Last updated: 2019-04-09

Checks: 5 1

Knit directory: rrresearch/

This reproducible R Markdown analysis was created with workflowr (version 1.2.0). The Report tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: uncommitted changes

The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20190216)

The command set.seed(20190216) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Repository version: f517bba

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/.DS_Store
    Ignored:    assets/.DS_Store
    Ignored:    assets/img/.DS_Store
    Ignored:    data/.DS_Store
    Ignored:    data/metadata/
    Ignored:    demos/demo-rmd-0_files/
    Ignored:    demos/demo-rmd-1_files/
    Ignored:    demos/demo-rmd_files/

Untracked files:
    Untracked:  analysis/05_literate-prog.Rmd
    Untracked:  analysis/10_pkg_functions.Rmd
    Untracked:  analysis/assets/
    Untracked:  analysis/outro.Rmd
    Untracked:  data/birds.csv
    Untracked:  data/raw/
    Untracked:  data/stocks.csv
    Untracked:  data/trees.csv
    Untracked:  data/vst_mappingandtagging.csv
    Untracked:  data/vst_perplotperyear.csv
    Untracked:  docs/figure/
    Untracked:  figure/
    Untracked:  project_man.Rmd
    Untracked:  rmd/
    Untracked:  slides/

Unstaged changes:
    Modified:   analysis/index.Rmd

Staged changes:
    Modified:   analysis/_site.yml

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
html	558735a	Anna Krystalli	2019-04-09	commit draft docs

Literate programming

Programming paradigm first introduced by Donald E. Knuth.

Treat program as a literature understandable to human beings

move away from writing programs in the manner and order imposed by the computer
focus instead on the logic and flow of human thought and understanding
single document to integrate data analysis (executable code) with textual documentation, linking data, code, and text

Why is this important in science:

Calls for reproducibility

Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.

Fully scripted analyses pipelines
- from raw data to published tables and figures
Publication of code and data

Calls for open science

… highlight problems with users jumping straight into software implementations of methods (e.g. in r) that may lack documentation on biases and assumptions that are mentioned in the original papers.

To help solve these problems, we make a number of suggestions including providing blog posts or videos to explain new methods in less technical terms, encouraging reproducibility and code sharing, making wiki-style pages summarising the literature on popular methods, more careful consideration and testing of whether a method is appropriate for a given question/data set, increased collaboration, and a shift from publishing purely novel methods to publishing improvements to existing methods and ways of detecting biases or testing model fit. Many of these points are applicable across methods in ecology and evolution, not just phylogenetic comparative methods.

tl;dr

Modern open source technologies have given us great power
With great power comes great responsibility
You can share some of that burden by using these tools to open your work up to feedback and contribution by others. The more eyes the better.
Use them to provide and context around your work. Help more humans understand

Literate programming in R

rmarkdown (.Rmd) integrates:

– a documentantion language (.md)

– a programming language (R)

Combine tools, processes and outputs into interactive evidence streams that are easily shareable, particularly through the web.

Rmarkdown overview

Features

Rstudio features fly through

What is R Markdown? from RStudio, Inc. on Vimeo.

The researchers perspective

a reproducible workflow in action

elements of R markdown

markdown {`.md`}

stripped down `html`. User can focus on communicating & disseminating

intended to be as easy-to-read and easy-to-write as possible.
most powerful as a format for writing to the web.
syntax is very small, corresponding only to a very small subset of HTML tags.
clean and legible across platforms (even mobile) and outputs.
formatting handled automatically
html markup language also handled.

code {r, python, SQL, … }

Code chunks defined through special notation. Executed in sequence. Exceution of individual chunks controllable
Analysis self-contained and reproducible
- Run in a fresh R session every time document is knit.
A number of Language Engines are supported by knitr
- R (default)
- Python
- SQL
- Bash
- Rcpp
- Stan
- JavaScript
- CSS
Can read appropriately annotated .R scripts in and call them within an .Rmd

outputs

Knit together through package knitr to

Many great packages and applications build on rmarkdown.

All this makes it incredibly versatile. Check out the gallery.

