• Code/Tools/approaches we have seen in class that you saw used over the week
I enjoyed to see here that Mike used many techniques to create his visual that we have also looked at in class by starting off with loading the tidyverse package. He then went on to use the pipe function to filter and mutate the data. https://twitter.com/MikeMaieli/status/1313594299154665472/photo/1.
I also noticed that a lot of the visualizations seen have included the function “theme” which ggplot has described as a way to give plots a “consistent customized look”. I am curious to learn about this, plus other tools once we begin explore ggplot.
I also really enjoyed this visualization by Dr.Christian Hoggard https://twitter.com/CSHoggard/status/1314158772319924224/photo/1. It was easy to view the number of schools decreasing as time when on, as well as the colour coded seeds which revealed that it was seeds 1 through 3 that made it to championships. These statistics were clear and concise and able to be understood at just a glance.
• Data Visualization (Figures) that could be improved (and how you would improve them)
While the creativity in this visualization was nice, I did find it more difficult to understand as well as a bit messy. I think in this case it would have been nice to stick to something a bit simpler such as a line/bar graph rather than doing zig zagged thick and thin lines to visualize the data. https://twitter.com/geokaramanis/status/1314551593971273732/photo/1
While I did find it interesting the statistics that Amit chose to focus on (“Wins don’t guarantee the NCAA championship”), I thought he could have done a better job at actually showing us the data, rather than just telling us the statistics in his pie chart. https://twitter.com/Amit_Levinson/status/1313557470766419969/photo/1
• Code/Tools/approaches we have seen in class that you saw used over the week
I enjoyed to see here that Mike used many techniques to create his visual that we have also looked at in class by starting off with loading the tidyverse package. He then went on to use the pipe function to filter and mutate the data. https://twitter.com/MikeMaieli/status/1313594299154665472/photo/1.
To create his visualization “goldenboy” used techniques such as mutate, select, summarize, arrange and filter, all which we have seen in class. https://twitter.com/GoldenB16117397/status/1314266208934465538/photo/1
• Code/Tools/approaches that you enjoyed or that surprised you that we have not seen in class
It was interesting to see here that Lorena used basketballs/hoops to be the datapoints on her graph using emoji font. It’s fun to see the creativity that can be used to display datasets. https://twitter.com/loreabad6/status/1315290120199831555/photo/1
I also noticed that a lot of the visualizations seen have included the function “theme” which ggplot has described as a way to give plots a “consistent customized look”. I am curious to learn about this, plus other tools once we begin explore ggplot.
• Data visualizations (figures) that you enjoyed
I really enjoyed the look of this circular visualization that was created in R using the circlize_0.4.11 package. I thought it was a creative way to display data in a fun and bubbly kind of way. https://github.com/charlie-gallagher/tidy-tuesday/tree/master/ncaa
I also really enjoyed this visualization by Dr.Christian Hoggard https://twitter.com/CSHoggard/status/1314158772319924224/photo/1. It was easy to view the number of schools decreasing as time when on, as well as the colour coded seeds which revealed that it was seeds 1 through 3 that made it to championships. These statistics were clear and concise and able to be understood at just a glance.
• Data Visualization (Figures) that could be improved (and how you would improve them)
While the creativity in this visualization was nice, I did find it more difficult to understand as well as a bit messy. I think in this case it would have been nice to stick to something a bit simpler such as a line/bar graph rather than doing zig zagged thick and thin lines to visualize the data. https://twitter.com/geokaramanis/status/1314551593971273732/photo/1
While I did find it interesting the statistics that Amit chose to focus on (“Wins don’t guarantee the NCAA championship”), I thought he could have done a better job at actually showing us the data, rather than just telling us the statistics in his pie chart. https://twitter.com/Amit_Levinson/status/1313557470766419969/photo/1