Kickstarter Data Visualization
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Project Deliverable
data analysis
data visualization
website design
GitHub page
My Roles
data processing
data visualization
visual design
Program
Rstudio
Shinyapps.io
Plot.ly
html, css
Team
Haowen Bao
Zexin Lyu
Yutian Lei
Project Summary
Kickstarter is a global crowdfunding platform that focuses on creativity and merchandising. We have found our dataset about Kickstarter from Kaggle Platform. This data includes total of 378661 observations from 2009 to 2018 including different features such as name of the Kickstarter projects, categories of projects, goals for fundraising, duration of the fundraising process, amount of money raised, and the status of the project(successful, fail, cancel, etc.).
This data visualization’s purpose is to find potential patterns under Kickstarter’s data throughout the year. The team created a website with R, plt.ly and Shinyapps.io to present our finding about the dataset.
Target Audience
The data visualization is tailored to the audiences, such as potential start-ups or investors. The visualization is designed to provide the audience with some insight into the Kickstarter platform, which could be potentially helpful to their future business decisions.
This is an interactive timeline which provides information about the success rate for different “main categories” of Kickstarter projects in each year.
projects under games and design categories became most popular and successful.
The responsive line graph on the bottom provides the trendline of the success rate for each specific subcategory throughout the years. It could be interacted with the drop down menu
This bar graph provides the information about the average goal (USD) for each main category.
The interactive bubble graph provides the information about the average goal(USD) for each specific subcategory.
The Sankey diagram provides the information about the flow of backers’ investment. From left to right, it shows the top 3 invested specific categories under each main category.
most of the investment made under design went to product design
This bar graph provides information of an average investment from one backer in a project under a specific category according to the user’s input.
Takeaways:
preparation: it is of significant importance to prepare the data before any analysis. outliers and incomplete data (during 2018) are removed in this data visualization.
visual: visual identity, such as the use of certain color palette and interaction, plays an inseparable role in engaging with the audience.
objectivity: the team should stay as objective as possible while performing data analysis.
What I would do better next time:
time management: time was wasted due to team members’ unfamiliarity with R and plot.ly. I would be more careful about assigning coding tasks with individual’s ability in mind.
organization: without organization, data can not assemble into useful information.