Superpower: Simple interface to powerful modern web technologies and libraries

Publish to the web for free!

RPubs: Publish rendered rmarkdown documents on the web with the click of a button http://rpubs.com/

GitHub: Host your site through `gh-pages` on GitHub. Can host entire websites, like this course material https://github.com/

Applications in research

`Rmd` documents

Can be useful for a number of research related materials

Vignettes: long form documentation.
- Analyses
- Documentation (code & data)
- Supplementary materials
Reports
Papers

Useful features: - bibliographies and citations

💻 Exercise Part 1

Throughout this workshop, we’ll be working with the gapminder dataset to produce a reproducible Rmarkdown vignette of our work. We’ll also be working in a project and setting our analysis report up to be shared online!

Create new project `gapminder-rmarkdown`!!

File > New Project… > New Directory > New Project > gapminder-rmarkdown

Create your first `.Rmd`!!

File > New File > RMarkdown… > Document

save and render it

Before knitting, the document needs to be saved. Give it a useful name, e.g. index.Rmd
Render the document by clicking on the knit button.

You can also render .Rmd documents to html using rmarkdown function render()

rmarkdown::render(input = "index.Rmd")

Publish your .Rmd

Register an account on RPubs
Publish your rendered document (don’t worry, you can delete or overwrite it later)

open the cheatsheet

install the packages we’ll need

install.packages(c("rmarkdown", "tidyverse", "plotly", "DT", "reprex", "broom", 
    "kableExtra"))

🚦 YAML header

https://bookdown.org/yihui/rmarkdown/markdown-syntax.html

The yaml header contains metadata about the document, most importantly the output. Different seetings can be set within different outputs. Here we’ll be focusing on on the html_document output.

Markdown was originally designed for HTML output, so it may not be surprising that the HTML format has the richest features among all output formats.

define outputs

To create an HTML document from R Markdown, you specify the html_document output format in the YAML metadata of your document:

basic `html_document`

---
title: "Untitled"
author: "Anna Krystalli"
date: "3/23/2018"
output: html_document
---

define a floating table of contents

You can add a table of contents (TOC) using the toc option and specify a floating toc using the toc_float option. For example:

---
title: "Untitled"
author: "Anna Krystalli"
date: "3/23/2018"
output:
  html_document:
    toc: true
    toc_float: true
---

choose a theme

There are several options that control the appearance of HTML documents:

theme specifies the Bootstrap theme to use for the page (themes are drawn from the Bootswatch theme library). Valid themes include default, cerulean, journal, flatly, darkly, readable, spacelab, united, cosmo, lumen, paper, sandstone, simplex, and yeti.

---
title: "Untitled"
author: "Anna Krystalli"
date: "3/23/2018"
output:
  html_document:
    toc: true
    toc_float: true
    theme: cosmo
---

choose code highlights

highlight specifies the syntax highlighting style. Supported styles include default, tango, pygments, kate, monochrome, espresso, zenburn, haddock, and textmate.

---
title: "Untitled"
author: "Anna Krystalli"
date: "3/23/2018"
output:
  html_document:
    toc: true
    toc_float: true
    theme: cosmo
    highlights: zenburn
---

💻 Exercise Part 2

Clear everything BELOW THE YAML header. You should be left with just this:

---
title: "Gapminder Analysis"
author: "Anna Krystalli"
date: "3/23/2018"
output: html_document
---

add a floating table of contents
set a theme of your choice (see avalable themes here and the associated bootstrap styles here)

🚦 Markdown basics

The text in an R Markdown document is written with the Markdown syntax. Precisely speaking, it is Pandoc’s Markdown.

text

    normal text

normal text

    *italic text*

italic text

    **bold text**

bold text

    ***bold italic text***

bold italic text

headers

rmarkdown

# Header 1
## Header 2
### Header 3
#### Header 4
##### Header 5
###### Header 6

rendered html

unordered lists

rmarkdown

- first item in the list
- second item in list
- third item in list

rendered html

first item in the list
second item in list
third item in list

ordered lists

rmarkdown

1. first item in the list
1. second item in list
1. third item in list

rendered html

first item in the list
second item in list
third item in list

quotes

rmarkdown

> this text will be quoted

rendered html

this text will be quoted

code

annotate code inline

rmarkdown

`this text will appear as code` inline

rendered html

this text will appear as code inline

evaluate r code inline

a <- 10

rmarkdown

the value of parameter *a* is `r a`

rendered html

the value of parameter a is 10

images

Provide either a path to a local image file or the URL of an image.

