1. Data Journalism
Web

2. 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?

3. Why data?

4. 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

5. 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.

6. 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

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

8. 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.

9. 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.)

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