1 RStudio

Create a new project in RStudio by going to File > New Project > New Directory. Select New Project if required. Then label the project name and the directory. An empty project is created. The R session has been refreshed. All variables are removed and the environment is cleared.

Create a new RMarkdown file by going to File > New File > RMarkdown.... Use the default options.

2 RMarkdown

---
title: "Reproducible research"
subtitle: "RaukR 2019 • Advanced R for Bioinformatics"
author: "Roy Francis"
output:
  rmarkdown::html_document:
    toc: true
    toc_float: true
    toc_depth: 3
    number_sections: true
    theme: united
    highlight: textmate
    df_print: paged
    code_folding: none
    self_contained: false
    keep_md: false
    encoding: "UTF-8"
    css: ["assets/lab.css"]
---

## Level 2 heading  
### Level 3 heading  
#### Level 4 heading  
##### Level 5 heading  
###### Level 6 heading 

+ Point one
+ Point two

> This is a block quote. This
> paragraph has two lines.

> 1. This is a list inside a block quote.
> 2. Second item.

2.3 Images

Images can be displayed from a relative local location using ![This is a caption](rr_lab_assets/gotland.jpg). For example:

This is a caption

By default, the image is displayed at full scale or until it fills the display width. The image dimension can be adjusted ![This is a caption](rr_lab_assets/gotland.jpg){width=40%}.

This is a caption

For finer control, raw HTML can be used. For example;

<img src="rr_lab_assets/gotland.jpg" width="150px">

Images can also be displayed using R code. Chunks option out.width in RMarkdown can be used to control image display size.

This image is displayed at a size of 200 pixels.

```{r,out.width=200}
knitr::include_graphics('rr_lab_assets/gotland.jpg')
``` 

This image is displayed at a size of 75 pixels.

```{r,out.width=75}
knitr::include_graphics('rr_lab_assets/gotland.jpg')
``` 

```
This is generic block code.
```

This is generic block code.

```{r}
str(iris)
```

str(iris)

## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

```{r,eval=FALSE}
ggplot(dfr4,aes(x=Month,y=fraction,colour=Year,group=Year))+
  geom_point(size=2)+
  geom_line()+
  labs(x="Month",y="Fraction of support issues")+
  scale_colour_manual(values=c("#000000","#E69F00","#56B4E9",
  "#009E73","#F0E442","#006699","#D55E00","#CC79A7"))+
  theme_bw(base_size=12,base_family="Gidole")+
  theme(panel.border=element_blank(),
        panel.grid.minor=element_blank(),
        panel.grid.major.x=element_blank(),
        axis.ticks=element_blank())
```

ggplot(dfr4,aes(x=Month,y=fraction,colour=Year,group=Year))+
  geom_point(size=2)+
  geom_line()+
  labs(x="Month",y="Fraction of support issues")+
  scale_colour_manual(values=c("#000000","#E69F00","#56B4E9",
  "#009E73","#F0E442","#006699","#D55E00","#CC79A7"))+
  theme_bw(base_size=12,base_family="Gidole")+
  theme(panel.border=element_blank(),
        panel.grid.minor=element_blank(),
        panel.grid.major.x=element_blank(),
        axis.ticks=element_blank())

```{r,eval=FALSE,echo=FALSE,fig.height=6,fig.width=7}
```

```{r,fig.height=6,fig.width=6}
plot(x=iris$Petal.Length,y=iris$Petal.Width)
```

2.6 Export

The RMarkdown notebook can be exported into various format. The most common formats are HTML and PDF.

2.6.1 HTML

The RMarkdown document can be previewed as an HTML inside RStudio by clicking the ‘Knit’ button.

The document can also be exported as an HTML file by running the code below:

rmarkdown::render("document.Rmd")

HTML documents can be opened and viewed in any standard browser such as Chrome, Safari, Firefox etc.

2.6.2 PDF

An Rmd document can be converted to a PDF. Behind the scenes, the markdown is converted to TeX format. The conversion to PDF needs a tool that understands TeX format and converts to PDF. This can be softwares like ‘MacTeX’, ‘MikTeX’ etc. which needs to be installed on the system beforehand.

