In the past few years, the University of Ottawa Library has significantly increased its outreach activities by sponsoring or co-sponsoring events in library spaces where rooms are equipped with cutting-edge technologies. As there are competing demands for library spaces, we have to prioritize requests in order to maximize their usage and provide our users with alternate spaces available on campus. However, there was no information about how campus spaces were used for events based on their types. Since uoCal, the online campus-wide calendar of events, contains comprehensive information on campus events, we decided to utilize uoCal data to analyze and visualize how campus spaces have been used based on event types and hours in order to make evidence-informed decisions. In this poster, we will demonstrate how we scraped thousands of event data available since 2011 from the uoCal website, conducted data wrangling for further data analysis, visualized datasets on the campus map, and analyzed datasets to answer our questions using R. We will also discuss challenges about web data scraping, especially large datasets from different web pages and data munging processes. Lastly, we will share our reproducible R scripts so that other people can re-use them for their purpose.