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Big Data has become the paradigm shift of our culture. It is an exciting field to pursue right now with every sector across the globe looking to harness the power of Data Science and make data driven decisions. But extracting value from data requires professionals who have undergone Data Science Training and possess a proper set of skills. One of those skills will include the ability to write good ‘R’ code.
‘R’ in Data Science
‘R’ is an open-source programming language used in Data Science, without which Data Science would not even be possible. It is used to perform statistics, data analysis, predictive modeling, visualization, graphical modules, etc and offers techniques such as clustering, time series analysis and so on.
This programming language is flexible, sophisticated, and user-friendly and is ideal for any kind of analysis or exploratory work. Its strong ecosystem and extensible features makes it a popular and powerful tool among Big Data Scientists.
Best practices for programming with R
1) Organize your data
To design an effective and durable statistical project, you want to be able to write clean codes which can be easily understood by others. For this, you need to organize the workflow of your project in a clear and logical manner. Create a project structure to keep track of where your data is going.
When you start, work only with your R source file. (R Script) Organize the source code in logical building blocks. Remember to store all source files of the project within the same directory. When modifying a code, keep in mind to edit only the source file and leave the raw data files uninterrupted.
2) Be clear about the requirements and dependencies of your code
Use the library function to list out all the relevant packages needed to run your code. This is an effective method to indicate which packages are essential for your code and also helps you find out whether all the dependencies of your code have been installed. Before you write the code, ensure you’re R packages are updated.
3) Describe the code
When you first start writing your code, make an annotated description of what the R code does. Describe it briefly at the beginning of your file. Follow this method when describing the subsequent blocks of codes. This can save a lot of time for you/others to understand or modify the code.
4) Maintain a consistent style
To keep your R script easily readable, you need to frequently check your data. One resourceful method of maintaining different types of data is by using a consistent style. Since R programming language has different structures, (vectors, data frames, matrices, etc) you can identify and segregate distinct components in your code. Standardized script names, consistent indents, good commenting practices are few key ways.
5) Remove temporary objects
When using R scripts for a long time, it can often run out of memory. The best practice here is to inspect all the objects in your script and remove objects that are no longer in use. Check if the objects have been deleted since R may sometimes take a while to tidy up its memory.
6) Test your code
Use software tools and different input parameters to test your code. A well maintained workflow will reflect your efficiency as a programmer. Ensure the logic used is accurate and most of the coding process is automated. For valuable insight, review your code with a colleague or experienced programmer.
For novice programmers, the next time you practice writing in R, keep the above points in mind. You may not get instant success but you will be on the right path. For professional programmers, watch how your coding becomes readable and consistent.
While there are a vast number of data science courses out there, very select courses dole out data scientists with in-depth knowledge of the subject. For instance, Manipal ProLearn has a structured and extensive curriculum that covers every evolving nuance of the field. You can check out the various data science courses available on the site here.
What are your ‘R’ hacks? Tell us in the comments!
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