slides.qmd

---
title: "Introduction to reproducible data analysis with R and Quarto"
title-slide-attributes:
    data-background-color: "#a13c65"
    data-background-image: "images/LMU_Logo.png"
    data-background-size: 15%
    data-background-position: 50% 83%
subtitle: "KLI Seminar 2023"
author:
  - name: Anne-Kathrin Kleine
format:
  revealjs: 
    slide-number: true
    width: 1500
    height: 1000
    chalkboard: 
      buttons: false
    preview-links: false
    footer: <https://quarto.org>
    theme: [simple, custom.scss]
    logo: "images/LMU_Logo.png"
    scrollable: false
    center: true
    incremental: false
    link-external-newwindow: true
resources:
  - demo.pdf
---

```{r include=FALSE}
library(fontawesome)
library(tidyverse)
library(quarto)
library(gt)
library(gtExtras)
```

# Schedule {background-color="#a13c65"}

## DAY 1

::: fragment
### 9.00-9.30: Introduction

-   Welcome and overview
-   Reproducibility and open science
:::

::: fragment
### 9.40-10.30: Getting Started with R and Quarto

-   Best practices for organizing your code and materials
-   File and folder structure
:::

------------------------------------------------------------------------

### 11.00-12.00: Creating data analysis projects in R and quarto

-   Basics of R programming
-   Creating a data analysis project
-   Integrating code, text, and output within a Quarto document

::: fragment
### 12.15-13.00: Working with data

-   Working with data in R: importing, manipulating, and exporting data
-   Data visualization and data analysis
:::

## DAY 2

::: fragment
### 09.00-10.00: Publishing Reproducible Data Analysis Scripts

-   Sharing your scripts and data
-   Publishing through GitHub, RPubs; integration with osf
:::

::: fragment
### 10.15-11.00: Hands-on Practice PART I

-   Guided exercise: Data analysis project using R and Quarto
-   Independent practice: Working on your data analysis project with support from workshop facilitator
:::

------------------------------------------------------------------------

### 11.30-12.15: Hands-on Practice PART II

::: fragment
### 12.00-13.00: Closing Remarks and Resources

-   Recap of the workshop
-   Additional resources for learning R, Quarto, reproducibility best practices, version control and collaboration
-   Q&A session
:::

## Workshop Requirements

-   Are you on the [latest version of RStudio](https://posit.co/download/rstudio-desktop/#download)?

. . .

-   Open RStudio

------------------------------------------------------------------------

## Install relevant packages

```{r}
#| echo: fenced
#| eval: false
pkg_list <- c("tidyverse", "haven", "flextable", "broom", "report", "effectsize", "rempsyc")
install.packages(pkg_list)
```

# Reproducibility and open science {background-color="#a13c65"}

------------------------------------------------------------------------

## Reproducibility and replicability

::: callout-tip
## Reproducibility

**Reproducibility** means that research data and code are made available so that others are able to reach the same results as are claimed in scientific outputs. Closely related is the concept of **replicability**, the act of repeating a scientific methodology to reach similar conclusions. These concepts are core elements of empirical research.

::: fragment
Reproducibility emphasizes re-using original data and methods for verification, while replicability focuses on achieving consistent results with repeats of the experiment under similar conditions but using new data.
:::
:::

::: {style="font-size: 50%;"}
From [The Open Science Training Handbook](https://open-science-training-handbook.github.io/Open-Science-Training-Handbook_EN/02OpenScienceBasics/04ReproducibleResearchAndDataAnalysis.html)
:::

------------------------------------------------------------------------

## Open Science for ensuring reproducibility and replicability

![](images/open_science.png)

::: {style="font-size: 50%;"}
Source: [The six core principles of Open Science](https://www.researchgate.net/figure/The-six-core-principles-of-Open-Science-which-guide-the-Open-Traits-Network_fig1_332352194)
:::

------------------------------------------------------------------------

## The benefits of Open Science for behavioral researchers

![](images/open-science-benefits.jpeg)

::: {style="font-size: 50%;"}
Source: [The benefits of Open Science](https://phoebekoundouri.org/what-is-open-science/)
:::

## The role of R and Quarto (RMarkdown) for Open Science

::: fragment
### R and Quarto are open-source

-   ensuring accessibility, collaboration, customizability, longevity
:::

::: fragment
### Producing shareable and collaborative high-quality documents that integrate text and R code for analysis and visualization within one document
:::