rmarkdown

![](../assets/cheat.png)

rendered html

resize images

html in rmarkdown

<img src="../assets/cheat.png" width="200px" />

rendered html

basic tables in markdown

rmarkdown


    Table Header  | Second Header
    ------------- | -------------
    Cell 1        | Cell 2
    Cell 3        | Cell 4

rendered html

Table Header	Second Header
Cell 1	Cell 2
Cell 3	Cell 4

Check out handy online .md table converter

links

rmarkdown

[Download R](http://www.r-project.org/)    
[RStudio](http://www.rstudio.com/)

rendered html

Download R

RStudio

mathematical expressions

Supports mathematical notations through MathJax.

You can write LaTeX math expressions inside a pair of dollar signs, e.g. $\alpha+\beta$ renders $\alpha+\beta$. You can use the display style with double dollar signs:

$$\bar{X}=\frac{1}{n}\sum_{i=1}^nX_i$$

\[\bar{X}=\frac{1}{n}\sum_{i=1}^nX_i\]

💻 Exercise: Part 3

Get more info on gapminder:

Do some quick online research on Gapminder. A good places to start: https://www.gapminder.org/

Create a `"Background"` section using headers

Write a short description

Write a short description of the Gapminder project (feel free to copy, paste and edit information).

Make use of markdown annotation to:

highlight important information
include links to sources or further information.

Add an image

Add an image related to Gapminder.

have a look online for an image.
include the source URL underneath for attribution.
see if you can resize it.

🚦 Chunks & Inline R code

inserting new chunks

You can quickly insert You can insert an R code chunk with:

the keyboard shortcut Ctrl + Alt + I (OS X: Cmd + Option + I)
the Add Chunk command in the RStudio toolbar
by typing the chunk delimiters ```{r} and ```.

chunk uses

There are a lot of things you can do in a code chunk:

you can produce text output, tables, or graphics.
You have fine control over all these output via chunk options, which can be provided inside the curly braces (between ```{r and }).
- For example, you can choose hide text output via the chunk option results = 'hide', or set the figure height to 4 inches via fig.height = 4.
Chunk options are separated by commas, e.g.,

```{r, chunk-label, results='hide', fig.height=4}

R code chunks execute code.

They can be used as a means to render R output into documents or to simply display code for illustration (eg with option eval=FALSE).

chunk notation

chunk notation in .rmd

```{r chunk-name}
print('hello world!')
```

rendered html code and output

print("hello world!")

[1] "hello world!"

Chunks can be labelled with chunk names, names must be unique.

chunk options

for more details see http://yihui.name/knitr/

uses

controlling whether code is displayed inline (echo setting)
controlling whether code is evaluated (eval setting)
controlling how figures are displayed (fig.width and fig.height settings)
suppressing warnings and messages (warning and message settings)
cacheing computations (cache setting)
controlling whether code is extracted when using purl (purl settings)

controlling code display with `echo`

chunk notation in .rmd

```{r hide-code, echo=FALSE}
print('hello world!')
```

rendered html code and output

[1] "hello world!"

controlling code evaluation with `eval`

chunk notation in .rmd

