Layers

Deepwater Horizon Oil Spill

Datasets

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

Loading NOAA Data

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

1. Download the data from http://heike.github.io/rwrks/02-r-graphics/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:

library(ggplot2)
library(maps)

load("noaa.rdata")

Floats

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

head(floats, n = 2)[,1:5]

##   callSign Date_Time JulianDay Time_QC Latitude
## 1 Q4901043 7/12/2010   2455390       1   24.823
## 2 Q4901043 7/12/2010   2455390       1   24.823

head(floats, n = 2)[,6:10]

##   Longitude Position_QC Depth Depth_QC Temperature
## 1   -87.964           1     2        1       29.83
## 2   -87.964           1     4        1       29.65

head(floats, n = 2)[,11:14]

##   Temperature_QC Salinity Salinity_QC  Type
## 1              1    36.59           1 Float
## 2              1    36.58           1 Float

Floats Plot

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

Gliders

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

Boats

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

Layering

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

Layers

To give you an idea…

states <- map_data("state")

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()

More Layering

• 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…

What is a Plot?

• 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)

Floats Decomposed

• Data: floats
• Mappings:

• Layers: Points

Floats Decomposed (Continued)

• Scales:

Facetting: None

qplot() vs ggplot()

qplot() stands for “quickplot”:

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

ggplot() stands for “grammar of graphics plot”

• Contructs the plot using components listed in previous slides

qplot() and ggplot() for Floats

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 can shorten that a bit

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()

What is a Layer?

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

Layer Examples

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

Piecing Things Together

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

The Result

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()