An updated version of xts is now on CRAN. This release patches a few issues with the features added in version 0.13.0 and addresses a few maintenance issues that popped up recently.
An updated version of xts is now on CRAN. This release adds several exciting changes: open-ended time-of-day subsetting, smarter conversions to xts from data.frames/data.tables/tibbles;
to.period() handles custom endpoint values,
print() truncates rows like data.table, and
str() provides more informative output. There are also changes to make xts more consistent with zoo, some minor speed improvements, and the usual smattering of bug fixes.
An updated version of xts is now on CRAN. This release is a big one, with lots of changes. Plotting functionality got a lot of attention. Another notable change is that
merge.xts() now supports suffixes. Plus the obligatory bug fixes and refinements to make xts more robust.
An updated version of xts reached CRAN on 2020-09-09. Time-of-day subsetting (e.g.
x["T10:00/T13:00"]) is 200x faster! (This post includes some notes on some nifty changes in 0.12.0 too, since I didn’t post about 0.12.0 when it was released.)
xts version 0.11-2 was published to CRAN yesterday. This is quick a bug-fix release.
xts version 0.11-1 was published to CRAN this morning. xts provides data structure and functions to work with time-indexed data. This release contains some awesome features that will transparently make your xts code even faster!
This xts release contains mostly bugfixes, but there are a few noteworthy features. Some of these features were added in version 0.10-1, but I forgot to blog about it. Anyway, in no particular order:
endpoints() gained sub-second accuracy on Windows (#202)! na.locf.xts() now honors x and xout arguments by dispatching to the next method (#215). Thanks to Morten Grum for the report. na.locf.xts() and na.omit.xts() now support character xts objects. Thanks to Ken Williams and Samo Pahor for the reports (#42).
A new, and long overdue, release of xts is now on CRAN! The major change is the completely new plot.xts() written by Michael Weylandt and Ross Bennett, and which is based on Jeff Ryan’s quantmod::chart_Series() code.
Do note that the new plot.xts() includes breaking changes to the original (and rather limited) plot.xts(). However, we believe the new functionality more than compensates for the potential one-time inconvenience. And I will no longer have to tell people that I use plot.
I’m excited to announce my DataCamp course on importing and managing financial data in R! I’m also honored that it is included in DataCamp’s Quantitative Analyst with R Career Track!
You can explore the first chapter for free, so be sure to check it out!
Course Description Financial and economic time series data come in various shapes, sizes, and periodicities. Getting the data into R can be stressful and time-consuming, especially when you need to merge data from several different sources into one data set.
We have been working on a new charting engine for xts::plot.xts for the past couple years. It started with Michael Weylandt’s work during the 2012 Google Summer of Code, and Ross Bennett took up the torch during the 2014 GSoC.
This new engine improves the functionality, modularity, and flexibility of plot.xts by building off the framework Jeff Ryan began with quantmod::chart_Series. The modular framework allows users to plot an xts object and incrementally build custom charts by adding panels of new data (including transformations of the original xts object).