1. Code/Tools/approaches we have seen in class
I like how the main Tidy Tuesday page shows us basic stats - the author used a tibble and pipes to mutate the data and create a plot. The data was cleaned using the clean_names function from the janitor package, as well as using the mutate function.
2. Code/Tools/approaches that you enjoyed that we have not seen in class
schmid07 added emoji's to his graph and used the functions ungroup to ungroup previously grouped data and left_join from the dplyr package to joint 2 tables together
Rony Coelho used and modified ggplot2 themes for graphing
3. Data visualizations (figures) that you enjoyed
I enjoyed the data visualisation by Mike Maieli showing the percentage of games Tennessee has won less than 85% and still won the NCAA tournament. I liked how there were comments at specific points on the graph.
I also liked the infographic Rony Coelho created based on this blog. It's a great way to share out data visually.
4. Data Visualization (Figures) that could be improved
While this graph by Ian Bell is interesting, I found it hard to read all the data points crowded together.
1. Code/Tools/approaches we have seen in class I like how the main Tidy Tuesday page shows us basic stats - the author used a tibble and pipes to mutate the data and create a plot. The data was cleaned using the clean_names function from the janitor package, as well as using the mutate function.
2. Code/Tools/approaches that you enjoyed that we have not seen in class
schmid07 added emoji's to his graph and used the functions ungroup to ungroup previously grouped data and left_join from the dplyr package to joint 2 tables together
Rony Coelho used and modified ggplot2 themes for graphing
3. Data visualizations (figures) that you enjoyed
I enjoyed the data visualisation by Mike Maieli showing the percentage of games Tennessee has won less than 85% and still won the NCAA tournament. I liked how there were comments at specific points on the graph.
I also liked the infographic Rony Coelho created based on this blog. It's a great way to share out data visually.
4. Data Visualization (Figures) that could be improved While this graph by Ian Bell is interesting, I found it hard to read all the data points crowded together.