index.Rmd

---
title: "Machine Learning in R"
subtitle: "Introduction to the Tidyverse"
author: "Simon Schölzel"
institute: "*Research Team Berens*"
date: "Winter Term 2021/2022"

output:
  xaringan::moon_reader:
    seal: false
    css: [./custom/xaringan-themer.css, ./custom/custom-theme.css]
    lib_dir: libs
    nature:
      titleSlideClass: [left, middle, inverse]
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
      ratio: '16:9'
---

```{r setup, include=F}
# knitr options
knitr::opts_chunk$set(
  comment = ">",
  collapse = F
)

# package management
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
  xaringanthemer, metathis, palmerpenguins, 
  tidyverse, magrittr, glue, showtext, plotly, patchwork, ggforce
)
pacman::p_load_gh("hadley/emo", "gadenbuie/countdown", "gadenbuie/xaringanExtra")

# xaringan custom setup
xaringanthemer::style_mono_accent(
  base_color = "#000000",
  link_color = "#0000EE",
  header_font_google = google_font("Josefin Sans"),
  text_font_google = google_font("Montserrat", "700b", "400"),
  code_font_google = google_font("Fira Mono"),
  colors = c(red = "#f34213", white = "#FFFFFF"),
  code_font_size = "smaller",
  code_inline_background_color = "#F4F4F4",
  outfile = "./custom/xaringan-themer.css"
)

xaringanExtra::use_panelset()
xaringanExtra::use_webcam()

xaringanExtra::use_extra_styles(
  hover_code_line = T,
  mute_unhighlighted_code = T
)

metathis::meta() %>%
  meta_name("github-repo" = "simonschoe/introduction-to-the-tidyverse") %>% 
  meta_social(
    title = "Introduction to the Tidyverse",
    description = "2 hours introductory workshop on the `tidyverse` meta-package.",
    url = "https://simonschoe.github.io/introduction-to-the-tidyverse",
    image = "https://simonschoe.github.io/introduction-to-the-tidyverse/img/share-card.png",
    image_alt = "Title slide for Introduction to the Tidyverse",
    og_type = "website",
    og_author = "Simon Schölzel",
    twitter_card_type = "summary_large_image"
  )
```

```{r, include=F}
countdown::countdown(
  minutes = 2, seconds = 0, 
  left = 0, right = 0, top = 0.5, bottom = 0.5,
  padding = "50px",
  margin = "7.5%",
  font_size = "5em"
)
```

class: center, middle, hide-count
count: false

# `r rmarkdown::metadata$title`
### `r rmarkdown::metadata$subtitle`

___

**`r rmarkdown::metadata$author`**

`r rmarkdown::metadata$date`  
.small[(updated: `r Sys.Date()`)]

<br><br>

<a href="https://www.wiwi.uni-muenster.de/"><img src="https://www.wiwi.uni-muenster.de/fakultaet/sites/all/themes/wwucd/assets/images/logos/secondary_wiwi_aacsb_german.jpg" alt="fb4-logo" height="45"></a> <a href="https://www.wiwi.uni-muenster.de/ctrl/aktuelles"><img src="https://www.wiwi.uni-muenster.de/ctrl/sites/all/themes/wwucd/assets/images/logos/berenslogo5.jpg" alt="ftb-logo" height="45"></a> <a href="https://www.wiwi.uni-muenster.de/iff2/de/news"><img src="https://www.wiwi.uni-muenster.de/iff2/sites/all/themes/wwucd/assets/images/logos/logo_iff2_en2.jpg" alt="ipb-logo" height="45"></a>

