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TUMO Creative Technologies

Personal Geographies: Exploring Your Community Through the Lens of Data Visualization

Day 5 (January 16th, 2021)

https://tumopersonalgeographies.github.io/

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Iterinary for Today’s Class:

Changes in course structure/pacing

Overview of Tableau dashboards and conclusion of first unit

Introduction to the programming language R

R vs. RStudio

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Iterinary for Today’s Class (cont.):

Syntactical recommendations for RStudio

Tidyverse (ggplot, dplyr)

Univariate vs. Bivariate analysis

Hypothesis testing and simple regression

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Changes in course structure/pacing

I’ve received responses to the Google Form from about half of the class so far and really appreciate all the feedback I’ve read from students…

Some respondents also chose to omit their name, which is totally fine. For those who did, however, please submit another response with answers to just the first 3 questions (your name, comfort level in R, and comfort level in Javascript). The form will play an important role in assigning partners for our following units.
There will be some new expectations for class engagement during presentations
Some groups were not working collaboratively on designs

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A few notes about your progress so far in Tableau

So far in walking around and viewing everybody’s work, here are some pointers...

If you want to create multiple graphs/visualizations for the same dataset, create a “New Worksheet” on the bottom-left hand pane of your screen
Make sure you’re using descriptive titles and axes labels for every graph
For purposes of academic integrity, it is imperative that you are always citing your data sources directly on the graph. This can be done in Tableau by left-clicking on any white/blank space in your card and checking the “Caption” option
“Filter” is a powerful tool to rank data by certain conditions (i.e. Top 10)

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An overview of Tableau dashboards

Tableau dashboards, which can be created on the bottom left hand pane of your UI, offer a powerful tool for presenting the results of multiple interactive visualizations on one screen…

You will each create dashboard summarizing the 3 visualizations you’re translating from digital mock-ups to interactive Tableau prototypes
Please include detailed title and text cards in your dashboard explaining background information about the dataset to contextualize what the fields mean and how the data was collected

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Activity 5.1: Capstone presentation for unit 1

(30 minutes)

Work collaboratively with your partner to prepare a 2-3 minute presentation (in PowerPoint or Google Slides) with the following components…

1. A 2-sentence summary of the dataset (relevant fields, data types, and collection methods)
2. Pictures of your hand-drawn sketches
3. Screenshots of your digital mock-ups
4. A screenshot of your interactive Tableau dashboard

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Introduction to the programming language R

R is a programming language that is optimized for statistical and graphical analysis…

Here are a few of the statistical techniques alluded to on the about page of the environment’s website:

“linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …”

There are hundreds of powerful data analysis libraries that are well-integrated into the R ecosystem

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Distinction between R and RStudio

What is an integrated development environment (IDE)?

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Distinction between R and RStudio

In a similar fashion to we’ll see in our D3.js unit, the difference between R and RStudio is analogous to that of Javascript and Webstorm…

R is the underlying language
RStudio is an integrated development environment (IDE) that allows programmers to seamlessly create, debug, and maintain tools using R

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Syntactical recommendations for development

in the RStudio environment

Here are some of the idiosyncrasies about coding in R which may be unlike other languages you’ve worked with before…

Code is executed in the form of “chunks”, independent blocks designed to facilitate tasks like data loading, wrangling, and visualization in a more modular fashion
While not part of the base R environment, the pipe operator “%>%” is a fixture of development in tidyverse and is used to string together a sequence of commands
R uses both the operators “<-” and “=” for different assignment purposes

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Syntactical recommendations for development

in the RStudio environment (cont.)

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Activity 5.2: Getting RStudio setup

(15 minutes)

Try booting up RStudio on your computer and going through the registration process.

I will walk around the room to make sure everyone’s software gets installed and to answer any questions as needed.