Chapter 1 Introduction

Temporal data analysis has assumed that the entry point to data analysis is at a model-ready data format, which provides little organization or conceptual oversight on how one should get the wild data into a tamed state. This mind-set is related to a long-held belief that exploratory data analysis is a highly ad hoc statistical area, impossible to teach or formalize. However, the tidyverse framework implemented in the statistical software R (R Core Team 2018), as originating in Wickham (2014), fundamentally overturns this thinking. Data plots and data wrangling, which the “tidy data” conceptualization supports, can be formally described using an abstract grammar. The grammar of graphics and data manipulation, as implemented in the ggplot2 (H. Wickham, Chang, et al. 2019) and dplyr (H. Wickham, François, et al. 2019) R packages respectively, form the core of the tidyverse suite of tools. My contributions extend the tidyverse way of thinking to the temporal domain, by providing tidy tools, built as R packages, for supporting fluent workflow in temporal data analysis.