We’re into the second day of Business Science Demo Week. What’s demo week? Every day this week we are demoing an R package: tidyquant (Monday), timetk (Tuesday), sweep (Wednesday), tibbletime (Thursday) and h2o (Friday)! That’s five packages in five days! We’ll give you intel on what you need to know about these packages to go from zero to hero. Second up is timetk, your toolkit for time series in R. Here we go!

We’ve got an exciting week ahead of us at Business Science: we’re launching our first ever Business Science Demo Week. Every day this week we are demoing an R package: tidyquant (Monday), timetk (Tuesday), sweep (Wednesday), tibbletime (Thursday) and h2o (Friday)! That’s five packages in five days! We’ll give you intel on what you need to know about these packages to go from zero to hero. First up is tidyquant, our flagship package that’s useful for financial and time series analysis. Here we go!

Predictive sales analytics to predict product backorders can increase sales and customer satisfaction. Using a Kaggle dataset, we use H2O AutoML predict backorders.

Today we are introducing tibbletime v0.0.2, and we’ve got a ton of new features in store for you. We have functions for converting to flexible time periods with the ~period formula~ and making/calculating custom rolling functions with rollify() (plus a bunch more new functionality!). We’ll take the new functionality for a spin with some weather data (from the weatherData package). However, the new tools make tibbletime useful in a number of broad applications such as forecasting, financial analysis, business analysis and more! We truly view tibbletime as the next phase of time series analysis in the tidyverse. If you like what we do, please connect with us on social media to stay up on the latest Business Science news, events and information!

Predict employee turnover using the H2O machine learning and Lime.

We are very excited to announce the initial release of our newest R package, tibbletime. As evident from the name, tibbletime is built on top of the tibble package (and more generally on top of the tidyverse) with the main purpose of being able to create time-aware tibbles through a one-time specification of an “index” column (a column containing timestamp information). There are a ton of useful time functions that we can now use such as time_filter(), time_summarize(), tmap(), as_period() and time_collapse(). We’ll walk through the basics in this post.

We’re excited to announce the alphavantager package, a lightweight R interface to the Alpha Vantage API! Alpha Vantage is a FREE API for retreiving real-time and historical financial data. It’s very easy to use, and, with the recent glitch with the Yahoo Finance API, Alpha Vantage is a solid alternative for retrieving financial data for FREE! It’s definitely worth checking out if you are interested in financial analysis. We’ll go through the alphavantager R interface in this post to show you how easy it is to get real-time and historical financial data. In the near future, we have plans to incorporate the alphavantager into tidyquant to enable scaling from one equity to many.

Tidy Time Series Analysis, investigate lags and autocorrelation to understand seasonality and form the basis for autoregressive forecast models.