index.Rmd

--- 
title: "Data Analysis and Visualization in R (IN2339)"
author: |
  | Chair of Computational Molecular Medicine
  | Technical University of Munich
date: "`r Sys.Date()`"
output:
  html_document:
    df_print: paged
documentclass: krantz
bibliography:
- book.bib
- packages.bib
biblio-style: apalike
link-citations: yes
colorlinks: yes
lot: no
lof: no
graphics: yes
urlcolor: blue
geometry: left=1.5in, right=1.5in, top=1.25in, bottom=1.25in
description: This book introduces concepts and skills that can help you tackle
  real-world data analysis challenges. It covers concepts from probability, statistical
  inference, linear regression and machine learning and helps you develop skills such
  as R programming, data wrangling with data.table, data visualization with ggplot2, file
  organization with UNIX/Linux shell, version control with GitHub, and reproducible
  document preparation with R markdown.
site: bookdown::bookdown_site
header-includes: \usepackage{float} \floatplacement{figure}{H}
always_allow_html: yes
subtitle: A practical introduction to Data Science
---


```{r include=FALSE}
# automatically create a bib database for R packages
knitr::write_bib(c(
  .packages(), 'bookdown', 'knitr', 'rmarkdown'), 'packages.bib')
```

# Preface {-}

This is the lecture script of the module Data Analysis and Visualization in R (IN2339).

This work is licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0)

# Acknowledgments {-}

This script has been first put together in the winter semester 2020/2021 by Felix Brechtmann, Alexander Karollus, Daniela Klaproth-Andrade, Pedro Silva, and Julien Gagneur with help from Xueqi Cao, Laura Martens, Ines Scheller, Vangelis Theodorakis, and Vicente Yépez.

We leveraged work from colleagues who helped creating lecture slides since 2017: Žiga Avsec, Ines Assum, Daniel Bader, Jun Cheng, Bašak Eraslan, Mathias Heinig, Jan Krumsieck, Christian Mertes, and Georg Stricker.


# Prerequisites {-}
Basics in probabilities are required. Chapters 13-15 ("Introduction to Statistics with R", "Probability" and "Random variables") of the Book "Introduction to Data Science" https://rafalab.github.io/dsbook/ make a good refresher. Make sure all concepts are familiar to you. Check your knowledge by trying the exercises. 

# Datasets {.unnumbered}
Datasets used in this script are available to download as a compressed file [here](http://shorturl.at/otwxO).

# Feedback {-}
For improvement suggestions, reporting errors and typos, please use the online document [here](https://docs.google.com/document/d/1tdWmtkDaNUnQLkG_cNfJ2KLHwnaldtQwZh6pUQh8Mpc/edit?usp=sharing).

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