In three previous posts, I wrote about different programming languages that I have considered learning. I mentioned about 15 different languages in those posts. F# was not on the list. Because my background is in R, I thought I was better off sticking to learning dynamically typed languages at this point. Moreover, I hold a longstanding bias against Microsoft and Windows and that bias was easy to transfer to F#.

When I started learning Scheme (R6RS), I took the common approach of learning a new language by implementing features from familiar languages (namely R). That approach sent me down the path of writing the dataframe library and porting gnuplot-pipe from Chicken to Chez Scheme. Those two libraries now allow me to conduct simple exploratory data analysis (EDA) in Scheme that should feel relatively familiar to R programmers. In this post, I will work through a simple example, which mostly serves to reinforce how much better suited R is for these types of tasks.

This post is part of a series on the dataframe library for Scheme (R6RS). In this post, I will contrast the dataframe library with functions from base R and the dplyr package for modifying and aggregating dataframes.

Akku is a package manager for Scheme that currently supports numerous R6RS and R7RS Scheme implementations [1]. I was slow to embrace Akku because I encountered some initial friction with installation and setup. Moreover, coming from R, I was more familiar with a global package management model than Akku's project-based workflow. In the meantime, I was content to manually manage the few libraries that I had downloaded from different repos and placed in a directory found by Chez's (library-directories).

As an impatient person, I typically use progress bars for any code that takes more than a few minutes to run. In a previous post, I wrote about creating ASCII progress bars in R and Racket. The Racket version depended on the raart module, which "provides an algebraic model of ASCII that can be used for art, user interfaces, and diagrams." Because I'm not aware of any such library for Chez Scheme [1], I was left feeling stuck.

One of the advantages of open source software is being able to view, review, and learn from source code. Both R and Chez Scheme provide tools for accessing source code.

This post is the fourth in a series on the dataframe library for Scheme (R6RS). In this post, I will contrast the dataframe library with functions from the dplyr R package for filtering, partitioning, and sorting dataframes. And discuss implementation decisions in the dataframe library.

This post is the third in a series on the dataframe library for Scheme (R6RS). In this post, I will contrast the dataframe library with functions from base R and the dplyr package for splitting, binding, and appending dataframes.

In my second post on this blog, I wrote about my interest in learning new programming languages and my thoughts on how to choose the next language to learn. It's been over a year since that post and I thought it would be worthwhile to reflect on what I've learned.

This post is the second in a series on the dataframe library for Scheme (R6RS). In this post, I will contrast the dataframe library with functions from the dplyr R package for selecting, dropping, and renaming columns.