Codes, tools, and approaches we have seen in class
There were a number of Twitter members that used tidyverse tools that we have used in class this week, including pipe (%>%), mutate, summarise, and filter. The majority of twitter posts that are discussed below in my commentary used some of the tools that we discussed in our wk04 class.
I appreciated that most of the Tidy Tuesday tweets included a link to their github code, allowing me to review their process to identify which codes and tools we are working with and what approaches they have taken.
Codes, tools, and approaches that I enjoyed that we have not seen in class
There were quite a few Tidy Tuesday submissions that use gglot, which we will be discussing in wk05.
A new plot that I have not seen before was geom_moon, which made a pie chart to differentiate percent of tournament losing teams that had more wins than the champion versus tournament champions that had the most/tied wins. It was visually appealing and simplified the interpretation of the data. https://twitter.com/Amit_Levinson/status/1313557470766419969/photo/1
Data visualization that I enjoyed
There were a number of unique data visualizations that I enjoyed, including the use of symbols in their figures.
One of the submissions plotted regular season wins per season and tournament wins per season, allowing you to see which teams were similar or dissimilar, and compare regular season and tournament wins. They also plotted teams with their logo, which made the interpretation visually appealing. https://twitter.com/CallmeAlfredo/status/1313439856119345153/photo/1
Similarly, Julia Silge put together a screencast of the process to prepare the model. I really liked the visualization of this product. They used ggplot and a colour scale to interpret three parameters in a two dimensional plot including tournament wins, seed number, and the number of teams. https://twitter.com/juliasilge/status/1313979395745275904
Data visualization that could be improved and how
One of the Tidy Tuesday submissions used a GT table to analyze the percent of conference wins by teams in 2018. They used the pipe function that we have seen and used different codes to manipulate table shape, layout, and colour which we have not discussed in class. However, I thought that the output (a table) was not visually appealing and did not facilitate any interpretation of the data. It could be improved by turning the output into a figure, a simple bar plot showing % wins.
Week 41- Tidy Tuesday Commentary
Codes, tools, and approaches we have seen in class There were a number of Twitter members that used tidyverse tools that we have used in class this week, including pipe (%>%), mutate, summarise, and filter. The majority of twitter posts that are discussed below in my commentary used some of the tools that we discussed in our wk04 class.
I appreciated that most of the Tidy Tuesday tweets included a link to their github code, allowing me to review their process to identify which codes and tools we are working with and what approaches they have taken.
Codes, tools, and approaches that I enjoyed that we have not seen in class There were quite a few Tidy Tuesday submissions that use gglot, which we will be discussing in wk05. A new plot that I have not seen before was geom_moon, which made a pie chart to differentiate percent of tournament losing teams that had more wins than the champion versus tournament champions that had the most/tied wins. It was visually appealing and simplified the interpretation of the data. https://twitter.com/Amit_Levinson/status/1313557470766419969/photo/1
Data visualization that I enjoyed There were a number of unique data visualizations that I enjoyed, including the use of symbols in their figures. One of the submissions plotted regular season wins per season and tournament wins per season, allowing you to see which teams were similar or dissimilar, and compare regular season and tournament wins. They also plotted teams with their logo, which made the interpretation visually appealing. https://twitter.com/CallmeAlfredo/status/1313439856119345153/photo/1
One Tidy Tuesday submitter put together a video on the process of making their Tidy Tuesday plot, showing how each figure, text, and titles adapted in the process. https://twitter.com/CedScherer/status/1314115370270236673
Similarly, Julia Silge put together a screencast of the process to prepare the model. I really liked the visualization of this product. They used ggplot and a colour scale to interpret three parameters in a two dimensional plot including tournament wins, seed number, and the number of teams. https://twitter.com/juliasilge/status/1313979395745275904
Data visualization that could be improved and how One of the Tidy Tuesday submissions used a GT table to analyze the percent of conference wins by teams in 2018. They used the pipe function that we have seen and used different codes to manipulate table shape, layout, and colour which we have not discussed in class. However, I thought that the output (a table) was not visually appealing and did not facilitate any interpretation of the data. It could be improved by turning the output into a figure, a simple bar plot showing % wins.