::: fragment
-   minimize "false" results in output documents because nothing needs to be copy-pasted
-   "coding" of research papers (easy integration of later modifications)
:::

# Change Your Mental Model {background-color="#a13c65"}

## 

::: columns
::: {.column width="50%"}
Source

![](images/word.png){width="700px" fig-alt="A blank Word document"}
:::

::: {.column width="50%"}
Output

![](images/word.png){width="700px" fig-alt="A blank Word document"}
:::
:::

## 

::: columns
::: {.column width="50%"}
Source

````         
---
title: "ggplot2 demo"
author: "Norah Jones"
date: "5/22/2021"
format: 
  html:
    fig-width: 8
    fig-height: 4
    code-fold: true
---

## Air Quality

@fig-airquality further explores the impact of temperature 
  on ozone level.

```{{r}}
#| label: fig-airquality
#| fig-cap: Temperature and ozone level.
#| warning: false
library(ggplot2)
ggplot(airquality, aes(Temp, Ozone)) + 
  geom_point() + 
  geom_smooth(method = "loess"
)
```
````
:::

::: {.column width="50%"}
Output ![](https://quarto.org/images/hello-knitr.png)
:::
:::

# Getting Started with R {background-color="#a13c65"}

## [Getting Started with R](https://rafalab.dfci.harvard.edu/dsbook/getting-started.html)

![](images/rstudio.png)

# Creating a data analysis project {background-color="#a13c65"}

## Create a folder for your R project

![](images/folder_new.png)

------------------------------------------------------------------------

## Create a Quarto document (report.qmd)

![](images/new_r.png)

------------------------------------------------------------------------

## Create an R project

![](images/rproj.png)

------------------------------------------------------------------------

## R projects

When a project is opened within RStudio the following actions are taken:

. . .

-   A new R session is started

. . .

-   The .Rprofile, .RData file, and .Rhistory files in the project's main directory are loaded

. . .

-   **The current working directory is set to the project directory**

. . .

-   Previously edited source documents are restored into editor tabs

. . .

-   Other RStudio settings (e.g. active tabs, splitter positions, etc.) are restored to where they were the last time the project was closed

# Working with Quarto {background-color="#a13c65"}

## How does Quarto work?

![](images/rmd-knitr.png){fig-alt="A diagram of how a RMD is turned into output formats via knitr and pandoc"}

------------------------------------------------------------------------

## So what is Quarto?

> Quarto is a command line interface (CLI) that renders plain text formats (`.qmd`, `.rmd`, `.md`) into static PDF/Word/HTML reports, books, websites, presentations and more

## One install, "Batteries included"

-   Quarto is bundled and comes pre-installed with RStudio [v2022.07.1](https://www.rstudio.com/products/rstudio/download/#download) and beyond!

-   [more infos](https://quarto.org/docs/computations/r.html#installation)

## Rendering

1.  Render button

. . .

![](https://quarto.org/docs/tools/images/rstudio-render.png){fig-alt="A screenshot of the render button in RStudio" width="1000px"}

## Rendering

2.  System shell via `quarto render`

::: {style="font-size: 60px;"}
``` {.bash filename="terminal"}
quarto render document.qmd # defaults to html
quarto render document.qmd --to pdf
quarto render document.qmd --to docx
```
:::

------------------------------------------------------------------------

### Quick excourse: *Power Shell* and *Terminal* {background-color="#DADEFD"}

::: columns
::: {.column width="50%"}
[Windows Power Shell](https://learn.microsoft.com/en-us/powershell/scripting/discover-powershell?view=powershell-7.2)

-   Click Start, type PowerShell, right-click Windows PowerShell, and then click Run as administrator

![](images/power_shell.png){width="600px"}

[Image source: Wikipedia](https://upload.wikimedia.org/wikipedia/de/f/f6/Windowspowershell_unter_Windows_8.png)
:::

::: {.column width="50%"}
[Mac Terminal](https://support.apple.com/en-gb/guide/terminal/apd5265185d-f365-44cb-8b09-71a064a42125/mac)

-   Click the Launchpad icon in the Dock, type Terminal in the search field, then click Terminal

![](images/terminal.png){width="700px"}

[Image source: Wikipedia](https://en.wikipedia.org/wiki/Terminal_(macOS)#/media/File:Appleterminal2.png)
:::
:::

## Rendering

3.  R console via `quarto` R package

::: {style="font-size: 60px;"}
```{r}
#| eval: false
#| echo: fenced
library(quarto)
quarto_render("document.qmd") # defaults to html
quarto_render("document.qmd", output_format = "pdf")
```
:::

# Working with a `.qmd` {background-color="#a13c65"}

## A `.qmd` is a plain text file

. . .

