tidyquant logo


The tidyquant package integrates the best resources for collecting and analyzing financial data, zoo, xts, quantmod, TTR, and PerformanceAnalytics, with the tidy data infrastructure of the tidyverse allowing for seamless interaction between each. You can now perform complete financial analyses in the tidyverse.


A brief introduction to tidyquant

timetk logo


The 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

Time Series Machine Learning with timetk

sweep logo


The 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 tidyverse.


Forecasting in the "tidyverse" with sweep

Forecasting in the tidyverse with sweep

tibbletime logo


Built on top of the tidyverse, tibbletime is an extension that allows for the creation of time aware tibbles through the setting of a time index.


Introducing the tibbletime functions

  1. time_filter() - Succinctly filter a tbl_time object by date.

  2. 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”.

  3. tmap() - The family of tmap functions transform a tbl_time input by applying a function to each column at a specified time interval.

  4. 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.

  5. 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.

  6. 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 tidyverse framework.

  7. create_series() - Use shorthand notation to quickly initialize a tbl_time object containing a date column with a regularly spaced time series.