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: Use Machine Learning to Predict and Optimize Product Backorders
Predictive sales analytics to predict product backorders can increase sales and customer satisfaction. Using a Kaggle dataset, we use H2O AutoML predict backorders.
It's tibbletime v0.0.2: Time-Aware Tibbles, New Functions, Weather Analysis and More
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!
HR Analytics: Using Machine Learning to Predict Employee Turnover
It's tibbletime: Time-Aware Tibbles
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.
alphavantager: An R interface to the Free Alpha Vantage Financial Data API
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, Part 4: Lags and Autocorrelation
Tidy Time Series Analysis, investigate lags and autocorrelation to understand seasonality and form the basis for autoregressive forecast models.
Tidy Time Series Analysis, Part 3: The Rolling Correlation
In the third part in a series on Tidy Time Series Analysis, we’ll use the runCor
function from TTR
to investigate rolling (dynamic) correlations. We’ll again use tidyquant
to investigate CRAN downloads. This time we’ll also get some help from the corrr
package to investigate correlations over specific timespans, and the cowplot
package for multi-plot visualizations. We’ll end by reviewing the changes in rolling correlations to show how to detect events and shifts in trend. If you like what you read, please follow us on social media to stay up on the latest Business Science news, events and information! As always, we are interested in both expanding our network of data scientists and seeking new clients interested in applying data science to business and finance. If interested, contact us.
BizSci Package Updates: Formerly timekit... Now timetk :)
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 sweep
and 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.
Tidy Time Series Analysis, Part 2: Rolling Functions
In the second part in a series on Tidy Time Series Analysis, we’ll again use tidyquant
to investigate CRAN downloads this time focusing on Rolling Functions. If you haven’t checked out the previous post on period apply functions, you may want to review it to get up to speed. Both zoo
and TTR
have a number of “roll” and “run” functions, respectively, that are integrated with tidyquant
. In this post, we’ll focus on the rollapply
function from zoo
because of its flexibility with applying custom functions across rolling windows. If you like what you read, please follow us on social media to stay up on the latest Business Science news, events and information! As always, we are interested in both expanding our network of data scientists and seeking new clients interested in applying data science to business and finance.
sweep: Extending broom for time series forecasting
We’re pleased to introduce a new package, sweep
, now on CRAN! Think of it like broom
for the forecast
package. The forecast
package is the most popular package for forecasting, and for good reason: it has a number of sophisticated forecast modeling functions. There’s one problem: forecast
is based on the ts
system, which makes it difficult work within the tidyverse
. This is where sweep
fits in! The sweep
package has tidiers that convert the output from forecast
modeling and forecasting functions to “tidy” data frames. We’ll go through a quick introduction to show how the tidiers can be used, and then show a fun example of forecasting GDP trends of US states. If you’re familiar with broom
it will feel like second nature. If you like what you read, don’t forget to follow us on social media to stay up on the latest Business Science news, events and information!
Tidy Time Series Analysis, Part 1
In the first part in a series on Tidy Time Series Analysis, we’ll use tidyquant
to investigate CRAN downloads. You’re probably thinking, “Why tidyquant?” Most people think of tidyquant
as purely a financial package and rightfully so. However, because of its integration with xts
, zoo
and TTR
, it’s naturally suited for “tidy” time series analysis. In this post, we’ll discuss the the “period apply” functions from the xts
package, which make it easy to apply functions to time intervals in a “tidy” way using tq_transmute()
!