In AndySouth/rmapteach:

This tutorial will be about you doing stuff. I will get you to try things probably before you understand fully what they are. Worry not.

It doesn't matter if you make mistakes.
We will come back to some of the concepts later.
Questions are encouraged.
This should at least give you a start to follow up on later.

The beauty (& sometimes otherwise) of R is that there are usually multiple ways of doing the same thing. Here I will show some modern ways there are others.

We will mostly start looking at ggplot2 by Hadley Wickham. This is a newer alternative to base graphics. Base graphics are still useful for creating customised plots but we will not look at them here.

A,B,C ... ggplot
1,2,3 ... maps
At the end ... golden time & going further

A) point plots with ggplot2 first go

# load the package
library("ggplot2")

# load some data on mammal sleep patterns from the package
data(msleep)

# to show the 'str'ucture of the data
str(msleep)


# first plot
ggplot(data = msleep, aes(x = bodywt, y = awake)) +
    geom_point()

# aes (for aesthetic) sets up links between the data and aspects of the plot
# + adds layers to the plot, in this case points
# geom* stands for the geometric objects you can add to a plot
# bodywt and awake are columns within the data

# add an extra column to the aes
ggplot(data = msleep, aes(x = bodywt, y = awake, col = vore)) +
    geom_point()

# if you change col= to a column that is numeric it changes the legend
ggplot(data = msleep, aes(x = bodywt, y = awake, col = sleep_rem)) +
    geom_point()

# you can store the plot as an object (in this case called p) which can make modifying it easier
p <- ggplot(data = msleep, aes(x = bodywt, y = awake, col = sleep_rem))
p <- p + geom_point()
# type the variable name to display the plot 
p

# use themes to change the overall appearance of plots
# note that when typing this in RStudio it will show you the options once you've typed th...
p + theme_bw()

A1) Exercise

Have a quick look at the online ggplot2 documentation here : http://docs.ggplot2.org/current/ Particularly look at geom_point and scroll down to the examples.

From within R you can get similar help but without the helpful plots by : ?geom_point

Try modifying a point plot with the msleep data.

B) point plots, modifying axes, label points, divide into subplots

# we can log bodywt to space the points out more
p <- ggplot(data = msleep, aes(x = log(bodywt), y = awake, col = vore)) +
     geom_point()
p

# label the points for data exploration
p <- ggplot(data = msleep, aes(x = log(bodywt), y = awake, label = name)) +
     geom_point() + 
     geom_text()
p

# divide into subplots using facet_wrap()
p <- ggplot(data = msleep, aes(x = log(bodywt), y = awake, label = name)) +
    geom_point() + 
    facet_wrap(~vore)
p

C) line plots with ggplot2

#load some time-series data from ggplot2
data(economics)

# to show the 'str'ucture of the data
str(economics)

# geom_line()
ggplot(economics, aes(date, unemploy)) + geom_line()

#note that above aes(date, unemploy) assumes that these are to set x & y respectively
# this : aes(x=date, y=unemploy) would give an identical result

# another dataset economics_long is in 'long' format
# instead of having one column for each attribute (e.g. pop and unemploy)
# it has a column (variable) which has a repeated factor values for each date
# this allows us to create a plot with multiple lines 

data(economics_long)

ggplot(economics_long, aes(date, value01, colour = variable)) +
  geom_line()

#note that for each variable the value01 column has been scaled between 0 & 1 to fit on same scale


# geom_path can be used to look at the relationship of 2 variables over time
# unemployment and savings rate
p <- ggplot(economics, aes(unemploy/pop, psavert))
p + geom_path(aes(colour = date))

# note how the unemployment rate is calculated by unemploy/pop within the aes call
# you can do other calculations between columns in there

C1) Exercise

Try using facet_wrap with the economics_long line plot to get subplots for each variable.

...

D) bigger (but still small!) data, histograms etc.

# the diamonds dataset has > 50K diamonds (rows) in it

data(diamonds)
str(diamonds)

# a scatter plot is not very informative
p <- ggplot(diamonds, aes(x = carat, y = price, col = color))
p + geom_point()

# setting the alpha (transparency) level to < 1 can improve things but not much
p <- ggplot(diamonds, aes(x = carat, y = price, col = color))
p <- p + geom_point(alpha=0.3)

# geom_smooth adds a smoothed conditional mean which can help reveal patterns
p + geom_smooth()

# geom_histogram can be used to look at distributions 
p <- ggplot(data=diamonds) + geom_histogram(aes(x=price))
p

# most of the diamonds are cheap ! (relatively)

D1) Exercise

Use any of your ggplot skills to find out something interesting about diamonds ...

...

