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By Supcik J.

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However, presentation graphics may have further constraints: ❼ size: may be strongly constrained in either a presentation or a publication. ) to make more room for large symbols and labels. ❼ color : avoid red-green contrasts, 10% of your audience won’t be able to see them. Color reproduction may be expensive in publications, and the number of gray scales may be limited because gray scales may not reproduce faithfully. Thankfully, most publications don’t use photo reproductions of figures any more, so you can use gray scales instead of ugly hash and stripe patterns.

The base graphics system is cruder/simpler, while the lattice graphics system (in the lattice package) is more sophisticated/complex. They overlap a lot in their functionality (both will do scatterplots, box-and-whisker plots, histograms, . . ), but lattice graphics tend to be a little bit fancier and do more automatic processing of your data, with the downside that they are harder to understand and customize. In the realm of 3D graphics, there are (confusingly) several more options, at different stages of development.

G. 0/3, 1/3, 2/3, 1) appear in the data set. Suppose we want to calculate the mean fraction taken for each number of seeds available. There are a variety of ways of doing this, from simple to complex and crude to sophisticated (see the lab for more details), but here I will to use the tapply() function. g. mean() or sd()) to each group. taken for each group defined by a different value of available (R automatically converts available into a factor temporarily for this purpose). avail) I’ll actually use a variant of barplot(), barplot2() (from the gplots package) to plot these values with standard errors (I find it mildly embarrassing that R does not supply error-bar plotting as a built-in function, but you can use the barplot2() (gplots package) and plotCI() (gplots or plotrix package) functions.

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An object-oriented database programming environment for Oberon by Supcik J.

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