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R Code to download Datasets from Kenneth French’s famous website.

Update

With version 1.1.0 we have added the possibility to format the data sets saved in the list as tibble for direct proceeding.

Motivation

One often needs those datasets for further empirical work and it is a tedious effort to download the (zipped) csv, open and then manually separate the contained datasets. This package downloads them automatically, and converts them to a list of xts-objects that contain all the information from the csv-files.

Contributors

Original code from MasimovR https://github.com/MasimovR/. Was then heavily redacted by me.

Installation

You can install FFdownload from CRAN with

install.packages("FFdownload")

or directly from github with:

# install.packages("devtools")
devtools::install_github("sstoeckl/FFdownload")

Examples

Example 1: Monthly files

In this example, we use FFDwonload to

  1. get a list of all available monthly zip-files and save that files as temp.txt.
library(FFdownload)
temptxt <- tempfile(fileext = ".txt")
# Example 1: Use FFdownload to get a list of all monthly zip-files. Save that list as temptxt.
FFdownload(exclude_daily=TRUE,download=FALSE,download_only=TRUE,listsave=temptxt)
FFlist <- readr::read_csv(temptxt) %>% dplyr::select(2) %>% dplyr::rename(Files=x)
FFlist %>% dplyr::slice(1:3,(dplyr::n()-2):dplyr::n())
#> # A tibble: 6 × 1
#>   Files                                          
#>   <chr>                                          
#> 1 F-F_Research_Data_Factors_CSV.zip              
#> 2 F-F_Research_Data_Factors_weekly_CSV.zip       
#> 3 F-F_Research_Data_Factors_daily_CSV.zip        
#> 4 Emerging_Markets_4_Portfolios_BE-ME_OP_CSV.zip 
#> 5 Emerging_Markets_4_Portfolios_OP_INV_CSV.zip   
#> 6 Emerging_Markets_4_Portfolios_BE-ME_INV_CSV.zip
  1. Next, after inspecting the list we specify a vector inputlist to only download the datasets we actually need.
tempd <- tempdir()
inputlist <- c("F-F_Research_Data_Factors","F-F_Momentum_Factor","F-F_ST_Reversal_Factor","F-F_LT_Reversal_Factor")
FFdownload(exclude_daily=TRUE,tempd=tempd,download=TRUE,download_only=TRUE,inputlist=inputlist)
  1. In the final step we process the downloaded files (formatting the output data.frames as tibbles for direct proceeding):
tempf <- paste0(tempd,"\\FFdata.RData")
getwd()
#> [1] "D:/OneDrive - University of Liechtenstein/ROOT/Packages/ffdownload"
FFdownload(output_file = tempf, exclude_daily=TRUE,tempd=tempd,download=FALSE,
           download_only=FALSE,inputlist = inputlist, format="tbl")
#>   |                                                                              |                                                                      |   0%  |                                                                              |==================                                                    |  25%  |                                                                              |===================================                                   |  50%  |                                                                              |====================================================                  |  75%  |                                                                              |======================================================================| 100%
  1. Then we check that everything worked and output a combined file of monthly factors (only show first 5 rows).
library(tidyverse)
library(timetk)
load(file = tempf)
FFdata$`x_F-F_Research_Data_Factors`$monthly$Temp2 %>% 
  left_join(FFdata$`x_F-F_Momentum_Factor`$monthly$Temp2, by="date") %>%
  left_join(FFdata$`x_F-F_LT_Reversal_Factor`$monthly$Temp2,by="date") %>%
  left_join(FFdata$`x_F-F_ST_Reversal_Factor`$monthly$Temp2,by="date") %>% head()
#> # A tibble: 6 × 8
#>   date      Mkt.RF   SMB   HML    RF   Mom LT_Rev ST_Rev
#>   <yearmon>  <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl>
#> 1 Jul 1926    2.96 -2.56 -2.43  0.22    NA     NA  -1.87
#> 2 Aug 1926    2.64 -1.17  3.82  0.25    NA     NA   1.43
#> 3 Sep 1926    0.36 -1.4   0.13  0.23    NA     NA  -0.17
#> 4 Okt 1926   -3.24 -0.09  0.7   0.32    NA     NA  -2.11
#> 5 Nov 1926    2.53 -0.1  -0.51  0.31    NA     NA   1   
#> 6 Dez 1926    2.62 -0.03 -0.05  0.28    NA     NA   2.01
  1. No we do the same with annual data:
FFfive <- FFdata$`x_F-F_Research_Data_Factors`$annual$`annual_factors:_january-december` %>% 
  left_join(FFdata$`x_F-F_Momentum_Factor`$annual$`january-december` ,by="date") %>%
  left_join(FFdata$`x_F-F_LT_Reversal_Factor`$annual$`january-december`,by="date") %>%
  left_join(FFdata$`x_F-F_ST_Reversal_Factor`$annual$`january-december` ,by="date") 
FFfive %>% head()
#> # A tibble: 6 × 8
#>   date      Mkt.RF    SMB    HML    RF   Mom LT_Rev ST_Rev
#>   <yearmon>  <dbl>  <dbl>  <dbl> <dbl> <dbl>  <dbl>  <dbl>
#> 1 Dez 1927   29.5   -2.04  -4.54  3.12  24.1  NA    -17.7 
#> 2 Dez 1928   35.4    4.51  -6.17  3.56  29.1  NA    -10.8 
#> 3 Dez 1929  -19.5  -30.7   11.7   4.75  21.1  NA    -15.0 
#> 4 Dez 1930  -31.2   -5.17 -11.5   2.41  25.7  NA     -0.86
#> 5 Dez 1931  -45.1    3.7  -14.0   1.07  23.8  -3.24  24.2 
#> 6 Dez 1932   -9.39   4.4   11.1   0.96 -21.8   9.27  30.5
  1. Finally we plot wealth indices for 6 of these factors:
FFfive %>% 
  pivot_longer(Mkt.RF:ST_Rev,names_to="FFVar",values_to="FFret") %>% mutate(FFret=FFret/100,date=as.Date(date)) %>% 
  filter(date>="1960-01-01",!FFVar=="RF") %>% group_by(FFVar) %>% arrange(FFVar,date) %>%
  mutate(FFret=ifelse(date=="1960-01-01",1,FFret),FFretv=cumprod(1+FFret)-1) %>% 
  ggplot(aes(x=date,y=FFretv,col=FFVar,type=FFVar)) + geom_line(lwd=1.2) + scale_y_log10() +
  labs(title="FF5 Factors plus Momentum", subtitle="Cumulative wealth plots",ylab="cum. returns") + 
  scale_colour_viridis_d("FFvar") +
  theme_bw() + theme(legend.position="bottom")
#> Warning in self$trans$transform(x): NaNs wurden erzeugt
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Removed 11 row(s) containing missing values (geom_path).

Acknowledgment

I am grateful to Kenneth French for providing all this great research data on his website! Our lives would be so much harder without this boost for productivity. I am also grateful for the kind conversation with Kenneth with regard to this package: He appreciates my work on this package giving others easier access to his data sets!