Visualizing data is powerful. It transforms the data into a format that is easily interpretable and it allows to reach out to wider audiences.
For this activity you will use the sample dataset and visualize the data using DataWrapper or a data visualization tool such as Google Chart or Tableau Public.
Download the Labour Force Survey, 2020 sample dataset (CSV file on Google Drive, 72MB).
Alternatively, you might want to search for open data from one of the sources below:
To Create Visualization:
- Download the prepared Labour Force Survey dataset of one your choice in csv format.
- Go to DataWrapper. Create a new chart and Import the csv file that you have exported in step 1. You can also create data visualization with your choice of data visualization tool such as Google Chart or Tableau Public.
- Share the visualized data you have created in the comment below
For Example
An example of a data visualization created using DataWrapper. The data is taken from Statistics Canada.
Complete this Activity
After you do this assignment, please share it with the comment below it can appear with other responses below. If your response exists at a public viewable URL, you can add the information directly to this site.
Image Credit: Image used on featured image: Martin Grandjean, Social Network Analysis Visualization, CC BY-SA 3.0
Here’s a link to my data visualization, made using DataWrapper: https://datawrapper.dwcdn.net/ILz32/1/. The data in question comes from the City of Toronto’s Open Data initiative (https://open.toronto.ca/dataset/licensed-dog-and-cat-names/), and displays the top 50 dog names registered in Toronto in 2019. Charlie is maintaining a pretty incredible lead!
I used Datawrapper to visualize foodbank visits in Toronto.
Here is a link for my data visualization, which includes a link to the data source used: https://datawrapper.dwcdn.net/2ueb4/1/
Have a look at April and March 2020!
I used Datawrapper to visualize “Trust in Media Reporting regarding widely reported topics of 2015”.
https://datawrapper.dwcdn.net/B8b3K/4/
Anber Rana
Jan 31, 2021 at 5:27am
I used Datawrapper to visualize ‘Energy Data” obtianed from Scholars Portal Dataverse and published by “Angus Reid Institute, 2017, “Energy Industry, 2014 [Canada]”, https://doi.org/10.5683/SP/XDZ49Y, Scholars Portal Dataverse, V1; 2014.12.18″
https://datawrapper.dwcdn.net/PCEE1/1/
Breakfast Cereals Protein (g) vs. Calories Comparison
https://datawrapper.dwcdn.net/X4m3y/1/
Toronto Bike Share ridership in 2020: https://datawrapper.dwcdn.net/5sAAG/1/
𝗧𝗼𝗽 𝟱𝟬 𝗖𝗮𝘁 𝗡𝗮𝗺𝗲𝘀 𝗶𝗻 𝗧𝗼𝗿𝗼𝗻𝘁𝗼 (𝟮𝟬𝟭𝟵) 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲𝗱 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮𝘄𝗿𝗮𝗽𝗽𝗲𝗿
https://datawrapper.dwcdn.net/gh5et/1/
NB: 427 cats were registered with no name provided. Named later? Witness protection program? Insufficient data to determine.
I had trouble accessing the Labour Force Survey data provided, so I used one of the data options provided through DataWrapper about the gender wage gap based on level of education. Super interesting, though not entirely surprising, results. See the visualization here: https://datawrapper.dwcdn.net/LPfhL/1/
I took some of the Labour Force Survey data and used Data Wrapper to visualize. https://datawrapper.dwcdn.net/1n39g/1/
I used DataWrapper to create a chart using their option for the Gender pay gap.
I was very thrilled to find all the different ways you can make your chart more accessible and to share it widely if wanting/needing.
https://datawrapper.dwcdn.net/2IkIG/1/
I created a chart in Datawrapper to visualize music sales based on format for 2019, the latest year with data available according to: https://open.canada.ca/data/en/dataset/fbd93028-2e74-4e27-9478-2c272b15e9b3
The results are certainly interesting, if not that surprising: https://datawrapper.dwcdn.net/Yhv6x/1/
I found DataWrapper to be pretty slow when I used it, but this is a great, easy to use tool for data visualizations. Great for novices, and for those who don’t have the expertise to use some of the more sophistocated tools.
I’m impressed with all the unique data sets people found – I could not think of anything as interesting, so I just used Global CO2 emissions from fossil-fuel burning, cement manufacture, and gas flaring, in million metric tonnes, 1850-2014
https://app.datawrapper.de/chart/xMHzK/publish
This is the visualization I created based on the sample data of the most visited museums in the world. I didn’t know about DataWrapper and it creates nice visualizations quickly, but I would only use it for datasets with few rows and columns. Since it is a web tool, I’m not sure if it would work well with larger datasets.
This was the visualization I created: https://datawrapper.dwcdn.net/hM8vX/1/
I used data on household spending by household type generated by Statistics Canada for this activity. Datawrapper is a great web tool but it had a difficult time working with larger datasets.
Here’s the visualization I created using Data Wrapper – https://datawrapper.dwcdn.net/9Vfji/1/
I used Datawrapper to visualize one of their sample data sets provided (Gender Pay Gap). Overall, I found the experience very smooth! Here is my final visualization in all of it’s very gender-binary ‘glory’: https://datawrapper.dwcdn.net/qLcAJ/1/.
This activity got me thinking about how much data can be conveyed, omitted and obscured depending on the selection of (in)appropriate visualizations. With the view that I selected, Datawrapper didn’t create a legend to explain the two different colour codes for women and men, so added this info in the description.
With some tweaking, I could see Datawrapper being useful for correctly reformatting data that has been scraped from PDFs.