The tidyfinance Package

The companion tidyfinance package bundles the small, repetitive tasks that show up in almost every project — chiefly one-line downloads of common datasets behind a single download_data call. This keeps the later chapters focused on ideas rather than plumbing. Use the R | Python toggle to switch.

Installing and exploring

After installing, list_supported_types shows everything the package can fetch by name — Fama–French factors, q-factors, macro predictors, WRDS tables, and more.

install.packages("tidyfinance")
library(tidyfinance)

list_supported_types()

# pip install tidyfinance
import tidyfinance as tf

tf.list_supported_types()

One-line downloads

Each dataset is one call: name the type and (optionally) a date range, and you get back a tidy table ready to join. No remembering URLs or file formats.

download_data(type = "factors_ff_3_monthly")

tf.download_data(domain="factors_ff_3_monthly")

download_data(
  type = "factors_ff_3_monthly",
  start_date = "2000-01-01",
  end_date = "2020-12-31"
)

tf.download_data(
  domain="factors_ff_3_monthly",
  start_date="2000-01-01",
  end_date="2020-12-31"
)

Why wrap it

Hiding boilerplate behind named functions has two payoffs: scripts read like the analysis you intend, and a fix to a download quirk lives in one place rather than scattered across files. The same download_data interface reappears in every data chapter, which is what keeps them concise.

Study notes following the Tidy Finance curriculum by Scheuch, Voigt, Weiss, and Frey. Prose is my own; the R/Python code is reproduced from the book's open-source source, licensed CC BY-NC-SA 4.0.