Literate programming

• Combine text, code, figures, tables, etc.
• Write papers without having to remember to swap figures
• Automatically generate reports

Getting started

install.packages(c("rmarkdown", "knitr", "tinytex"))
tinytex::install_tinytex()

• File -> New File -> R Markdown
  • Enter a title and author(s).
  • Choose ‘Default Output Format’ as HTML.
• Generates a basic stub of a .Rmd document
  • Customize “front matter” at the top of the document between the ---.
• Delete everything below the second ---

Markdown

• Basic approach to formatting text
• Let’s you do
  • # Headers
  • *italics*
  • **bold**
  • [links](http://google.com)
  • Lists
    • *
    • 1.

## Concept

Exploration of population dynamic patterns at **The Portal Project**.
How do counts of rodents like *Dipodomys* species change through time?

In this document I will:

1. Load data from the [Portal Project Teaching Database](http://figshare.com/articles/Portal_Project_Teaching_Database/1314459)
2. Process it into population time series
3. And make initial visualizations

• Easy to read for humans

• Easy to convert into other things for computers

• Press Knit to create HTML from the document
• Can also create PDF & Word versions of our files
  • PDF Requires pandoc and TeX installation
  • Use the Knit dropdown or change output: pdf_document
• Markdown is common on lots of websites
• Used to create all of the exercises and lectures in this course
• Github will automatically render it
  • https://github.com/ethanwhite/CV/blob/master/CV.md

R chunks

• R Markdown allows you to include code to run in the document
• Click on Insert and choose R

## Required Packages

```{r}
library(dplyr)
library(ggplot2)
```

• Knitting runs the code and prints its output

## Data

```{r}
data <- read.csv("https://ndownloader.figshare.com/files/2292172")
head(data)
```

Chunk options

• Chunks have lots of useful options
  • Options are described at: http://yihui.name/knitr/options/
  • Options will be listed in RStudio if you press tab inside the {r} brackets at the top of the chunk

```{r, message=FALSE}
library(dplyr)
library(ggplot2)
```

• cache=TRUE reuses results of the code chunk in subsequent “knits”. Save time re-calculating or re-downloading it each time.

```{r, cache=TRUE}
data <- read.csv("https://ndownloader.figshare.com/files/2292172")
head(data)
```

• You can run code inside your text, too:

The data includes `r length(unique(data$species_id))` species.

Analysis Example

## Analysis

Get the time-series of counts for all species.
          
```{r}
time_series <-
  data %>%
  group_by(species_id, year) %>%
  summarize(count = n())
head(time_series)
```

• echo=FALSE let’s you show the results of the code chunk without showing the code.

## Plot the time-series.

```{r, message=FALSE, echo=FALSE, cache=TRUE}
ggplot(time_series, aes(x = year, y = count)) +
  geom_line() +
  facet_wrap(~species_id)
```

Citations

• Create a bibtex file with citations
• Get bibtex entries from Google Scholar
  • Search reference
  • Click on "
  • Select Bibtex
  • Copy text
  • Paste into .bib file
• Add this file as the source for citations in the YAML

bibliography: bibliography.bib

• Cite papers

[@white2018]

• When you knit in-text citations will be formated and the full references will be displayed at the bottom of the document.

Notebook

• In RStudio run chunks using Ctrl-Shift-Enter or Cmd-Shift-Enter
• Displays results in the editor
• Notebook
• output: html_notebook or File -> New File -> R Notebook
• Resulting .nb.html file has interactive control of code blocks

R Presentations

• File -> New File -> R Presentation
  • Save to location.
  • Rpres generates MD and directory of figures.
• Template Rpres file includes title page and sample code blocks.

Untitled
========================================================
author: Ethan P. White
date: 
autosize: true


Outline
========================================================

- Show making slides in R
- Include code on slides
- Includes graphs on slides

Slide With Code
========================================================

```{r}
data <- read.csv("https://ndownloader.figshare.com/files/2292172")
```

Histogram of Masses
========================================================


```{r, echo = FALSE}
library(ggplot2)
ggplot(data, aes(x = weight, color = species_id)) +
geom_histogram()
```

• Preview in RStudio panel
• Convert to HTML
  • More -> View in Browser or Save As Web Page...