For a lot of quantitative corporate personnel, the raging debate between the tools of choice for analytics has been known to cause some rival enthusiasm instead of the age-old political debates on Thanksgiving!
The SAS vs. R debate was already hotly underway for the past couple of years, but recently many analytics professionals and aspiring analysts have requested us to include a comparison of Python in our debates. So, we decided to keep things light and simple and only asked a single question – “which analytics tool do your prefer to use: SAS, R Programming or Python?”
Read Also: Elementary Character Functions in SAS
Gradually our survey results have been showing a growing demand for open source tools over the past few years. In fact so much so, that this year almost 61.3% of respondents in a survey conducted by KDnuggets chose R and Python over 38.6 percent of people still opting for SAS. As it is SAS is a great tool for large companies to conduct their data analytics.
Are you keen on learning more about these numbers? So were we, so tallied a few survey results and opinions of analytics professionals to determine which is the better data analytics tool to learn first. And here is what we found…
We have all established that Big Data is big and all the noise about Big Data is not just hype but reality. With the increase in technology the data generated on Earth is doubling in every 40 months and huge heaps of data keeps coming in from multiple sources. Let’s look at some data to really understand how Big Data is evolving:
- The population of the world is 7 billion, and out of these 7 billion, 5.1 billion people use a smart phone device.
- On an average everyday almost 11 billion texts are sent across the globe.
- The global number of Google searches everyday is 5 billion
But there is an imbalance as we have been creating data but not consuming it enough for proper use. We generate 25 quintillion bytes of data daily through our regular online activities including online communications, online behaviour, video streaming services and much more.
Studies carried out in 2012 showed that the world generated more than 2 zetabytes of data which is roughly equal to 2 trillion gigabytes. By the year 2020, we will generate 35 trillions of data and to manage this growing amount of data we will need 10 times the servers we use now and at least 50 times more data management systems and 75 times the files to manage it all.
The industry is still not equipped to handle such an explosion of data as 80% of it is unstructured data. It is beyond the scope of traditional statistical analysis tools to handle this amount of data as it is too complicated and unorganized.
The talent pool required to effectively manage Big Data will fall short by at least 100 thousand minds as there are only 500 thousand computer scientists but less than 3000 mathematicians. But to truly utilize the complete potential of Big Data we need more human resource and more tools.
The solution to tackle this even bigger problem of Big Data is Big Data Analytics. It is fresh new way of thinking about the company objectives and the strategies created to achieve them. Big Data analytics is the answer behind where the hidden opportunities lie.
SAS, R programming , Hadoop, Pig, Spark and Hive are a few advanced tools that are currently in use in the data analysis industry. SAS experts are higly in demand in the job market recently as it is slowly emerging to be an increasingly popular tool to handle data analysis problems. To learn more about SAS training institutes follow our latest posts in DexLab Analytics.
For more information please read our blog at http://www.dexlabanalytics.com/blog/the-evolution-of-big-data-in-business-decision-making
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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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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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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As per a recent survey, the training wheels of data science mostly turn to Python. Most industry experts revealed that Python remains to be the no. 1 tool in data science. Many even suggest that R programming skill is turning out to be the top dog in the data industry today after Python.
While many argue that there is no reason to believe that Python’s reign on the data world will not last. But also these are the same people who only thought so, when data science was a place for PhD-holding propeller-heads. Now data science is a mainstream industry, with fresh recruits from a varied range of fields. Initially Python was thought to have the widest range of utilities, but newer and more advanced technologies are cropping up every day.
With data science and management slowly emerging to be elemental in all industries, so is R programming.
Why Python is being swallowed?
It is a common notion that comparing two different programming languages is whimsical as each has their separate “use scenarios”. For instance, it might still seem plausible comparing C++and Swift, but it may not be very informative on the font of revealing new news.
Similar is the case for comparing Python with R programming; both are used by data scientists for data analysis. But R software was initially developed keeping the needs of statisticians. While on the other hand, Python has a more generalized purpose. Earlier in the industry Python had the most number of job opportunities, with its usability in web applications and other such similar uses.
But there has been a sudden change in preferences within the industry which is interesting to note, that led to the sudden rise in the popularity of R programming.
As per the multifaceted ranking from IEEE spectrum, the topmost 5 programming languages currently are – Java, C, C++, R and Python. Another interesting fact to note about R programming language is that it rose from the 9th to the 6th position within a single year. So, it is understandable that it is slowly emerging into the top ranking programming languages now.
Are experts snacking on Python first and then feasting on R?
Many experts expect that there will be an impending fusion in the realm of big data with the melding of R and Python. While Python is the generalist language for developers, but R is a data experts’ language, for those who know their way in data. Many had the question if both R and Python would be in use in the long-run, are both such languages useful in the future.
Today we have the answer to this question as the biggest experts in the field have claimed that more often than not R and Python are used together. But still there is ample reason to believe that R will soon take over the data science world over Python. Because with time, as data planning, management and analysis becomes invaluable in businesses regardless of the department of operation, R will also gain greater popularity rather than Python. And that is not only the forecast for data science industry but overall in the corporate world.
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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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