From Ch. 2 of Wilke's book, I used mapping and scales to make the data interpretation clear. I labeled both axes, mapping "Release Year" to the x-axis and "Number of Movies" to the y-axis. Both scales are quantitative and evenly spread out for consistency and clarity.
I am confused as to how there could possibly be data all the way out until the year 2045...
Reading the book made me more conscious of the colours that I used. I had to pay more attention to aesthetics if I wanted to have a good graphic. Usually I will leave it with default colours.
I would like to learn how to use more transforms so that i can do more with my data.
I used a consistant scale so that I don't trick the people who sees it.
I don't know what sex 1 and 2 are, so I just used the default color vega-lite let me.
The reasoning behind using different marks for categorical data influenced my decision to use the bar mark to represent the countries I selected.
I want to fix the weird bit at the bottom because the bar mark goes over the axis.
Referencing Ch. 1 of Wilke's book, I strove to make the graph clear, informative, and aesthetically-pleasing (avoiding the ugly, bad, and wrong). It uses simple colors and faint grid lines with clear axis titles. If I could have, I would have liked to improve on the color scheme (maybe using a log scale?) because the on the right half of the graph, all of the bars are the same (or similar) color.
I find the built-in color schemes fascinating!
I was attracted by the weather visualizations I found in the book while reading about scales, so I wanted to use a weather dataset.Wilke's talks about scales, and I've been paying attention to that as I graph.
I would like to explore more in the direction of heatmaps using vegalite.
his distinction between graphics and charts influenced my design by focusing on mapping variables (horsepower to position and origin to color) rather than just producing a standard bar chart. I also reduced visual clutter by hiding x-axis labels, aligning with his emphasis on clarity and revealing patterns in the data.
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In both of my graphics, I was particularly careful about perceptions and the way I set the sizes on the graphics in an attempt to avoid producing a 'bad' graphic and misleading the viewer.
If I knew how, I think it could be helpful to arrange the varieties of barley in the order of the smallest yield to the largest yield, or vice versa, for better comparison.
One thing that I took away from Wilke's book was to reduce visual clutter. I did this by mapping origin to both color and the x-axis, for a clean and easy to read graphic.
One note on my graph is that I used the average of miles per gallon, so that the data would be accurately represented by a bar graph.
I made sure to follow the guidelines telling the appropriate scale to use based on the datatype. I would have loved to use a heatmap in my graphic but I am not sure how exactly that would play out. Maybe another dataset would be more fitting.
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I made sure to not make the graphs of different color, because Wilke's made sure to tell us that unnecessary coloring of plots would make them considered as ugly.
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Wilke's book influenced my design of graphic by mapping of data values onto aesthetics. I applied quantitative variables to the y-axis to display magnitude. I mapped the qualitative variables to the color aesthetic. To improve my graphic, I would have put multiple layers of lines or scales for each year, instead of showing one single line or whole a lot of points.
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The quote: "In addition to continuous and discrete numerical values, data can come in the form of discrete categories, in the form of dates or times, and as text", helped me to design the graph with the scale of years.
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As I designed my graphic, I tried to be mindful of what aesthetics can represent, which type of data. For example, I decided that it was appropriate to have color represent the different categories of sex. I could have also used different types of line to represent the different types of categories.
I would like to be able to change the colors of the lines mapped to 'male' and 'female'. I was also exploring trends in other job categories, and noticed that some jobs, like pilot, begin to exist only after a certain point, so I think it would be great to be able to filter out the prior years.
I’ve learned that the importance of using scales that create a clear one-to-one mapping between data values and visual elements, so that the key numerical data uses position to represent the most accurate visual channel, and uses color to distinguish categories. It is a good way to avoid confusing visualization.
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In order to prevent the the graph from being cluttered and falling into what Wilke calls ugly, I faceted using a slider bar to let the user choose year.
There was a few variables I was missing info on, I did not know where this population data is coming from and I do not know whether 1 or 0 is male/female so I would have tried to figure those out if I knew more about the data
One thing that I was influenced by was the standard change per x or y axis tick. This gave me the perspective on why to keep it uniform on both axis to not mis represent the data.
What I'd do to improve the graphic is to make it a density map to help with the overlay of points. Similar to figure 2.3 from the reading.
One of the parts of Wilke's book that influence the design of my graphic was reducing visual clutter. I left the graphic to represent three variables instead of more, so that it was still readable and concise.
One thing that I would improve is zooming in on the data points to a more full sized visual if I knew how.
I switched from faceting by species to faceting by Island to prevent the creation of an ugly graphic due to a massive color blob of data.
I would have loved to create a 4th graph to show the trend lines of each Island-Species combo.
I got the idea of changing the size of each point to show additional variable, instead of x and y axis. By adding the size as the fertility, I could show the relationship between the life_expect over years as the fertility decreased(the size shrinks)
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I’ve learned that the importance of using scales that create a clear one-to-one mapping between data values and visual elements, so that the key numerical data uses position to represent the most accurate visual channel, and uses color to distinguish categories. It is a good way to avoid confusing visualization.
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I made sure to follow the guidelines telling the appropriate scale to use based on the datatype. I would have loved to use a heatmap in my graphic but I am not sure how exactly that would play out. Maybe another dataset would be more fitting.
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I set the quantitative variables as position and categorical (sex) as color.
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In this graph I made sure to map shape aesthetic to the no of cylinders the subject contained.
I would love to learn how to add a slider.