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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-"
)
```
# FFdownload <a href='https://github.com/sstoeckl/FFdownload'><img src='man/figures/logo.png' align="right" height="139" /></a>
<!-- badges: start -->
[](https://www.repostatus.org/#active)
[](https://github.com/sstoeckl/ffdownload/actions/workflows/R-CMD-check.yaml)
[](https://cran.r-project.org/package=FFdownload)
[](https://CRAN.R-project.org/package=FFdownload)
[](https://cranlogs.r-pkg.org/badges/grand-total/FFdownload)
[](https://cran.r-project.org/package=FFdownload)
[](https://cran.r-project.org/package=FFdownload)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://sstoeckl.github.io/ffdownload/)
<!-- badges: end -->
# `R` Code to download Datasets from [Kenneth French's famous website](http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html).
# Update
Version 1.1.1 corrects a small error for publication on CRAN.
## 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
```{r cran-installation, eval = FALSE}
install.packages("FFdownload")
```
or directly from github with:
```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("sstoeckl/FFdownload")
```
## Examples
### Example 0: Easy Access
This is the quick-starter example. It just retrieves the data and provides it for easy usage!
```{r example_0, eval=TRUE, message=FALSE}
library(FFdownload)
library(tidyverse)
FFdownload(inputlist = c("F-F_Research_Data_5_Factors_2x3"), output_file = "FFdata.RData", format = "tbl")
load("FFdata.RData")
FFdata$`x_F-F_Research_Data_5_Factors_2x3`$monthly$Temp2 |>
tidyr::pivot_longer(cols = -date, names_to = "FFFactors", values_to = "Value") |>
group_by(FFFactors) |> mutate(Price=cumprod(1+Value/100)) |>
ggplot2::ggplot(aes(x = date, col = FFFactors, y = Price)) + geom_line(lwd=1.2) +
theme_bw() + theme(legend.position="bottom")
```
### 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*.
```{r example_1a, eval=TRUE, message=FALSE}
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)
```
```{r example_1b, message=FALSE, warning=FALSE}
FFlist <- readr::read_csv(temptxt) %>% dplyr::select(2) %>% dplyr::rename(Files=x)
FFlist %>% dplyr::slice(1:3,(dplyr::n()-2):dplyr::n())
```
2. Next, after inspecting the list we specify a vector `inputlist` to only download the datasets we actually need.
```{r example_2, message=FALSE, warning=FALSE}
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)
```
3. In the final step we process the downloaded files (formatting the output data.frames as tibbles for direct proceeding):
```{r example_3a, message=FALSE}
tempf <- paste0(tempd,"\\FFdata.RData")
getwd()
FFdownload(output_file = tempf, exclude_daily=TRUE,tempd=tempd,download=FALSE,
download_only=FALSE,inputlist = inputlist, format="tbl")
```
4. Then we check that everything worked and output a combined file of monthly factors (only show first 5 rows).
```{r example_3b, message=FALSE}
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()
```
5. No we do the same with annual data:
```{r example_3c, message=FALSE}
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()
```
6. Finally we plot wealth indices for 6 of these factors:
```{r FFpic}
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")
```
# 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!