The output argument in the YAML matter must be changed to pdf_document, and the Rmd file can be converted as follows:

rmarkdown::render("document.Rmd")

The PDF output can also be specified as such:

rmarkdown::render("document.Rmd",output_format=pdf_document())

Sometimes TeX converters may need additional libraries which may need to be installed. And all features of HTML are not supported on TeX which may return errors.

See here for other export formats.

3 Report

This is a do-it-yourself challenge to RMarkdown Notebook/Report. Have a look at the HTML page below and try to recreate the page. Instructions and helpful tips are given below.

This is the challenge report to prepare.

1. Create a new RMarkdown document
2. Set YAML matter

• Set title, subtitle, Author name.
• Set date to be automatically computed in this format “25/04/2018”. See here on how to format dates. Solution: date: "`r format(Sys.Date(),format='%d/%m/%Y')`"
• Show a floating table of contents on the top left of the page. See here on how to do this.
• Set automatic numbering of sections. Check ?html_document.
• Set theme to ‘flatly’.
• Set code highlighting to ‘kate’.
• Notice the little buttons on the right to show/hide code. Show this button and set all code to hidden by default. See here on how to do this.
• Set the default data.frame printing option to pageable HTML table. See here.

3. Add a Level 1 heading
4. Add some text and a Level 2 heading
5. Create an R chunk to display the structure of cars dataset. Display the code and the results. See here to read more about chunk options.
6. Display the first six rows and all columns of the cars dataset as a table.
7. Create a new sub-section and create a scatterplot with the two variables from cars dataset. Hide the code used and only show the plot. Create a square aspect ratio plot by setting equal figure height and width in the chunk options. Add a caption for the plot using chunk options.
8. Create a new sub-section, download a random image and include it in the document.
9. Render the document in RStudio by clicking on the ‘Knit’ button.
10. Render the document to an HTML file using a console command.
11. You can try to render this document to a PDF. Note that you will need to install additional software for this. For a lightweight latex distribution for knitr, see here. Change output type from html_document to pdf_document and remove all arguments except toc: true. Try rendering using ‘Knit’.

4 Pres • ioslides

ioslides is an HTML and javascript based presentation system. RMarkdown provides a way to use this framework through R purely using R code. Here is an example of an ioslide presentation.

This is a do-it-yourself challenge to ioslides Presentation using RMarkdown. Have a look at the presentation below and try to recreate it. Instructions and helpful tips are given below.

This is the final result for challenge ioslide presentation.

1. Create a new RMarkdown document.
2. Set YAML matter output to output: ioslides_presentation. See here for a guide to using ioslides presentation in R. See ?ioslides_presentation() for the options.
3. Set theme and highlight as needed.
4. New slides are defined using # with level 1 heading or ---- without a heading. Subtitle can be specified using a pipe symbol, like this # Title | Subtitle. Slides starting with # contains only the title and/or subtitle. ## adds a standard content slide with a heading on the top.
5. Add a title slide

# Title Slide | Subtitle

7. Add a standard slide with a heading and a bulleted list. For example

## Level 2 Heading

- Point one
- Point two

The bullets can set to show incrementally on click:

## Level 2 Heading

> - Point one
> - Point two

8. Create a new slide and add some R chunk. For example:

```{r}
data(iris)
str(cars)
```

9. Create a new slide and display a table by executing an R chunk. For example:

```{r}
head(cars)
```

10. Create a new slide and display an R plot. For example:

```{r,fig.height=3,fig.width=5,fig.cap="This is a scatterplot."}
plot(cars$speed,cars$dist)
```

11. Add an image to a new slide. For example:

![](rr_lab_assets/gotland.jpg){width=60%}

12. When viewing the presentation, press O to see an overview of the slides.

5 Pres • revealjs

revealJS is an HTML and javascript based presentation system. The revealjs R package provides a way to use this framework through R purely using R code. Here is an example of an revealJS presentation.

This is a do-it-yourself challenge to RevealJS 2-D Presentation using RMarkdown. Have a look at the presentation below and try to recreate it. Instructions and helpful tips are given below.

This is the challenge RevealJS presentation.

