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()!

This report was originally published on The Data Incubator Blog. You can view the the report in it's entirety here: Ranked 16 R Packages for Machine Learning The most frequently asked question in our data science training program is "what is the best programming language for machine learning?" The resulting discussion, depending on the day, either ends in a hotly contested debate between R, Python, and MATLAB fans, or a full on WWE wrestling match. In other words, it depends. However, there is no doubt R is language of choice for the majority of data scientists who want to understand data, especially those looking to leverage its great machine learning packages. R also boasts being open source which is great for anyone looking to get started with machine learning in their spare time.

OpenML is an online machine learning platform where researchers can easily share data, machine learning tasks and experiments as well as organize them online to work and collaborate more efficiently. In this paper, we present an R package to interface with the OpenML platform and illustrate its usage in combination with the machine learning R package mlr. We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks. Furthermore, we also show how to upload results of experiments, share them with others and download results from other users. Beyond ensuring reproducibility of results, the OpenML platform automates much of the drudge work, speeds up research, facilitates collaboration and increases the users' visibility online.

This paper describes Picat's planner, its implementation, and planning models for several domains used in International Planning Competition (IPC) 2014. Picat's planner is implemented by use of tabling. During search, every state encountered is tabled, and tabled states are used to effectively perform resource-bounded search. In Picat, structured data can be used to avoid enumerating all possible permutations of objects, and term sharing is used to avoid duplication of common state data. This paper presents several modeling techniques through the example models, ranging from designing state representations to facilitate data sharing and symmetry breaking, encoding actions with operations for efficient precondition checking and state updating, to incorporating domain knowledge and heuristics. Broadly, this paper demonstrates the effectiveness of tabled logic programming for planning, and argues the importance of modeling despite recent significant progress in domain-independent PDDL planners.