```{r dont-eval, eval=FALSE}
print('hello world!')
```

rendered html code and output

print("hello world!")

setting document level default options

knitr::opts_chunk$set(echo = TRUE, warning = F, message = F)

💻 Exercise Part 4

For this exercise we’ll be accessing the gapminder data through the gapminder R package.

Create an “`Installation`” section using headers

Write installation instruction

Write brief instructions (including code) for others to access the dataset in R. Have a look at the package documentation on GitHub for inspiration.

In R we often need to describe a setup proceedure that involves specifying the installation of required packages. However, installation of packages in not handled in .Rmd! (For the moment, install packages through the console).

In our case, we’ll want to include the code for installing the gapminder package but not evaluate it in the .Rmd.

🚦 Displaying data

printing `data.frame`s

data(airquality)
head(airquality)

  Ozone Solar.R Wind Temp Month Day
1    41     190  7.4   67     5   1
2    36     118  8.0   72     5   2
3    12     149 12.6   74     5   3
4    18     313 11.5   62     5   4
5    NA      NA 14.3   56     5   5
6    28      NA 14.9   66     5   6

printing `tibble`s

library(tibble)
as_tibble(airquality)

# A tibble: 153 x 6
   Ozone Solar.R  Wind  Temp Month   Day
   <int>   <int> <dbl> <int> <int> <int>
 1    41     190   7.4    67     5     1
 2    36     118   8      72     5     2
 3    12     149  12.6    74     5     3
 4    18     313  11.5    62     5     4
 5    NA      NA  14.3    56     5     5
 6    28      NA  14.9    66     5     6
 7    23     299   8.6    65     5     7
 8    19      99  13.8    59     5     8
 9     8      19  20.1    61     5     9
10    NA     194   8.6    69     5    10
# … with 143 more rows

`Displaying knitr::kable()` tables

library(knitr)
data(airquality)
kable(head(airquality), caption = "New York Air Quality Measurements")

New York Air Quality Measurements
Ozone	Solar.R	Wind	Temp	Month	Day
41	190	7.4	67	5	1
36	118	8.0	72	5	2
12	149	12.6	74	5	3
18	313	11.5	62	5	4
NA	NA	14.3	56	5	5
28	NA	14.9	66	5	6

Displaying interactive `DT::datatable()` tables

library(DT)
data(airquality)
datatable(airquality, caption = "New York Air Quality Measurements")

Summarising data with `skimr::skim()`

Provides a frictionless approach to displaying summary statistics that can be quickly skimmed quickly to understand their data.

skimr::skim(airquality)

Skim summary statistics
 n obs: 153 
 n variables: 6 

── Variable type:integer ───────────────────────────────────────────────────────────────
 variable missing complete   n   mean    sd p0    p25   p50    p75 p100
      Day       0      153 153  15.8   8.86  1   8     16    23      31
    Month       0      153 153   6.99  1.42  5   6      7     8       9
    Ozone      37      116 153  42.13 32.99  1  18     31.5  63.25  168
  Solar.R       7      146 153 185.93 90.06  7 115.75 205   258.75  334
     Temp       0      153 153  77.88  9.47 56  72     79    85      97
     hist
 ▇▇▇▇▆▇▇▇
 ▇▇▁▇▁▇▁▇
 ▇▆▃▃▂▁▁▁
 ▃▃▃▃▅▇▇▃
 ▂▂▃▆▇▇▃▃

── Variable type:numeric ───────────────────────────────────────────────────────────────
 variable missing complete   n  mean   sd  p0 p25 p50  p75 p100     hist
     Wind       0      153 153  9.96 3.52 1.7 7.4 9.7 11.5 20.7 ▁▃▇▇▅▅▁▁

Printing model summaries with `broom`

library(broom)  # great for extracting model parameter summaries
lm(lifeExp ~ gdpPercap, data = gapminder::gapminder) %>% broom::glance() %>% 
    knitr::kable()

r.squared	adj.r.squared	sigma	statistic	p.value	df	logLik	AIC	BIC	deviance	df.residual
0.340713	0.3403256	10.49132	879.5766	0	2	-6422.205	12850.41	12866.73	187335.3	1702

💻 Exercise Part 5

install.packages("gapminder")

Start a new section called `"Dataset"`

Display an example of the dataset

Write a short description of the dataset

What size is the data? (How many variables? How many rows of data points. See if you can extract and include such info inline)
what type of object is it? (see ?class)
Use some of the functions you’ve learnt to extract such information (eg ?dim, ?ncol etc).

Summarise the data

(e.g. ?summary, ?skimr)

🚦 plots

library("ggplot2")
ggplot(gapminder::gapminder, aes(x = continent, y = lifeExp)) + geom_boxplot(outlier.colour = "hotpink") + 
    geom_jitter(position = position_jitter(width = 0.1, height = 0), alpha = 1/4)

p <- gapminder::gapminder %>% ggplot(aes(x = gdpPercap, y = lifeExp, color = continent)) + 
    geom_point()

interactive plots with plotly

Wraps nicely around plotting library ggplot2

library(plotly)

ggplotly(p)

💻 Exercise Part 6a

Replicate some of the plots above but hide the code that generates them.
OPTIONAL Can you adapt the model and plotting code to use log10 transformed data?
- hint: explore ?log10 function for using in lm and the scale_... functions in ggplot2 (cheatsheet)
Add some comments for each plot

Exercise Part 6b

Publish your report on Rpubs

Add you link to our googledoc

🚦 Advanced `.Rmd`

reading chunks of code

`R` -> `Rmd`

You can read in chunks of code from an annotated .R (or any other language) script using knitr::read_chunks()

Chunks are defined by the following notation. Names must be unique.

# ---- descriptive-chunk-name1 ----
code("you want to run as a chunk")

# ---- descriptive-chunk-name2 ----
code("you want to run as a chunk")

code in `.R` script `hello-world.R`

hello-world.R

# ---- demo-read_chunk ----
print("hello world")

read chunks from `hello-world.R`

knitr::read_chunk(here::here("demos", "hello-world.R"))

call chunk by name

rmarkdown r chunk notation

