03-simple-data-exploration.md

Simple visualization and mapping

Preliminaries

If you haven't worked through the other two sets of instructions for this week go back there and do this now.

Next, set the working directory to where you would like to work using the Session - Set Working Directory - Choose Directory... menu option. When you do that you should see a response something like

setwd("~/Documents/teaching/GISC-422/labs/scratch")

in the console. It will be a different location than above, depending on your machine.

Meet the command line...

OK.

The key thing to understand about R is that it is a command line driven tool. That means you issue typed commands to tell R to do things (this was a normal way to interact with a computer and get work done before the early 1990s, and is making a comeback, like vinyl, but not cool). There are some menus and dialog boxes in RStudio to help you manage things, but mostly you interact with R by typing commands at the > prompt in the console window. To begin, we'll load up a dataset, just so you can see how things work and to help you get comfortable. As with most computer related stuff, you should experiment: you will learn a lot more that way.

Reading data

We will do this using the readr package from the tidyverse, which means we first have to load it:

library(readr)

Now we can use a command in readr to read the comma-separated-variable (CSV) data file:

quakes <- read_csv('earthquakes.csv')

You should get a response something like

Parsed with column specification:
cols(
  CUSP_ID = col_double(),
  NZMGE = col_double(),
  NZMGN = col_double(),
  ELAPSED_DAYS = col_double(),
  MAG = col_double(),
  DEPTH = col_double(),
  YEAR = col_double(),
  MONTH = col_double(),
  DAY = col_double(),
  HOUR = col_double(),
  MINUTE = col_double(),
  SECOND = col_double()
)

If that doesn't happen make sure you have set the correct working directory, that the data file is in there, and that you typed everything correctly. Ask for help, if necessary.

The response tells you that the file has been opened successfully. You should see an entry for the dataset appear in the Environment part of the interface, called quake because that's the name of the variable we asked R to read the data into. You can look at the data in a more spreadsheet like way either by typing (the capital V is important)

View(quakes)

or by clicking on the view icon at the right hand side of the entry for quakes in the environment list.

These are data concerning earthquakes recorded in the months after the 7.1 earthquake in Christchurch in September 2010.

In R, data tables are known as dataframes and each column is an attribute or variable. The various variables that appear in the table are

• CUSP_ID a unique identifier for each earthquake or aftershock event
• NZMGE and NZMGN are New Zealand Map Grid Easting and Northing coordinates
• ELAPSED_DAYS is the number of days after September 3, 2010, when the big earthquake was recorded
• MAG is the earthquake or aftershock magnitude
• DEPTH is the estimate depth at which the earthquake or aftershock occurred
• YEAR, MONTH, DAY, HOUR, MINUTE, SECOND provide detailed time information

Exploring data

Now, if we want to use R to do some statistics, these data are stored in a variable named quakes (in my example, you may have called it something different). I can refer to columns in the dataframe by calling them quakes$MAG (note the $ sign). So for example, if I want to know the mean magnitude of the aftershocks in this dataset I type

mean(quakes$MAG)

or the mean northing coordinate

mean(quakes$NZMGN)

and R will return the value in response. Probably more informative is a boxplot or histogram, try:

boxplot(quakes$MAG)

or

hist(quakes$MAG)

and you should see statistical plots similar to those shown below.

It gets tedious typing quakes all the time, so you can attach the dataframe so that the variable names are directly accessible without the quakes$ prefix by typing

attach(quakes)

and then

hist(MAG)

will plot the specified variable. Be careful using attach as it may lead to ambiguity about what you are plotting if you are working with different datasets that include variables with the same names.

Try the above commands just to get a feel for things.

A simple (crappy) map

You can make a simple map of all the data by plotting the NZMGE variable as the x (i.e. horizontal axis) and NZMGN as the y axis of a scatterplot:

plot(NZMGE, NZMGN)

Because base R is not a GIS it doesn't know about things like projections, so this is a very crude map.

NOTE: from here on I am not going to show results of commands, just the commands!

There are R packages to handle geographical data better than this (we will look at those in a little later) but for now don't worry about it too much. To see if there is a relationship between earthquake depth and magnitude, try this

plot(DEPTH, MAG)

and because R is a statistics package, we can easily fit and plot a simple linear regression model to the data

regmodel <- lm(MAG ~ DEPTH)
plot(DEPTH, MAG)
abline(regmodel, col = 'red')

Note here the use of <- to assign the model, made by the linear model lm() command to a new variable, called regmodel. You can get more details of this model by typing regmodel or summary(regmodel). If you know anything about regression models, these may be of interest to you. Also note how, I've requested that the line be plotted in red (col = 'red'), so it can be seen more easily.

We can make more complex displays. For example

plot(ELAPSED_DAYS, MAG)

Shows how the magnitude of the aftershocks changed in the days after the initial large earthquake, with the second large event happening around 6 months (180 days) later. A more complicated plot still would be

boxplot(MAG ~ cut(ELAPSED_DAYS, seq(0,200,20)))

Give that a try and see what you get. To label the chart more informatively we need to add information for x and y axis labels

boxplot(MAG ~ cut(ELAPSED_DAYS, seq(0, 200, 20)), xlab = "Days after Sept 3, 2010", ylab = "Magnitude")

Next up: simple maps.