1 maps

Install and load the tmap package

install.packages("tmap")

library("tmap")

1.1 Getting started with polygon maps

# load data from tmap
data(Europe)
# set shapes and fill them
tm_shape(Europe) +
  tm_fill("purple")

# load data from tmap
data(World)
# set shapes and fill them
tm_shape(World) +
  tm_fill("green")

# check what data are associated with a map using 'str(*@data)', str stands for structure
str(World@data)
str(Europe@data)
# note that str(Europe) shows all the geometry information too and is not very useful here

# check the contents of Factor variables using 'levels'
levels(Europe$part)
# levels(Europe@data$part) gives the same output, you don't always need the @data to get at the variables 

# plot a subset of the map using '[which(*),]'
tm_shape(Europe[which(Europe$part=='Northern Europe'),]) +
  tm_fill("red")


# check the contents of numeric variables using 'hist'
hist(Europe$pop_est)

# fill polygons on a map based on data variables
# factor
tm_shape(Europe) +
  tm_fill("part")
# numeric
tm_shape(Europe) +
  tm_fill("pop_est")

# the previous commands used default colours, you can modify these with 'palette'
tm_shape(Europe) +
  tm_fill("pop_est", palette = "YlGnBu")
# to see available palettes 
RColorBrewer::display.brewer.all()

# to add a histogram to the legend
tm_shape(Europe) +
  tm_fill("pop_est", palette = "YlGnBu", legend.hist = TRUE)

# maps are stored as a SpatialPolygonsDataFrame from the package sp
# you can find this out by typing
class(Europe)

1.2 Exercise : can you plot different areas, different variables, different colours ...

# to see help use '?'
?tm_fill

# syntax is like ggplot2 (e.g. + to add layers)

# start with one of the code examples above and change something ...

2.1 Getting started with point maps

# load some tmap point data for metropolitan areas
data(metro)
# ?metro gives information about the data
# this is also an sp object with associated variables
class(metro)
str(metro@data)

# plot points as bubbles
tm_shape(metro) +
    tm_bubbles("pop2010", legend.size.show = FALSE)

# plot points on top of a European map
tm_shape(Europe) +
  tm_borders() +
tm_shape(metro) + 
  tm_bubbles("pop2010")

2.2 Exercise - can you change one of the point maps ?

...

3.1 Getting started with raster maps

# load some data from tmap
data(land)

class(land)

str(land@data)

tm_shape(land) + 
    tm_raster("trees", breaks=seq(0, 100, by=20) )

tm_shape(land) + 
    tm_raster("trees", breaks=seq(0, 100, by=50) )

# categorical land cover
tm_shape(land) + 
    tm_raster("cover")

# using faceting

# facet by cover to get one map for each cover type
tm_shape(land) + 
    tm_raster("cover", legend.show = FALSE) +
    tm_facets("cover", free.coords=TRUE, drop.units=TRUE)   


# facet by country to get one cover map per country
tm_shape(land) + 
    tm_raster("cover") + # , legend.show = FALSE)
tm_shape(Europe[which(Europe$part=="Northern Europe"),]) +
    tm_borders() +
    tm_facets("name", free.coords = TRUE)

4 map your own data in tmap

You can use both your own geometry and/or attribute data in tmap and other mapping options.

# create a very simple data frame as an example
dF <- data.frame( country=c("Spain", "United Kingdom"),
                  weather=c("hot", "cold") )

# tmap can join this onto a map (other options are available too)
Eu_and_dat <- append_data(Europe, dF, key.shp="name", key.data="country")

tm_shape(Eu_and_dat) +
  tm_fill("weather", palette=c("red","blue"))

4.1 Exercise

# Can you plot some other data by country ?
# remember the country names in the tmap map are in Europe$name

# the gapminder package contains some country data
install.packages("gapminder")
library(gapminder)
?gapminder
str(gapminder)

Eu_and_gap <- append_data(Europe, gapminder[gapminder$year==2007,], key.shp="name", key.data="country")

tm_shape(Eu_and_gap) +
   tm_fill("lifeExp")

5 Golden time

Take whatever you want from above and below, with your own data or the example datasets and play with it. Show your neighbour what you've done.

Going further

# other useful packages, resources

# a new package for getting world bank data
install.packages("wbstats")
# http://www.r-bloggers.com/new-r-package-to-access-world-bank-data/

# packages for creating the circular migration plots I showed this morning
install.packages("migest")
install.packages("circlize")

# raster package, great for mapping satellite data etc.
install.packages("raster")
library(raster)

# rmapshaper for simplifying polygon boundaries
install.packages("rmapshaper")
library(rmapshaper)

# leaflet a package for creating interactive maps
install.packages("leaflet")