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Package {envsocty3LT3} is an open educational resource that aims to
combine various advantages of working with the R statistical
computing project:
- Ease of distribution
- Reproducibility
- Availability of templates for computational notebooks
- Rigor in documentation of data sets and computational products
See the Quickstart guide below on instructions to install and start using the package.
This course package is designed for use in the course ENVSOCTY 3LT3 Transportation Geography offered by the School of Earth, Environment and Society at McMaster University. The course package includes the following components:
- Document templates with Readings.
- Document templates with Exercises.
- Data sets used in the Readings and Exercises.
- Custom functions.
Readings are designed to be like mini-chapters in a book. What makes them different from a conventional book is that they are interactive and editable, which means that you can work with them in ways not possible with a conventional printed book.
These include:
- Exercise 1: Energy consumption trends
- Exercise 2: Geographic information for transportation analysis
- Exercise 3: Network analysis and the potential for walking mobility and accessibility in neighbourhoods
- Exercise 4: Spatial interaction analysis in the Hamilton CMA
- Exercise 5: Logistics and deliveries
Exercises are the documents that you will use to complete most of your coursework. The templates are pre-formatted for you, so you do not have to think about how to prepare the document, and can instead devote your attention and energy to learn while creating great content.
This course does not assume knowledge of, or experience working with
R. So, no previous knowledge is required, other than some experience
using computers in general, and maybe a word processor (e.g., Microsoft
Word) and spreadsheets (e.g., Microsoft Excel). To use the package you
will begin from the very basics: how to install and use the necessary
software: R and an Interactive Development Environment (e.g., RStudio)
as explained next.
What is R?
R is an open-source language for statistical computing. It was created
in the early 1990s by Ross
Ihaka and Robert
Gentleman
at the University of Auckland, New Zealand, as a way to provide their
students with an accessible, no-cost statistical application for their
courses. R is now maintained by the R Development Core Team, and it
continues to be developed by hundreds of contributors around the globe.
R is an attractive alternative to other software packages for data
analysis (e.g., Microsoft Excel, Matlab, Stata, ArcGIS) due to its
open-source character (i.e., it is free), its flexibility, and large
user community. The size of the R community means that if there is
something you want to do (for instance, estimate a linear regression
model or plot geographical information), it is very likely that someone
has already developed a package for it in R.
A good way to think about R is as a core package, with a library of
optional packages that can be attached to increase its core
functionality. R can be downloaded for free at:
R comes with a built-in console (a graphical user interface), but
better alternatives to the basic interface include Interactive
Development Environments like RStudio, which can also be downloaded for
free:
https://www.rstudio.com/products/rstudio/download/
R requires you to work using the command line, which is going to be
unfamiliar to many of you accustomed to user-friendly graphical
interfaces. Do not fear. People worked for a long time using the command
line, or using even more cumbersome systems, such as punched cards in
early computers. Graphical user interfaces are convenient, but they have
a major drawback, namely their inflexibility. A program that functions
based on graphical user interfaces allows you to do only what is
hard-coded in the user interface. Command line, as you will soon
discover, is somewhat more involved, but provides much more flexibility
in operation, and the ability to be more creative.
To begin, install R and RStudio in your computer. This
video (5:23 min) shows
how to install these application.
If you are working in the GIS lab at McMaster you will find that these
have already been installed there. If you have used R and have a
previous instal, update it to R version 4.1.1 (2021-08-10) – “Kick
Things”. The course package was developed using “Kick Things”!
RStudio is an Interactive Development Environment (IDE for short). It
takes the form of a familiar window application, and it provides a
complete interface to interact with the language R. The application
consists of a window with toolbars and several panes. Some panes include
several tabs. There are the usual drop-down menus for common operations.
See Figure 1 below.
The editor pane allows you to open and work with text and other files.
In these files you can write instructions that can be passed on to R
for execution. Writing something in the editor does not execute the
instructions, it merely records them for possible future use.
The console pane is where instructions are passed on to the program.
When an instruction is typed (or copied and pasted) there, R will
understand that it needs to do something. The instructions must be
written in a way that R understands, otherwise errors will occur.
The top-right pane includes a tab for the environment; this is where all data objects that are currently in memory are reported. The History tab in the same pane acts like a log: it keeps track of all instructions that have been executed in the console. Depending on your project, you may see other tabs there.
The last pane in the bottom-right includes a few other useful tabs. The
File tab allows you to navigate directories in your computer, change the
working directory, see what files are where, and so on. The Plot tab is
where plots are rendered, when instructions require R to do so. The
Packages tab allows you to manage packages, which as mentioned above,
are pieces of code that can augment the functionality of R. The Help
tab is where you can consult the documentation for
functions/packages/see examples, and so on. The Viewer tab is for
displaying web content locally. Many R functions create html output
and it is in this pane where this kind of content can be previewed.
