Data Visualization - Revision history

SpookySimon at 19:23, 11 January 2023

2023-01-11T19:23:07Z

SpookySimon: moving stuff arouns

2023-01-11T18:32:59Z

moving stuff arouns

SpookySimon: adding heading

2023-01-11T18:32:01Z

adding heading

SpookySimon: adding category

2023-01-09T19:00:04Z

adding category

SpookySimon: /* Common Concerns */ adding context to further reading links

2023-01-09T17:31:20Z

Common Concerns: adding context to further reading links

SpookySimon: /* Common Concerns */

2023-01-09T16:44:46Z

Common Concerns

SpookySimon: /* Common Concerns */ adding image

2023-01-09T15:56:09Z

Common Concerns: adding image

SpookySimon: /* Common Concerns */

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Common Concerns

SpookySimon: /* Graphs */ heading

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Graphs: heading

SpookySimon: /* Basic Points */ better link to example

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Basic Points: better link to example

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 When using a table to convey information, it is recommended to color code the information to enable the reader to see the trend or comparison intended by the information. As described above, color is already associated with meaning so even crude coloration can dramatically improve the presentation. For a good example of a table with no color and lots of data, see the wiki table for [[Fuel#Comparison_of_Line_Types|comparison of fuel line types]].
 {{clear}}
+=Distributions=
 =CAE and CFD=
 CAE and CFD are great ways to show information about a design. It is important to include what the pretty colors mean. Because they are so compelling, simulation results can easily lead a team to commit to a decision that is not in their best interest.
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 * Slightly harder to import data than excel
 * The standard plotting in Matlab is very familiar to most judges
 ==R==
 R is a free program used for statistical analysis and can be a great tool. It has a very steep learning curve and is likely more time than it is worth for extracting, processing, and visualizing the kinds of data most teams will be working with. The plotting functions can be extremely powerful and customizable but require the relatively large upfront investment of learning the language. If someone on the team already knows R, there's a good chance they'll know how to make a plot readable.

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 ===Size===
 ==Other==
 * shape
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 ** Use trendline sparingly, only when r2 or slope is point of the graph
 -->
 ==Excel==
 * Easiest to input raw data
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 R is a free program used for statistical analysis and can be a great tool. It has a very steep learning curve and is likely more time than it is worth for extracting, processing, and visualizing the kinds of data most teams will be working with. The plotting functions can be extremely powerful and customizable but require the relatively large upfront investment of learning the language. If someone on the team already knows R, there's a good chance they'll know how to make a plot readable.
 [[Category:Data Acquisition]]
 [[Category:References]]

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 ===Size===
+=Visualization Programs=
 ==Other==
 * shape
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 * Extremely easy to make it not look like dookie
 * Excel likes to make text grey, fine on computer, bad in print
-* Default blue is hard to read in print, also kinda ugly color
+* Default blue can be hard to read in print, also kinda ugly color
 * Not good at showing 3d data, forget 4d
 ** Can be done with some creativity or plug ins

← Older revision		Revision as of 19:00, 9 January 2023
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	Many data acquisistion systems will export GPS coordinates or a .KML file that can be used to show location data. This can be used to plot the drive cycle as a map and vehicle data can be plotted in color to visualize how the vehicle behaves at each point on the track.		Many data acquisistion systems will export GPS coordinates or a .KML file that can be used to show location data. This can be used to plot the drive cycle as a map and vehicle data can be plotted in color to visualize how the vehicle behaves at each point on the track.
	[[Category:Data Acquisition]]		[[Category:Data Acquisition]]
		+	[[Category:References]]

