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1. Link to PlotMe bukkit page: permission for regular default players in PlotMe world:plotme.usePlotMe permission.
2. The Last Road is a MUSH - a multi-player text-based storytelling game. Check out the MUSH 101 Tutorial. This is the Web Portal, which lets you interact with the game from a web browser.
3. Creative plots is a flatworld separated into 64×64 plots for people to claim as their own to work on Server projects, Arenas for mini-games, Personal projects, test redstone, test ship designs etc! Plots can be merged into mega plots, friends added and plenty of settings, with Worldedit and VoxelSniper being limited to each plot or megaplot!

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Intro

For a long time, R has had a relatively simple mechanism, via the maps package, for making simple outlines of maps and plotting lat-long points and paths on them.

More recently, with the advent of packages like sp, rgdal, and rgeos, R has been acquiring much of the functionality of traditional GIS packages (like ArcGIS, etc). This is an exciting development, but not always easily accessible for the beginner, as it requires installation of specialized external libraries (that may, on some platforms, not be straightforward) and considerable familiarity with GIS concepts.

More recently, a third approach to convenient mapping, using ggmap has been developed that allows the tiling of detailed base maps from Google Earth or Open Street Maps, upon which spatial data may be plotted. Today, we are going to focus on mapping using base maps from R’s tried and true maps package and also using the ggmap package. We won’t cover the more advanced GIS-related topics nor using rgdal, or sp to plot maps with different projections, etc. Nor will cover the somewhat more simplified approach to projections using the mapproj package.

As in our previous explorations in this course, when it comes to plotting, we are going to completely skip over R’s base graphics system and head directly to Hadley Wickham’s ggplot2 package. Hadley has included a few functions that make it relatively easy to interact with the data in R’s maps package, and of course, once a map layer is laid down, you have all the power of ggplot at your fingertips to overlay whatever you may want to over the map. ggmap is a package that goes out to different map servers and grabs base maps to plot things on, then it sets up the coordinate system and writes it out as the base layer for further ggplotting. It is pretty sweet, but does not support different projections.

Today’s Goals

1. Introduce readers to the map outlines available in the maps package
   • Show how to convert those data into data frames that ggplot2 can deal with
   • Discuss some ggplot2 related issues about plotting things.
2. Use ggmap to make some pretty decent looking maps

I feel that the above twp topics should cover a large part of what people will need for making useful maps of field sites, or sampling locations, or fishing track lines, etc.

For today we will be skipping how to read in traditional GIS “shapefiles” so as to minimize the number of packages that need installation, but keep in mind that it isn’t too hard to do that in R, too.

Prerequisites

You are going to need to install a few extra packages to follow along with this lecture.

Load up a few of the libraries we will use

Plotting maps-package maps with ggplot

The main players:

• The maps package contains a lot of outlines of continents, countries, states, and counties that have been with R for a long time.
  
• The mapdata package contains a few more, higher-resolution outlines.
• The maps package comes with a plotting function, but, we will opt to use ggplot2 to plot the maps in the maps package.
  
• Recall that ggplot2 operates on data frames. Therefore we need some way to translate the maps data into a data frame format the ggplot can use.

Maps in the maps package

• Package maps provides lots of different map outlines and points for cities, etc.
  
• Some examples: usa, nz, state, world, etc.

Makin’ data frames from map outlines

• ggplot2 provides the map_data() function.
  • Think of it as a function that turns a series of points along an outline into a data frame of those points.
  • Syntax: map_data('name') where “name” is a quoted string of the name of a map in the maps or mapdata package

• Here we get a USA map from maps:

• Here is the high-res world map centered on the Pacific Ocean from mapdata

The structure of those data frames

These are pretty straightforward:

• long is longitude. Things to the west of the prime meridian are negative.
• lat is latitude.
• order. This just shows in which order ggplot should “connect the dots”
• region and subregion tell what region or subregion a set of points surrounds.
• group. This is very important! ggplot2’s functions can take a group argument which controls (amongst other things) whether adjacent points should be connected by lines. If they are in the same group, then they get connected, but if they are in different groups then they don’t.
  • Essentially, having to points in different groups means that ggplot “lifts the pen” when going between them.

