Using the tidyfinance package

# Using the `tidyfinance` package

`tidyfinance` is an R package on CRAN that contains a set of helper functions for empirical research in financial economics, addressing a variety of topics covered in Tidy Finance with R (TFWR). We designed the package to provide easy shortcuts for the applications that we discuss in the book. If you want to inspect the details of the package or propose new features, feel free to visit the package repository on Github.

In this vignette, we demonstrate the features of the initial release. We decided to focus on functions that allow you to download the data that we use in TFWR.

## Install the package

You can install the released version of `tidyfinance` from CRAN via:

    install.packages("tidyfinance")

You can install the development version of tidyfinance from GitHub using the `pak` package:

    pak::pak("tidy-finance/r-tidyfinance")

## Download data

Let’s start by loading the package

    library(tidyfinance)

The main function is `download_data(type, start_date, end_date)` with supported type:

    list_supported_types()
    #> # A tibble: 320 × 3
    #>    type                     dataset_name                   domain
    #>    <chr>                    <chr>                          <chr>
    #>  1 factors_q5_daily         q5_factors_daily_2024          Global Q
    #>  2 factors_q5_weekly        q5_factors_weekly_2024         Global Q
    #>  3 factors_q5_weekly_w2w    q5_factors_weekly_w2w_2024     Global Q
    #>  4 factors_q5_monthly       q5_factors_monthly_2024        Global Q
    #>  5 factors_q5_quarterly     q5_factors_quarterly_2024      Global Q
    #>  6 factors_q5_annual        q5_factors_annual_2024         Global Q
    #>  7 factors_ff_3_monthly     Fama/French 3 Factors          Fama-French
    #>  8 factors_ff_3_weekly      Fama/French 3 Factors [Weekly] Fama-French
    #>  9 factors_ff_3_daily       Fama/French 3 Factors [Daily]  Fama-French
    #> 10 factors_ff_5_2x3_monthly Fama/French 5 Factors (2x3)    Fama-French
    #> # ℹ 310 more rows

So, for instance, if you want to download monthly Fama-French Three-Factor data, you can call:

    download_data("factors_ff_3_monthly", "2020-01-01", "2020-12-31")

Under the hood, the function uses the `frenchdata` package (see its documentation here) and applies some cleaning steps, as in TFWR. If you haven’t installed `frenchdata` yet, you’ll get prompted to install it first before you can download this specific data type.

You can also access q-Factor data in this way, by calling:

    download_data("factors_q5_daily", "2020-01-01", "2020-12-31")

To ensure that we can extend the functionality of the download functions for specific types, we also provide domain-specific download functions. The `download_data("factors_ff_3_monthly")` actually calls `download_data_factors("factors_ff_3_monthly")`, which in turn calls `download_data_factors_ff("factors_ff_3_monthly")`. Why did we decide to have these nested function approach?

Suppose that the q-Factor data changes its URL path and our original function does not work anymore. In this case, you can replace the default `url` value in `download_data_factors_q(type, start_date, end_date, url)` to apply the usual cleaning steps.

This feature becomes more apparent for other data sources such as `wrds_crsp_monthly`. Note that you need to have valid WRDS credentials and need to set them correctly (check `?set_wrds_connection()` and WRDS, CRSP, and Compustat in TFWR). If you want to download the standard monthly CRSP data, you can call:

    download_data("wrds_crsp_monthly", "2020-01-01", "2020-12-31")

If you want to add further columns, you can add them via `additional_columns` to `download_data_wrds_crsp()`, for instance:

    download_data_wrds_crsp(
      "wrds_crsp_monthly",
      "2020-01-01",
      "2020-12-31",
      additional_columns = "mthvol"
    )

Note that the function downloads CRSP v2 as default, as we do in our book since February 2024. If you want to download the old version of CRSP before the update, you can use the `version = v1` parameter in `download_data_wrds_crsp()` .

As another example, you can do the same for Compustat:

    download_data_wrds_compustat(
      "wrds_compustat_annual",
      "2000-01-01",
      "2020-12-31",
      additional_columns = c("acoxar", "amc", "aldo")
    )

Check out the list of supported types and the corresponding download functions for more information on the respective customization options. We decided to provide limited functionality for the initial release on purpose and rather respond to community request than overengineer the package from the start.

## Browse content from TFWR

We include functions to check out content from TFWR in your browser. If you want to list all available R chapters, simply call the following function:

    list_tidy_finance_chapters()
    #>  [1] "setting-up-your-environment"
    #>  [2] "introduction-to-tidy-finance"
    #>  [3] "accessing-and-managing-financial-data"
    #>  [4] "wrds-crsp-and-compustat"
    #>  [5] "trace-and-fisd"
    #>  [6] "other-data-providers"
    #>  [7] "beta-estimation"
    #>  [8] "univariate-portfolio-sorts"
    #>  [9] "size-sorts-and-p-hacking"
    #> [10] "value-and-bivariate-sorts"
    #> [11] "replicating-fama-and-french-factors"
    #> [12] "fama-macbeth-regressions"
    #> [13] "fixed-effects-and-clustered-standard-errors"
    #> [14] "difference-in-differences"
    #> [15] "factor-selection-via-machine-learning"
    #> [16] "option-pricing-via-machine-learning"
    #> [17] "parametric-portfolio-policies"
    #> [18] "constrained-optimization-and-backtesting"
    #> [19] "wrds-dummy-data"
    #> [20] "cover-and-logo-design"
    #> [21] "clean-enhanced-trace-with-r"
    #> [22] "proofs"
    #> [23] "hex-sticker"
    #> [24] "changelog"