---

## Agenda

**1 Learning Objectives**

**2 Introduction to the `tidyverse`**  
> 2.1 What is the `tidyverse`  
2.2 The Concept of Tidy Data
  
**3 `palmerpenguins`: Palmer Archipelago (Antarctica) Penguin Data**

**4 The Core `tidyverse` Packages**  
> 4.1 `magrittr`: A Forward-Pipe Operator for `R`  
4.2 `tibble`: Simple Data Frames  
4.3 `readr`: Read Rectangular Text Data  
4.4 `tidyr`: Tidy Messy Data  
4.5 `dplyr`: A Grammar of Data Manipulation  
4.6 `purrr`: Functional Programming Tools  
4.7 `ggplot2`: Create Elegant Data Visualisations Using the Grammar of Graphics

```{r, echo=F}
xaringanExtra::use_progress_bar(color = "#000066", location = "bottom")
```

---

## 1 Learning Objectives `r emo::ji("idea")`

This lecture teaches you important tools for working with tabular data sets in `R`. It introduces and showcases a suite of packages which ease your data science workflow in terms of data import, data cleaning, data transformation and data visualization.

More specifically, after this lecture you will
- be familiar with the main tools of the `tidyverse` and how it differs from `base R`,<br><br>
- know your way around in working with the core packages of the `tidyverse` for importing, tidying, transforming and visualizing data,<br><br>
- be proficient in processing (*non-tidy*) data of any shape and quality,<br><br>
- be able to produce high-quality, fully customizable visualizations,<br><br>
- have improved your overall data literacy.

???
especially highlight the last point: how you think about data, how you approach working with data whenever you open a new data set, build a mental model for data transformation operations

---

```{r child='./01_intro.Rmd', eval=T}
```

---

```{r child='./02_tidy_data.Rmd', eval=T}
```

---

```{r child='./03_magrittr.Rmd', eval=T}
```

---

```{r child='./04_tibble.Rmd', eval=T}
```

---

```{r child='./05_readr.Rmd', eval=T}
```

---

```{r child='./06_tidyr.Rmd', eval=T}
```

---

```{r child='./07_dplyr.Rmd', eval=T}
```

---

```{r child='./08_purrr.Rmd', eval=T}
```

---

```{r child='./09_ggplot2.Rmd', eval=T}
```

---

## Thank You!

.pull-left[
.center[`r emo::ji("thinking_face")` **Right now**]<br>
```{r, echo=F, fig.align='center'}
knitr::include_graphics("https://tenor.com/view/homer-daydreaming-thinking-simpsons-gif-8949118.gif")
```
]
.pull-right[
.center[`r emo::ji("nerd_face")` **After having mastered the `tidyverse`**]<br><br>
```{r, echo=F, fig.align='center'}
knitr::include_graphics("https://tenor.com/view/homer-gif-10571731.gif")
```
]

.footnote[
*Note: Eventually, not everything is great in the `tidyverse`. You should always be aware of its [downsides](https://github.com/matloff/TidyverseSkeptic/blob/master/READMEFull.md) and know when to return to using `base R`.*
]

---

## Further Resources

**Wickham, H./Grolemund, G. (2017):** R for Data Science: Visualize, Model, Transform, Tidy, and Import Data. URL: https://r4ds.had.co.nz/tidy-data.html. (*Best read for starting in the `tidyverse`*)

**Wickham, H./Navarro, D./Lin Pedersen, T. (2020):** ggplot2: Elegant Graphics for Data Analysis. 3rd. edition, Online Publication 2020. URL: https://ggplot2-book.org/.  (*Additional resource for diving deeper into the world of `ggplot2`*)

Stay up-to-date with recent developments in the `tidyverse`: https://www.tidyverse.org/blog/

Watch live-coding sessions related to the [TidyTuesday](https://github.com/rfordatascience/tidytuesday) Project, e.g., the episodes by David Robinson: https://www.youtube.com/user/safe4democracy/videos

## Credits

Educational resources are inspired by [workshop materials](https://github.com/rstudio-education/remaster-the-tidyverse) of Garrett Grolemund and [blog posts](https://education.rstudio.com/blog/2020/07/teaching-the-tidyverse-in-2020-part-1-getting-started/) by Mine Çetinkaya-Rundel of the RStudio Education team.

`tidyverse` [artworks and illustration](https://github.com/allisonhorst/stats-illustrations) are provided by Allison Horst.