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Understandably, we get a little impatient when we do not know how much time a process is going to take, for example, a for loop or a file downloading or an application starting up. Therefore to cater to the basic human urge to see and quantify progress, we can use some progress bars in our R scripts.

To distract us from this wait, we have the option of using the txtProgressBar from the built-in R utils package. Let us take the example of a FOR loop with a little sleep time so that we can actually observe the progressBar in…


Ace your next side-project and become a more complete software engineer

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Background:

Over the course of this lock-down amidst this ongoing pandemic I was able to develop some interesting side-projects in a limited period of time. Having created a few of these side-projects, I was able to loosely define a proper pipeline to help create side projects in a more structured method. I am a firm believer in creating SOPs (Standard Operating Procedures) for as many tasks as possible to unburden oneself. So, here I present to you my top 12 tips from my project pipeline to help streamline your next side-project.

My top 12 tips:

Recently, I have been in touch with a lot of…


Learn to link to different sections of your article ^_^

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Background:

A while back I wrote a Medium article describing my selection in GSoC 2020 as a student developer for the R Project for Statistical Computing. To put it mildly, the article was extremely well received, getting to a stage where it started getting recommended even to me 😄

The only negative feedback I received from the article was that it was too lengthy and often the readers felt lost in the article because of itsvastness. So, I decided to tinker around a bit and I added a Table of Contents sections and made all the entries of the table clickable


How I got an opportunity to intern under one of the largest organizations in one of the most competitive GSoC till date.

My story of becoming a GSoC fellow under R Project for Statistical Computing

Table of Contents:

  1. Preface
  2. Flashback
  3. Reconnaissance
  4. Analysis
  5. Setting Things Right
  6. Go All Out
  7. Stay Sane
  8. Make Your Proposal Count
  9. What If I Don’t Make The Cut?
  10. Happy Pictures

Preface:

My entire GSoC journey has been a pretty enjoyable ride, full of excitement, unexpected turns and yes, expected blind turns. Through this article I only wish to share my journey with you, and not to give you any advice per se. However, if you find some parts of this voyage impressionable, you may very well adopt it.

I had been requested by many people, to share how I “cracked GSoC” and what tips I could…


An advanced guide for file handling in R Shiny (including PDFs)

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My last post intrigued quite a lot of people because of the novel and innovative nature of my last R Shiny app. This use case of Shiny was mostly unheard of, as primarily this is not what R Shiny was designed for. However, due to some personal and professional reasons, I decided to undertake the project of “Creating a centralized platform for my university exam resources” in R Shiny and here in this article, I’ll share the solution to the biggest hurdle you might face in file handling using R Shiny.

The Problem: Persistent Data in R Shiny

Whenever you are dealing with persistent data storage in…


Leveraging R Shiny for better exam preparation

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With the rising uncertainty around the promotion criterion for university students in the midst of the COVID-19 pandemic, I decided to create a centralized platform to aggregate examination resources from the students themselves.

Motivation:

For any person, the daily updates of new cases and deaths related to COVID-19 is traumatic. University students, however, have to deal with a plethora of additional issues ranging from rescinded job offers to the uncertainty of getting promoted to the next semester. This has been a cause of rising anxiety among my batch mates too. …


A list of alternate function calls for better performance :)

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My last post about R code optimizations was about a set of strategical changes and mindset shifts required to write code better suited for the R programming language. Here, in this post I’ll go over alternate function calls that you could use to bolster your code performance without any restructuring of code per se.

Compiling Functions:

As we know R code is interpreted when it is run, this makes the functions written in R a little slower than their counterparts in C/C++, where the functions (the entire code, actually) gets compiled first and then executed. R however, does have compiling ability that…


The love affair of R with everything vector ^-^

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If you have had a background in coding in “traditional” programming languages like C++ or Java and then switched to R, a lot of thing might have baffled you. Among the plethora of new concepts, you might have come across the phrase “write vectorized R code”. If you were like me, you would have simply ignored this and went on ahead with just adjusting to the new syntax of R.

A lot of beginner R users are not comfortable with the term “vectorize”, and not really familiar with the method. So, here, I’ll present to you why your code should…


Exploring all option of fast and efficient I/O

In my last post, you got to know about how to speed up your I/O speed without loading any external libraries. Today, we would explore a few more options of efficient I/O that can be leveraged with external libraries. So let’s jump right in.

Different Approaches for I/O:

Probably the most versatile package for data reading/writing in R is the rio package. The rio package can be used to read/access data of almost all formats using a single “import” function. You are not even required to specify the file format as a parameter in this function, because the extension in the filename takes care…


Looking beyond read.csv( )

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My last story generated quite a buzz in my university, as it was in regards to how I did my part to fight against a global pandemic as a university student. Many of my friends were intrigued by a specific line of code in that story. That line was:

test_df <- readRDS("input_data/cleaned_alumni_2.rds")

Even with zero idea of what readRDS is,I think it would be amply clear to you that here, we are reading a data file named “cleaned_alumni_2” with a strange “.rds” extension from a folder named “input_data”.

If you arrived at the above inference yourself, congratulations, it is absolutely…

Rahul Saxena

I am a university student, support my work: https://www.buymeacoffee.com/saxenism

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