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Steps to Creating a Parquet Data Repository from WRDS

This guide will help you download WRDS data and store it locally as Parquet files using the db2pq Python package. No administrator privileges are required for these steps. As a side-effect, after running the following, you will have a working installation of Python on your system.

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Notes

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library(db2pq)
library(dplyr, warn.conflicts = FALSE)
library(farr, warn.conflicts = FALSE)

wrds_update_pq("stksecurityinfohist", "crsp", force = TRUE)
#> Forcing update based on user request.
#> Beginning file download at 2026-03-21 11:15:00 UTC.
#> Completed file download at 2026-03-21 11:15:10 UTC.
wrds_update_pq("dsf_v2", "crsp")

iangow

Setting Up a Python Environment for Quarto + Jupyter Using uv and .venv

This guide explains how to:

1. Install uv
2. Create a project with pyproject.toml
3. Use a project-local .venv
4. Add dependencies to the project
5. Sync the environment
6. Register the environment as a Jupyter kernel

iangow

library(dplyr, warn.conflicts = FALSE)
library(tidyr)
library(wrds)
#> ── Checking WRDS credentials ───────────────────────────────────── wrds 0.0.1 ──
#> ✔ Credentials found for user iangow
library(farr, warn.conflicts = FALSE)

db <- wrds_connect()

iangow

library(readxl)
library(httr2)
library(tidyverse)

# Define file URL and destination path
url <- "https://www.jobsandskills.gov.au/sites/default/files/2024-12/occupation_profiles_data_-_november_2024.xlsx"
destfile <- "occupation_profiles_data.xlsx"

# Create request with headers

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Setting up the ODBC driver on MacOS

The following instructions improve on those here by avoiding the need to:

a. Compile the Homebrew binary itself
b. Set the path for the Homebrew binary

Steps

1. Install HomeBrew using the .pkg file available here.

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libname home '.';

PROC SQL;        
  CREATE TABLE crsp_dates AS
  SELECT date, intnx('MONTH', date, 0, 'BEGINNING') AS month format=yymmdd10.
  FROM crsp.msi
  ORDER BY date;
QUIT;

DATA crsp_dates;

iangow

libname home '.';

PROC SQL;        
  CREATE TABLE crsp_dates AS
  SELECT date, intnx('MONTH', date, 0, 'BEGINNING') AS month format=yymmdd10.
  FROM crsp.msi
  ORDER BY date;
QUIT;

DATA crsp_dates;

iangow

library(farr)
library(tidyverse)

original <- read_csv("~/Downloads/WalkerDataCodeMar2021/original.csv")

original |>
  group_by(fyear) |>
  summarize(auc = auc(prob, aaer), .groups = "drop") |>
  mutate(avg_auc = mean(auc))

iangow

install.packages(c("formatR", "markdown", "tinytex", "downlit", "xml2"))