Data analysis and data science are increasingly useful tools in today’s world. Being able to utilize various platforms to achieve these goals is – more than ever before – a necessary skill.
There are a variety of tools you can do this with – however, programming in R is arguably one of the most direct and purpose-built.
Together with Apache Spark, you can interact with very powerful techniques and ideas using a familiar interface and a standard computer.
In this article, we’ll be covering:
Let’s get into it!
R programming is a general term referring to the various things that you can do with the tools provided in R.
On the surface, it doesn’t seem like R is “really programming” – what you’re writing certainly looks like code, but all you’re doing is using pre-created functions and calculators. However, there’s more functionality in R than just this!
R allows its users to create new functions to suit their own particular personal tasks.
While you can do this within R itself (and R takes much of its language and conventions from another programming language, S), R also allows you to integrate code in C, C++, or Fortran should you require more complex computation.
R has a variety of benefits as a programming language. It is free and open source, and available to all major modern computing platforms (Windows, macOS, Linux, and other unix systems).
This means that it’s tremendously accessible and allows you to use it just about anywhere.
As we mentioned earlier, R strongly follows the conventions of another language: S. S is another statistical programming language, which is commonly chosen when analyzing data.
However, S is not open source. As a result, R allows users greater control over what their installation contains and is therefore more flexible than S.
R is a language designed to be used primarily for statistical analysis and modeling.
It contains a suite of tools designed to help users interact with data, particularly when this data may be otherwise tedious to process through “standard” means such as spreadsheets.
It also provides visual elements to assist in the presentation of data and in general assists in preparing data for reports and casual consumption.
R programming is very similar to other kinds of programming, albeit designed specifically for statistical analysis.
It primarily operates on data structures that are fed to the program and provides a wide variety of tools to do so.
For full information on how the language works, you can check out the manuals here. R is designed as a full programming language, meaning that you can do most of the things you’d expect to be able to do in other languages.
For many (if not most) purposes, you’ll be fine just knowing R. The language provides a substantial set of tools for you to analyze data and define your own routines and functions to assist you in this task.
However, if you’re running computationally intensive routines, you may want to look at R’s ability to integrate with the C, C++, or Fortran languages.
Adding routines in these languages is a good plan if you’re finding that your functions or analysis choices are running slowly in R itself.
In this case, you will obviously need to have a grasp on whichever of these languages you choose to use!
R can be integrated with Apache Spark to augment the use of both platforms. To do so, you’ll want to use an interface. There are two major choices for this: SparkR and sparklyr.
Don’t be fooled by the similar names; these two interfaces are quite different, both in terms of what you as a user see on the surface and how they work under the hood.
One key difference is the way each interface handles functions from the dplyr suite. These functions are designed to improve the ease of data manipulation and are quite popular among the R user community.
sparklyr provides explicit support (referencing it even on its front page) for dplyr functions.
However, SparkR has some functions which share names with those from dplyr. This can cause problems if you are trying to use dplyr functions in your analysis!
There are several online analyses on the differences between the interfaces and which ones users prefer.
The general consensus seems to be that sparklyr is preferred, for a variety of reasons including its lack of conflict with dplyr and better support for user-created functions.
Apache Spark is a platform designed to provide powerful data science, batch and stream processing and ML tools.
Data science is driven by data analysis, so applying tools to help you better analyze the data is a logical next step to improve the efficiency of your processes. R is one such tool, although Spark supports Python, SQL, Scala, and Java as well.
The main reason R programming can be a good choice for Apache Spark is due to the highly statistics- and data-focused nature of the language, as opposed to the other choices which are more general.
It’s also a natural choice if you’re already familiar with R, as this means you’ll have a smoother transition to using Apache Spark.
R programming is a very useful tool to integrate with Apache Spark.
With extensive documentation and user experiences behind it, along with a purpose-built function set for data analysis, it’s arguably one of the best languages that can be integrated with Apache Spark.
You can choose how you want to integrate R with Spark for your particular use case, but at the end of the day, you’ll be working in mostly the same way with what is hopefully a familiar language.
Sources
https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf
https://www.r-project.org/about.html
https://cran.r-project.org/manuals.html
https://spark.apache.org/docs/latest/sparkr.html
https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html
https://eddjberry.netlify.app/post/2017-12-05-sparkr-vs-sparklyr/
https://stackoverflow.com/questions/39494484/sparkr-vs-sparklyr
https://learn.microsoft.com/en-us/azure/databricks/sparkr/sparkr-vs-sparklyr
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