I have previously written about how to read and write JSON files in R and Racket. In re-reading that old post, I'm struck by how it shows me tinkering without understanding. Now that I have pivoted from learning Racket to learning Chez Scheme, I'm revisiting JSON as a data serialization format and actually reading about JSON instead of just playing with JSON packages.
I was reading a blog post that mentioned that Julia has "[w]eak conventions about namespace pollution" and it got me thinking about how I manage namespace pollution in R and Chez Scheme. The short answer is that I don't. I developed bad habits in R centered around writing overly terse code.
I've been an enthusiastic Mac user for about 12 years, but hardware problems with a recent MacBook Pro and friction surrounding the Catalina upgrade pushed me to evaluate other Unix-like systems. I pulled out an old ASUS laptop that originally had Windows 7(?) installed, but was most recently running CloudReady. I first tried installing FreeBSD because it seemed like an intriguing alternative, but the installation failed on the old hardware. I then tried Debian, but also failed. Finally, I reached for Ubuntu and, true to its reputation as being beginner friendly, was able to successfully complete the installation .
I recently wrote a little library,
chez-docs, to make accessing documentation easier while learning Chez Scheme (blog post). The main procedure,
chez-docs only returns results for exact matches with
proc . To aid in discovery, I've added a procedure,
find-proc, that provides exact and approximate matching of search strings.
In the process of learning Chez Scheme, I've missed R's ability to quickly pull up documentation from the console via
?. I've toyed with the idea of trying to format the contents of the Chez Scheme User's Guide for display in the REPL (similar to Clojure Docs). But that is probably too big of a task for me at this point. It recently occurred to me, though, that I can write a simple library,
chez-docs, with only one procedure,
doc, that will make it a bit easier to access the Chez Scheme User's Guide.
I have added functionality for reading and writing CVS files to my Chez Scheme library,
chez-stats. In a previous post, I compared reading CSV files in R and Racket and made the following observation.
Recently, I switched from learning Racket to Chez Scheme. I wanted to try to repeat some of my previous Racket exercises in Chez Scheme, but quickly ran into a barrier when my first choice required drawing random variates from a normal distribution. I looked for existing Chez Scheme libraries but came up empty. I considered SRFI 27: Sources of Random Bits, which includes example code for generating random numbers from a normal distribution, and reached out for guidance. Ultimately, I decided that it would be a good exercise to write a library for generating random variates from different distributions. As I started to write the random variate procedures, I realized that I minimally needed procedures for calculating mean and variance to test the output of the random variate procedures. And, thus, the scope of the library started to expand and the
chez-stats library was born.
I recently decided to switch my attention from learning Racket to Chez Scheme. One of the reasons that I chose Racket was because of how easy it is to get up and running. Setting up a development environment for Chez requires jumping through a few more hoops. In this post, I document those hoops. Disclaimer: The suggestions in this post may not represent best practice. I will update the post as I become more experienced with Chez and Emacs.
Over the last 6 months, I have been learning Racket in my free time. One of my first posts on this blog laid out my reasons for choosing Racket. The relatively low barrier to entry (e.g., easy installation of Racket and packages, DrRacket IDE, good docs, etc.) allowed me to build momentum with Racket and I was feeling satisfied with my choice of Racket as an alternative to Python.
In a previous post, I used GUI toolkits to make progress bars in R and Racket. However, I usually prefer the ASCII progress bars of the
progress package in R. The
progress package includes several options for formatting the progress bar. I particularly like the option to display the estimated time remaining. However, for this post, we will stick to the basic progress bar.