Basic Usage

library(dplyr)
library(tidyr)
library(lubridate)
library(frenchdata)
library(ggplot2)

The frenchdata package provides functions to download the finance data sets provided by the data library website of Prof. Kenneth French.

We can visit the website using the function:

browse_french_site()

this will open the page on your default browser.

As a first step we get the list of data sets that are currently available to download:

data_sets <- get_french_data_list()

The get_french_data_list() function returns an object of the S3 class french_data_list:

data_sets
#> 
#> ── Kenneth's French data library
#> ℹ Information collected from: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html on Sun May 16 12:14:07 2021
#> 
#> ── Files list
#> # A tibble: 297 x 3
#>    name                        file_url                   details_url           
#>    <chr>                       <chr>                      <chr>                 
#>  1 Fama/French 3 Factors       ftp/F-F_Research_Data_Fac… Data_Library/f-f_fact…
#>  2 Fama/French 3 Factors [Wee… ftp/F-F_Research_Data_Fac… Data_Library/f-f_fact…
#>  3 Fama/French 3 Factors [Dai… ftp/F-F_Research_Data_Fac… Data_Library/f-f_fact…
#>  4 Fama/French 5 Factors (2x3) ftp/F-F_Research_Data_5_F… Data_Library/f-f_5_fa…
#>  5 Fama/French 5 Factors (2x3… ftp/F-F_Research_Data_5_F… Data_Library/f-f_5_fa…
#>  6 Portfolios Formed on Size   ftp/Portfolios_Formed_on_… Data_Library/det_port…
#>  7 Portfolios Formed on Size … ftp/Portfolios_Formed_on_… Data_Library/det_port…
#>  8 Portfolios Formed on Size … ftp/Portfolios_Formed_on_… Data_Library/det_port…
#>  9 Portfolios Formed on Book-… ftp/Portfolios_Formed_on_… Data_Library/det_form…
#> 10 Portfolios Formed on Book-… ftp/Portfolios_Formed_on_… Data_Library/det_form…
#> # … with 287 more rows

Searching for a data set

To download a data set we need to identify its name first from the list data_sets obtained previously. A simple way of performing this search is to use the View() function provided by RStudio on the files_list element that contains the the files list:

View(data_sets$files_list)

Using the filter on Rstudio viewer

and use the filter to narrow down the search, for instance if I’m looking for the 5 factors I can quickly identify the correct data set name.

Downloading a data set

To download a specific data, for instance the monthly Fama/French 5 Factors (2x3) set we can use:

ff_5_factors <- 
  download_french_data("Fama/French 5 Factors (2x3)")

This will return an object of class french_dataset:

ff_5_factors
#> 
#> ── Kenneth's French data set
#> ℹ This file was created by CMPT_ME_BEME_OP_INV_RETS using the 202103 CRSP database. The 1-month TBill return is from Ibbotson and Associates Inc. 
#> 
#> Information collected from: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/ftp/F-F_Research_Data_5_Factors_2x3_CSV.zip on Sun May 16 12:14:08 2021
#> 
#> ℹ For details on the data set call the function `browse_details_page()` on this object
#> 
#> ── Subsets in the file:
#> # A tibble: 2 x 2
#>   name                               data                   
#>   <chr>                              <list>                 
#> 1 ""                                 <spec_tbl_df [693 × 7]>
#> 2 "Annual Factors: January-December" <spec_tbl_df [57 × 7]>

The file contains two subsets of data:

ff_5_factors$subsets
#> # A tibble: 2 x 2
#>   name                               data                   
#>   <chr>                              <list>                 
#> 1 ""                                 <spec_tbl_df [693 × 7]>
#> 2 "Annual Factors: January-December" <spec_tbl_df [57 × 7]>

The first one is the default one and is unnamed, and the second one is on “Annual Factors: January-December”. We can access the data on the first subset:

monthly_ff_5_factors <- ff_5_factors$subsets$data[[1]]
monthly_ff_5_factors
#> # A tibble: 693 x 7
#>      date `Mkt-RF`   SMB   HML   RMW   CMA    RF
#>     <dbl>    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 196307    -0.39 -0.45 -0.94  0.66 -1.15 0.27 
#>  2 196308     5.07 -0.82  1.82  0.4  -0.4  0.25 
#>  3 196309    -1.57 -0.48  0.17 -0.76  0.24 0.27 
#>  4 196310     2.53 -1.3  -0.04  2.75 -2.24 0.290
#>  5 196311    -0.85 -0.85  1.7  -0.45  2.22 0.27 
#>  6 196312     1.83 -1.9  -0.06  0.07 -0.3  0.290
#>  7 196401     2.24  0.08  1.53  0.22  1.5  0.3  
#>  8 196402     1.54  0.31  2.86  0.06  0.85 0.26 
#>  9 196403     1.41  1.4   3.37 -2.01  2.93 0.31 
#> 10 196404     0.1  -1.5  -0.66 -1.35 -1.08 0.290
#> # … with 683 more rows

We can now browse use this data.frame directly, for instance, to plot all each of the factors and the risk-free rate overtime:

monthly_ff_5_factors %>% 
  mutate(date = ym(date)) %>% 
  pivot_longer(cols = -date, 
               names_to = "factor", 
               values_to = "value") %>% 
  ggplot(data = .,
         mapping = aes(x = date, y = value, 
                       group = factor, 
                       color = factor)) +
  geom_line() + 
  labs(caption = "Source: Kenneth French Data Library") +
  facet_wrap( ~ factor)

We can browse the details page for this specific data set using:

browse_details_page(ff_5_factors)

Downloading and saving a data set

We can also save the original uncompressed file when downloading and reading it, by specifying a valid path name:

ff_5_factors <- 
  download_french_data("Fama/French 5 Factors (2x3)",
                       dir = ".",
                       dest_file = "fama_french_5_factors.zip")

This will download the file, read it, assign the result to the object ff_5_factors, and save the results to the file fama_french_5_factors.zip on the current directory.

If we prefer to use the original file name just leave the dest_file parameter unset:

ff_5_factors <- 
  download_french_data("Fama/French 5 Factors (2x3)",
                       dir = ".")