Two of our esteemed OPIGlets presented a workshop on collaborative research using Jupyter Notebook this week at ISMB in Chicago. Their workshop highlights the importance of finding ways to share your work conveniently and reproducibly. So on a related note, I thought I would share a brief introduction to another useful tool, R Markdown with RStudio, which I use to present updates to various supervisors and to remember what I did three months (or three days) ago. This method of sharing work is highly readable, reproducible, and narrative-driven.

I use R for much of my data analysis and all of my visualisation, and I count the tidyverse among my most beloved friends. If you’re so inclined, it’s easy to execute python, bash, and more from within R Markdown. You also don’t need to use RStudio to use R Markdown, but that’s a whole other story.

Starting a new markdown file in RStudio will generate a template script explaining most of what you need to know. If I showed you that then I’d be out of a blog post, but I will at least link to the R Markdown Reference Guide.

R Markdown files consist of text written in markdown, and code chunks that can be individually executed and displayed inline within RStudio. To “knit” the whole thing together, the knitr package is used to execute and combine code chunks, then pandoc converts the whole thing into an attractive document.

Here’s an example. The metadata at the top sets up the document. I’ll be generating an html document here, but notice some other tempting examples commented out. Yes, you can use it for Latex (swoon). You can even make a Word document, but really, why would you?

---
title: "Informative Title"
author: "Clare E. West"
date: "10/07/2018"
output: html_document
#output: beamer_presentation
#output: pdf_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(knitr)
library(ggplot2)
library(tidyr)
library(dplyr)
```

## Big Title
### Smaller title

R Markdown scripts have the extension .Rmd

R Markdown is __so__ *fun*. You can read all about it [here](https://www.rstudio.com/wp-content/uploads/2015/03/rmarkdown-reference.pdf).

```{r}
print("Hello world")
```

Notice that chunks are enclosed within three backticks, with the language and options in braces. Single commands can be executed inline using single backticks.

As highlighted in the example above, global options are set like this:

knitr::opts_chunk$set(echo = TRUE)

“echo=TRUE” means that the code in each chunk is displayed in the final product; this is useful to show collaborators (or your future self) exactly how you did something. Change this option (“echo = FALSE”) globally or in individual blocks to prevent code from printing. This is useful to hide uninteresting commands, or when presenting to people who don’t have the time or inclination to read your code (hard to imagine). Notice I’ve also used “include = FALSE”  for the library-loading code chunk, which means evaluate but don’t include in the output. Another useful option is “eval = FALSE”, which means don’t even run this chunk.

So let’s see what that looks like when we render it:

The above example output as HTML

The above example output as Latex

Plots generated in code chunks or images from other sources can be embedded. Set the width in the options. “fig.width” sets the width (in inches) of the figure generated, while “out.width” scales the image in the final documents, for which the units will depend on the document type. Within RStudio, these are previewed inline below the code chunk.

## Including plots/images
```{r fig.width = 4, fig.height = 3, out.width = "400px", echo=FALSE}
t  %>% group_by(Tour, Winner, N, Tournament) %>% filter(WRank <= 20) %>% summarise(WPts = max(WPts))  %>% ggplot(aes(x=N, y=WPts, group=Winner, colour=(Winner=="Murray A."))) + geom_point() + geom_line() + labs(x="Tournament Number",y="Ranking Points") + scale_colour_discrete("",labels=c("Not Andy Murray", "Andy Murray")) + theme_bw() + theme(legend.position = "bottom", legend.margin = margin(0, 0, 0, 0))
knitr::include_graphics("https://s.yimg.com/ny/api/res/1.2/69ZUzNSMYb09GKd8CNJeew--~A/YXBwaWQ9aGlnaGxhbmRlcjtzbT0xO3c9ODAwO2g9NjAw/http://media.zenfs.com/en_us/News/afp.com/0102e1f7d0d3c35303c8a62d56a5eb79c2c8b4d8.jpg")
```

Rather than just printing data R-style, you can nicely format it into a table using kable (part of knitr). I also style mine using kableExtra, which makes it look nice and gives you extra options. By default tables fill the full width, you can override this using e.g. kable_styling(full.width = FALSE, position = “left”). When making a latex document, use kable(table, booktabs = T, “latex”) to get a (reproducible) latex-style table.

Here’s how to use python and bash. Thanks to the package reticulate, you can even share objects between your R and Python chunks. Exclude reticulate (knitr::opts_chunk$set(python.reticulate=FALSE) if you prefer to keep your languages separate.

### Mix it up with python
```{python}
a='Wow python'
print(a.split()[0])
```

What a wild ride. 

### or bash

