Tagged as "dataframe"

Filter, partition, and sort dataframes in Chez Scheme

This post is the fourth in a series on the dataframe library for Chez Scheme. In this post, I will contrast the dataframe library with functions from the dplyr R package for filtering, partitioning, and sorting dataframes.

Split, bind, and append dataframes in Chez Scheme

This post is the third 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 splitting, binding, and appending dataframes.

Select, drop, and rename dataframe columns in Chez Scheme

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 the dplyr R package for selecting, dropping, and renaming columns.

A dataframe record type for Chez Scheme

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. Dataframes are column-oriented, tabular data structures useful for data analysis found in several languages including R, Python, Julia, and Go.

Reading and writing JSON files in R and Chez Scheme

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.

Reading and writing JSON files in R and Racket

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.1 My default was set by primarily working in R, where reading and writing CSV files plays a central role in data analysis.

Reading CSV files in R and Racket

In a previous post, I wrote about reading and writing data to file while retaining the structure and attributes of the data (i.e., data serialization). However, I more commonly pass data around as text files (usually, CSV files).