ and runs with step sizes of ‘n’. Taking a more realistic example, if we implement seq(1, 10, 3) returns with the following vectors – 1, 4, 7 and 10.
This command is somewhat similar to the range(a, b, n) in Python, except in Python only half-open intervals are used so, the vector 10 would not be included which was returned in case of the R example. The default step size augments in case of both R and Python is 1.
Boolean operators used in R:
The Boolean operators used in R are T or True for true values and F or False for false values.
As for the operators & and |, they are applied on the vectors element-wise. Conditional elements use && and || and they use lazy evaluations like in C. in such cases the operators do not use the second augment if the first augment works to determine the return value.
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In 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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With computerized information development anticipated to increment by 4,300% all over the world by 2020 and viable weights rising, organizations should now like never before convene the growing requests of their customers.
This digital upheaval is additionally giving remarkable chances to enhance the general client experience by means of big data analytics, as per a study conducted by Big Data Hadoop certification in Gurgaon. This is the procedure of gathering and deciphering these boundless amounts of information to separate the important, savvy, and helpful information that gives worth to a customer.
The following are 3 tips to utilize Big Data to improve general customer experience.
Actualize proactive bill shock administration
Bill shock is client agony from unforeseen allegations and is normally the consequence of broadband clients’ powerlessness to evaluate their huge information utilization, particularly while roaming. These disappointed clients can adversely affect the correspondence administration supplier’s repute and at last prompt income misfortune. Broadband organizations can stay away from this by giving continuous authorization activities and choices, through content warnings or email, permit free limited skimming, and divert clients to exchange information arrangements to dodge upcoming concerns.
Make more intelligent customized shopping encounters
Opt-in versatile showcasing correspondences of focused items and administrations can then be offered through customized messages particular to every phase of the purchaser cycle – mindfulness, engagement, thought, change and steadfastness. Suppose somebody selects to get promoting messages from a retailer who has an outlet in the neighborhood shopping center. GPS-incorporated tracking recognizes that the client is close to the store and sends the client an instant message alarming them to a unique one-day offer. With the client’s advantage provoked, she heads into the store and buys utilizing the coupon code as a part of the instant message.
Diminish holding up time in the line
A service organization, for instance, can deal with this perpetual agony of getting, as to orchestrate a home repair visit by getting the purchaser’s favored channel of correspondence, affirming the evening before in a mechanized way by means of that favored channel, and illuminating the client that the administration tech will call at 8:00 a.m. to tell the purchaser where he remains in the everyday line. This joys the client and disposes of the expense of up to three inbound telephone calls.
You have to know clients as people if you need to win them and then you’ll make more intelligent choices about their needs and practices.
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