DS 2003: Communicating with Data

Palmer penguins

Measurements of 344 penguins (three species) from three islands in the Palmer Archipelago, Antarctica: bill length and depth, flipper length, body mass, sex.

• penguins.csv (seaborn-data)

Or: pip install palmerpenguins, then from palmerpenguins import load_penguins

Used in: Fri 8/28 Lab 0: Python review; Fri 9/18 Lab 3: anatomy of a figure; Fri 9/25 Lab 4: encodings, color & chart types; Fri 10/23 Lab 7 (async, family weekend): interactive EDA with Plotly

Smithsonian Holocene volcanoes

The Smithsonian Global Volcanism Program's list of volcanoes active in the last ~12,000 years: name, country, region, type, elevation, and how strong the evidence of eruption is.

• Smithsonian_VOTW_Holocene_Volcanoes.csv

Used in: Fri 9/4 Lab 1: pandas & data types; Fri 9/18 Lab 3: anatomy of a figure

Fast food nutrition

Nutrition facts for 515 menu items from eight fast food chains: calories, fat, sodium, carbs, sugar, protein, and a few vitamins.

• fastfood.csv

Used in: Fri 9/11 Lab 2: grouping & aggregating; Fri 9/18 Lab 3: anatomy of a figure; Fri 9/25 Lab 4: encodings, color & chart types; Mon 9/28 Question storm

NYC flights, 2013

Every flight that departed New York City's three airports (JFK, LGA, EWR) in 2013: scheduled and actual times, delays, carrier, origin, destination, distance.

• flights.csv

Or: pip install nycflights13, then import nycflights13 as nf; nf.flights

Used in: Fri 9/11 Lab 2: grouping & aggregating; Fri 9/18 Lab 3: anatomy of a figure; Fri 9/25 Lab 4: encodings, color & chart types; Fri 10/23 Lab 7 (async, family weekend): interactive EDA with Plotly

Gapminder

Life expectancy, population, and GDP per capita for 142 countries, every five years from 1952 to 2007. Built into Plotly, so there's nothing to download.

Load it with: import plotly.express as px, then px.data.gapminder()

Used in: Fri 10/30 Lab 8: ipywidgets: controls & linked views; Fri 11/13 Lab 10 (light): annotating charts in matplotlib

Titanic passengers

One row per passenger on the Titanic's maiden voyage: ticket class, sex, age, fare, port of embarkation, and whether they survived. A classic for showing how categories combine.

• titanic.csv (seaborn-data)

Used in: Fri 11/13 Lab 10 (light): annotating charts in matplotlib; Mon 11/23 Project 2 work session

Superstore sales

Orders from a fictional US office-supply retailer (Tableau's classic sample data): region, segment, category and sub-category, sales, profit, quantity, order date. Its nested categories make it a good fit for tree charts.

• superstore.csv

Used in: Fri 11/6 Lab 9: hierarchical data: interactive treemaps & sunbursts; Mon 11/23 Project 2 work session

Bad chart redesign

Data behind the four charts from the Fixing bad charts activity (Wed 9/30) and Lab 5. Some are collected from the original source; where the raw data wasn't available, it's a synthetic best guess.

Chart A: US household income & financial assets (see chart)

• tang2023_fig4_household_income.csv
• tang2023_fig3_financial_assets_pct_gdp.csv

Chart B: ranked game rating over time (see chart)

• gaming_scores.csv

Chart C: Florida firearm murders & national crime rates (see chart)

• reuters_florida_firearm_murders.csv
• florida_population.csv
• pew_2013_crime_rates.csv

Chart D: slime mold traits by group (synthetic) (see chart)

• biology_dataset_synthetic.csv

Used in: Wed 9/30 Fixing bad charts; Fri 10/2 Lab 5: bad chart redesign