seasthedata is an R package that makes it easy to seasonally adjust tidy data using X13. It is a thin wrapper around the seasonal library (see github; cran).
The benefit of seasthedata is that it accepts tibbles or data.frames with date columns (instead of ts) and respects grouping variables. This means you can easily seasonally adjust a large number of series that are in long form.
Install the package using the R devtools package:
library(devtools)
install_github("angusmoore/seasthedata", ref= "stable")
You may need to first install the devtools package if you don't already have it (install.packages("devtools")).
Installing may fail if devtools cannot correctly determine your proxy server. If so, you'll get the following error message when you try to install:
Installation failed: Timeout was reached: Connection timed out after 10000 milliseconds
If you get this message, try setting your proxy server with the following command, and then running the install again:
Sys.setenv(https_proxy = curl::ie_get_proxy_for_url("https://www.google.com"))
The library is a thin wrapper around the seasonal library, which itself wraps
the US Census Bureau X13 binary.
library(seasthedata)
library(dplyr)
library(tibble)
# First, just seasonally adjust a tibble of data with a date column
ungrouped_data <- tibble(dates = seq.Date(from = as.Date("1949-01-01"), by = "month",
length.out = 144), y = as.vector(AirPassengers))
seasthedata(ungrouped_data)
# Now create some fake GROUPED data, where we have two series - group A and B
grouped_data <- bind_rows(mutate(ungrouped_data, group = "A"),
mutate(ungrouped_data, group = "B"))
grouped_data <- group_by(grouped_data, group)
seasthedata(grouped_data)
Documentation for this package can be found here.