  Visualizations of tabular data are widely used; understanding their
effectiveness in different task and data contexts is fundamental to scaling
their impact. However, little is known about how basic tabular data
visualizations perform across varying data analysis tasks and data attribute
types. In this paper, we report results from a crowdsourced experiment to
evaluate the effectiveness of five visualization types --- Table, Line Chart,
Bar Chart, Scatterplot, and Pie Chart --- across ten common data analysis tasks
and three data attribute types using two real-world datasets. We found the
effectiveness of these visualization types significantly varies across task and
data attribute types, suggesting that visualization design would benefit from
considering context dependent effectiveness. Based on our findings, we derive
recommendations on which visualizations to choose based on different tasks. We
finally train a decision tree on the data we collected to drive a recommender,
showcasing how to effectively engineer experimental user data into practical
visualization systems.
