
Create Page Layout Visualizations in R - mpweiher
https://github.com/EmilHvitfeldt/ggpage
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claytonjy
This is really neat; I'm working with OCR output and hand-rolled something
very similar to `ggpage_plot` about two weeks ago to recreate the original
layouts with colored labels. Super helpful to get a sense of how to engineer
some spatial features to feed into classification models. Having this around
might have saved me some time!

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gghh
Interesting project, and surely a great showcase of R graphic capabilities.
For somebody like me coming from python/matplotlib, which gives almost
unlimited freedom in creating complex visualizations, when approaching R
graphing should I focus on basic R ("graphics" package) or learn some ggplot2?

At first it looked like basic graphics is quite limited, but after I learned
that I can draw rectangles and polygons I re-evaluated it a lot. OTOH I can't
avoid the feeling that all ggplot2 gives me is some canned styles and a very
uncomfortable syntax, like "ggplot(...) + geom_bar(...) + theme(...)" where
`+` means something I can't fully comprehend (because "The Grammar of
Graphics").

Please help me change my mind if I'm being ill-informed, I do want to take the
most I can from R graphing. ggplot2 is hugely popular so it must be doing
something right.

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Tarq0n
For most everyday plotting tasks freedom is overrated. What ggplot gives you
is unparalleled productivity, combined with a 'grammar of graphics' API that
makes it very pleasant to specify and experiment with visualizations. It's
also easy to make ggplot look publication ready with little effort.

If you need something highly customized it can be quite a bit of work, but at
least in my experience you almost never do.

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pgroth
Is there anything like this for python and or javascript?

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lottin
I really wish this trend of using pseudo-pipes in R would stop.

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mdlincoln
I find that a fascinating reaction given how rapidly %>% have been taken up
across a large segment of the R universe, to great excitement! Personally, I
find it far MORE legible than endlessly-nested function calls.

It results in code that more closely resembles executed order of operations
(e.g. filter -> mutate -> group -> summarize). Context is also key: it's most
often used for data processing pipelines in specific analytical scripts or
literate-code documents - less so used when defining generalizable/testable
functions in packages (again, just a personal perspective - YMMV of course)

~~~
extr
you nailed it. dplyr is better the further you are from doing heavy duty data
analysis or creating production code. if you're writing some simple transforms
to put data into a report, fine. someone is probably going to want to look at
that at some point and it's much, much easier to understand. but for anything
else i stick with data.table.

