Module 9: Multivariates

Hi everyone!

This week we learned about multivariate visualizations and the five principles of design. We explored corrgrams to visually see correlations, heat maps to capture trends, and multivariate scatterplots to understand relationships.

The five principles:
(1) Alignment - create a sharper and ordered design
(2) Repetition - create association and consistency
(3) Contrast - emphasize or highlight key elements
(4) Proximity - group or visual connection between elements
(5) Balance - symmetrical (equal weight) or asymmetrical (contrast)

I utilized the built-in R dataset airquality that has a record of ozone, temperature, solar radiation, and wind measurements for multiple dates in 1973 New York.

Purpose: What is the relationship between Ozone, temperature, solar radiation, and wind measurements in 1973 New York?

I began with a corrgram to gain a general understanding of the strength of relations between these variables. This design is strong in contrast through the red and blue shades indicative of positive or negative relationships and alignment to see each variable by column and row for ease of comparison. 

It is clear Ozone and Temperature have a very positive relationship and Wind has a negative relation with all other variables making it an interesting factor to explore.


Next, I created a heat map to visualize the date component with each variable by seeing the fluctuation for each variable throughout the months. The strength of this design is in symmetrical balance allowing easy comparisons between months and variables, proximity by each shade being right next to each other, and repetition of similar shaded blocks and value units to showcase trends. 

The apparent trend is high fluctuations throughout the year in solar radiation and a rather stable temperature pattern throughout the months.


Lastly, I wanted to visualize these variable in a scatterplot to understand how the most fluctuating variable (solar radiation) and the least fluctuating variable (temperature) relate and the role of ozone and wind in this relationship. The strength of this design is the repetition of size and color indicating a higher or lower value for each variable making it a easy visualization to comprehend.

This scatterplot shows a rather random relationship between solar radiation and temperature, but increasing ozone levels with solar radiation and temperature. We can also see lower winds accompanied by higher ozones and vice versa. 

Conclusion: These three multivariate visualizations suggest the relationship between temperature and solar radiation can be explained by lower temperatures and solar radiation having high winds and low ozone, while higher temperatures and solar radiation having low winds and high ozone. I find it fascinating that a linearly weak relationship between solar radiation and temperature can be well-explained by the other two air quality measures' stronger relationship.

Check the full code out in GitHub!

-Ramya's POV

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