tidyquant package integrates the best resources for collecting and analyzing financial data,
PerformanceAnalytics, with the tidy data infrastructure of the
tidyverse allowing for seamless interaction between each. You can now perform complete financial analyses in the
A brief introduction to
What users are saying about tidyquant
Thanks very much for the tidyquant package, it's an absolute breeze to work with. I am a self-taught user of the "tidyverse" and your package saves me (a) a ton of work and (b) helps produce understandable and manageable code. The latest integration with the PerformanceAnalytics package is even better! Thank you.
timetk package enables the user to more easily work with time series objects in R. The package has tools for inspecting, analyzing and manipulating the time-based index and converting time-based objects to and from the many time series classes. The package is well-suited for time series data mining and time series machine learning using the time series signature.
Time Series Machine Learning with timetk
sweep package enables
broom-style "tidying" of ARIMA, ETS, BATS, and other models and forecast objects used in the
forecast package. The output is a "tidy" data frame that fits into the data science workflow of the
Forecasting in the "tidyverse" with sweep
Introducing the tibbletime functions
time_filter()- Succinctly filter a tbl_time object by date.
time_summarise()- Similar to dplyr::summarise but with the added benefit of being able to summarise by a time period such as “yearly” or “monthly”.
tmap()- The family of tmap functions transform a tbl_time input by applying a function to each column at a specified time interval.
as_period()- Convert a tbl_time object from daily to monthly, from minute data to hourly, and more. This allows the user to easily aggregate data to a less granular level.
time_collapse()- When time_collapse is used, the index of a tbl_time object is altered so that all dates that fall in a period share a common date.
rollify()- Modify a function so that it calculates a value (or a set of values) at specific time intervals. This can be used for rolling averages and other rolling calculations inside the
create_series()- Use shorthand notation to quickly initialize a
tbl_timeobject containing a
datecolumn with a regularly spaced time series.