class: middle #### tidy * data structure * workflow * tool suite --- Open an intro to ANY statistics textbook... .center[ <img src="assets/figures/intro-stats.png" width="70%"> ] ...and you will find that statistics (analysis, plotting - anything, really) starts once you have tidy data. .footnote[D. Cook [To the Tidyverse and Beyond: Challenges for the Future in Data Science](bit.ly/rstudio-cook)] --- class: middle, inverse "It is often said that 80% of data analysis is spent on the process of cleaning and preparing the data"¹ "All data are crap, it is just a matter of how much work you have to do to make them useful" _good ol' Ben_ .footnote[¹Dasu and Johnson 2003, Exploratory Data Mining and Data Cleaning] --- class: middle #### data structure: tidy data * each variable in a column * each observation in a row * each value in a cell .footnote[Wilson et al. (2017) [Good enough practices in scientific computing](https://doi.org/10.1371/journal.pcbi.1005510)] --- class: middle #### tidy data structure .center[ <img src="assets/figures/tidy-data.png" width="90%"> ] .footnote[[R for Data Science](https://r4ds.had.co.nz/)] --- class: middle, inverse .center[ <img src="assets/figures/tidy_jeopardy.jpg" width="90%"> ]