Once you have installed R and RStudio you are ready to install the
course package {envsocty3LT3}. The package is available from
GitHub, and to install it you
need to run the following code in your R console:
install.packages("remotes")
remotes::install_github("paezha/envsocty3LT3")This will download the package to your personal library of packages and
install it to make the package available for use locally. Behind the
scenes, {envsocty3LT3} uses LaTeX to convert documents to PDF. For this
you need to have install LaTeX in your system. The simplest approach on
any platform is with R package tinytex,
as follows:
install.packages(c('tinytex', 'rmarkdown'))
tinytex::install_tinytex()After restarting R Studio, confirm that you have LaTeX with the following command:
tinytex:::is_tinytex() After installing the course package, this is the recommended workflow for using it in this course.
Follow the steps below to create a new project. A project is the best way to keep your work in this course nicely organized.
You can create a new project using the buttons in the toolbar. Figure 2
shows one way of doing this:
Figure
2. Create new project - option 1
Figure 3 shows an alternative way of doing the same, using the button
for managing projects in the R Studio interface:
Figure
3. Create new project - option 2
You then need to select a new directory to store your new project. Give
the new directory a name, and save it in a place that you can easily
find (for instance, the folder where you keep your academic work).
Figure 4 illustrates the steps to do this:
Figure 4. Choose to store the project in a new directory
After you click ‘Create Project’, you will have an R session with your
new project. This will look like the image in Figure 5.
Figure 5.
Your project keeps all your files nicely organized
You need to restart R Studio once after installing the package before you can access the readings and exercises.
After doing so, you will find that all your readings are included in the
course package. Each reading is like a mini-chapter in a book (instead
of asking you to buy a book, we will give you the contents). But
readings can be much more than that. To begin working with your
preliminary reading, you begin by creating a new file and choosing R
Markdown from a template. Select template Reading-0 from the course
package and give it a name. After you click ‘OK’, a new R Markdown file
will open in your editor. Also, notice that a new folder appears in your
project to keep this file. The process is illustrated in Figure 6.
Figure
6. Creating a new file from a template
Your new file is an R Markdown document. This is a text file with chunks of code that can be executed. Reading 0 will introduce you to the use of R Markdown. The document is editable, which means that you can annotate it. To begin with, you can add your name to the list of authors of the document. You can execute code by clicking on the ‘play’ icon on the top-right corner of a chunk of code. The template also includes a definition for a textbox. You can introduce a textbox in the text using this format:
:::{.textbox data-latex=""}
This is my annotation.
:::
Figure 7 illustrates these steps.
Figure 7. Working with your reading
Once you are happy with your work using this file, you can create a pdf
file to study by knitting the document. Knitting will convert the R
Markdown to a pdf file. Click the Knit button in the top left corner to
do this. You can knit your document at any time, and as many times as
you want; remember, you can always start afresh by creating a new R
Markdown file with the same template. See Figure 8 for an example of
knitting. 
Figure 8. Click ‘Knit’ on the toolbar to convert your R Markdown
into a pdf file
Figure 9 shows the result of knitting your R Markdown file.
Figure
9. The result of knitting is a pdf file with your reading
Since you can edit and annotate the text, you can essentially customize the chapter so that it is a unique reflection of your learning. As you progress with the course and complete all the readings, you will have a collection of unique chapters that track your very personal learning experience in this course.
Working on your exercises (which you will submit for grading) is very
similar to working with your readings. First, you need to create a new R
Markdown file from a template. For the first exercise, you would select
the template for Exercise-1. Figure 10 illustrates the steps. Use the
following naming convention for your exercise files:
exercise-number-studentnumber. Once you click ‘OK’ a new R Markdown
file will appear in your editor, as well as a new folder where this file
resides.
Figure 10. Creating a new file to work on an exercise
To begin, you can edit the header of the document with your personal
information, like name and student number (see Figure 11).
Figure 11. Entering your personal information in the header of the
R Markdown document
You can also run chunks of code (see Figure 12).
Figure 12. Running chunks of code in your exercise
And importantly, to work on your exercise, you can enter your answers as
text and create new chunks of code to do any calculations you need for
your answers, as shown in Figure 13.
Figure 13. Working to answer the questions in your exercise
After you complete your exercise, you will need to return to the header
and complete two sections: highlights and threshold_concepts. The
highlights ask you to reflect on your learning experience working on the
exercise. Try to write concisely in approximately 200 words. The
threshold concepts are key ideas that once you grasp them they change
your understanding of a topic, phenomenon, subject, method, etc. Write
between three and five threshold concepts that apply to your learning
experience working on this exercise. Figure 14 illustrates this step.
Figure 14. Writing the highlights of the exercise
The highlights and threshold concepts are the last element of your
exercise, and after writing them you can knit the document to generate
the pdf file for submission. Click the Knit button in the top left
corner to knit (see Figure 15).
Figure 15. Knitting the exercise
You are now ready to submit the exercise following the instructions in the course outline.