index.Rmd

--- 
title: "An Introduction to Data Analysis"
author: "Michael Franke"
date: "last rendered at: `r format(Sys.time(), tz = 'Europe/Berlin')`"
site: bookdown::bookdown_site
output:
  bookdown::gitbook:
    css: [styles.css, webppl-editor.css]
    highlight: kate
    fig_caption: TRUE
    split_by: "section"
    includes:
      in_header: gitbook_header.html
    config:
      toc:
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        scroll_highlight: yes
      sharing: no
documentclass: scrbook
bibliography: [bibliography/book.bib, bibliography/packages.bib]
biblio-style: apalike
link-citations: yes
description: "Introductory text for statistics and data analysis (using R)"
github-repo: "michael-franke/intro-data-analysis" 
---


#  {-}

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<img src="visuals/00-title-page.png" alt="title_pic">  
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This book provides basic reading material for an introduction to data analysis.
It uses R to handle, plot and analyze data. 
After covering the use of R for data wrangling and plotting, the book introduces key concepts of data analysis from a Bayesian and a frequentist tradition.
This text is intended for use as a first introduction to statistics for an audience with some affinity towards programming, but no prior exposition to R.

Many people have supported this project actively by providing text, examples, code or technical support. Many thanks to (in alphabetic order): 
Tobias Anton,
Florence Bockting,
Noa Kallioinen,
Minseok Kang,
Marcel Klehr,
Özge Özenoglu,
Maria Pershina,
Timo Roettger,
Polina Tsvilodub and
Inga Wohlert.

Work on this web-book has been kindly supported financially by the Lower Saxony Ministry for Science and Culture (project "Innovation plus 2020/21").

```{r initialize, echo=FALSE, message=FALSE, warning=FALSE, include=FALSE}
source("00_initialize.R")
```

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<!-- ### Plots -->

<!-- This is a plot of quite a famous dataset [@anscombe1973]: -->

<!-- ```{r ch-preface-a_plot_example_01, fig.cap="Anscombe's Quartet: four different data sets all of which receive the same correlation score."} -->
<!-- tibble( -->
<!--   grp = rep(c("I", "II", "III", "IV"), each = 11), -->
<!--   x = c(anscombe$x1, anscombe$x2, anscombe$x3, anscombe$x4), -->
<!--   y = c(anscombe$y1, anscombe$y2, anscombe$y3, anscombe$y4) -->
<!-- ) %>%  -->
<!--   ggplot(aes(x, y)) + -->
<!--     geom_smooth(method = lm, se = F, color = "black") + -->
<!--     geom_point(color = project_colors[3], size = 2) + -->
<!--     scale_y_continuous(breaks = scales::pretty_breaks()) + -->
<!--     scale_x_continuous(breaks = scales::pretty_breaks()) + -->
<!--     labs(title = "Anscombe's Quartet", x = NULL, y = NULL, -->
<!--          subtitle = bquote(y == 0.5 * x + 3 ~ (R^2 %~~% .667) ~ "for all datasets")) + -->
<!--     facet_wrap(~grp, ncol = 2, scales = "free_x") + -->
<!--     theme(strip.background = element_rect(fill = "#f2f2f2", colour = "white")) -->
<!-- ``` -->

<!-- Here's a plot showcasing our custom palellet for discrete factors: -->

<!-- ```{r ch-preface-Setosa, fig.cap="A figure to test the contrast of the first three project colors"} -->
<!-- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) + geom_point(size=3) -->
<!-- ``` -->

<!-- ### Shiny Apps -->

<!-- Embedding Shiny Apps is as easy as pie! -->

<!-- ```{r shiny_example} -->
<!-- knitr::include_app("https://marauderpixie.shinyapps.io/dist_chisq/", height = "800px") -->
<!-- ``` -->