We have several announcements regarding Business Science R packages. First, as of this week the R package formerly known as
timekit has changed to
timetk for time series tool kit. There are a few “breaking” changes because of the name change, and this is discussed further below. Second, the
tidyquant packages have several improvements, which are discussed in detail below. Finally, don’t miss a beat on future news, events and information by following us on social media.
timetk package (formerly
timekit) is a relatively new package that is aimed at assisting users with working with time series in R. It helps users switch back and forth between time based “tibbles” (tidy data frames with dates or date times) and the other time series objects in R (
ts, etc). Equally important,
timetk includes functions that help setup time series for data mining and machine learning. Here’s an example from Vignette #4: Forecasting Using a Time Series Signature with timetk:
Why the name change? The name change was made to help differentiate from timekit.io. It was in both parties best interest to differentiate, which is less confusing to users of both organizations’ software.
The primary change is to the name of the package, and most functions are the same. There are however a few “breaking changes” that are a result of the name change. We made the transition very simple. All you need to do to refactor is “Ctrl+F” or “Cmd+F” and find and replace “timekit” with “timetk”, and everything will work. We promise!
Here’s some examples of the changes. Notice the functions are all the same. Only changes are related to “timekit”.
has_timekit_index()now changes to
has_timetk_index(). This function is used to detect if a
tsobject has a “timetk index” (non-regularized date or datetime, which are present in
tsobjects coerced with
tk_ts()). Again, just refactor and you will be fine.
Functions with the boolean argument
timekit_idxhave the argument changed to
timetk_idx. Examples include
tk_index(timetk_idx = TRUE)and
tk_tbl(timetk_idx = TRUE). The
timetk_idxargument enables retrieving a non-regularized date or datetime series rather than the regularized time series typically present in
tsobjects. Note that this only is applicable if the
tk_ts()coercion function is used during initial coercion to a
tsobject. Refer to the Vignette #1: Time Series Coercion Using timetk for more details.
That’s it. It should be very easy to make the transition to
timetk via a simple refactor. Please let us know if you have any issues. You can contact us at email@example.com or via social media below.
sweep package is designed to “tidy” the model and forecast output of packages that use the
ts system. The most popular example is the
forecast package. The package uses
broom-style tidiers (
sw_sweep) to convert the output to “tibbles”. Here’s an example from our recent sweep blog post where we collected GDP for each US state and plotted “tidy” ARIMA forecasts.
The main addition to
sweep v0.2.0 is support for the
robets package. The addition was user supplied via pull request (thanks Joel Gombin). We highly encourage users interested in converting models in other
ts-based packages to “tidy” output to submit pull requests! Let us know if you are interested in helping.
The other main change is to the
sw_sweep() function, which is used to convert
forecast objects to “tidy” data frames. Because it uses
timetk under the hood to convert the
ts object time series to date or datetime, we changed the
timekit_idx argument to
timetk_idx. Again, just refactor to change to the
sw_sweep(timetk_idx) argument. For more information, refer to the Introduction to sweep Vignette .
tidyquant package bridges a gap between the “tidyverse” and many of the financial and time series packages that depend on the
zoo time series objects. The main benefit is the scale-ability to perform grouped operations, which can be difficult in the
xts system when managing multiple time series. Here’s a simple example to show why you might consider using
tidyquant. The script below retrieves the past 10-years of stock prices for every stock in the SP500, then calculates the average and standard deviation of the daily returns.
Here’s a useful plot you can make with the data. An investor can easily focus on stocks that have lower risk and higher reward metrics.
What are the changes? There’s two main changes in
tq_index(), the function used to get stock indexes such as SP500, DOW, and RUSSELL2000 now collects its data from SPDRs. The
The return from
tq_index() now include weight, sector, and shares_held from the associated SPDR.
|AAPL||Apple Inc.||0.0377152||Information Technology||59372276|
|MSFT||Microsoft Corporation||0.0269475||Information Technology||87913770|
|AMZN||Amazon.com Inc.||0.0196867||Consumer Discretionary||4517403|
|FB||Facebook Inc. Class A||0.0184511||Information Technology||26915296|
|JNJ||Johnson & Johnson||0.0166280||Health Care||30675892|
|XOM||Exxon Mobil Corporation||0.0160500||Energy||48243920|
tidyquant::as_xts() functions are now deprecated. These were used to convert between xts and time-based tibble objects. You can still use them (for now), but you will receive a warning. Rather, you should transition to the more robust
tk_xts() functions, which do the same thing in a more automated way.
timetk::tk_xts() to coerce any time based object to
xts. Dates are converted and dropped automatically. Use
silent = TRUE to eliminate messages describing date column being dropped and converted to index. This replaces
tidyquant::as_xts() which only worked with tibble objects.
timetk::tk_tbl() to coerce any time based object to
tibble. This replaces
tidyquant::as_tibble() which only worked with
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