The function returns a character vector containing the names of the chapters available in TFWR. If you want to look at a specific chapter, you can call:

    open_tidy_finance_website("beta-estimation")

This opens either the specific chapter you requested or the main index page in your default web browser.

## Regression helpers

We discuss winsorization in TFWR, so we figured providing this function could be useful:

    library(dplyr)

    set.seed(123)
    data <- tibble(x = rnorm(100)) |>
      arrange(x)

    data |>
      mutate(x_winsorized = winsorize(x, 0.01))
    #> # A tibble: 100 × 2
    #>        x x_winsorized
    #>    <dbl>        <dbl>
    #>  1 -2.31        -1.97
    #>  2 -1.97        -1.97
    #>  3 -1.69        -1.69
    #>  4 -1.55        -1.55
    #>  5 -1.27        -1.27
    #>  6 -1.27        -1.27
    #>  7 -1.22        -1.22
    #>  8 -1.14        -1.14
    #>  9 -1.12        -1.12
    #> 10 -1.07        -1.07
    #> # ℹ 90 more rows

If you rather want to replace the bottom and top quantiles of your distribution with missing values, then you can use `trim()`

    data |>
      mutate(x_trimmed = trim(x, 0.01))
    #> # A tibble: 100 × 2
    #>        x x_trimmed
    #>    <dbl>     <dbl>
    #>  1 -2.31     NA
    #>  2 -1.97     -1.97
    #>  3 -1.69     -1.69
    #>  4 -1.55     -1.55
    #>  5 -1.27     -1.27
    #>  6 -1.27     -1.27
    #>  7 -1.22     -1.22
    #>  8 -1.14     -1.14
    #>  9 -1.12     -1.12
    #> 10 -1.07     -1.07
    #> # ℹ 90 more rows

We also discuss the importance of providing summary statistics of your data, so there is also a function for that:

    create_summary_statistics(data, x, detail = TRUE)
    #> # A tibble: 1 × 15
    #>   variable     n   mean    sd   min   q01   q05   q10    q25    q50   q75   q90
    #>   <chr>    <int>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl>
    #> 1 x          100 0.0904 0.913 -2.31 -1.97 -1.27 -1.07 -0.494 0.0618 0.692  1.26
    #> # ℹ 3 more variables: q95 <dbl>, q99 <dbl>, max <dbl>

## Experimental functions

We have two more experimental functions in the sense that it is unclear in which direction they might evolve. First you can assign portfolios based on a sorting variable using `assign_portfolio()`:

    data <- tibble(
      id = 1:100,
      exchange = sample(c("NYSE", "NASDAQ"), 100, replace = TRUE),
      market_cap = runif(100, 1e6, 1e9)
    )

    data |>
      mutate(
        portfolio = assign_portfolio(
          pick(everything()),
          "market_cap",
          list(n_portfolios = 5, breakpoint_exchanges = "NYSE")
        )
      )
    #> # A tibble: 100 × 4
    #>       id exchange market_cap portfolio
    #>    <int> <chr>         <dbl>     <int>
    #>  1     1 NASDAQ   784790691.         4
    #>  2     2 NASDAQ    10420475.         1
    #>  3     3 NASDAQ   779286817.         4
    #>  4     4 NYSE     729661261.         4
    #>  5     5 NASDAQ   630501721.         4
    #>  6     6 NASDAQ   481429919.         3
    #>  7     7 NASDAQ   157480215.         1
    #>  8     8 NYSE       9207304.         1
    #>  9     9 NASDAQ   453005936.         3
    #> 10    10 NASDAQ   492801035.         3
    #> # ℹ 90 more rows

Second, you can estimate the coefficients of a linear model specified by one or more independent variable using `estimate_model()`:

    data <- tibble(
      ret_excess = rnorm(100),
      mkt_excess = rnorm(100),
      smb = rnorm(100),
      hml = rnorm(100)
    )

    estimate_model(data, "ret_excess ~ mkt_excess + smb + hml")
    #> # A tibble: 1 × 4
    #>   intercept mkt_excess     smb    hml
    #>       <dbl>      <dbl>   <dbl>  <dbl>
    #> 1     0.123    -0.0399 -0.0287 0.0207

## Concluding remarks

We are curious to learn in which direction we should extend the package, so please consider opening an issue in the package repository. For instance, we could support more data sources, add more parameters to the `download_*` family of functions, or we could put more emphasis on the generality of portfolio assignment or other modeling functions.