This post is the second in a series on the dataframe library for Chez Scheme. In this post, I will contrast the dataframe library with functions from base R and the dplyr package for selecting, dropping, and renaming columns.
As an exercise in my Chez Scheme learning journey, I have implemented a dataframe record type and procedures to work with the dataframe record type.
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.
In learning about reading CSV files in Racket, I have started to reconsider whether storing small(ish) datasets in CSV files is the best default behavior.
In a previous post, I wrote about reading and writing data to file while retaining the structure and attributes of the data (i.
When programming in R, I generally pass data around by reading and writing text files (typically, CSV files). The ubiquity of CSV files means that many different types of software will open them easily (e.
When building a simulation model in R, I might want to group related input parameters into a data structure. For example, in a life cycle model with resident and anadromous fish, you might use different fecundity parameters for each life history type.