Choose one of David Robinson’s tidytuesday screencasts, watch the video, and summarise. https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ
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Tidy Tuesday screencast: analyzing Bob Ross paintings in R (by David Robinson) ## Q2 When was it published?
This was publushed to youtube on August 12, 2019
Hint: What’s the source of the data; what does the row represent; how many observations?; what are the variables; and what do they mean?
The source of the data is from Theme_set package, he’s also using tidyverse. The row represents each episode, its title, and whether or not the element is present.Each column, or obserservation, represents weather or not a certain feature made it into one of Bob Ross’s painting in a particular episode. Some examples of veriables present were barns, birds, and.. Apple Frames? There are 66 elements total in this data set.
Hint: For example, importing data, understanding the data, data exploration, etc.
He finds the most common elements across all episodes and seasons (trees, mountains, and clouds). He also zooms into various parts of the data set, such as Steve Ross being a guest painter. David finds out that Steve painted 11 paintings across the shows 31 seasons and 403 episodes.
I learned a lot more about how to explore data and specefic parts of the data sets themselves. His repeated use of cleaning the data throughout was also a very cool aspect of thr tidy tuesday screencast. I had no idea you could edit so much within the data set, when he used the add_count function some very interesting data would become clear. David Robinson does a wonderful job manipulating the data sets. Something iI learned in class that was also showcased in this screencast was the use of tidyverse.
Most of the variation in Bob Ross’s paintings come from nature rather than ocean/sea. With the least variation being mountains in particular.The four principal components of Bob Ross paintings are Mountains vs Oceans, Lakes and Rivers, Cabins in the snow, and winter vs summer.
I enjoyed watching the process of how statistical data analysis can be applied in real life situations. His ability to select and use relevant data is also incredible. This type of anaysis culd be used for stocks, prices of goods, and consumer behaviors. I truly nderstand that the aplications of R are endless.