47 lines
2.7 KiB
Plaintext
47 lines
2.7 KiB
Plaintext
# Whole game {#sec-whole-game-intro .unnumbered}
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```{r}
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#| results: "asis"
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#| echo: false
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source("_common.R")
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```
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Our goal in this part of the book is to give you a rapid overview of the main tools of data science: **importing**, **tidying**, **transforming**, and **visualizing data**, as shown in @fig-ds-whole-game.
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We want to show you the "whole game" of data science giving you just enough of all the major pieces so that you can tackle real, if simple, data sets.
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The later parts of the book, will hit each of these topics in more depth, increasing the range of data science challenges that you can tackle.
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```{r}
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#| label: fig-ds-whole-game
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#| echo: false
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#| out.width: NULL
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#| fig-cap: >
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#| In this section of the book, you'll learn how to import,
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#| tidy, transform, and visualize data.
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#| fig-alt: >
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#| A diagram displaying the data science cycle: Import -> Tidy ->
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#| Understand (which has the phases Transform -> Visualize -> Model in a
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#| cycle) -> Communicate. Surrounding all of these is Program
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#| Import, Tidy, Transform, and Visualize is highlighted.
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knitr::include_graphics("diagrams/data-science/whole-game.png", dpi = 270)
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```
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Five chapters focus on the tools of data science:
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- Visualisation is a great place to start with R programming, because the payoff is so clear: you get to make elegant and informative plots that help you understand data.
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In @sec-data-visualisation you'll dive into visualization, learning the basic structure of a ggplot2 plot, and powerful techniques for turning data into plots.
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- Visualisation alone is typically not enough, so in @sec-data-transform, you'll learn the key verbs that allow you to select important variables, filter out key observations, create new variables, and compute summaries.
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- In @sec-data-tidy, you'll learn about tidy data, a consistent way of storing your data that makes transformation, visualization, and modelling easier.
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You'll learn the underlying principles, and how to get your data into a tidy form.
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- Before you can transform and visualize your data, you need to first get your data into R.
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In @sec-data-import you'll learn the basics of getting `.csv` files into R.
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- Finally, in @sec-exploratory-data-analysis, you'll combine visualization and transformation with your curiosity and skepticism to ask and answer interesting questions about data.
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Nestled among these chapters that are five other chapters that focus on your R workflow.
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In @sec-workflow-basics, @sec-workflow-pipes, @sec-workflow-style, and @sec-workflow-scripts-projects, you'll learn good workflow practices for writing and organizing your R code.
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These will set you up for success in the long run, as they'll give you the tools to stay organised when you tackle real projects.
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