R Tutorial

Created by: Joey K Lee

Edited by: Andras Szeitz

About

This is an intro to the R programming language which we will use throughout the rest of the course and hopefully you’ll use when you leave UBC and start your lives in research, industry, and beyond!

Requirements:

  • First: download and install R – make sure you download the right one for your system.
    • For Windows users, you can choose between 32-bit and 64-bit versions of R, you need to choose which one is right for your computer. If you’re really unsure, then choose the 32-bit, BUT if you don’t mind google searching “32-bit or 64-bit windows for “
    • SUPER IMPORTANT for Windows: We’ve noticed some issues with R and the packages if you install R and R studio to to the “C:\Program Files” which is the default option.
      • Instead try to install R and RStudio on your “C:\” drive. If you have already installed R to your “C:\Program Files” and have been noticing problems, uninstall R and Rstudio and reinstall them in your “C:\” drive.
  • Second: download and install R studio (make sure to install R first!)

Outline

Why are we learning R?

Why are we using R after spending all that time learning Processing? Well, first I would go back to the fact that programming languages are like materials, each with a distinct qualities, benefits, and limitations. In this way, it doesn’t make sense to say one language is better or worse than another, rather that they are just different. So why learn R? Here’s why I chose to introduce R in this course:

  • High Level Programming Language: R is what we consider a high level programming language. With a few lines of code, you can do a lot of stuff which is super nice when we’re learning.
  • Easy to setup and has a nice Integrated Development Environment (IDE): R is nice because it sort of lives within itself – it is easy to download, install, and setup, it is cross platform (it can be used in Windows, Mac OS, etc), and has a nice IDE called RStudio which we will use to help us write our code.
  • Large user community: Like Processing, R has a HUGE user community that has grown to include domains of all sorts. This means also if you have a specific question that most likely someone has already found the answer and posted it online. The New York Times Data team is using R (among other things), for example!
  • Super handy packages: Because of the large user community people have developed packages for doing all sorts of cool things. Want to make an interactive map? Bam! There’s an R package for that. Want to make a hillshade of a DEM in R? There’s a package for that too! You’ll start to see that packages will be your best friend, so use your favorite search engine and find the right packages for you!
  • Nice plots: R kicks ass for plotting – and this is a data visualization class after all. With one line of code you can already start looking at your data, add a few more and you already have something pretty to look at. As a way of exploring and displaying your data, R is a nice way to go.
  • Local expertise: Its always handy to have people around to help you if you get stuck. There are plenty of people teaching R at UBC and also some expert grad students and faculty in Geography.
  • Open source mapping: When we dive further into R we’ll start to see that we can start to do some really fancy geoprocessing in R – Basically your own, free, GIS 🙂 When you leave UBC and no longer have access to proprietary software, you’ll be happy to know that you have the tools to continue mapping away.

Getting Started

Throughout this workshop, try your best to make connections between what you learned in the intro to Processing to what R has to offer. There are some fundamental differences you will begin to notice, but you’ll really quickly begin to see the advantages (and disadvantages) of using R.


R Studio IDE:

This is the R studio IDE. We will use R studio as our environment for running our code, making our scripts, and viewing our plots. It’s sort of like our Processing PDE, with the major difference being that R is an interpreted language meaning we can run our code line-by-line versus Processing which is compiled which means Processing runs all of the code at once. You’ll see why this is advantages for working with and exploring data. Anyways, because of this key difference, R studio is setup to help us explore our data line-by-line.

rstudio

Your typical R studio IDE will look like the above and includes:

  • Script window: A place to put your script (your entire code)
  • R console: every line of code your run from your script will be run, evaluated and printed here.
  • Global Environment: This will show all the variables you declare and assign. The fancy thing about R studio is that it will tell you what kind of variable it is and even let your preview your data that you read in!
  • Plot, Packages, Help, & more: Your plots will be shown here. Also you will be able to find packages and access R’s help documentation.

Keeping Track:

  • Print everything – check that things are working and see how you change and manipulate data with functions and operations.
  • Plot everything – again, check that things are working and see how you change and manipulate data with functions and operations.
  • Comment your code – it will make it easier for you to remember what you were trying to do, and make it easier for others to read and understand your code

Quick Syntax review:

Mathematical operators in R

While most of the mathematical operators are the same as in Processing, there’s a few differences. Let’s check out those mathematical operators in R again.

