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w1_getting_started.Rmd
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w1_getting_started.Rmd
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---
title: "Week 1 - Getting Started"
author: "Nicholas Good ([email protected])"
output:
rmarkdown::html_document:
toc: true
toc_float: true
theme: yeti
---
```{r global_options, include=FALSE}
knitr::opts_chunk$set(echo=TRUE, warning=FALSE, message=FALSE, results = 'hide')
```
---
# Course Materials
Course material will be made available via [GitHub](https://github.com/nickgood/r_for_fire_data). The classes will be largely example driven. We'll cover some areas in more detail than others, frequently returning to topics as we progress.
```{r}
# Navigate to the course Github page and view this document.
```
---
# Installing R
R is available from [CRAN](https://cran.r-project.org). There are distributions for multiple platforms (Linux, OS X and Windows). CRAN (The Comprehensive R Archive Network) is a network of servers from which the lastest versions of base R, R packages, and R documentation can be downloaded. Choose a CRAN mirror close to your current location.
```{r}
# Install the precompiled R distribution for your platform.
```
---
# Installing RStudio
Install RStudio from [rstudio.com](https://www.rstudio.com/products/rstudio/download/). RStudio is your interface with R. Download the free version of *RStudio Desktop*.
```{r}
# Install RStudio.
```
---
# Navigating RStudio
We'll start by taking a brief tour of RStudio and get a feel for some basic R tasks. RStudio has a default four pane window layout.
The ***source*** pane allows you to open files for editing. RStudio have developed a number of file types that you can use to document and present your work.
<br>
The ***console*** pane is where you can pass commands to R.
<br>
```{r, eval = FALSE}
# Let's try out some commands. One at a time, type each of the following commands into the console and press return:
seq(1, 10, 0.5)
round(pi, 1)
Sys.time()
?list.files()
plot(cars)
# Does R care if you use upper or lower case characters?
# What does prefixing a command with the ? symbol do?
```
The ***environment*** pane allows you to view R objects et cetera in your working environment. Different object types are grouped together in the ***environment*** panel. There is also ***history*** tab in this panel, where you can view commands you have run.
<br>
At present your ***environment*** pane will be empty. We'll add some items to the environment in the next section.
The final panel contains the ***files***, ***plots***, ***packages***, ***help***, and ***viewer*** tabs. The ***files*** tab allows you to browse your local system directory. Graphics can be viewed in the ***plots*** tab. Package information is displayed in the ***packages*** tab. You can use the ***help*** tab to search for function information. Documents generated by RStudio can be viewed in the ***viewer*** tab.
<br>
Let's take a look at the different tabs:
```{r}
# Select the files, plots, packages, help, and viewer tabs in turn.
# What do you see in each tab?
```
RStudio settings can be customized via the ***preferences*** menu item.
<br>
---
# Working directory
The working directory is the folder that works in unless you tell R otherwise. The `getwd()` command returns your current working directory. The `setwd(...)` command allows you to set the working directory.
```{r}
# Run the getwd() command in the console
# Where is your current working directory?
```
# RStudio projects
When working with R you'll very soon want to create a way to organize your work. RStudio facilitates organizing your work using `projects`. Let's create our first RStudio project. Select `file` -> `new project` -> `new directory` -> `empty project`, select a directory and name for your project.
```{r}
# Run getwd() again. What does it return?
# Run setwd(..)
# Set the working directory back to your project directory
```
# Saving your work
There are multiple ways to save your work in R/RStudio. There are basic R scripts (`.R files`) and more elaborate markdown (`.Rmd`) documents for example
Let's create a file to save our work in. We'll create an `R Notebook` file, a special type of file that can be used to document our work in an easy to read and reproducible format.
```{r}
#1. Open a new R Notebook: file -> new file -> R Notebook
#2. Save the notebook file and rename it.
#3. Preview the file by pressing the preview button.
#4. Edit and resave the file (e.g. give it a new title e.g. "week one")
#5. Run the code chunk using the green arrow.
#6. Preview the file again.
#7. Remove the example text.
#8. You can use this file for the rest of today's class.
```
# Libraries
R comprises a *base* version that we installed earlier. However, many of the tools we will use are will be from add-on *packages*.
To download a new package you can navigate to `tools` -> `install packages`. Or you can install via the command line:
```{r, eval = FALSE}
install.packages("tidyverse")
```
Once a package is installed you need to tell R to make it available for use. We can do this with the `library()` function:
```{r}
library(tidyverse)
```
You can use the RStudio menu (`tools` -> `check for package updates`) to update to the lastest stable package releases.
---
---
# R objects and assignment
R is [object oriented](http://adv-r.had.co.nz/OO-essentials.html#base-types). To create an object use the assignment operator `<-`. The `<-` operator is recommended over `=`, which also works (most of the time).
```{r, eval = FALSE}
# One at a time, type each of the following commands into the console and press return.
my_dataframe <- data_frame(x = seq(1, 20, 1), y = rnorm(20))
my_lm <- lm(y ~ x, my_dataframe)
# Take a look in your environment window, what do you see?
```
* R objects have a class associated with them. Different methods (functions) can be applied to objects of different classes. You can use the `class()` function to determine an object's class.
```{r, eval = FALSE}
# Use the `class()` function to determine the class of each object in your working environment.
```
* Class specific results can be produced when a method is applied to an object of a given class
```{r, eval = FALSE}
# Run the following code one line at a time:
summary(my_dataframe)
plot(my_dataframe)
summary(my_lm)
plot(my_lm)
```
* Note: R can get a little tricky when [evaluating](http://adv-r.had.co.nz/Computing-on-the-language.html) objects. For example when `""` are required around inputs can take a little time to get your head around
---
# Names
R is fairly liberal in terms of object names it will allow. For your sanity its worth imposing some rules of your own:
* use lower case characters.
* separate words with `_`.
* never start a name with a number.
* use `a-z`, `0-9` and `_` only.
---
# Comments
Use `#` to comment your code.
```{r, results = 'markup'}
# this is a comment
# comments are not evaluated
print(8 * 10) # comments can be placed after code on the same line
# use comments to concisely document your code
```
---
# Help!
To display text for a function type a `?` before the function
```{r}
?help
```
View the help documentation for the following functions:
```{r, eval = FALSE}
mean()
lm()
data_frame()
sort()
```
---