Code/tools/approaches that we have seen in class that you saw used over the week:
I noticed several different functions that were covered in the week four lecture. Tidyverse functions such as arrange, summarize, group_by, filter and mutate. A lot of participants chose to follow a single team or conference and filtered data to only use the rows that pertained to them. I saw the use of the pipe function I also saw the use of some of the packages mentioned like readr and dplyr.
Code/tools/approaches that we have seen in class that you saw used over the week:
It was interesting to see how long some of the projects were, some used several hundred lines of code and created pretty elaborate displays, which were more advanced that something I could yet produce, but provide something to aspire to. Some of the most notable things which I had not seen before were related to imaging the data, things such as (cowplot), (magick), and (patchwork).
Data visualizations (figures) that you liked:
All of them basically! I though it was so interesting how so many people could be working on a single data set but produce so many different interpretations and figures. I saw basically every type of basic graph and some more elaborate ones as well. I thought most were pretty clear and easy to follow, I especially enjoyed the simple line graph by @elaine_mitchell where the data points were little basketballs. She did not include her code, but I found another figure that used little baskets as data points (@loreabad6) using something called emojifont. It was just a little thing, and not necessarily something that you would use when preparing data for publication but for creating a project just for fun, I thought it was great.
Data visualizations (figures) that could be improved (and how you would improve them):
There was an interesting figure from @geokaramanis showing winning percentage for 15 teams for the full spectrum of the data set (1982-2018), with markers for years that the teams were champions or runners up. The lines vary back and forth over a lone which represents a winning percentage of 75%. I want to say, I actually really like this figure, it takes into account a lot of data and shows it in a way that I have never seen before. And once you figure out what is being represented, I think it's a really interesting interpretation of the data, but some changes could make it easier to understand. Many of these are simple visual changes, for example the data label on the lines that represent the different winning percentages are only shown for one team, and there is other text adjacent to it making it difficult to read, it is also very small. The logos of each team are included, which is a nice touch and makes the graph look like something that could be used in a sports broadcast, but roughly half of the logos are obscured by the beginning of the respective plot line. Creating more space for the logo so that it was unobscured would make the figure more polished. The data line also varies, seemingly randomly in width, and creates a rather jarring image. Simply using a line of standard with would communicate the function of the graph more clearly. Also, perhaps this is just a personal opinion, but to me it seems the graph is upside down; earlier years should be at the bottom working up to more recent.
Code/tools/approaches that we have seen in class that you saw used over the week:
I noticed several different functions that were covered in the week four lecture. Tidyverse functions such as arrange, summarize, group_by, filter and mutate. A lot of participants chose to follow a single team or conference and filtered data to only use the rows that pertained to them. I saw the use of the pipe function I also saw the use of some of the packages mentioned like readr and dplyr.
Code/tools/approaches that we have seen in class that you saw used over the week: It was interesting to see how long some of the projects were, some used several hundred lines of code and created pretty elaborate displays, which were more advanced that something I could yet produce, but provide something to aspire to. Some of the most notable things which I had not seen before were related to imaging the data, things such as (cowplot), (magick), and (patchwork).
Data visualizations (figures) that you liked:
All of them basically! I though it was so interesting how so many people could be working on a single data set but produce so many different interpretations and figures. I saw basically every type of basic graph and some more elaborate ones as well. I thought most were pretty clear and easy to follow, I especially enjoyed the simple line graph by @elaine_mitchell where the data points were little basketballs. She did not include her code, but I found another figure that used little baskets as data points (@loreabad6) using something called emojifont. It was just a little thing, and not necessarily something that you would use when preparing data for publication but for creating a project just for fun, I thought it was great.
Data visualizations (figures) that could be improved (and how you would improve them):
There was an interesting figure from @geokaramanis showing winning percentage for 15 teams for the full spectrum of the data set (1982-2018), with markers for years that the teams were champions or runners up. The lines vary back and forth over a lone which represents a winning percentage of 75%. I want to say, I actually really like this figure, it takes into account a lot of data and shows it in a way that I have never seen before. And once you figure out what is being represented, I think it's a really interesting interpretation of the data, but some changes could make it easier to understand. Many of these are simple visual changes, for example the data label on the lines that represent the different winning percentages are only shown for one team, and there is other text adjacent to it making it difficult to read, it is also very small. The logos of each team are included, which is a nice touch and makes the graph look like something that could be used in a sports broadcast, but roughly half of the logos are obscured by the beginning of the respective plot line. Creating more space for the logo so that it was unobscured would make the figure more polished. The data line also varies, seemingly randomly in width, and creates a rather jarring image. Simply using a line of standard with would communicate the function of the graph more clearly. Also, perhaps this is just a personal opinion, but to me it seems the graph is upside down; earlier years should be at the bottom working up to more recent.
Go Lady Vols!