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index.html
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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>reveal-md</title>
<link rel="stylesheet" href="./css/reveal.css">
<link rel="stylesheet" href="./css/theme/white.css" id="theme">
<link rel="stylesheet" href="./css/highlight/zenburn.css">
<link rel="stylesheet" href="./css/print/paper.css" type="text/css" media="print">
</head>
<body>
<div class="reveal">
<div class="slides"><section ><section data-markdown><script type="text/template"># 2017-01-10 `R` for Reproducible Scientific Analysis
</script></section><section data-markdown><script type="text/template">
Please use the EtherPad to take communal notes:
[http://pad.software-carpentry.org/2017-01-09-dundee](http://pad.software-carpentry.org/2017-01-09-dundee)
</script></section></section><section ><section data-markdown><script type="text/template">
## Introduction to `R` and `RStudio`
</script></section><section data-markdown><script type="text/template">
### Learning objectives
* Familiarity with the RStudio IDE
* Set up a working directory with version control
* Introduce `R` syntax
* Introduce package management
</script></section><section data-markdown><script type="text/template">
### Why `R`/`RStudio`?
* Open source & free
* Better than commercial alternatives
* Available on all platforms
* Widely used, good community
* Is a programming language:
- automation
- reproducibility
- sharing
</script></section><section data-markdown><script type="text/template">
### "But I already know Excel!"
</script></section><section data-markdown><script type="text/template">
### `R`/`RStudio` presentation
**Live Presentation**
</script></section><section data-markdown><script type="text/template">
### Best practices
* No single 'best' way - only good principles
* Treat your data as read only (separate folder)
* (cf. `Excel`'s interactive modification)
* Data cleaning
* Keep 'cleaned' data in a separate folder
* All generated output should be considered disposable
</script></section><section data-markdown><script type="text/template">
### Creating files/directories
**Live Presentation**
</script></section><section data-markdown><script type="text/template">
### Challenge 1
* Download [gapminder-FiveYearData.csv](https://raw.githubusercontent.com/widdowquinn/2016-01-11-dundee/gh-pages/lessons/R_gapminder/data/gapminder-FiveYearData.csv) to `data` subfolder (link on Etherpad)
* Create a new subdirectory called `graphs` in your working directory
* Modify `.gitignore` so that Git does not track the `graphs/` directory
* Commit these changes to the local repository
(10min)
</script></section><section data-markdown><script type="text/template">
### `R` as a calculator
**Live Presentation**
```
> 1 + 100
[1] 101
> log10(10)
[1] 1
> 1 != 2
[1] TRUE
> x <- 1/40
```
</script></section><section data-markdown><script type="text/template">
### Good variable names
* letters, numbers, underscores and periods allowed
* cannot start with a number
* no spaces
* don't use existing names (e.g. `mean`, `df`, etc.)
* Explicit (e.g. `weight_kg`)
* Long variable names: **be consistent**
* `periods.between.words`
* `underscores_between_words`
* `camelCaseToSeparateWords`
</script></section><section data-markdown><script type="text/template">
### MCQ1
What is the value of each variable after running the following program?
```
mass <- 47.5
age <- 122
mass <- mass * 2.3
age <- age - 20
```
* `mass = 47.5, age = 102`
* `mass = 109.25, age = 102`
* `mass = 47.5, age = 122`
* `mass = 109.25, age = 122`
</script></section><section data-markdown><script type="text/template">
### Package Management
**Live Presentation**
```
installed.packages()
install.packages("packagename")
update.packages()
remove.packages("packagename")
library(packagename)
```
</script></section><section data-markdown><script type="text/template">
### Challenge 2
* Install the following packages:
* `plyr`
* `gapminder`
* `dplyr`
* `tidyr`
(5min)
</script></section><section data-markdown><script type="text/template">
### Functions, and getting help
* Function ≈ "canned script", automates something complicated or convenient
* Some functions built-in (`base` packages e.g. `sqrt()`, `log()`, `lm()`)
* Some functions require package import with `library()`
```
fname(arg1, arg2, ...)
```
* Functions usually take *argument*s - some *default*s (e.g. which symbol is used for a plot)
* Functions (always) return *value*s
</script></section><section data-markdown><script type="text/template">
### Getting help for functions
**Live Presentation**
```
args(fname)
?fname
help(fname)
??fname
help.search("text")
vignette(fname)
```
</script></section><section data-markdown><script type="text/template">
### Where can I get more help?
* Ask locally: Colleagues? R user group?
* Google: [Search with your error message](http://lmgtfy.com/?q=R+error+message)
* Stack Overflow (`[r]` tag): [http://stackoverflow.com/questions/tagged/r](http://stackoverflow.com/questions/tagged/r)
* `R-help` mailing list: [https://stat.ethz.ch/mailman/listinfo/r-help](https://stat.ethz.ch/mailman/listinfo/r-help)
* Other topic-specific mailing lists: [http://www.r-project.org/mail.html](http://www.r-project.org/mail.html)
</script></section><section data-markdown><script type="text/template">
### Asking the right questions
* Make it easy for the person you're asking!
* Make the simplest possible example that reproduces the error
* Provide code and data
* For small data: `dput()` to write the data
* For large data use `saveRDS()` or provide the raw data file and script
* Use `sessionInfo()` to provide information about your platform
</script></section></section><section ><section data-markdown><script type="text/template">
## Functions
</script></section><section data-markdown><script type="text/template">
### Learning objectives
* To understand why we divide programs into small, single-purpose functions
* To understand how to define a function
* To understand how to test a function (and why)
</script></section><section data-markdown><script type="text/template">
### What is a function
* Gathers a sequence of operations into one action
* Preserves operations for future use
* Keeps them under a memorable, meaningful name (readability)
* Can be applied to several different inputs at different times (reproducibility)
* Functions are the building-blocks of programming
</script></section><section data-markdown><script type="text/template">
### Defining a function in `R`
**Live Presentation**
```
my_sum <- function(a, b) {
the_sum <- a + b
return(the_sum)
}
```
</script></section><section data-markdown><script type="text/template">
### Challenge 1
(5min)
* Can you write a function, called `kelvin_to_celsius`, that converts temperature from Kelvin to degrees Celsius?
* Put the function in the `functions-lesson.R` script and save it, then `source` the file.
* Hint: 0K = -273.15C; 0C = 273.15K
</script></section><section data-markdown><script type="text/template">
### Challenge 2
(5min)
* Can you write a function, called `fahr_to_celsius`, that converts temperature from Fahrenheit to degrees Celsius?
* Put the function in the `functions-lesson.R` script and save it, then `source` the file.
</script></section><section data-markdown><script type="text/template">
### Testing functions
* Testing functions to demonstrate that they work is critical
* Test against known good and bad values
* Tests can be automated (e.g. `testthat` package)
**Live Presentation**
```
fahr_to_celsius(32)
kelvin_to_celsius(-10)
```
</script></section><section data-markdown><script type="text/template">
### Not the best approach…
…you always need negative examples
</script></section></section></div>
</div>
<script src="./lib/js/head.min.js"></script>
<script src="./js/reveal.js"></script>
<script>
function extend() {
var target = {};
for (var i = 0; i < arguments.length; i++) {
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{ src: './plugin/highlight/highlight.js', async: true, callback: function() { hljs.initHighlightingOnLoad(); } },
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{ src: './plugin/notes/notes.js', async: true },
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Reveal.initialize(options);
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</body>
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