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How-to xts

This vignette demonstrates the step-by-step FFdownload() workflow — browse available datasets, download, and process separately — using xts as the output format. This separation is valuable for reproducible research: you can save a dated snapshot of the raw zip files and re-process them at any time without re-downloading.

For a simpler one-step approach (especially for interactive use), see vignette("FFD-tibble-how-to") and the FFget() function.

library(FFdownload)
outd     <- paste0("data/", format(Sys.time(), "%F_%H-%M"))
outfile  <- paste0(outd, "FFData_xts.RData")
listfile <- paste0(outd, "FFList.txt")

Step 1: Browse available datasets

Option B — listsave (classic approach, still supported)

FFdownload(exclude_daily=TRUE, download=FALSE, download_only=TRUE, listsave=listfile)
#> Step 1: getting list of all the csv-zip-files!
read.delim(listfile, sep=",")[c(1:4, 73:74), ]
#>     X                                        x
#> 1   1        F-F_Research_Data_Factors_CSV.zip
#> 2   2 F-F_Research_Data_Factors_weekly_CSV.zip
#> 3   3  F-F_Research_Data_Factors_daily_CSV.zip
#> 4   4  F-F_Research_Data_5_Factors_2x3_CSV.zip
#> 73 73              F-F_Momentum_Factor_CSV.zip
#> 74 74        F-F_Momentum_Factor_daily_CSV.zip

Verifying your search strings with FFmatch()

Before downloading, use FFmatch() to confirm that your (partial) names map to the datasets you intend:

FFmatch(c("F-F_Research_Data_Factors_CSV", "F-F_Momentum_Factor_CSV"))

Step 2: Download selected datasets

inputlist <- c("F-F_Research_Data_Factors_CSV","F-F_Momentum_Factor_CSV")
FFdownload(exclude_daily=TRUE, tempd=outd, download=TRUE, download_only=TRUE, inputlist=inputlist)
#> Step 1: getting list of all the csv-zip-files!
#> Step 2: Downloading 2 zip-files
list.files(outd)
#> [1] "F-F_Momentum_Factor.csv"           "F-F_Momentum_Factor_CSV.zip"      
#> [3] "F-F_Research_Data_Factors.csv"     "F-F_Research_Data_Factors_CSV.zip"

The action parameter (new in v1.2.0) is equivalent and more readable:

FFdownload(exclude_daily=TRUE, tempd=outd, action="download_only", inputlist=inputlist)

The cache_days parameter prevents re-downloading files that are already fresh:

# Reuse any cached file younger than 7 days; only download if stale
FFdownload(exclude_daily=TRUE, tempd=outd, action="download_only",
           inputlist=inputlist, cache_days=7)

Step 3: Process downloaded files

FFdownload(exclude_daily=TRUE, tempd=outd, download=FALSE, download_only=FALSE,
           inputlist=inputlist, output_file=outfile)
#> Step 1: getting list of all the csv-zip-files!
#> Step 3: Start processing 2 csv-files
#>   |                                                                              |                                                                      |   0%  |                                                                              |===================================                                   |  50%  |                                                                              |======================================================================| 100%
#> Be aware that as of version 1.0.6 the saved object is named FFdata rather than FFdownload to not be confused with the corresponding command!

To also get the data back directly (skipping a separate load() call), add return_data=TRUE:

FFdata <- FFdownload(exclude_daily=TRUE, tempd=outd, download=FALSE,
                     download_only=FALSE, inputlist=inputlist,
                     output_file=outfile, return_data=TRUE)

To replace French’s missing-value sentinels (-99, -999, -99.99) with NA during processing:

FFdownload(exclude_daily=TRUE, tempd=outd, download=FALSE, download_only=FALSE,
           inputlist=inputlist, output_file=outfile,
           na_values=c(-99, -999, -99.99))

Step 4: Inspect the result

load(outfile)
ls.str(FFdata)
#> x_F-F_Momentum_Factor : List of 3
#>  $ annual :List of 1
#>  $ monthly:List of 1
#>  $ daily  : Named list()
#> x_F-F_Research_Data_Factors : List of 3
#>  $ annual :List of 1
#>  $ monthly:List of 1
#>  $ daily  : Named list()

The output is a named list. Each element corresponds to one dataset and contains three sub-lists: $monthly, $annual, and $daily. Within each sub-list, sub-tables are named after the section headings in French’s CSV. When a section has no heading the name defaults to Temp1, Temp2, etc. — in factor files the main returns table is always Temp2.

# Discover sub-table names for the factors dataset
names(FFdata[["x_F-F_Research_Data_Factors"]]$monthly)
#> [1] "Temp2"
names(FFdata[["x_F-F_Research_Data_Factors"]]$annual)
#> [1] "annual_factors:_january-december"

Step 5: Use the data

Code below merges all monthly xts objects, trims to post-1963, and plots cumulative wealth indices (credit: Joshua Ulrich):

monthly         <- do.call(merge, lapply(FFdata, function(i) i$monthly$Temp2))
monthly_1963    <- na.omit(monthly)["1963/"]
monthly_returns <- cumprod(1 + monthly_1963 / 100) - 1
plot(monthly_returns)

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