R is probably one of the most underrated programming languages of all. Also, it is often considered as an underdog language for Data Science as Python is said to have an upper edge over it.
But when it comes to completeness for Data Science, R Programming Language is a top-notch choice for it. R programming has got it all what it takes to be a perfect language for Data Science though it didn't get the recognition that it deserved.
From all the features that make it a perfect choice for Data Science, here I am with an attempt to highlight a few not so known features of R programming.
Here are 5 features of R Programming Language that I wish I had known earlier.
1. You can parameterize R markdown documents
By defining parameters in the YAML header of your R markdown document and giving them values, you can make a type of template for the document. Then you can either knit the document with the default values or knit it with the parameters to get a document with different analyses and different data represented in the same format.
This process allows you to build templates that you can use later to generate new web apps with different data and content but a similar format.
2. You can write word or PowerPoint documents using R markdown
By changing a single line in the YAML header of your document you can make word or Powerpoint documents with R markdown code. You can embed your R code and your data in them. Make them as interactive as you want. Detailed presentations of your analysis and documentation of your projects ready within the R environment.
3. You can analyze data using spark cluster from R
Fitting big machine learning models on huge datasets is quite a task with R alone. Did you know that using the sparklyr package, you can use Spark on your desktop or even a spark cluster to that for you, all without exiting R even once?
4. Everything is a vector
All data structures in R are either a type of vector or are derived from vectors. Due to this, it makes sense that all data structures should be convertible to other data structures. In my experience, the easiest way of converting one data structure into another is to change the structure’s dimensions.
5. You can host your shiny apps
The shiny package allows you to create interactive web apps that can embed your R code and data. It is also possible to host the apps online on cloud servers like shinyapps.io. The rsconnect package can even cut this process short to just a few lines of code.
Wrapping it up...
In this era of Data Science where Python has always been in the spotlight, the importance of R Programming often gets overshadowed. I think that it is the right for all the Data Science aspirants out there to look forward to learning R.
R is seriously an easy language to learn and can be easily be learned by people from a non-technical background as well.I would suggest trying out all of the above features. Who knows you might end up learning something new. Share it if you do.
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Until next time!!