… more than just data … it’s also a palindrome
… astsa
includes data sets and scripts for analyzing
time series in both the frequency and time domains including state space
modeling as well as supporting the Springer
text, Time
Series Analysis and Its Applications: With R Examples and the Chapman
& Hall text Time
Series: A Data Analysis Approach using R. Most scripts are designed
to require minimal input to produce aesthetically pleasing output for
ease of use in live demonstrations and course work.
We do not always push the latest version of the package to CRAN, but the latest working version of the package will always be at Github.
⛔ WARNING: If loaded, the package
dplyr
may (and probably will) corrupt the base scripts
filter
and lag
that a time series analyst uses
often. An easy fix if you’re analyzing time series (or teaching a class)
is to (tell students to) do the following if dplyr
is going
being used:
# [1] either detach it if it's loaded but no longer needed
detach(package:dplyr)
# [2] or fix it yourself when loading dplyr
# this is a great idea from https://stackoverflow.com/a/65186251
library(dplyr, exclude = c("filter", "lag")) # remove the culprits
= dplyr::lag # then correct ...
dlag = dplyr::filter # ... the blunders
dfilter # Now use `dlag` and `dfilter` in dplyr scripts and
# `lag` and `filter` can be use as originally intended
# [3] or just take back the commands
= stats::filter
filter = stats::lag
lag # in this case, you can still use these for dplyr
= dplyr::lag
dlag = dplyr::filter dfilter
A list of data sets, scripts, and demonstrations of the
capabilities of astsa
can be found at FUN
WITH ASTSA… it’s more fun than high school.
The code for the graduate level text is here: TSA5.
The updated code for the data science text is here: TSDA2.
Python