Operator        Description
+               addition
-               subtraction
*               multiplication
/               division
^ or **         exponentiation
x %% y          modulus (x mod y) 5%%2 is 1
x %/% y         integer division 5%/%2 is 2

Logical Operators in R

Operator        Description
<               less than
<=              less than or equal to   >               greater than
>=              greater than or equal to
==              exactly equal to
!=              not equal to
!x              Not x
x | y           x OR y
x & y           x AND y
isTRUE(x)       test if 'x' is TRUE

Assignment Operators and Subsetting in R

Operator        Description
<-              assigns a value to a name
=               assigns a value to a name
[]              extracts a part of an object
$               extracts a part of a vector

Gettin’ down and di-R-ty:

After learning about the fundamental building blocks of programming (and the data visualization pipeline), the goal now is to apply our understanding by taking code and data and producing visual output.

Instead of learning how to code R from the bottom up (which we did with Processing), we will go from the top down, meaning: we are going to learn by running examples and in the process train ourselves to deconstruct the patterns and functions we pick up along the way.

But before we get into some examples, let’s do a quick tour of running code in R, some R syntax and data types!


Your First R commands: “Hello World”

Let’s start by just running a few commands in the R Console. To run a command in the console window, simply:

  • type your statement and
  • press [enter].

In the console window, let’s write 3 statements and run them. Here’s the result of running the 3 examples below.

r-intro

  • Addition
    5 + 3
    
  • Logical Operation
    100 < 200
    
  • String
    "Oops I did it again"
    

Running code from the script window

Now that you’ve seen how R reads, evaluates, and prints the statements you feed it line-by-line, .

When running code line-by-line in the script window you:

  1. Highlight the lines you want to run:

highlight.png

  1. then “run the code” by pressing at the same time:
    • On Mac: [cmd] + [enter]
    • On Windows: [ctrl] + [enter]

The results will then be “printed” to the console:

script-print.png

Now, to familiarize yourself with running code line-by-line from the script window, let’s write another 3 statements, but this time insert a comment between each of the statements to neatly organize your code.

NOTE: In R, comments are made by using the hash symbol (#)

Try writing 3 examples of your own in the script window and seeing what is printed to the console:

  • Commenting:
    # --- Gettin' Down and di-R-ty: an intro to R --- #
    # remember anything after the comment symbol is not evaluated
    
  • Multiplication
    # Multiplication
    6 * 5
    
  • Exponentiation
    # Exponents
    5**2
    
  • String
    # Strings
    "Hello World"
    

FYI, we can also use the print() function to print statements for us like this:

# --- heres another way to print in R --- #
print("Hello World!")
print(5*10)
print(10-3)

Run this and see that it return the same thing! Pretty neat stuff.

NOTICE SOMETHING? WE DON’T NEED TO USE SEMI-COLONS (;) IN R! This means that R doesn’t use semi-colons to signify the end of a statement in contrast to Processing. Rather R uses line breaks to interpret each statement. Excited? Sad? Indifferent? In any case, just remember you don’t need semi-colons to end statements in R. Are you beginning to see how programming languages all have their own flavor?

Now that you see how to run code line-by-line, let’s dive further into some more exciting stuff.

Help! Information!

If you are trying to use a function and you aren’t getting the output you are expecting, or maybe it’s straight-up not working, RStudio has very useful help documentation for all the functions and packages you might use! To get the information about what parameters a function is expecting, and in what format, just type ?’function’ in the console. ie:

# Getting help on mean:
?mean

untitled


Variables, Data, & Plotting

Before we take a leap into a real-world example, it’s worth noting a few fundamental things about R variables, data, and plotting.

R Variables:

Unlike in Processing which is strongly typed, R is loosely typed meaning that we don’t need to specify the type of data that we are assigning to each variable – notice we don’t have to specify “int” or “float” or “String” – R knows that type of data we are assigning!:

# I'm an integer variable:
x <- 12
print(x)

# I'm a float variable:
y <- 16.5
print(y)

# I'm a string variable:
z <- "Hello"
print(z)

Fundamental R Data Types:

Here are a number of fundamental R data objects that over time you will come to fully understand what they are, what makes them special, and when and where you would use them. For the 4 major data objects below, all we need to now is that:

Vectors (pretty much just a 1-D Array):

A vector in R is basically a collection of elements of the same data type (e.g numeric, character, boolean.) – a 1-D array. We create a vector in R by using the c() function. The c() is short for “concatenate” which means “paste together or chain together in a series”.

