Data visualization: Building a static illustration

Transcript

1. JOUR 5200 – May 15, 2019 Trish Audette-Longo Data visualization: #ottflood trends
2. When was #ottflood trending? • For the final visualization using this data set, we will create a graphic using Canva to show when the hashtag #ottflood was used most over the period of time studied.
3. Things to avoid… • Misrepresenting information.
5. • SOURCE: https://medium.economist.com/mis takes-weve-drawn-a-few- 8cdd8a42d368
6. Things to avoid… • Instead of adding clarity or providing an “a-ha” moment for readers, making data more difficult to understand.
10. When was #ottflood trending? • For the final visualization using this data set, we will create a graphic using Canva to show when the hashtag #ottflood was used most over the period of time studied. • As a follow-up question, we will ask: when did users mention “climate” when they discussed #ottflood?
11. Pivot table • To understand the timeline of #ottflood use, we will pivot our data set, so that the number of tweets posted on different days can be counted.
12. 1. Highlight your entire spreadsheet.
13. 1. Highlight your entire spreadsheet. 2. Select “PivotTable” under the “Insert” tab.
14. 1. Highlight your entire spreadsheet. 2. Select “PivotTable” under the “Insert” tab. 3. In Excel, click “OK” to the Table/Range including the whole spreadsheet and the Pivot Table report going into a new worksheet.
15. 1. Highlight your entire spreadsheet. 2. Select “PivotTable” under the “Insert” tab. 3. In Excel, click “OK” to the Table/Range including the whole spreadsheet and the Pivot Table report going into a new worksheet. 4. Your objective is to count the number of tweets posted each day. So, the first field to add to the report is the Date, so that dates will be extracted and listed in rows.
16. 1. Highlight your entire spreadsheet. 2. Select “PivotTable” under the “Insert” tab. 3. In Excel, click “OK” to the Table/Range including the whole spreadsheet and the Pivot Table report going into a new worksheet. 4. Your objective is to count the number of tweets posted each day. So, the first field to add to the report is the Date, so that dates will be extracted and listed in rows. 5. The second field to add is the posts themselves, which you drag down to the bottom right corner and insert under Values. Now you have a count of the total number of #ottflood tweets posted each day.
17. Creating a graph • In Canva, select a template and edit it.
18. Creating a graph • Add a line graph from the available elements in the menu on the left-hand side of your screen.
19. Creating a graph • To illustrate the #ottflood timeline, double-click on the line graph, opening a chart on the left. Add the results from the pivot table. • Option: remove every other date for legibility without sacrificing clarity.
21. Creating a graph • Thanks to the word cloud exercise, we know “climate” came up rarely in the #ottflood conversation, but when did it come up most?
22. Discussion: • What does this data visualization add to readers’ understanding of how people experienced the flood in 2019?
23. Takeaways & questions?
24. Where we are in data visualization  How to clean up a spreadsheet and make it more legible.  How to build a Google My Map using a spreadsheet.  How to clean up, share and embed a Google My Map.  How to build a Word Cloud.  How to make a Pivot Table report.  How to draw a line graph.
25. Based on this data… • We are going to create three different data visualizations:  A map  A word cloud  A graph showing how #ottflood was used