Practical-intro-to-R.Rmd

---
title: "A practical introduction to R"
author: "Robin Lovelace"
date: "March 27, 2015"
output: ioslides_presentation
---

```{r, include=FALSE}
pkgs <- c("png", "grid", "shiny", "knitr")
lapply(pkgs, library, character.only = T)
# opts_knit$set(root.dir = "~/repos/Creating-maps-in-R/")
```

# Installing and using the software

## Installing and using R and RStudio

- Install the latest version of R: http://cran.rstudio.com/ 
- Install the latest version of RStudio for your system:
http://www.rstudio.com/products/rstudio/download/
- Get acquainted to RStudio and the panels - play around!
http://dss.princeton.edu/training/RStudio101.pdf

```{r, echo=FALSE}
grid.raster(readPNG("figure/rstudio-proj.png"))
```

## Productivity with RStudio

Key shortcuts in RStudio:

```{r, echo=FALSE}
shortcuts <- data.frame(Command = c(
  "Alt + Shift + K", 
  "Ctrl + Enter",
  "Ctrl + R",
  "Tab"),
  Action = c("Show shortcuts",
    "Run current line of code",
    "Run all lines of code in the script",
    "Autocomplete*"))
kable(shortcuts)
```

## Code used to generate that table

```{r, eval=FALSE}
shortcuts <- data.frame(Command = c(
  "Alt + Shift + K", 
  "Ctrl + Enter",
  "Ctrl + R",
  "Tab"),
  Action = c("Show shortcuts",
    "Run current line of code",
    "Run all lines of code in the script",
    "Autocomplete*"))
kable(shortcuts)
```

## R packages

There are 7,000+ 'add-on' packages to 'supercharge' R.

Easiest way to install them, from RStudio:

```
Tools -> Install Packages
```

or using keyboard shortcuts:

```
Alt + T ... then k
```

## Install packages for this tutorial

Can be installed and loaded in 6 lines of code:

```{r, eval=FALSE}
pkgs <- c("devtools", "shiny", "rgdal", "rgeos", "ggmap", "raster")
install.packages(pkgs) # install the official packages!
library(devtools) # enables installation of leaflet
gh_pkgs <- c("rstudio/leaflet", "robinlovelace/stplanr") 
install_github(gh_pkgs) # install packages on github
lapply(c(pkgs, "leaflet", "stplanr"), library, character.only = T)
```

```{r, include=FALSE}
pkgs <- c("devtools", "shiny", "rgdal", "rgeos", "ggmap", "raster")
library(devtools) # enables installation of leaflet
lapply(c(pkgs, "leaflet", "stplanr"), library, character.only = T)
```

# Create and plot data

## Basic data types

Anything that exists in R is an object. Let's create some with the `<-` symbol (`=` does the same job, before you ask!)

```{r}
vector_logical <- c(TRUE, TRUE, FALSE)
vector_character <- c("yes", "yes", "Hello!")
vector_numeric <- c(1, 3, 9.9)

class(vector_logical) # what are the other object classes?
```

Use the "Environment tab" (top right in RStudio) to see these

## Automating things

To ask R what objects it has, we can use `ls()`.

(Anything that happens is a function)

```{r}
ls()
```

Now we can automate the question: what class?

```{r}
obs <- ls()[grep("ve", ls())]
sapply(X = mget(obs), FUN = class)
```

## Getting help in R

To find out what just happened, we can use R's internal help

The most commonly used help functions are:

```{r}
help(apply) # get help on apply
?apply 
?sapply
??apply
```

The `*apply` family of functions are R's internal `for` loops. What about `get()`

```{r}
?mget
```


## The data frame

The fundamental data object in R. 

Create them with `data.frame()`

```{r}
data.frame(vector_logical, vector_character, n = vector_numeric)
```

Oops - we forgot to assign that. Tap `UP` or `Ctl-UP` in the console, then enter:

```{r}
df <- data.frame(vector_logical, vector_character, n = vector_numeric)
```

## Plotting data in R

`plot()` is polymorphic. Try `plot(df)` and `?plot`:

```{r, echo=FALSE}
plot(df) # note: now we can do things with the df
?plot    # get help on plotting
```

## Subsetting data in R

The `[]` brackets, appending the object name, subset data.

A comma separates each dimension; nothing means everything:

```{r}
df[1,] # all of the the 1st line of df
```

In a 2d dataset, the following selects the 3rd line in the 3rd column:

```{r}
df[3,3]
```

## Making objects spatial

There are 4 fundamental `Spatial*` data types in R, enabled by
the foundational spatial package `sp`:

```{r, eval=FALSE}
SpatialPixels()
SpatialPoints()
SpatialLines()
SpatialPolygons()
```

These are `S4` R objects, with strictly defined `slots`.
All such `Spatial*` objects have `proj4string` and `bbox` slots.
Each can be extended as a `Spatial*DataFrame` to include data.

```{r}
lnd <- shapefile("data/london_sport.shp")
class(lnd)
```