Here’s 2 examples of a vectors:

# Here's some data of months of the year and the precipitation in Vancouver
months <- c(1, 2, 3, 4, 5, 6, 7, 8, 9 , 10, 11, 12)
precipitation <- c(186, 94, 118, 85, 60, 59, 38, 39, 48, 126, 183, 177)

Lists:

An R list is a collection of elements (similar to a vector) BUT the key difference is that a list can contain different data types. Lists can be created by using the list() function and filling in values to be stored in the list.

Here’s an example of a list of all the same values:

# Hey I'm a list:
my_list <- list(3, 6, 9, 12,15)

Here’s a list of lists of mixed data types:

# ---- a list ---- #
my_fav_things <- list(pi, 42, "Vancouver")

As you can see, depending on your data, this could be useful way of organizing and structuring your data. Looking kind of like Javascript Object Notation (JSON) with those keys and values, eh?

Dataframes:

A dataframe is essentially data in a tabular form (think an excel spreadsheet) – it is composed of rows and columns of equal length. Each column is a vector ( aka 1-D array), meaning it contains all of the same data type (except for the header row) and is recursive, meaning that we can apply functions and mathematical operations on each column. Holy guacamole, does this mean R is really great at processing tabular data like excel sheets, csv files, and maybe even shapefiles? Yes! That is exactly right!

Very generally, dataframes are composed of vectors, which are like each column. OH! That means we can take our month and precipitation data and create a data frame?! yes! All we have to do is use the data.frame() function:

A dataframe of the rain data:

# Here's some data of months of the year and the precipitation in Vancouver
months <- c(1, 2, 3, 4, 5, 6, 7, 8, 9 , 10, 11, 12)
precipitation <- c(186, 94, 118, 85, 60, 59, 38, 39, 48, 126, 183, 177)

# rain dataframe
rain_data <- data.frame(months, precipitation)
print(rain_data)

This gives us a nice tabular dataset 🙂

precip-df

We use R’s selector operator ($) to select each column:

# select the precpitation column and calculate the annual average
mean_precip <- mean( rain_data$precipitation )
print(mean_precip)

We see that the mean precipitation in Vancouver is 101.0833 mm.

Plotting!

Ok, so now we are familiar with some of the basic data objects.  But just looking at a vector of numbers or a dataframe doesn’t make it easy to see what information the data holds. The great thing about R is how easily we can make plots to visualize our data! Here, we’ll plot the monthly precipitation data as a scatterplot:

# A simple scatterplot:
plot(x = months, y = precipitation)

precip-p.png

Or how about a line plot:

# a line plot:
plot(x = months, y = precipitation, type = "l")

precip-l.png

Or how about both lines and points:

# scatterplot with lines - "b" stands for both:
plot(x = months, y = precipitation, type = "b")

precip-b.png
Or how about a barplot:

# plot it as a bar plot
barplot(height = precipitation, names.arg = months)

precip-bar

Say, we wanted to plot the annual average on top of the monthly values, we could do something like this:

# create the plot:
plot(x = rain_data$month, y = rain_data$precipitation, type = "b" )

# add the mean line:
abline(h = mean_precip)

precip-mean.png

How easy and great is that? Sure sure, you’re probably thinking “This is lame, I could do this in Excel” – but what if you had to make these graphs for every year in Vancouver since 1965? You could write a loop to automate that in R instead of clicking over and over again in Excel… AND if you needed to change a color or an axis label, you could just run your script again… just sayin’.

*NOTE: We usually won’t be making our own dataframes from scratch, but rather reading them in the form of delimited text files like .csv, .xls, .tsv, .dat, etc. *

and finally, Matrices:

Think of matrices as a raster or bitmap image. While the x and y dimensions can be different (e.g. like a photograph) all the columns must be the same data type.

As a quick example, let’s use one of R’s preloaded datasets – a matrix (e.g. raster) of Auckland’s Maunga Whau Volcano:

# store the volcano data to a variable:
cano <- volcano
print(cano)

Whoa! we get a bunch of print outs that essentially show this structure/numbers which represent the topography of this volcano:
cano-ex

Now let’s plot it, but instead of using plot() let’s use the image() function:

# image() fuction to plot the cano
image(cano)

Wow! Such colors!

cano-img.png

What other goodies can we plot? How about some contours?

