slides.Rmd

author: "Laurent Modolo [laurent.modolo@ens-lyon.fr](mailto:laurent.modolo@ens-lyon.fr)"
date: "08 Nov 2019"
output:
  beamer_presentation:
    theme: metropolis
    slide_level: 2
    fig_caption: no
    df_print: tibble
    highlight: tango
    latex_engine: xelatex
  slidy_presentation:
    highlight: tango

knitr::opts_chunk$set(echo = FALSE)

R Markdown

R Markdown provides an unified authoring framework for data science, combining your code, its results, and your prose commentary. R Markdown documents are fully reproducible and support dozens of output formats, like PDFs, Word files, slideshows, and more.

You need the rmarkdown package, but you don’t need to explicitly install it or load it, as RStudio automatically does both when needed.

knitr::include_graphics('img/rmarkdownflow.png')

R Markdown

R Markdown files are designed to be used in three ways:

• For communicating to decision makers, who want to focus on the conclusions, not the code behind the analysis.
• For collaborating with other data scientists (including future you!), who are interested in both your conclusions, and how you reached them ( i.e. the code).
• As an environment in which to do data science, as a modern day lab notebook where you can capture not only what you did, but also what you were thinking.

Markdown language

Text formatting

*italic* or _italic_

• italic or italic

**bold** __bold__

• bold bold

`code`

• code

superscript^2^ and subscript~2~

• superscript^2^ and subscript2

Markdown language

Headings

# 1st Level Header

## 2nd Level Header

### 3rd Level Header

Markdown language

Lists

*   Bulleted list item 1
*   Item 2
    * Item 2a
    * Item 2b

1.  Numbered list item 1
1.  Item 2. The numbers are incremented automatically in the
    output.

Markdown language

Links and images

<http://example.com>

[linked phrase](http://example.com)

![optional caption text](path/to/img.png)

Markdown language

Tables

First Header  | Second Header
------------- | -------------
Content Cell  | Content Cell
Content Cell  | Content Cell

Rmarkdown: markdown meet R

R code will be evaluated and printed

```{r} ```{r}r '' summary(cars$dist)

```` ``` ````


```{r}
summary(cars$dist)

Rmarkdown: markdown meet R

Inline R Code

There were `r nrow(cars) ` cars studied

There were r nrow(cars) cars studied

When inserting numbers into text use format()

Chunk options

Chunk output can be customised with options, arguments supplied to chunk header. Knitr provides almost 60 options that you can use to customize your code chunks.

Option	Run code	Show code	Output	Plots	Messages	Warnings
eval = FALSE	-		-	-	-	-
include = FALSE		-	-	-	-	-
echo = FALSE		-
results = "hide"			-
fig.show = "hide"				-
message = FALSE					-
warning = FALSE						-

Rmarkdown: kable

The tables syntax is tedious to use...

First Header  | Second Header
------------- | -------------
Content Cell  | Content Cell
Content Cell  | Content Cell

\pause

do it in R !

```{r kable} ```{r}r '' knitr::kable( mtcars[1:5, 1:5], caption = "A knitr kable." )

```` ``` ````

## Rmarkdown: `kable`

do it in R !

```{r kable}
knitr::kable(
  mtcars[1:5, 1:5], 
  caption = "A knitr kable."
)

Caching

Each knit of a document starts from a completely clean slate.

• This is great for reproducibility
• ensures that you’ve captured every important computation in code.
• can be painful if you have some computations that take a long time

The solution is cache = TRUE

Caching

```{r raw_data} ```{r}r '' rawdata <- readr::read_csv("a_very_large_file.csv")

```` ``` ````

```` ```{r processed_data, cache = TRUE, dependson = "raw_data"} ````
```{r}`r ''`
processed_data <- rawdata %>% 
  filter(!is.na(import_var)) %>% 
  mutate(new_variable = complicated_transformation(x, y, z))

```

YAML Header

You can control many “whole document” settings by tweaking the parameters of the YAML header

---
author: "Laurent Modolo"
date: "28 Nov 2019"
output:
  beamer_presentation:
    theme: metropolis
    highlight: tango
    latex_engine: xelatex
  slidy_presentation:
    highlight: tango
---

Bibliographies and Citations

Pandoc can automatically generate citations and a bibliography in a number of styles.

bibliography: rmarkdown.bib

You can change the style of your citations and bibliography by referencing a CSL (citation style language) file in the csl field:

bibliography: rmarkdown.bib
csl: apa.csl

Bibliographies and Citations

User the bibtex key:

Blah blah [@smith04].

Separate multiple citations with a ;:

Blah blah [@smith04; @doe99].

You can add arbitrary comments inside the square brackets:

Blah blah [see @doe99, pp. 33-35; also @smith04, ch. 1].

Remove the square brackets to create an in-text citation:

says blah, or @smith04 [p. 33] says blah.

Add a - before the citation to suppress the author's name:

Smith says blah [-@smith04].

Rmarkdown is not just for R

```{bash}

ls -l

```

ls -l

Rmarkdown is not just for R

```{python}

x = 1 ** 4
print(x)

```

x = 10 ** 4
print(x)