1. Install R package revealjs and load the library.
2. Create a new RMarkdown document.
3. Set YAML matter output to output: revealjs_presentation. See here for a guide to using RevealJS presentation in R. See ?revealjs_presentation for options.
4. Set theme and highlight as needed.
5. New slides are defined using # with level 1 heading,## with level 2 heading or ---- without a heading. The best feature of RevealJS is that slides are not restricted to horizontal (linear) flow. Slides can also flow vertical from any of the horizontal slides. A level 2 heading ## signifies that the content under that flows vertically.
6. Add a level 1 heading and add some text content.
7. Create a new slide vertically by adding a level 2 heading and add some R chunk. For example:

## Level 2 Heading

```{r}
data(iris)
str(cars)
```

9. Create a new slide vertically using level 2 heading and display a table by executing an R chunk. For example:

## Level 2 Heading

```{r}
head(cars)
```

10. Create a new slide vertically and display an R plot. For example:

## Level 2 Heading

```{r,fig.height=3,fig.width=5,fig.cap="This is a scatterplot."}
plot(cars$speed,cars$dist)
```

11. Create a new slide horizontally. Add an image to a new slide. For example:

![](rr_lab_assets/gotland.jpg)

12. When viewing the presentation, press O to see an overview of the slides.

6 Pres • xaringan

xaringan is an R package that provides bindings to remarkjs HTML and javascript based presentation system. Here is an example of a remarkjs presentation. Here is an example that actually uses the xaringan package in R.

The remarkjs repo and wiki pages and the xaringan repo and wiki are good sources of documentation.

This is a do-it-yourself challenge to remarkjs presentation system using RMarkdown in xaringan. Have a look at the presentation below and try to recreate it. Instructions and helpful tips are given below.

This is the challenge Xaringan presentation.

1. Install R package xaringan and load the library.
2. Create a new RMarkdown document.
3. Set YAML matter output to output: xaringan::moon_reader. Use ?moon_reader() for options.
4. Set theme and highlight as needed.
5. New slides are defined using ---. Headings as usual are # for level 1 heading,## with level 2 heading etc.
6. Add a level 1 heading and add some bullet points.

# Bullet points

- Point One
- Point Two

7. Note that Xaringan/remarkjs does not use the regular pandoc markdown, therefore some features may not work or may be different. For example superscript is created using 2<sup>10</sup> rather than 2^10^. Subscript is created using 2<sub>10</sub> rather than 2~10~.

8. Add some code content. For example;

```{r}
data(iris)
str(cars)
```

9. Slides can be named which helps to create links back to this slide. Create a new slide and add a name like this name: name. Add a heading and add an R plot on this slide.

---
name: plots

# Plots

```{r,fig.height=3,fig.width=5,fig.cap="This is a scatterplot."}
plot(cars$speed,cars$dist)
```

Note that the slide name is added immediately under the new slide creator ---.

11. Create a new slide with a table and name the slide table.

---
name: table

# Table

```{r}
head(cars)
```

From this slide, create a link to go back to plots.

Click [here](#plots) to go to plots.

12. Create a new slide and add an image.

---
name: image

# Image

![](rr_lab_assets/gotland.jpg)

If this is too big, you can manually set the width using raw HTML <img src="rr_lab_assets/gotland.jpg" width="250px">

13. It is possible to highlight parts of code. For example, in this code,

```{r}
ggplot(iris,aes(x=Petal.Length,Sepal.Width,col=Species))+
  geom_point(size=2)+
  labs(x="Petal Length",y="Sepal Width")+
  scale_colour_manual(values=c("#000000","#E69F00","#56B4E9",
  "#009E73","#F0E442","#006699","#D55E00","#CC79A7"))+
  theme_bw(base_size=12)+
  theme(panel.border=element_blank(),
        panel.grid.minor=element_blank(),
        panel.grid.major.x=element_blank(),
        axis.ticks=element_blank())
```

say, we want to highlight theme_bw(base_size=12). We can add {{}} around it. Add the below code to a new slide called highlighting.

```{r}
ggplot(iris,aes(x=Petal.Length,Sepal.Width,col=Species))+
  geom_point(size=2)+
  labs(x="Petal Length",y="Sepal Width")+
  scale_colour_manual(values=c("#000000","#E69F00","#56B4E9",
  "#009E73","#F0E442","#006699","#D55E00","#CC79A7"))+
  {{theme_bw(base_size=12)}}+
  theme(panel.border=element_blank(),
        panel.grid.minor=element_blank(),
        panel.grid.major.x=element_blank(),
        axis.ticks=element_blank())
```

