DISCLAIMER: Before you begin, be aware that the protagonist of any fictional account that this write-up might contain would be a male and would be referred to as a ‘he’ or at times, simply as ‘coder’. That’s not because the … Continue reading Open Source: The itch, the hustle and the merge
Always code as if the guy who ends up maintaining your code will be a violent psychopath who knows where you live. – Martin Golding In previous posts, I had discussed how Rperform can be used to obtain and visualize package performance data. However, real-world software development is a collaborative process. Thus, automating performance testing for your package is not only a good idea, it’s a critical one; testing projects locally might not be good enough. This post will cover usage of Rperform with Travis CI for automated performance testing. More importantly, we will be able to assess performance impact … Continue reading Analyze pull requests and Travis builds using Rperform
Developer: Akash Tandon Mentors: Joshua Ulrich, Toby Dylan Hocking Official Project Link: Rperform: performance analysis of R package code This project meant to deal primarily with development of Rperform’s functionalities to allow developers to obtain potential performance impacts of a pull request (PR) without having to merge, extension of the package’s existing performance metric measurement and visualization functions, and development of a coherent user interface for developers to interact with. About Rperform Rperform is a package that allows R developers to track and visualize quantitative performance metrics of their code. It focuses on providing changes in a package’s performance metrics, … Continue reading GSoC 2016 Report – Rperform
I recently participated in a weekend-long data science hackathon, titled ‘The Smart Recruits’. Organized by the amazing folks at Analytics Vidhya, it saw some serious competition. Although my performance can be classified as decent at best (47 out of 379 participants), it was among the more satisfying ones I have participated in on both AV (profile) and Kaggle (profile) over the last few months. Thus, I decided it might be worthwhile to try and share some insights as a data science autodidact. The problem The competition required us to use historical data to create a model to help an organization … Continue reading Data Science Competitions 101: Anatomy and Approach
“In God we trust. All others must bring data.” – W. Edwards Deming In a previous post, I had discussed how Rperform uses the grammar of graphics approach to visualize an R package’s performance in terms of runtime and memory usage. The visualizations contribute significantly towards Rperform’s mission to allow package developers to quantify, analyze and visualize performance. However, at times you, the developer, might want to play with the data instead to perform analysis of your own. After going through this post, that is exactly what you would be able to do. Background If you are new to Rperform, consider … Continue reading Obtaining package performance data using Rperform
The greatest value of a picture is when it forces us to notice what we never expected to see. ―John Tukey Replace ‘picture’ in the above quote with ‘data visualization’ and it will still ring true; maybe even more so. To provide valuable insights to package developers is exactly what Rperform strives to do through it’s visualization functions. Background If you are new to Rperform, consider going through it’s Github README once. In a nutshell, Rperform is an R package that allows package developers to track and visualize quantitative performance metrics of their code, over time. It focuses on providing … Continue reading Visualizing package performance using Rperform and Grammar of Graphics
Rperform had started as a GSoC 2015 project with an aim to “to provide a package with functions that make it easy for R package developers to track quantitative performance metrics of their code, over time.” Much of the functionality required for the same was implemented over the course of last summer. This included various performance visualization functions and integration with the Travis-CI workflow, among other things. The project has been accepted into the GSoC program again under the organization, R project for statistical computing. I will be working on it over the summer with my mentors, Toby Dylan Hocking … Continue reading Rperform in Google Summer of Code 2016
tl;dr Code for scraping GSoC data from Google Developers website for the years 2005-08 and storing it as csv files can be found here: GSoC data digging on Github Two csv files will be created: org_numbers.csv (containing information about the … Continue reading Digging into GSoC Data: Scraping 101 (Part 1)
For the last two years or so, I have been involved with Python in an on-off relationship of sorts. I possessed syntactical familiarity, had completed a MOOC and couple of web projects using Django. I had also read up on … Continue reading Python in the open!
There’s a plethora of generic content regarding GSoC preparation already available on the internet.* This blog post will concentrate on specific tips which should help anyone looking to get involved with The R-Project, or any R-centric Open-Source project, begin their journey. *The post assumes basic programming knowledge (loops, functions, etc.) and familiarity with git/github.* The basics To begin with, familiarity with the basic R commands/constructs (function, apply family, etc) would be required. Previous experience of writing small-to-mid sized programs and a good IDE can really come in handy. RStudio is highly recommended. The following resources should help in beginning your R journey. -> … Continue reading R, Open Source and GSoC
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