README.md

In angusmoore/seasthedata: Easy seasonal adjustment of tidy data

seasthedata

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.

Installation

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"))

## Usage
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) ```

Package documentation

Documentation for this package can be found here.

angusmoore/seasthedata documentation built on Sept. 26, 2021, 12:08 p.m.