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How-to in the Tidyverse
library(FFdownload)
library(tidyverse)
outd <- paste0("data/", format(Sys.time(), "%F_%H-%M"))
outfile <- paste0(outd, "FFData_tbl.RData")
This vignette covers the tidyverse workflow. For the step-by-step xts
workflow (with separate download and processing stages), see
vignette("FFD-xts-how-to").
Quick start: FFget() (new in v1.2.0)
FFget() is the simplest way to get a single dataset into
your session. It returns a tibble directly — no intermediate
.RData file, no load() call. Missing-value
sentinels (-99, -999, -99.99) are
converted to NA by default.
# Monthly FF 3-factor data as a tibble, NAs handled automatically
ff3 <- FFget("F-F_Research_Data_Factors", subtable = "Temp2")
head(ff3)
#> # A tibble: 6 × 5
#> date Mkt.RF SMB HML RF
#> <yearmon> <dbl> <dbl> <dbl> <dbl>
#> 1 Jul 1926 2.89 -2.55 -2.39 0.22
#> ...
Use frequency = NULL to get all frequencies at once, or
subtable = NULL to get all sub-tables within a
frequency:
# All sub-tables for the monthly frequency
ff3_monthly <- FFget("F-F_Research_Data_Factors", subtable = NULL)
names(ff3_monthly) # e.g. "Temp2"
# Annual data as xts
ff3_ann_xts <- FFget("F-F_Research_Data_Factors", frequency = "annual", format = "xts")
Dataset discovery: FFlist() and
FFmatch()
Before downloading, browse all available datasets and check that your
search strings match the intended files:
fl <- FFlist()
nrow(fl) # 100+ non-daily datasets
fl |> filter(grepl("5_Factors|Momentum", name))
FFmatch(c("Research_Data_Factors", "Momentum_Factor"))
#> # A tibble: 2 × 4
#> requested matched edit_distance similarity
#> <chr> <chr> <int> <dbl>
#> 1 Research_Data_Factors F-F_Research_Data_Factors 3 0.87
#> 2 Momentum_Factor F-F_Momentum_Factor 4 0.78
Bulk download with FFdownload() (classic API)
For downloading multiple datasets in one call and/or saving a dated
snapshot for reproducible research, use FFdownload()
directly with format = "tibble".
For a detailed explanation of the download / process separation, see
vignette("FFD-xts-how-to"). Here we download and process in
one step:
inputlist <- c("F-F_Research_Data_Factors_CSV","F-F_Momentum_Factor_CSV")
FFdownload(exclude_daily=TRUE, tempd=outd, download=TRUE, download_only=FALSE,
inputlist=inputlist, output_file=outfile, format="tibble")
#> Step 1: getting list of all the csv-zip-files!
#> Step 2: Downloading 2 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!
New parameters
Replace French’s missing-value codes with NA during
processing:
FFdownload(exclude_daily=TRUE, tempd=outd, download=TRUE, download_only=FALSE,
inputlist=inputlist, output_file=outfile, format="tibble",
na_values=c(-99, -999, -99.99))
Return the data list directly in addition to saving the file:
FFdata <- FFdownload(exclude_daily=TRUE, tempd=outd, download=TRUE,
download_only=FALSE, inputlist=inputlist,
output_file=outfile, format="tibble",
return_data=TRUE)
Working with 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()
Verify that the sub-tables are tibbles:
str(FFdata$`x_F-F_Research_Data_Factors`$monthly$Temp2)
#> tibble [1,194 × 5] (S3: tbl_df/tbl/data.frame)
#> $ date : 'yearmon' num [1:1194] Jul 1926 Aug 1926 Sep 1926 Okt 1926 ...
#> $ Mkt.RF: num [1:1194] 2.89 2.64 0.38 -3.27 2.54 2.62 -0.05 4.17 0.14 0.47 ...
#> $ SMB : num [1:1194] -2.55 -1.14 -1.36 -0.14 -0.11 -0.07 -0.32 0.07 -1.77 0.39 ...
#> $ HML : num [1:1194] -2.39 3.81 0.05 0.82 -0.61 0.06 4.58 2.72 -2.38 0.65 ...
#> $ RF : num [1:1194] 0.22 0.25 0.23 0.32 0.31 0.28 0.25 0.26 0.3 0.25 ...
Merge the two datasets (the date column is a
yearmon object and serves as the join key):
FFfour <- FFdata$`x_F-F_Research_Data_Factors`$monthly$Temp2 %>%
left_join(FFdata$`x_F-F_Momentum_Factor`$monthly$Temp2, by="date")
FFfour %>% head()
#> # A tibble: 6 × 6
#> date Mkt.RF SMB HML RF Mom
#> <yearmon> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Jul 1926 2.89 -2.55 -2.39 0.22 NA
#> 2 Aug 1926 2.64 -1.14 3.81 0.25 NA
#> 3 Sep 1926 0.38 -1.36 0.05 0.23 NA
#> 4 Okt 1926 -3.27 -0.14 0.82 0.32 NA
#> 5 Nov 1926 2.54 -0.11 -0.61 0.31 NA
#> 6 Dez 1926 2.62 -0.07 0.06 0.28 NA
Plot cumulative wealth indices using pivot_longer() and
ggplot2:
FFfour %>%
pivot_longer(Mkt.RF:Mom, 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="FF4 Factors", subtitle="Cumulative wealth plots", ylab="cum. returns") +
scale_colour_viridis_d("FFvar") +
theme_bw() + theme(legend.position="bottom")
#> Ignoring unknown labels:
#> • ylab : "cum. returns"
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