```{r demo-read_chunk}

```

rendered html code and output

print("hello world")

[1] "hello world"

Check chunks in the current session

knitr:::knit_code$get()

$`demo-read_chunk`
[1] "print(\"hello world\")"

Extracting code from an `.Rmd`

`Rmd` -> `R`

You can use knitr::purl() to tangle code out of an Rmd into an .R script. purl takes many of the same arguments as knit(). The most important additional argument is:

documentation: an integer specifying the level of documentation to go the tangled script:
- 0 means pure code (discard all text chunks)
- 1 (default) means add the chunk headers to code
- 2 means add all text chunks to code as roxygen comments

purl("file-to-extract-code-from.Rmd", documentation = 0)

extract using `purl`

Here i’m running a loop to extract the code in demo-rmd.Rmd for each documentation level

file <- here::here("demos", "demo-rmd.Rmd")
for (docu in 0:2) {
    knitr::purl(file, output = paste0(gsub(".Rmd", "", file), "_", docu, ".R"), 
        documentation = docu, quiet = T)
}

`demo-rmd_0.R`

knitr::opts_chunk$set(echo = TRUE)
summary(cars)
plot(pressure)

`demo-rmd_1.R`

## ----setup, include=FALSE------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)

## ----cars----------------------------------------------------------------
summary(cars)

## ----pressure, echo=FALSE------------------------------------------------
plot(pressure)

`demo-rmd_2.R`

#' ---
#' title: "Untitled"
#' author: "Anna Krystalli"
#' date: "3/23/2018"
#' output:
#'   html_document:
#'     toc: true
#'     toc_float: true
#'     theme: cosmo
#'     highlight: textmate
#' 
#' ---
#' 
## ----setup, include=FALSE------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)

#' 
#' ## R Markdown
#' 
#' 
#' This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <http://rmarkdown.rstudio.com>.
#' 
#' When you click the **Knit** button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
#' 
## ----cars----------------------------------------------------------------
summary(cars)

#' 
#' ## Including Plots
#' 
#' You can also embed plots, for example:
#' 
## ----pressure, echo=FALSE------------------------------------------------
plot(pressure)

#' 
#' Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.
#' 
#'

💻 Exercise: Part 7*

read in a chunk

Open an .R script
Cut the code from one or more of your chunks and paste it into the .R script
Annotate the code up as named chunk(s)
Read the chunk(s) in your .R script into your .Rmd (?read_chunk())
Include the code in your .Rmd workflow by labelling an empty chunk with your chunk(s) name(s)

purl your document

Once your document is ready, try and extract the contents of your .Rmd into an .R script.

?purl

🚦 html in rmarkdown

marking up with html tags

This text marked up in html

<strong>Bold text</strong>

renders to this

Bold text

**This text marked up with Bootstrap alert css classes

<div class="alert alert-warning"><small>this a is warning message</small></div>

renders to

this a is warning message

<div class="alert alert-success"><small>this a is success message</small></div>

renders to

this a is success message

embedding tweets

This snipped copied from twitter in the embed format

<blockquote class="twitter-tweet" data-lang="en"><p lang="en" dir="ltr">How cool does this tweet look embedded in <a href="https://twitter.com/hashtag/rmarkdown?src=hash&amp;ref_src=twsrc%5Etfw">#rmarkdown</a>! 😎</p>&mdash; annakrystalli (@annakrystalli) <a href="https://twitter.com/annakrystalli/status/977209749958791168?ref_src=twsrc%5Etfw">March 23, 2018</a></blockquote>
