I enjoyed this ridge plot that overlapped the distribution of ultra running events for five different nations. They created their own theme like we did in our assignment this week and used the function stat_density_ridges from the ggridges package. I've never seen this done before I thought it was really neat! Since we have never tried a distribution plot in the assignments before I think this would be really fun for me to try sometime using the overlapping layout.
I've always liked figures with multiple plots on them and now I understand how you can do it! This user loaded the patchwork package into R and also loaded google fonts into R (I didn't know you could do that). They created two histograms using geom_histogram. Since there is a lot of text on the figure they were able to create data for the titles to write lots of titles, same with creating data for insights for each plot. Everything was annotated in depth and then they used the patchwork package sort of like how we use dplyr::select to go patchwork::inset_element and add all the plots together.
Data visualization that could be improved and how I would improve them
Here is someone who I think is new to TidyTuesday so I found this a great way for me to look through their code and see what they did to prepare myself for my first TidyTuesday. They took five runners and showed the times in seconds. The plot is already very simple but I think I would make some changes like excluding a legend. I did this in my assignment earlier but now I realize that you don't really need a legend when it's the same as the x-axis. Since the runners are listed below, the legend is unnecessary. I think that this is great for me to see that sometimes it is best to leave things more simple and I will remember that for my next assignment.
I found this plot that someone recreated using someone else's package. But there was an error or bug in the code that prevented changing the colours of each of the lines. I think that having them all orange made the plot very confusing to understand. I think the way the information is represented here is very hard to understand because you can't really follow from country to country. I think they definitely need to fix the code in order to change the colour scheme.
Week 7 TidyTuesday Commentary
By: Kelsey Voss
Topic: Ultra-running over the last 23 years
Code/Tools/Approaches we have seen in class that I saw this week
Code/Tools/Approaches that I have not seen in class
Data visualization and plot code that I enjoyed
https://twitter.com/annapurani93/status/1454119652905271303?s=20
I enjoyed this ridge plot that overlapped the distribution of ultra running events for five different nations. They created their own theme like we did in our assignment this week and used the function stat_density_ridges from the ggridges package. I've never seen this done before I thought it was really neat! Since we have never tried a distribution plot in the assignments before I think this would be really fun for me to try sometime using the overlapping layout.
https://twitter.com/IcaroBSC/status/1453856368054657025?s=20
I've always liked figures with multiple plots on them and now I understand how you can do it! This user loaded the patchwork package into R and also loaded google fonts into R (I didn't know you could do that). They created two histograms using geom_histogram. Since there is a lot of text on the figure they were able to create data for the titles to write lots of titles, same with creating data for insights for each plot. Everything was annotated in depth and then they used the patchwork package sort of like how we use dplyr::select to go patchwork::inset_element and add all the plots together.
Data visualization that could be improved and how I would improve them
https://twitter.com/NikiThadani/status/1454859941060218885?s=20
Here is someone who I think is new to TidyTuesday so I found this a great way for me to look through their code and see what they did to prepare myself for my first TidyTuesday. They took five runners and showed the times in seconds. The plot is already very simple but I think I would make some changes like excluding a legend. I did this in my assignment earlier but now I realize that you don't really need a legend when it's the same as the x-axis. Since the runners are listed below, the legend is unnecessary. I think that this is great for me to see that sometimes it is best to leave things more simple and I will remember that for my next assignment.
https://twitter.com/richard_vogg/status/1453622421005774849?s=20
I found this plot that someone recreated using someone else's package. But there was an error or bug in the code that prevented changing the colours of each of the lines. I think that having them all orange made the plot very confusing to understand. I think the way the information is represented here is very hard to understand because you can't really follow from country to country. I think they definitely need to fix the code in order to change the colour scheme.