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 ==Common Concerns==
 [[File:Grid Lines.png|right|middle|thumb|Impact of Grid Lines]]
-Cluttering a graph is often a symptom of not putting enough work into the visualization rather than too much. Default graphing programs will often add superfluous elements. One of the most common and egregious elements in this vein are grid lines. Grid lines should never be added to a chart by default, only if needed, and when added, often with more moderation than a default program will do so<ref>Few, Stephen (February 2005) [https://www.perceptualedge.com/articles/dmreview/grid_lines.pdf "Grid Lines in Graphs are Rarely Useful"] <em>Perceptual Edge</em>. </ref>. When the purpose of a graph is to demonstrate a relationship between two data sets, and the specific number values are not important, gridlines get in the way of comprehension. Tick marks and color differentiation <!--link to color section when its written--> should be more than enough in most cases to elucidate the point. On the image showing the impact of gridlines, the graph is used to show the relationship of efficiency score to lap time: the highest scoring two cars are faster than average, and the lowest scoring are slower than average. The exact lap times, and the value differences between them are not important to convey this point. It is important to note that the fastest cars are not the most efficient, and that the third most efficient car is in the slowest quartile. This should lead to a conversation about which data to include as relevant, and how to show this discrimination on a graph.
+Cluttering a graph is often a symptom of not putting enough work into the visualization rather than too much. Default graphing programs will often add superfluous elements. One of the most common and egregious elements in this vein are grid lines. Grid lines should never be added to a chart by default, only if needed, and when added, often with more moderation than a default program will do so. When the purpose of a graph is to demonstrate a relationship between two data sets, and the specific number values are not important, gridlines get in the way of comprehension. Tick marks and color differentiation <!--link to color section when its written--> should be more than enough in most cases to elucidate the point. On the image showing the impact of gridlines, the graph is used to show the relationship of efficiency score to lap time: the highest scoring two cars are faster than average, and the lowest scoring are slower than average. The exact lap times, and the value differences between them are not important to convey this point. It is important to note that the fastest cars are not the most efficient, and that the third most efficient car is in the slowest quartile. This should lead to a conversation about which data to include as relevant, and how to show this discrimination on a graph.
 Another chart element that often ends up being confusing is a legend. It takes up room that the actual data could occupy and forces the reader to stop looking at the information itself while trying to understand it, which dramatically impedes comprehension. Instead, it is recommended to use direct labels on the data. There are times and places that a legend is necessary, but similar to gridlines, it should be a when it becomes helpful, not because it is easy or already generated.  <br />
-See here for further information:
+Resources for further reading:
-* https://stephanieevergreen.com/directly-label/
+* Explanation of accessibility and legibility concerns by Ann Emery can be read [https://depictdatastudio.com/accessibility-quick-wins-remove-legends-and-directly-label/ here]
-* https://www.datarevelations.com/avoid-color-legends/
+* Tutorial by Stephanie Evergreen on Excel implementation found on her [https://stephanieevergreen.com/directly-label/ website]
-* https://depictdatastudio.com/accessibility-quick-wins-remove-legends-and-directly-label/
+* Explanation of concept and tutorial by Steve Wexler can be found [https://www.datarevelations.com/avoid-color-legends/ here]
 Trend lines are less frequently abused than grid lines or legends, but can clutter the graph just as significantly. Trend lines are used when the graph is being used to convey a pattern or trend, or when it being used to forecast.

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 Graphs are the most common form of data visualization. Despite their ubiquity, often graphs fall short of their ability to convey information. The way the graphs are constructed can be as important as the data they are displaying. Graphs can fail in a number of ways: being cluttered, being vague, being complex, being imperceptible, etc.
 ==Common Concerns==
 [[File:Grid Lines.png|right|middle|thumb|Impact of Grid Lines]]
 Another chart element that often ends up being confusing is a legend. It takes up room that the actual data could occupy and forces the reader to stop looking at the information itself while trying to understand it, which dramatically impedes comprehension. Instead, it is recommended to use direct labels on the data. There are times and places that a legend is necessary, but similar to gridlines, it should be a when it becomes helpful, not because it is easy or already generated.  <br />
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 * https://depictdatastudio.com/accessibility-quick-wins-remove-legends-and-directly-label/
-Trend lines are less frequently abused than grid lines or legends, but can clutter the graph just as significantly. Trend lines are used when the graph is being used to convey a pattern or trend, or when it being used to forecast. <br />
+Trend lines are less frequently abused than grid lines or legends, but can clutter the graph just as significantly. Trend lines are used when the graph is being used to convey a pattern or trend, or when it being used to forecast.
 Further reading:
 * https://www.storytellingwithdata.com/blog/2020/10/20/thoughts-on-trendlines