<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

renders to this

How cool does this tweet look embedded in #rmarkdown! 😎
— annakrystalli (@annakrystalli) March 23, 2018

Embbed gifs, videos, widgets in this way

Parting words

Getting help with markdown

To get help, you need a reproducible example

github issues
stackoverflow
slack channels
discussion boards

`reprex`

Use function reprex::reprex() to produce a reproducible example in a custom markdown format for the venue of your choice

"gh" for GitHub (default)
"so" for StackOverflow,
"r" or "R" for a runnable R script, with commented output interleaved.

using reprex

Copy the code you want to run.
all required variables must be defined and libraries loaded
In the console, call the reprex function

reprex::reprex()

+ <small>the code is executed in a fresh environment and "code + commented output" is returned invisibly on the clipboard.</small>

Paste the result in the venue of your choice.
- Once published it will be rendered to html.

`bookdown`

Authoring with R Markdown. Offers:

cross-references,
citations,
HTML widgets and Shiny apps,
tables of content and section numbering

The publication can be exported to HTML, PDF, and e-books (e.g. EPUB) Can even be used to write thesis!

pkgdown

For buidling package documentation

Can use it to document any functional code you produce and demonstrate it’s us ethrough vignettes

workflowr pkg

Build analyses websites and organise your project

The workflowr R package makes it easier for researchers to organize their projects and share their results with colleagues.

blogdown

For creating and mantaining blogs.

Check out https://awesome-blogdown.com/, a curated list of awesome #rstats blogs in blogdown for inspiration!

Resources

Solutions: My example

sessionInfo()

R version 3.5.2 (2018-12-20)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.3

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] plotly_4.7.1     kableExtra_1.0.1 broom_0.5.1      DT_0.5          
 [5] knitr_1.22       forcats_0.4.0    stringr_1.4.0    dplyr_0.8.0.1   
 [9] purrr_0.3.2      readr_1.3.1      tidyr_0.8.3      tibble_2.1.1    
[13] ggplot2_3.1.0    tidyverse_1.2.1 

loaded via a namespace (and not attached):
 [1] httr_1.4.0        jsonlite_1.6      viridisLite_0.3.0
 [4] here_0.1          modelr_0.1.3      shiny_1.2.0      
 [7] assertthat_0.2.0  highr_0.7         emo_0.0.0.9000   
[10] cellranger_1.1.0  yaml_2.2.0        pillar_1.3.1     
[13] backports_1.1.3   lattice_0.20-38   glue_1.3.1       
[16] digest_0.6.18     promises_1.0.1    rvest_0.3.2      
[19] colorspace_1.4-0  htmltools_0.3.6   httpuv_1.5.0     
[22] plyr_1.8.4        pkgconfig_2.0.2   haven_2.0.0      
[25] magick_2.0        xtable_1.8-3      scales_1.0.0     
[28] webshot_0.5.1     processx_3.2.1    whisker_0.3-2    
[31] later_0.8.0       git2r_0.24.0.9001 generics_0.0.2   
[34] gapminder_0.3.0   withr_2.1.2       skimr_1.0.5      
[37] lazyeval_0.2.1    cli_1.1.0         magrittr_1.5     
[40] crayon_1.3.4      readxl_1.3.0      mime_0.6         
[43] evaluate_0.13     ps_1.3.0          fs_1.2.7         
[46] fansi_0.4.0       nlme_3.1-137      xml2_1.2.0       
[49] tools_3.5.2       data.table_1.12.0 hms_0.4.2        
[52] formatR_1.5       munsell_0.5.0     callr_3.1.1      
