Package {gravityshop} is an open educational resource to teach a
workshop on spatial interaction analysis using R and computational
notebooks.
The package 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
The package is designed for use in a 6-session workshop (approximately
12 hours) that teaches the principles of principles of spatial
interaction analysis, including the peculiarities of interaction data,
exploratory techniques, and modelling using the gravity model, all using
R. The package includes the following components:
- Document templates with Sessions
- Data sets used in the Sessions or for individual practice/research
The Sessions are R markdown documents with the main contents of the workshop. These documents resemble lecture notes, but with a difference: they are literate programming documents that are are interactive and editable, which means that you can work with them in ways not possible with conventional printed notes.
- Why
R? - Installing the software:
Rand R Studio - Packages
- Installing packages: CRAN and other sources
- Getting help
- (Break)
- Creating a project
- Directory structure
- Creating new files: types of files
- Literate programming
- Data objects/classes and basic operations
- Why measuring things?
- What is EDA?
- Why visualization?
- Reading external data
- Reading external data
- Reading external data
The workshop does not assume knowledge of, or experience working with
R. So, no previous knowledge regarding the software/language is
required, besides 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 applications.
If you have used R and have a previous instal, update it to R
version 4.2.0 (2022-04-22) – “Vigorous Calisthenics”. The package was
developed using “Vigorous Calisthenics”!
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.
Figure 1. RStudio IDE
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
package {gravityshop}. 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/gravityshop")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 package, this is the recommended workflow for using it in the workshop.
Follow the steps below to create a new project. A project is the best way to keep your work in this workshop 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 file templates for the Sessions.
After restarting the application, you will find that the templates are
available when you choose to create a new R Markdown file. To work with
templates, you start by creating a new R Markdown file with the option
of using a template. As an example, select template eda-session-1-IT
from package {gravityshop} and give it a name. The instructor will
indicate which specific files to use (the example is for the Italian
Edition of the workshop). 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. eda-session-1-* will introduce
you to the use of R Markdown, literate programming, and the basics of
working with R. 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 a computational notebook
Once you are happy with your work using this file, you can create a pdf
file (for archiving or 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 workshop notes
Since you can edit and annotate the text, you can essentially customize the document so that it is a unique reflection of your learning. As you progress with the workshop and complete all the sessions, you will have a collection of unique documents that track your very personal learning experience.