Tutorials

Online visualization tools make it easy to create a map from a spreadsheet. Here’s a brief overview of current tools.

A short overview of the importance of spreadsheets

Pivot tables are an effective tool for quickly summarizing the facts from a mass of records

A walkthrough of basic spreadsheet and pivot table usage (Part 1 of 3) We have a lot of ways to easily create beautiful, elaborate visualizations. But let’s see what we can do when we prioritize the story of the data over its visual presentation.

A walkthrough of managing earthquake data and doing basic visualizations with spreadsheets (Part 2 of 3)

A walkthrough of using Pivot Tables to summarize earthquake data across two dimensions, allowing for even more insightful histograms (Part 3 of 3)

The syntax for retrieving and displaying data from a SQLite tables

How to specify the quantity and arrangement of data rows returned by the SQL database.

How to retrieve rows based on whether they match specified values.

How to filter SQL data using comparison operators, such as “greater than” and “not equal to”. Mostly, this is a review of how tricky logical expressions can be.

Real-world data is often messy, so we need messy ways of matching values, because matching only on exact values can unintentionally filter out relevant data.

Think of these as spreadsheet functions.

A short lesson on how to give human-readable names for otherwise messily-named values and identifiers.

With the use of the GROUP BY clause, we gain the ability to aggregate our data based on values in a given column or columns. At the very least, this let’s us count the number of unique values in that column.

How to calculate sum, average, and other aggregates with the GROUP BY clause.

With GROUP BY, we can specify the groups of data for which we want to sum, count, and average.

Getting started with baby names.

How to create SQL tables and import raw data using the DB Browser for SQLite client.

An overview of the importance and syntax of JOIN queries and how to use them to find commonalities between different tables.

Sometimes when we compare two tables, we care more about their differences.

By using table aliases, we can join a table to a subset of itself, or to the results of another query.

A tutorial on using geospatial analysis, shapefiles and datasets from the U.S. Geological Survey, Census, and Department of Education to visualize the impact of Oklahoma’s earthquakes and explore possible investigative projects.

How to map simple geographic shapefiles with CartoDB and do some basic customization of visual features.

Did 2009 statewide reforms change how the NYPD polices misdemeanor drug crimes? How to go beyond the CartoDB map wizard and write our own CartoCSS to show greater depth of data.

How to combine a point data with shapefile data on the same CartoDB map (tutorial in progress)

How to use SQL to learn about Medicare, contemporary issues in Medicare billing practices, the math and evidence behind the WSJ’s “Medicare Unmasked” project, and the general problems with real-world datasets.