[55] compiler_3.5.2    rlang_0.3.1       grid_3.5.2       
[58] rstudioapi_0.9.0  htmlwidgets_1.3   crosstalk_1.0.0  
[61] labeling_0.3      rmarkdown_1.12    gtable_0.2.0     
[64] R6_2.4.0          lubridate_1.7.4   utf8_1.1.4       
[67] workflowr_1.2.0   rprojroot_1.3-2   stringi_1.3.1    
[70] Rcpp_1.0.1        tidyselect_0.2.5  xfun_0.5

Introduction to rmarkdown

Literate programming

Why is this important in science:

Calls for reproducibility

Calls for open science

tl;dr

Literate programming in R

Rmarkdown overview

Features

The researchers perspective

a reproducible workflow in action

elements of R markdown

markdown {.md}

stripped down html. User can focus on communicating & disseminating

code {r, python, SQL, … }

outputs

Publish to the web for free!

RPubs: Publish rendered rmarkdown documents on the web with the click of a button http://rpubs.com/

GitHub: Host your site through gh-pages on GitHub. Can host entire websites, like this course material https://github.com/

Applications in research

Rmd documents

💻 Exercise Part 1

Create new project gapminder-rmarkdown!!

Create your first .Rmd!!

save and render it

Publish your .Rmd

open the cheatsheet

install the packages we’ll need

🚦 YAML header

define outputs

basic html_document

define a floating table of contents

choose a theme

choose code highlights

💻 Exercise Part 2

🚦 Markdown basics

text

headers

unordered lists

ordered lists

quotes

code

annotate code inline

evaluate r code inline

images

resize images

basic tables in markdown

links

mathematical expressions

\[\bar{X}=\frac{1}{n}\sum_{i=1}^nX_i\]

💻 Exercise: Part 3

Get more info on gapminder:

Create a "Background" section using headers

Write a short description

Add an image

🚦 Chunks & Inline R code

inserting new chunks

chunk uses

chunk notation

chunk options

uses

controlling code display with echo

controlling code evaluation with eval

setting document level default options

💻 Exercise Part 4

Create an “Installation” section using headers

Write installation instruction

🚦 Displaying data

printing data.frames

printing tibbles

Displaying knitr::kable() tables

Displaying interactive DT::datatable() tables

Summarising data with skimr::skim()

Printing model summaries with broom

💻 Exercise Part 5

Start a new section called "Dataset"

Display an example of the dataset

Write a short description of the dataset

Summarise the data

🚦 plots

markdown {`.md`}

stripped down `html`. User can focus on communicating & disseminating

GitHub: Host your site through `gh-pages` on GitHub. Can host entire websites, like this course material https://github.com/

`Rmd` documents

Create new project `gapminder-rmarkdown`!!

Create your first `.Rmd`!!

basic `html_document`

Create a `"Background"` section using headers

controlling code display with `echo`

controlling code evaluation with `eval`

Create an “`Installation`” section using headers

printing `data.frame`s

printing `tibble`s

`Displaying knitr::kable()` tables

Displaying interactive `DT::datatable()` tables

Summarising data with `skimr::skim()`

Printing model summaries with `broom`

Start a new section called `"Dataset"`

🚦 Advanced `.Rmd`

`R` -> `Rmd`

code in `.R` script `hello-world.R`

read chunks from `hello-world.R`

Extracting code from an `.Rmd`

`Rmd` -> `R`

extract using `purl`

`demo-rmd_0.R`

`demo-rmd_1.R`

`demo-rmd_2.R`

`reprex`

`bookdown`