-   Metadata (YAML)

::: {style="font-size: 60px;"}
``` yaml
format: html
engine: knitr
```
:::

. . .

-   Code

::: {style="font-size: 60px;"}
```{{r}}
library(dplyr)
mtcars |> 
  group_by(cyl) |> 
  summarize(mean = mean(mpg))
```
:::

. . .

-   Text

::: {style="font-size: 60px;"}
``` markdown
# Heading 1
This is a sentence with some **bold text**, *italic text* and an 
![image](image.png){fig-alt="Alt text for this image"}.
```
:::

## Metadata: YAML

The [YAML](https://yaml.org/) metadata or header is:

> influences the final document in different ways. It is placed at the very beginning of the document and is read by each of Pandoc, Quarto and `knitr`. Along the way, the information that it contains can affect the code, content, and the rendering process.

## YAML

::: {style="font-size: 70px;"}
``` yaml
title: "My Document"
format: 
  html:
    toc: true
    code_folding: 'show'
```
:::

See more formats and other YAML metadata options [here](https://quarto.org/docs/output-formats/all-formats.html)

## Why YAML?

To avoid manually typing out all the options, every time!

. . .

::: {style="font-size: 70px;"}
``` {.bash filename="terminal"}
quarto render document.qmd --to html
```
:::

<br>

. . .

::: {style="font-size: 70px;"}
``` {.bash filename="terminal"}
quarto render document.qmd --to html -M code fold:true
```
:::

<br>

. . .

::: {style="font-size: 70px;"}
``` {.bash filename="terminal"}
quarto render document.qmd --to html -M code-fold:true -P alpha:0.2 -P ratio:0.3
```
:::

## Quarto workflow

Executing the Quarto Render button in RStudio will call Quarto render in a background job - this will prevent Quarto rendering from cluttering up the R console, and gives you and easy way to stop.

![](images/background-job.png)

## Markdown

> Quarto uses markdown as its underlying document syntax. Markdown is a plain text format that is designed to be easy to write, and, even more importantly, easy to read

## Text Formatting

+-----------------------------------+-------------------------------+
| Markdown Syntax                   | Output                        |
+===================================+===============================+
| ```                               | *italics* and **bold**        |
| *italics* and **bold**            |                               |
| ```                               |                               |
+-----------------------------------+-------------------------------+
| ```                               | superscript^2^ / subscript~2~ |
| superscript^2^ / subscript~2~     |                               |
| ```                               |                               |
+-----------------------------------+-------------------------------+
| ```                               | ~~strikethrough~~             |
| ~~strikethrough~~                 |                               |
| ```                               |                               |
+-----------------------------------+-------------------------------+
| ```                               | `verbatim code`               |
| `verbatim code`                   |                               |
| ```                               |                               |
+-----------------------------------+-------------------------------+

## Headings

+-------------------+-----------------+
| Markdown Syntax   | Output          |
+===================+=================+
| ```               | # Header 1      |
| # Header 1        |                 |
| ```               |                 |
+-------------------+-----------------+
| ```               | ## Header 2     |
| ## Header 2       |                 |
| ```               |                 |
+-------------------+-----------------+
| ```               | ### Header 3    |
| ### Header 3      |                 |
| ```               |                 |
+-------------------+-----------------+
| ```               | #### Header 4   |
| #### Header 4     |                 |
| ```               |                 |
+-------------------+-----------------+

## Code

```{r}
#| echo: fenced
#| output-location: column
#| fig-cap: Temperature and ozone level.
#| warning: false
library(ggplot2)
ggplot(airquality, aes(Temp, Ozone)) + 
  geom_point() + 
  geom_smooth(method = "loess")
```

# Data importing, manipulating, and exporting {background-color="#a13c65"}

## Get data

-   Download [the example dataset](https://github.com/AnneOkk/attitudes_study/tree/main/Data)
-   Store data file in "data/raw" folder

## Reading data in

```{r}
#| echo: fenced
## Read in data
library(readxl)
library(tidyverse)
data <- read_excel("Exercise/data/raw/data.xlsx", col_names = T) # if you created an R project you can use the direct path "data/raw/data.xlsx"
```

