Comparison of different for loops methods in parallel distance computation with R, C, and OpenMP
Based on the formulas presented in my previous article, I evaluate looping strategies in a parallel distance computation. A classic nested loop approach is compared to a single loop approach based on a linear index used to compute the indices of the rows being evaluated.
Convert a linear index to lower triangular subscripts
In this post, I present a way to convert a linear index to lower triangular subscripts, i.e. row and column number in a triangular matrix. The proposed functions work for different numbering directions (rowwise, colwise, and diagwise), including or excluding the diagonal.
A gentle introduction to data.table
This post presents a quick introduction to data.table, an indispensable package for data wrangling in R. The main objective is to present the data.table syntax, showing how to perform basic but essential data manipulation tasks.
A data.table and pandas small stroll
data.table and pandas are two major libraries for efficient manipulation of data frames in R and Python, respectively. Being quite familiar with data.table, I wrote this post as an exercice to learn more about pandas. Commands to execute common data munging tasks are presented side-by-side to facilitate the comparison.
Learning Japanese with data.table and ggplot2
This post is about drawing hiragana and katakana tables using R, data.table, and ggplot2. The objective was to better illustrate the rules that underlie these two Japanese writing systems.
Dendrograms in R, a lightweight approach
I wanted a “lightweight” and flexible ggplot2-based solution to draw dendrograms, in particular with the possibility to highlight clusters using different branch colors. I finally ended up writing a small set of functions, using ggdendro and ggplot2 only.
Turning images into ridgeline plots
I was wondering what images turned into ridgeline plots would look like. This post illustrates some experiments performed with ggplot2 and ggridges.
A data.table and dplyr tour
data.table and dplyr are two R packages that both aim at an easier and more efficient manipulation of data frames. This article shows how they can be used to execute common data munging tasks. The commands are presented side-by-side to facilitate the comparison.
Self-studying Python and Machine Learning: 2018 in review
Both for personal interest and professional development, I started to learn Python and machine learning concepts. This article summarizes my 2018 learning path.