Lab 05-A Introduction to Tableau

RE 519 Data Analytics and Visualization | Autumn 2025


In this lab, we are going to use Tableau, a business intelligence and analytics platform that allows users to create interactive visualizations and dashboards to understand data. It provides free access to students. Please try to download Tableau for Students in advance.

The due day for each lab can be found on the course wesbite. The submission should include Rmd, html, and any other files required to rerun the codes. For Tableau, submit your Tableau packaged workbook (.tbwx) This will include any excel files that you attach so that when you share the file anyone with Tableau can open it!

From Lab 4, the use of any generative AI tool (ChatGPT, Copilot, etc.) is allowed. But, I still encourage you to write codes by yourself and use AI tools as a way to debug and explore new knowledge. More information about Academic Integrity and the Use of AI.


Lab 05-A: Introduction to Tableau

In this lab, we will use Seattle Airbnb listing data published by Inside Airbnb. Inside Airbnb is a mission driven project that provides data and advocacy about Airbnb’s impact on residential communities. They published a report on Platform Failures: How Short-Term Rental Platforms like Airbnb fail to cooperate with cities and the need for strong regulations to protect housing which may be interesting to you.

The data we will use was published on 21 June, 2025 and you can explore the data using their visualization tool. Please check the data dictionary below.

Click to view the full data dictionary

We are going to build an interactive dashboard that visualizes key insights from our dataset. The goal is not only to display information but also to enable users to explore, filter, and compare different variables dynamically. After that, we will publish online through Tableau Public as well as your GitHub page. Also, we will continue working on dashboard in next lab, so do not worry if some functions you want do not show in this lab. The final results will be like this:

The Tableau Workspace

Workspace. Source: Tableau Desktop and Web Authoring Help

Key Steps

Connect to Data

Global Formatting

Maps

Average Price

Number of Room by Type

Licenses

Top Hosts

Dashboard

Interactivity

Publish

Hierarchy

Worksheet, Dashboard, Story

📚 TODO: Tableau

14 points

Create visualizations, at least 5 worksheets, using 2022 5-year estimate data from American Community Survey (ACS). The dataset is about housing affordability in King County, WA. You can download the data here. The geojson (What is it?) data also include geospatial column (geometry).You can check the data dictionary below.

If you have a strong interest in using other datasets for this assignment, you are free to do that.

Calculation field. You can build your own variables using existing variables, such as defining a affordability index. This page may be helpful to you.

Arrange these sheets into a dashboard. Make sure that any filters that you apply on your dashboard work on all (relevant) sheets. If you want, play around with creating multiple story points.

Submit your Tableau packaged workbook (.tbwx) This will include any data files that you attach so that when you share the file anyone with Tableau can open it!

Raw ACS Variables Dictionary

Variable ACS Code Description
med_income B19013_001 Median household income (past 12 months)
gini B19083_001 Gini Index of Income Inequality
med_rent B25064_001 Median gross rent (monthly)
med_home_value B25077_001 Median home value (owner-occupied units)
total_pop B01003_001 Total population
poverty_rate B17001_002 Individuals below poverty line (numerator)
total_poverty B17001_001 Total population used for poverty calculation
bachelors B15003_022 Population with a bachelor’s degree
graduate B15003_023 Population with a master’s degree or higher
total_edu B15003_001 Total education population (age ≥25)
white B03002_003 White alone
black B03002_004 Black or African American alone
asian B03002_006 Asian alone
hispanic B03002_012 Hispanic or Latino (any race)
total_race B03002_001 Total population by race
unemployed B23025_005 Unemployed population (age 16+)
labor_force B23025_003 Civilian labor force (age 16+)

Derived Indicator Dictionary

Derived Variable Formula Interpretation
poverty_pct poverty_rate / total_poverty Share of population below poverty line
college_pct (bachelors + graduate) / total_edu Share of population with ≥ bachelor’s degree
white_pct white / total_race White population share
black_pct black / total_race Black or African American share
asian_pct asian / total_race Asian population share
hispanic_pct hispanic / total_race Hispanic or Latino share
unemployment_rate unemployed / labor_force Civilian unemployment rate

Acknowledgement

The materials are developed by Haoyu Yue based materials from Dr. Feiyang Sun at UC San Diego, Siman Ning and Christian Phillips at University of Washington, Dr. Charles Lanfear at University of Cambridge.