Plot the USA map

• Maps in this format can be plotted with the polygon geom. i.e. using geom_polygon().
• geom_polygon() drawn lines between points and “closes them up” (i.e. draws a line from the last point back to the first point)
• You have to map the group aesthetic to the group column
• Of course, x = long and y = lat are the other aesthetics.

Simple black map

By default, geom_polygon() draws with no line color, but with a black fill:

What is this coord_fixed()?

• This is very important when drawing maps.
• It fixes the relationship between one unit in the y direction and one unit in the x direction.
• Then, even if you change the outer dimensions of the plot (i.e. by changing the window size or the size of the pdf file you are saving it to (in ggsave for example)), the aspect ratio remains unchanged.
• In the above case, I decided that if every y unit was 1.3 times longer than an x unit, then the plot came out looking good.
  • A different value might be needed closer to the poles.

Mess with line and fill colors

• Here is no fill, with a red line. Remember, fixed value of aesthetics go outside the aes function.

• Here is violet fill, with a blue line.

Adding points to the map

• Let’s add black and yellow points at our lab and at the NWFSC in Seattle.

See how important the group aesthetic is

Here we plot that map without using the group aesthetic:

That is no bueno! The lines are connecting points that should not be connected!

State maps

We can also get a data frame of polygons that tell us above state boundaries:

Plot all the states, all colored a little differently

This is just like it is above, but we can map fill to region and make sure the the lines of state borders are white.

Boom! That is easy.

Plot just a subset of states in the contiguous 48:

• Read about the subset command. It provides another way of subsetting data frames (sort of like using the [ ] operator with a logical vector).

• We can use it to grab just CA, OR, and WA:

Man that is ugly!!

• I am just keeping people on their toes. What have we forgotten here?
  • group
  • coord_fixed()

• Let’s put those back in there:

Phew! That is a little better!

Zoom in on California and look at counties

• Getting the california data is easy:

• Now, let’s also get the county lines there

• Plot the state first but let’s ditch the axes gridlines, and gray background by using the super-wonderful theme_nothing().

• Now plot the county boundaries in white:

Get some facts about the counties

• The above is pretty cool, but it seems like it would be a lot cooler if we could plot some information about those counties.
  
• Now I can go to wikipedia or http://www.california-demographics.com/counties_by_population and grab population and area data for each county.
• In fact, I copied their little table on Wikipedia and saved it into data/ca-counties-wikipedia.txt. In full disclosure I also edited the name of San Francisco from “City and County of San Francisco” to “San Francisco County” to be like the others (and not break my regex!)
• Watch this regex fun:

• We now have the numbers that we want, but we need to attach those to every point on polygons of the counties. This is a job for inner_join from the dplyr package

• And finally, add a column of people_per_mile:

Now plot population density by county

If you were needing a little more elbow room in the great Golden State, this shows you where you can find it:

Lame!

• The popuation density in San Francisco is so great that it makes it hard to discern differences between other areas.
• This is a job for a scale transformation. Let’s take the log-base-10 of the population density.
• Instead of making a new column which is log10 of the people_per_mile we can just apply the transformation in the gradient using the trans argument

Still not great

I personally like more color than ggplot uses in its default gradient. In that respect I gravitate more toward Matlab’s default color gradient. Can we do something similar with ggplot?

That is reasonably cool.

zoom in?

Note that the scale of these maps from package maps are not great. We can zoom in to the Bay region, and it sort of works scale-wise, but if we wanted to zoom in more, it would be tough.

Let’s try!

• Whoa! That is an epic fail. Why?
• Recall that geom_polygon() connects the end point of a group to its starting point.
• And the kicker: the xlim and ylim functions in ggplot2 discard all the data that is not within the plot area.
  • Hence there are new starting points and ending points for some groups (or in this case the black-line permiter of California) and those points get connected. Not good.