```{bash, echo=TRUE}
ls | head 
```

Oh look, there's our output, ready to share.

Finally, if you hate GUIs – and you know I do – you can ditch the interactive notebook part and just generate documents from R Markdown files like this:

rmarkdown::render("BlogExample.Rmd")

D3 is a JavaScript Library that allows the creation of interactive data visualisations. D3 stands for Data-Driven Documents and its advantage is that it is using internet standards as HTML, CSS, and SVG as the foundation. This gives maximal compatibility across all moderns browsers. It is widely used by journalists, data scientists, and starts to be used by academic scientists, too.

Here I want to present a simple way of creating interactive illustrations of collaboration networks, e.g., for your own website. Before going into details, have a look at the example below, it presents some of Rosalind Franklin’s papers and her co-authors.

[d3-source canvas=”wpd3-3903-2″]

The network illustration is a so-called bipartite network because it consists of two types of nodes, blue nodes represent scientists and orange nodes represent publications. These nodes are connected if a scientist is an author on a publication. This way of presenting is a so-called force layout, which simulates repelling Coulomb forces between all nodes and spring-like attractive forces act on nodes that are connected via links. You can drag the nodes around and explore the behaviour of the network.

You will have noticed that you can also see the name of the publications and co-authors when you hover over the nodes. For some of them, a representative figure or photograph is shown, too. You can also double-click the nodes and will be directed to the webpage of the publication or author.

For larger collaboration networks the visualisation is still possible but might get a bit messy. Thus, not showing any figures is advised. Furthermore, the name of the nodes should be shown either below the illustration or as a tooltip (the later is not possible in WordPress but on normal web pages as here). The illustration below shows all 124 publications written by OPIG, together with the 310 authors. Here, I had to remove the Head of the Group Charlotte Deane, as she is on almost all of these papers and would clutter the illustration, unfortunately. In network science, we call such a node an ego node.

[d3-source canvas=”wpd3-3903-0″]

If you want to play around with this, check out my Github repository. The creation of the network is fairly simple. You only need a Bibtex file and can then execute a Python script that creates a JSON file that is read into the JavaSript. This can then be incorporated in all web pages easily.

PS: To enable D3 in WordPress you need a special plugin.  Apparently, it is not up to date with the current stable D3 version 4 but you can load any missing functions manually.

Here I’ll share my set-up for writing Latex with gvim instead of a separate Latex editor. If you are text-editor averse, this blog post is not for you. But if, like me, you love vim and hate useless GUIs, this might be helpful.

We’re lucky to have nice big screens in the Stats Department, but I tend to prefer writing on my MacBook (I find it’s easier to transport to e.g. a cafe, my home, etc). Until now, I’ve been happily using TexMaker for writing, but during a recent period of intense Latexing I started to find the useable screen space oppressively small. The unnecessary GUI had to go.   

No offence TexMaker but I don’t like you

One of our good friends in Statistical Genetics recommended some things to help me with the transition to just using good old (g)vim, which I will now recommend to you.

The key thing is the LaTex-Box plug-in for vim, which gives you the compilation commands, as well as the essentials such as smart indentation, highlight matching, command completion, etc. I used pathogen to install it (see the GitHub for instructions).

Of course, you can then customise your .vimrc file to add more helpful things. This can be the simple preferences, such as using a light background when using gvim:

if has(“gui_running”)

        set background=light

endif

You can also do more complicated magic like tabbing through available commands, and the ability to minimise sections, etc. Sidenote: to make working with paragraphs easier, I recommend setting the up/down arrows to move the cursor to the next line in the GUI rather than the next actual line. I prefer overriding this behaviour only in gvim, while leaving the normal behaviour in vim (for actual coding). But each to their own.

To get started, open a .tex file, then compile and view the document with the command Latexmk.

Command suggestions are an example of a magical feature added in .vimrc

The configurations for this command are set in the file .latexmkrc. Mine looks like this:

$recorder = 1;
$pdf_mode = 1;
$bibtex_use = 2;
$pdflatex = "pdflatex --shell-escape %O %S";
$pdf_previewer = "start open -a skim %O %S";

My pdf viewer of choice on Mac is Skim, which autoupdates. I view the source and preview at the same time using split view. Please admire the beauty below:

Wow what a beautiful screen

My favourite part is that whenever you save (w), it recompiles and updates the preview. As someone who accidentally types :w everywhere that isn’t vim, it’s nice that this is now productive. It also recompiles automatically if the .bib file is updated. Note that if you have errors at compilation (I’m sure you don’t), you can view them with the command LatexErrors.

Now you too can be a (nearly) GUI-free lightweight Latexer. Enjoy!