What are essential rules for designing and producing plots? Background: 
Previously on Cross Validated, we have had questions on:


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*What is best practice when preparing plots?

*What are good tips available online for plotting two numeric variables?
It was suggested by @david in the comments to this question that we should have a community wiki question with one visualization rule per answer that the community could vote on.
Question 
What are the essential rules on designing and producing graphical representations of data?
Rules 


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*One rule per answer

*Ideally, include a brief explanation of why you think it is a good idea

*Answers with examples (code and image) of good and bad practice preferred.

 A: Leave time to edit. Making a good graph takes time and it often takes (at least for me) multiple tries. 
A: Don't oppose red and green.  Color can be helpful, but when using color always bear in mind that a substantial minority of people are red-green colorblind.  I once was showing some data to someone, and he couldn't make out what was going on in my graphs--it was a waste and I felt pretty stupid.  Other forms of colorblindness are very rare, but red-green is fairly common.  This page has a lot of good information.  Here are some tips:


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*If you only need two colors, use blue and yellow--don't use red and green.  

*If you need a gradient, go from blue to yellow while changing saturation and lightness simultaneously--don't use the rainbow.  

*If you need to encode more than two elements (e.g., points on a scatterplot from more than two groups, or several lines) back your colors up with different plotting symbols / line styles as well.  For example, distinct plotting symbols: o + < s w, or lines: solid, dotted, dashed, dot-dashed, etc (you can also add plotting symbols to your lines or change line weights).

A: Don't use stacked bar graphs. And on a related note, if you have a Likert scale item, don't feel the need to show the proportion for every response to each item. Those graphs make my eyes bleed.
Don't use pie-charts.
Don't duplicate data that is contained in a graph by throwing in a table. 
Use a sans serif font like Arial for graph titles, etc, because those types of fonts are designed to be used that way.
No post on design is complete without a book reference, I really like Statistical Rules of Thumb. Chapter 9 is the bit relevant to the discussion here, and the bits I point to when asked why I hate stacked bar graphs and pie charts. :)
Confession: in one of my first student consulting roles for a small NGO client I gave them a report that had lots of stacked bar graphs, printed in colour (this was the mid 1990s). I think I managed to get yellow, purple, and red into those puppies.
A: Don't mess with the axes. Don't cut off the first hundred units just because then the slope of the graph looks more impressive. The image will stick and people will remember a much larger effect than was actually measured.
A: Substance over Form: Choose the appropriate plot, style, coloring or other graphical parameters to show what you want the plot to show, rather than what your graphing package necessarily allows.
A: Being familiar with the three dimensions of colour can be helpful.
If you use several colours, they should ideally differ on several of those dimensions, not just one.
Value. The graph should remain readable even in black and white.
This simple rule should account for colour blindness, low-quality printers 
and bad lighting conditions.
Even if you use different hues, make sure that the values are sufficiently different.
In particular, the plots should be dark on a light background (or the opposite), 
but not grey on a grey value.
The worst example would be a blue plot of a red background -- both are middle values, 
i.e., would give very similar greys after conversion in black and white.
Saturation. 
Saturation should be used with moderation: a pure red line may be fine, but a thicker,
less saturated red line will be more readable 
(the increased thickness helps distinguish colours and allows you to reduce saturation).
On the other hand, a pure red area is painful to look at: do not use saturated colours to fill areas.
The Brewer colour palettes 
(designed for maps, not line plots) give examples of low-saturation colour choices.
The worst example would be, again, a saturated background (blue on red or red on blue).
Hue. 
As mentioned by @gung, avoid the red/green (traffic lights) combination: 
there are much more colour-blind people than you think.
Especially with hue, less is more. For instance, to plot "diverging" values
(i.e., quantities that can be positive or negative), only use two hues
(for positive and negative values),
so that the reader can immediately distinguish what is high and what is low.
Using a discrete gradient can result in a much more readable plot: 
the boundaries between the colours become visible and form a contour plot.
You may want to read S. Few's 
Practical Rules for Using Color in Charts
or refer to any material about "Colour Theory" for art or design students.
A: Place as much of the required information within the figure itself. Do not require the reader to reference the caption, e.g. to identify the meaning of various symbols or colors. Place whatever information (or supplementary information) that cannot go into the figure itself in the caption. The idea is to minimize the effort required by a graph viewer to extract the relevant information--best: graph is self-explanatory, next best: supplementary information required can quickly be gleaned from the caption, worst: the viewer must closely read through the whole results section searching for some crucial detail to figure out what is going on.
A: Make the plot as simple as possible. In Tufte's words, 'minimize the data-ink ratio'.
For example, avoid:


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*more colors or shapes than required

*more tick marks than necessary

*3-D effects on a 2-D plot.

*using a legend when objects can be labeled directly