True zoom.

• If you want to keep all the data the same but just zoom in, you can use the xlim and ylim arguments to coord_cartesian(). Though, to keep the aspect ratio correct we must use coord_fixed() instead of coord_cartesian().

• This chops stuff off but doesn’t discard it from the data set:

ggmap

The ggmap package is the most exciting R mapping tool in a long time! You might be able to get better looking maps at some resolutions by using shapefiles and rasters from naturalearthdata.com but ggmap will get you 95% of the way there with only 5% of the work!

Three examples

• I am going to run through three examples. Working from the small spatial scale up to a larger spatial scale.
  1. Named “sampling” points on the Sisquoc River from the “Sisquoctober Adventure”
  2. A GPS track from a short bike ride in Wilder Ranch.
  3. Fish sampling locations from the coded wire tag data base.

How ggmap works

• ggmap simplifies the process of downloading base maps from Google or Open Street Maps or Stamen Maps to use in the background of your plots.
• It also sets the axis scales, etc, in a nice way.
  
• Once you have gotten your maps, you make a call with ggmap() much as you would with ggplot()
• Let’s do by example.

Sisquoctober

• Here is a small data frame of points from the Sisquoc River.

• ggmap typically asks you for a zoom level, but we can try using ggmap’s make_bbox function:

• Now, when we grab the map ggmap will try to fit it into that bounding box. Let’s try:

• Nope! That was a fail, but we got a warning about it too. (Actually it is a little better than before because I hacked ggmap a bit…) Let’s try using the zoom level. Zoom levels go from 3 (world scale to 20 (house scale)).

• That is decent. How about if we use the “terrain” type of map:

• That is cool, but I would search for a better color for the lettering…

How about a bike ride?

• I was riding my bike one day with a my phone and downloaded the GPS readings at short intervals.

• We can plot the route like this:

• See how we have mapped elevation to the color of the path using our rainbow colors again.
• Note that getting the right zoom and position for the map is sort of trial and error. You can go to google maps to figure out where the center should be (right click and choose “What’s here?” to get the lat-long of any point. )

• The make_bbox function has never really worked for me.

Fish sampling locations

For this, I have whittled down some stuff in the coded wire tag data base to georeferenced marine locations in British Columbia where at least one Chinook salmon was recovered in between 2000 and 2012 inclusive. To see how I did all that you can check out this

Let’s have a look at the data:

So, we have 1,113 points to play with.

What do we hope to learn?

• These locations in BC are hierarchically structured. I am basically interested in how close together sites in the same “region” or “area” or “sector” are, and pondering whether it is OK to aggregate fish recoveries at a certain level for the purposes of getting a better overall estimate of the proportion of fish from different hatcheries in these areas.

• So, pretty simple stuff. I just want to plot these points on a map, and paint them a different color according to their sector, region, area, etc.

• Let’s just enumerate things first, using dplyr:

• That looks good. It appears like we could probably color code over the whole area down to region, and then down to area within subregions.

Makin’ a map.

• Let us try again to use make_bbox() to see if it will work better when used on a large scale.

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• Cool! That was about as easy as could be. North is in the north, south is in the south, and the three reddish points are clearly aberrant ones at the mouths of rivers.

Coloring it by region

• We should be able to color these all by region to some extent (it might get overwhelming), but let us have a go with it.
• Notice that region names are unique overall (not just within N or S) so we can just color by region name.

• Once again that was dirt easy, though at this scale with all the different regions, it is hard to resolve all the colors.

Zooming in on each region and coloring by area

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• It is time to really put this thing through its paces. (Keeping in mind that make_bbox() might fail…)
• I want to make series of maps. One for each region, in which the the areas in that region are colored differently.
• How? Let’s make a function: you pass it the region and it makes the plot.
• Keep in mind that there are no factors in this data frame so we don’t have to worry about dropping levels, etc.

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So, with that function we just need to cycle over the regions and make all those plots.

Note that I am saving them to PDFs because it is no fun to make a web page with all of those in there.

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