This week, I am going to cover the post by Javier Tamayo-Leiva, where he is portraying the impact of global seafood production on global biological sustainability using the data provided for the week. His code can be found here.
He has used some of the tools we have seen in class. Specifically, packages "tidyverse" and "janitor". However, to my pleasant surprise, he has used a lot more packages in order to produce his outstanding plot: "here", "tidytuesdayR", "scales", "BBmisc", "gggibbous", "Redmonder", "ggtext", and "systemfonts".
First of all, he created a couple directories, something we are familiar with, by using dir.create(). Then, surprisingly, he created a couple if loops in order to read the data using read_limit_check() from "tidytuesdayR" package, which returns the stored rate limits that are remaining. I am not sure if it is necessary to employ read_limit_check() here rather than simply reading the .csv files directly.
Secondly, the data was manipulated using familiar to us tools such as clean_names(), filter(), mutate(), pivot_longer(), group_by(), arrange(), as well as ignoring NAs with the na.rm flag. Overall, during the data manipulation steps, there were quite a few interesting flags used, which I might need to learn in order to be more efficient in using the mentioned functions.
Finally, the author proceeds to use gglplot2 in order to create the plot. I am somewhat familiar with ggplot, but there was a lot of new and unfamiliar to me material in his code, which is great, as this is an opportunity to learn more. Overall, the code was very neat: the author was using a lot of packages and functions, flags, as well as some loops and pipes in order to create a compact clean product.
The plot produced is again outstanding as it clearly shows the production of different fish and seafood in millions of tones per year between 1961 and 2013. I think, the author should have been more specific and named the y-axis "Annual production, mil tones". I am also curious as to how this graph would change if we display the annual production per capita. The "moon" plot was interesting, clearly showing that not only the production has increased but also the proportion of overexploited production. One more comment regarding the overall presentation of the plot: I believe the colors chosen could have been a bit more diverse/distinct and a bit brighter, especially the background color.
Please leave a comment regarding what you like or don't like about the code and plot as well as whether or not you agree with my analysis. Thank you.
This week, I am going to cover the post by Javier Tamayo-Leiva, where he is portraying the impact of global seafood production on global biological sustainability using the data provided for the week. His code can be found here.
He has used some of the tools we have seen in class. Specifically, packages "tidyverse" and "janitor". However, to my pleasant surprise, he has used a lot more packages in order to produce his outstanding plot: "here", "tidytuesdayR", "scales", "BBmisc", "gggibbous", "Redmonder", "ggtext", and "systemfonts".
First of all, he created a couple directories, something we are familiar with, by using
dir.create(). Then, surprisingly, he created a coupleifloops in order to read the data usingread_limit_check()from "tidytuesdayR" package, which returns the stored rate limits that are remaining. I am not sure if it is necessary to employread_limit_check()here rather than simply reading the .csv files directly.Secondly, the data was manipulated using familiar to us tools such as
clean_names(),filter(),mutate(),pivot_longer(),group_by(),arrange(), as well as ignoring NAs with thena.rmflag. Overall, during the data manipulation steps, there were quite a few interesting flags used, which I might need to learn in order to be more efficient in using the mentioned functions.Finally, the author proceeds to use gglplot2 in order to create the plot. I am somewhat familiar with ggplot, but there was a lot of new and unfamiliar to me material in his code, which is great, as this is an opportunity to learn more. Overall, the code was very neat: the author was using a lot of packages and functions, flags, as well as some loops and pipes in order to create a compact clean product.
The plot produced is again outstanding as it clearly shows the production of different fish and seafood in millions of tones per year between 1961 and 2013. I think, the author should have been more specific and named the y-axis "Annual production, mil tones". I am also curious as to how this graph would change if we display the annual production per capita. The "moon" plot was interesting, clearly showing that not only the production has increased but also the proportion of overexploited production. One more comment regarding the overall presentation of the plot: I believe the colors chosen could have been a bit more diverse/distinct and a bit brighter, especially the background color.
Please leave a comment regarding what you like or don't like about the code and plot as well as whether or not you agree with my analysis. Thank you.