I quite liked this week's #TidyTuesday figures and posts. As my MSc projects relates to beluga migration and management, the visualisations gave me several ideas that could be used to illustrate the work from my thesis.
1. Code/Tools/approaches seen in class that I saw used over the week
Of course this plot includes all the basic ggplot elements we saw in class.
This code uses the column renaming and the subsetting functions that we learned in class to wrangle and prepare the data before making the final figure.
2. Code/Tools/approaches that I enjoyed or that surprised me that we have not seen in class
I am always interested when I see good maps made in R- I would be interested in delving a bit further into this during the ggplot section of the course since I think it would be helpful for most people's projects! This includes this map by @g_abgrall and this one by @rony_coelho.
3. Data visualisation (figures) that I enjoyedThis visualisation by @cnicault conveyed a lot of information while being extremely readable and appealing to the eye.
4. Data visualisation (figures) that could be improved (and how I would improve them)
I found the stream graph of fish production by country to be very beautiful but difficult to interpret. Generally stream graphs don't have a value on the x-axis denoting positive values as the shapes move upwards or negative values as the shapes move downwards-- in my experience it is usually the width of the bands that are supposed to convey information (as shown in this other TidyTuesday post from this week, which is uglier but considerably less misleading). I would suggest that the creator of this figure do away with the values on the x-axis as these make the figure confusing, and do not seem to be appropriate to the data they are trying to convey.
I quite liked this week's #TidyTuesday figures and posts. As my MSc projects relates to beluga migration and management, the visualisations gave me several ideas that could be used to illustrate the work from my thesis.
1. Code/Tools/approaches seen in class that I saw used over the week Of course this plot includes all the basic ggplot elements we saw in class. This code uses the column renaming and the subsetting functions that we learned in class to wrangle and prepare the data before making the final figure.
2. Code/Tools/approaches that I enjoyed or that surprised me that we have not seen in class I am always interested when I see good maps made in R- I would be interested in delving a bit further into this during the ggplot section of the course since I think it would be helpful for most people's projects! This includes this map by @g_abgrall and this one by @rony_coelho.
3. Data visualisation (figures) that I enjoyed This visualisation by @cnicault conveyed a lot of information while being extremely readable and appealing to the eye.
4. Data visualisation (figures) that could be improved (and how I would improve them) I found the stream graph of fish production by country to be very beautiful but difficult to interpret. Generally stream graphs don't have a value on the x-axis denoting positive values as the shapes move upwards or negative values as the shapes move downwards-- in my experience it is usually the width of the bands that are supposed to convey information (as shown in this other TidyTuesday post from this week, which is uglier but considerably less misleading). I would suggest that the creator of this figure do away with the values on the x-axis as these make the figure confusing, and do not seem to be appropriate to the data they are trying to convey.