14. Try out some math expressions. Paste a few of the line below to your slide.

$e^{i\pi} + 1 = 0$
$$\frac{E \times X^2 \prod I}{2+7} = 432$$
$$\sum_{i=1}^n X_i$$
$$\int_0^{2\pi} \sin x~dx$$

Add a -- between each line to display them incrementally.

$e^{i\pi} + 1 = 0$

--

$$\frac{E \times X^2 \prod I}{2+7} = 432$$

--

$$\sum_{i=1}^n X_i$$

--

$$\int_0^{2\pi} \sin x~dx$$

15. When viewing the presentation, press H to see an overview of keyboard shortcuts during presentation.

16. remarkjs has a nifty feature where you can press C to clone the presentation in a new browser window. Now both these copies are linked. You can change one of them to the presenter mode by pressing P. Now changing the slide in one window changes the other. Convenient when a presenter view/audience view is needed.

7 Session info

## R version 3.5.2 (2018-12-20)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
## 
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     datasets  grDevices utils     graphics  methods   base     
## 
## other attached packages:
##  [1] revealjs_0.9     xaringan_0.9     ggrepel_0.8.0    stringr_1.4.0   
##  [5] captioner_2.2.3  gdtools_0.1.8    gridExtra_2.3    kableExtra_1.1.0
##  [9] ggplot2_3.1.1    tidyr_0.8.3      dplyr_0.8.0.1    bookdown_0.9    
## [13] knitr_1.23      
## 
## loaded via a namespace (and not attached):
##  [1] httr_1.4.0        pkgload_1.0.2     jsonlite_1.6     
##  [4] viridisLite_0.3.0 shiny_1.3.2       assertthat_0.2.1 
##  [7] highr_0.8         yaml_2.2.0        remotes_2.0.4    
## [10] sessioninfo_1.1.1 pillar_1.3.1      backports_1.1.4  
## [13] glue_1.3.1        digest_0.6.19     promises_1.0.1   
## [16] rvest_0.3.3       colorspace_1.4-1  cowplot_0.9.4    
## [19] htmltools_0.3.6   httpuv_1.5.1      plyr_1.8.4       
## [22] pkgconfig_2.0.2   devtools_2.0.2    rmdshower_2.1.1  
## [25] purrr_0.3.2       xtable_1.8-3      scales_1.0.0     
## [28] webshot_0.5.1     processx_3.3.0    later_0.8.0      
## [31] tibble_2.1.1      usethis_1.5.0     ggpubr_0.2       
## [34] withr_2.1.2       lazyeval_0.2.2    cli_1.1.0        
## [37] magrittr_1.5      crayon_1.3.4      mime_0.6         
## [40] memoise_1.1.0     evaluate_0.14     ps_1.3.0         
## [43] fs_1.2.7          xml2_1.2.0        pkgbuild_1.0.3   
## [46] Cairo_1.5-10      rsconnect_0.8.13  tools_3.5.2      
## [49] data.table_1.12.2 prettyunits_1.0.2 hms_0.4.2        
## [52] plotly_4.9.0      munsell_0.5.0     callr_3.2.0      
## [55] compiler_3.5.2    tinytex_0.13      rlang_0.3.4      
## [58] grid_3.5.2        rstudioapi_0.10   htmlwidgets_1.3  
## [61] crosstalk_1.0.0   base64enc_0.1-3   labeling_0.3     
## [64] rmarkdown_1.13    testthat_2.0.1    gtable_0.3.0     
## [67] curl_3.3          reshape2_1.4.3    R6_2.4.0         
## [70] ggiraph_0.6.1     rprojroot_1.3-2   readr_1.3.1      
## [73] desc_1.2.0        stringi_1.4.3     Rcpp_1.0.1       
## [76] tidyselect_0.2.5  xfun_0.7

Built on: 08-Jun-2019 at 19:58:37.