Open-source software dominates in certain areas. A lot of data science relies on thousands of open-source packages that are continually being improved in part because anyone can see how they work. Yet the open-source model has not taken off in academia. A lot of the publicly available code in accounting research relates to two seemingly obscure topics: Fama-French industries and winsorization. I discuss both in this note.

I use the WRDS data set `comp.g_secd` to do some benchmarking. A representative query that takes 6 minutes using SAS on the WRDS servers, takes about 1 minute using the WRDS PostgreSQL server, and about 0.2 seconds using a local parquet file. The parquet file occupies less than 4 GB on my hard drive, which compares with about 145 GB for the SAS file on the WRDS server. While creating the parquet file takes 45 minutes, this may be a reasonable trade-off for a researcher who is analysing `comp.g_secd` frequently and does not need the very latest iteration of `comp.g_secd` for research purposes.

A number of modern data frame libraries have emerged that address weaknesses of pandas. In this note, I use polars and Ibis to show how one can use these libraries to get the data into a form in which pandas can shine.

SAS might be another approach to manipulating data for pandas. My Python package wrds2pg offers a sas_to_pandas() function that can run code on the WRDS server and return the results as a pandas dataframe. While not quite as fast as using Ibis with the PostgreSQL server, SAS performs pretty well with this task.

Welcome to the digital home of our workshop! Insect ecology is uniquely messy—zero-inflated counts, overdispersed populations, and more variables than a centipede has legs. This guide is designed to take you from "R-anxiety" to "R-competence," focusing on the specific statistical hurdles we face as entomologists.