## Manipulating data

```{r}
#| echo: fenced
# Name correction
names(data) <- gsub("d2priv", "dapriv2", names(data))

# Own function
recode_5 <- function(x) {               
  x * (-1)+6
}

# Use of mutate_at to apply function
data_proc <- data %>%
  mutate_at(vars(matches("gattAI1_3|gattAI1_6|gattAI1_8|gattAI1_9|gattAI1_10|gattAI2_5|gattAI2_9|gattAI2_10")), recode_5)
```

## Exporting data

```{r}
#| echo: fenced
library(haven)
write_sav(data_proc, "Exercise/data/processed/data_proc.sav") # if you created an R project you can use the direct path "data/processed/data_proc.sav"
```

# The R folder {background-color="#a13c65"}

## Storing and sourcing custom functions from the R folder

-   You may store your custom functions in a separate file (e.g., "R/custom-functions.R") that you may source in your report document ("report.qmd")

**In R/custom-functions.R**:

```{r}
#| echo: fenced
# Own function
recode_5 <- function(x) {               
  x * (-1)+6
}
```

**Reading in your custom functions**:

```{r}
#| echo: fenced
source("Exercise/R/custom-functions.R")
```

# Tables and visualisations {background-color="#a13c65"}

## Reading in data

```{r}
#| echo: fenced
data <- haven::read_sav("Exercise/data/processed/data_proc.sav")
```

## Creating a correlation table

### Creating composites

```{r}
#| echo: fenced
data <- data[ , purrr::map_lgl(data, is.numeric)] %>% # select numeric variables
  select(matches("gattAI1|soctechblind|trust1|anxty1|SocInf1|Age")) # select relevant variables

comp_split <- data %>% sjlabelled::remove_all_labels(.) %>% 
  split.default(sub("_.*", "", names(data))) # creating a list of dataframes, where each dataframe consists of the columns from the original data that shared the same prefix (all characters before the underscore)

comp <- purrr::map(comp_split, ~ rowMeans(.x, na.rm=T)) #calculating the row-wise mean of each data frame in the list `comp_split`, with the output being a new list (`comp`) where each element is a numeric vector of row means from each corresponding data frame in `comp_split`

comp_df <- do.call("cbind", comp) %>% as.data.frame(.) # binding all the elements in the list `comp` into a single data frame, `comp_df`
```

## Creating the correlation table

```{r}
#| echo: fenced
cor_tab <- corstars(comp_df, removeTriangle = "upper")
cor_tab
```

## Creating correlation plot

```{r}
#| echo: fenced
cor_matrix <- cor(comp_df[1:6])

corrplot::corrplot(cor_matrix, method="color", type="upper", order="hclust", 
         addCoef.col = "black", # Add correlation coefficient on the plot
         tl.col="black", # Text label color
         tl.srt=90, # Text label rotation
         title="Correlation matrix", mar=c(0,0,1,0))

```

## Scatterplot

```{r}
#| echo: fenced
scatter <- ggplot(comp_df, aes(x=SocInf1, y=trust1, color=Age)) +
  geom_point() +
  labs(x="Anxiety", y="GattAI1", color="Age") +
  theme_minimal() +
  ggtitle("Scatterplot of social influence and trust colored by age")
scatter
```

## Saving scatterplot

```{r}
#| echo: fenced
ggsave(filename="Exercise/output/figs/scatter.png", plot=scatter)
```

# Referencing, style and the config folder {background-color="#a13c65"}

## Bibliography and csl style documents

::: {style="font-size: 60px;"}
``` yaml
___
bibliography: "/config/refs.bib"
csl: "/config/apa.csl"
---
```
:::

![](images/zotero_bib.png)

## Select a csl style

[Zotero Style Repository](https://www.zotero.org/styles?q=APA)

![](images/zotero_style.png)

## Store a template word document

::: {style="font-size: 60px;"}
``` yaml
format: 
  docx:
    reference-doc: "/config/template_apa.docx"
```
:::

# Folder structure overview {background-color="#a13c65"}

## Current folder structure

![](images/folder_structure.png)

## Back to "report.qmd"

. . .

### The visual editor and inserting text and citations

![](images/visual_ed.png)

# Questions and outlook

## Questions?

## Outlook

-   Tomorrow we will look into data analysis publication for true reproducibility

. . .

-   You will get the chance to work on your own data analysis project or use the material provided