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 Graphs are the most common form of data visualization. Despite their ubiquity, often graphs fall short of their ability to convey information. The way the graphs are constructed can be as important as the data they are displaying. Graphs can fail in a number of ways: being cluttered, being vague, being complex, being imperceptible, etc.
 ==Common Concerns==
-Cluttering a graph is often a symptom of not putting enough work into the visualization rather than too much. Default graphing programs will often add superfluous elements. One of the most common and egregious elements in this vein are grid lines. Grid lines should never be added to a chart by default, only if needed, and when added, often with more moderation than a default program will do so. Tick marks and color differentiation <!--link to color section when its written--> should be more than enough in most cases to elucidate the point.
+Cluttering a graph is often a symptom of not putting enough work into the visualization rather than too much. Default graphing programs will often add superfluous elements. One of the most common and egregious elements in this vein are grid lines. Grid lines should never be added to a chart by default, only if needed, and when added, often with more moderation than a default program will do so. Tick marks and color differentiation <!--link to color section when its written--> should be more than enough in most cases to elucidate the point.<ref>Few, Stephen (February 2005) [https://www.perceptualedge.com/articles/dmreview/grid_lines.pdf "Grid Lines in Graphs are Rarely Useful"] <em>Perceptual Edge</em>. </ref>
 <!-- example here-->
 Another chart element that often ends up being confusing is a legend. It takes up room that the actual data could occupy and forces the reader to stop looking at the information itself while trying to understand it, which dramatically impedes comprehension. Instead, it is recommended to use direct labels on the data. There are times and places that a legend is necessary, but similar to gridlines, it should be a when it becomes helpful, not because it is easy or already generated.  <br>

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 =Basic Points=
-A good visualization will show the reader what would be difficult to convey in words. This requires the visual be easy to understand, and demonstrate an obvious conclusion to avoid ambiguity. Care must be taken to the medium that the visual is in as physical copies are subject to a variety of factors that cannot be seen on a screen. Physical, printed copies must be designed in accordance with the printer, the size, and the light conditions it should be read in. Print journals are notorious for their use of small, greyscale visuals. For an ironic example, see <em>The  End of the  Rainbow?  Color Schemes for Improved Data  Graphics</em> shown in low resolution black and white <ref> Light, A., Bartlein, P.: The end of the rainbow? Color schemes for improved data graphics. EOS, Transactions, American Geophysical Union 85 (2004) 385, 391 </ref>
+A good visualization will show the reader what would be difficult to convey in words. This requires the visual be easy to understand, and demonstrate an obvious conclusion to avoid ambiguity. Care must be taken to the medium that the visual is in as physical copies are subject to a variety of factors that cannot be seen on a screen. Physical, printed copies must be designed in accordance with the printer, the size, and the light conditions it should be read in. Print journals are notorious for their use of small, greyscale visuals. For an ironic example, see the copy of <em>The  End of the  Rainbow?  Color Schemes for Improved Data  Graphics</em> shown in low resolution black and white [https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2004EO400002 at the AGU online library] <ref> Light, A., Bartlein, P.: The end of the rainbow? Color schemes for improved data graphics. EOS, Transactions, American Geophysical Union 85 (2004) 385, 391. <br> A color correct version may be viewed at the EOS archive here: https://eos.org/features/the-end-of-the-rainbow-color-schemes-for-improved-data-graphics</ref>
 =Graphs=