At a high level, the tidyverse is a language for solving data science challenges with R code. Its primary goal is to facilitate a conversation between a human and a computer about data. Less abstractly, the tidyverse is a collection of R packages that share a high-level design philosophy and low-level grammar and data structures, so that learning one package makes it easier to learn the next.

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This paper presents the reshape package for R, which provides a common framework for many types of data reshaping and aggregation. It uses a paradigm of 'melting' and 'casting', where the data are 'melted' into a form which distinguishes measured and identifying variables, and then 'cast' into a new shape, whether it be a data frame, list, or high dimensional array. The paper includes an introduction to the conceptual framework, practical advice for melting and casting, and a case study.

Understanding evolutionary relationships among crops, their wild progenitors, and close relatives provides the requisite framework for conserving and using crop genetic diversity (Fielder et al., 2015; Dempewolf et al., 2017; Migicovsky and Myles, 2017). While the evolutionary histories of many annual crop species have been reconstructed, well-resolved phylogenies remain elusive for many crop genera, in particular those that include woody perennials (Barakat et al., 2012). Long-lived plants such as woody vines and trees have several basic biological attributes that complicate phylogenetic reconstruction: they are often obligate outcrossers that are highly heterozygous, undergo extensive interspecific hybridization, exhibit little among-population variation, and commonly share haplotypes among species (Petit and Hampe, 2006). Traditional approaches to molecular phylogenetics, including the sequencing of chloroplast and nuclear genes, have contributed to the resolution of relationships in some groups (Soltis et al., 1999; Rokas et al., 2003). The advent of high-throughput sequencing and analysis has greatly enhanced our capacity to analyze hundreds of thousands of sites from across the genome and offers great potential to advance resolution of relationships in groups that have posed challenges to traditional phylogenetic approaches (e.g., Cavender-Bares et al., 2015; Hipp et al., 2014, Uribe-Convers et al., 2016). Approximately 75% of woody perennial crops are clonally propagated, including most fruit and nut trees (

Many data analysis problems involve the application of a split-apply-combine strategy, where you break up a big problem into manageable pieces, operate on each piece independently and then put all the pieces back together. This insight gives rise to a new R package that allows you to smoothly apply this strategy, without having to worry about the type of structure in which your data is stored.The paper includes two case studies showing how these insights make it easier to work with batting records for veteran baseball players and a large 3d array of spatio-temporal ozone measurements.

This article discusses ggplot2, an open source R package, based on a grammatical theory of graphics. The underlying theory has been discussed in depth elsewhere so this article illustrates some of the consequences of the theory for creating new graphics, the importance of programmable graphics, and the rich ecosystem that has grown up around ggplot2.

In spatial statistics the ability to visualize data and models superimposed with their basic social landmarks and geographic context is invaluable. ggmap is a new tool which enables such visualization by combining the spatial information of static maps from Google Maps, OpenStreetMap, Stamen Maps or CloudMade Maps with the layered grammar of graphics implementation of ggplot2. In addition, several new utility functions are introduced which allow the user to access the Google Geocoding, Distance Matrix, and Directions APIs. The result is an easy, consistent and modular framework for spatial graphics with several convenient tools for spatial data analysis.

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