# contour lines of matrix
contour(cano)

cano-contours.png

And believe it or not, enter the 3rd dimension:

# Get some perspective
persp(cano, expand = 0.3, phi = 35, theta = 10)

cano-persp.png


Reading Dataframes from files:

We just saw how to make a dataframe using the data.frame() function using a set of vectors. Now let’s read in data from a file as a dataframe using R’s read.csv() function:

# --- reading in data from a csv file --- #
# store the filename & path to a variable
fileName <- 'https://raw.githubusercontent.com/joeyklee/aloha-r/master/data/rain2014.csv'

# pass the filename variable to the "read.csv()" function
# use "header = TRUE" if there's a header
rain_csv <- read.csv(fileName, header = TRUE)

If we look at the data again, we see that it looks exactly like the dataframe we created earlier:

precip-df

Now, we can do exactly the same steps as before EXCEPT we need to change the data name from rain_data to rain_csv:

# select the precpitation column and calculate the annual average
mean_precip <- mean(rain_data$precipitation)
print(mean_precip)

rain-csv-plot.png


R Packages (aka libraries)

R packages (aka libraries) may be one of the most useful things about R. Libraries are basically bundled up scripts that people (software developers, researchers, designers, artists, etc) have written to help take complicated tasks or computations, wrapped up in simple(r) to use functions to make programming easier, more fun, and easy to read.

In this way, you can say that Processing is a language and also a “library” because it makes functions available (e.g. ellipse() and rect()) which would take 10 to 20 lines of code in Java and puts them into an easy to use function.

In R, we have countless numbers of libraries to help us to tasks. Remember earlier I mentioned that there are R packages that can help you do geoprocessing and even make your own interactive web map? Well, now we’re going to introduce a package that allows us to read in shapefiles into R so that we can use it as a basemap for our crime plot.

NOTE: It may seem like R libraries are just doing magic behind the scenes, but they are simply making more functions available to you to use that aren’t already included in the base R library – think of it as an actually library, you go there to get books that you don’t have at home 😉

SO to start, we need to install the package we want to use. After doing a few google searches, I found an R package called “GISTools” that allows us to read shapefiles into R, make choropleth maps, do geoprocessing, etc – essentially your own, free and open source GIS.

installing and adding a library

We install packages in R by using the install.packages() function:

# ---------- Using our first R package to display a shapefile! ---------- #
# install the maptools library
install.packages("GISTools")
install.packages('scales')

NOTE: If you get an error like “cannot write to lib” or something on windows — you need to change the read/write permissions on that folder. You can do so by navigating to that folder > right click > properties > check the box that says “read/write”.

After we install our R package, we need to import it to our script. We do so using the library() function:

# import the GISTools functions by calling the library() function
library(GISTools)
library(scales)

Now that we have our library imported, we have can read in some shapefiles that are conveniently sitting in our data folder. Similar to how we read in our .csv file, we just have to include the path name to each of our .geojson files.

# --- read in shps --- #
# Building shp
fname_buildings <- "https://raw.githubusercontent.com/joeyklee/aloha-r/master/data/example/buildings.geojson"
buildings <- readOGR(fname_buildings, 'OGRGeoJSON')
plot(buildings) # plot to inspect

# Roads shp
fname_roads <- "https://raw.githubusercontent.com/joeyklee/aloha-r/master/data/example/roads.geojson"
roads <- readOGR(fname_roads, 'OGRGeoJSON')
plot(roads) # plot to inspect

# Co2 points
fname_co2 <- "https://raw.githubusercontent.com/joeyklee/aloha-r/master/data/example/co2.geojson"
co2 <- readOGR(fname_co2, 'OGRGeoJSON')
plot(co2)

By plotting the data, we can see what we have! Now, let’s combine the plots together to make a very basic map:
ex1.png

# Plot the buildings
plot(buildings, 
     col = "#808080", 
     border = F,
     bg = "#FFFFFF",
     main = "CO2 Mixing Ratios Downtown Vancouver")

ex2

# Add the roads to the plot with "add = TRUE"
plot(roads,
     col = "#000000",
     lwd = 1,
     add = TRUE)

ex3.png

# Add the CO2 observation points to the plot with "add = TRUE"
vals = rescale(co2@data$co2, c(0.5, 5))
plot(co2,
     col = "#FF6600",
     pch = 20,
     alpha = 0.5,
     cex = vals,
     add = TRUE)

We can even add a north arrow in R (or later in Illustrator):

# We can even add a north arrow:
north.arrow(xb = -123.144035, yb = 49.273001, len = 0.0005)

And a scale bar:

# scale bar:
map.scale(xc = -123.151295, 49.271729, len = 0.01,
          ndivs = 2, subdiv = 3, units = "test")

Now you’re more than primed to construct and deconstruct our first real-world R project!

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