NOAA Data:

• National Oceanic and Atmospheric Administration
• Temperature and Salinity Data in Gulf of Mexico
• Measured using Floats, Gliders and Boats

US Fisheries and Wildlife Data:

• Animal Sightings on the Gulf Coast
• Birds, Turtles and Mammals
• Status: Oil Covered or Not

Both data sets have geographic coordinates for ever observation

NOAA data is a .rdata file so we need to read it specially:

1. Download the data from http://heike.github.io/rwrks/summerschool/data/noaa.rdata
2. Run the getwd() command to find your current working directory
3. Place noaa.rdata in the directory from step 2.
4. Run the command below:

load("noaa.rdata")

Lets take a peek at the top of the floats NOAA data.

head(floats, n = 2)

##   callSign Date_Time JulianDay Time_QC Latitude Longitude Position_QC
## 1 Q4901043 7/12/2010   2455390       1   24.823   -87.964           1
## 2 Q4901043 7/12/2010   2455390       1   24.823   -87.964           1
##   Depth Depth_QC Temperature Temperature_QC Salinity Salinity_QC  Type
## 1     2        1       29.83              1    36.59           1 Float
## 2     4        1       29.65              1    36.58           1 Float

qplot(Longitude, Latitude, colour = callSign, data = floats) + 
    coord_map()

qplot(Longitude, Latitude, colour = callSign, data = gliders) + 
    coord_map()

qplot(Longitude, Latitude, colour = callSign, data = boats) + 
    coord_map()

This data has the same context - a common time and common place

• Want to aggregate information from different sources onto a common plot
• Start with a common background the lat/long grid
• With ggplot2 we will superimpose data onto this grid in layers

To give you an idea…

ggplot() +
    geom_path(data = states, aes(x = long, y = lat, group = group)) + 
    geom_point(data = floats, aes(x = Longitude, y = Latitude, colour = callSign)) +   
    geom_point(aes(x, y), shape = "x", size = 5, data = rig) + 
    geom_text(aes(x, y), label = "BP Oil Rig", 
              size = 5, data = rig, hjust = -0.1) + 
    xlim(c(-91, -80)) + ylim(c(22,32)) + coord_map()

• Most maps (and many plots) have multiple layers of data. The layers may be from the same or different datasets.
• ggplot2 builds around this same idea. Very easy to add additional layers to the plot. To do this we need to understand a little more about the underlying theory…

• A default dataset
• A coordinate system
• layers of geometric objects (geoms)
• A set of aesthetic mappings (taking information from the data and converting into an attribute of the plot)
• A scale for each aesthetic
• A facetting specification (multiple plots based on subsetting the data)

qplot() stands for "quickplot":

• Automatically chooses default settings to make life easier
• Less control over plot construction

ggplot() stands for "grammar of graphics plot"

• Constructs the plot using components listed in previous slides

Two ways to construct the same plot for float locations:

qplot(Longitude, Latitude, colour = callSign, data = floats) 

Or:

ggplot(data = floats, 
       aes(x = Longitude, y = Latitude, colour = callSign)) +
  geom_point() + 
  scale_x_continuous() + 
  scale_y_continuous() + 
  scale_colour_discrete()

We fortunately don't need to be so verbose - Even ggplot() will automatically pick default scales:

ggplot(data = floats, 
       aes(x = Longitude, y = Latitude, colour = callSign)) +
  geom_point()

Find the ggplot() statement that creates this plot:

A layer added ggplot() can be a geom…

• The type of geometric object
• The statistic mapped to that object
• The data set from which to obtain the statistic

… or a position adjustment to the scales

• Changing the axes scale
• Changing the color gradient

More geoms described at http://docs.ggplot2.org/current/

Want to build a map using NOAA data

• Coordinate system (mapping Long-Lat to X-Y)
• Add layer of state outlines
• Add layer of points for float locations
• Add layers for Oil Rig marker and label
• Adjust the range of x and y scales

ggplot() +
    geom_path(data = states, aes(x = long, y = lat, group = group)) + 
    geom_point(data = floats, aes(x = Longitude, y = Latitude, colour = callSign)) +   
    geom_point(aes(x, y), shape = "x", size = 5, data = rig) + 
    geom_text(aes(x, y), label = "BP Oil Rig", size = 5, data = rig, hjust = -0.1) + 
    xlim(c(-91, -80)) + 
    ylim(c(22, 32)) + coord_map()

1. Read in the animal.csv data:

animal <- read.csv("http://heike.github.io/rwrks/02-r-graphics/data/animal.csv")

1. Plot the location of animal sightings on a map of the region
2. On this plot, try to color points by class of animal and/or status of animal
3. Advanced: Could we indicate time somehow?

Using the ggmap package, we can download satellite images using the Google Maps API for specified regions of the world

library(ggmap)
gmap <- get_map(location=c(rig$x, rig$y), zoom=6)

## Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=28.736628,-88.365997&zoom=6&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false

ggmap(gmap, extent = 'normal') + 
  geom_point(aes(Longitude, Latitude), shape="+", size=5, colour="black", data=subset(animal, Condition=="Dead")) +
  ggtitle("Crosses indicate locations of dead bird sightings")