Data Journalism Web

Why data? In his proposed 2013 budget, President Obama suggested $3.7 trillion in government spending, which includes an increase of 103 percent in federal highway administration budget, a decrease of 79.3 percent in funding for the federal transit administration, $2.5 trillion in mandatory spending, and $1.1 trillion in discretionary spending. The Health and Human Services department would receive the largest portion of the budget, with social security and defense spending following. This is just a fraction of the information presented in the New York Times data visualization of President Obama’s budget proposal. How can readers make sense of all these facts?

Why data? http://www.nytimes.com/interactive/2012/02/13/us/politics/2013-budget-proposal-graphic.html

Two ways to use data Shape data Use collected information and display it visually in the form of charts, graphs, tables ... Searchable database Compile collected data into a searchable database for users

Examples: Two ways to use data Searchable database “Look up your California kindergarten’s vaccination rate” This Los Angeles Times interactive database accompanied a story on vaccination in California. Shape data “A Map of Baseball Nation” From the New York Times, The Upshot blog took MLB fan data from Facebook and turned it into a map.

How to find data Think “public records” — what open government documents are available to the public? How could I go about getting them? restaurant inspections school/district performance school spending crime rates in community Collect your own data surveys polls observations

How to collect data Google Forms — useful for administering surveys, tracking observations, creating rudimentary charts and graphs. Feeds directly to a responses spreadsheet.

Visualizing data — clean up Look at the collected data for flaws, inconsistencies. Decide which data are most relevant. Clean up that data, if necessary, and import it to your tool of choice (next slide). Look at the outcome and return to the data to make changes as needed.

A note on “cleaning” data Cleaning up data means getting rid of extra information and making the data easier for your visualization tool to interpret. This might include: Removing duplicate entries Checking for obvious errors Separating combined information into multiple rows or columns Reformatting dates/numbers for compatibility Removing irrelevant categories Standardizing entries (e.g. users entered month as January, Jan, 1, or 01… All need to be the same.)

Visualizing data — tools In order of difficulty: Google spreadsheets (summary) Infogram Piktochart Plotly Datawrapper Google Charts Tableau Public Raw Chart.js D3.js