metR
Tools for Easier Analysis of Meteorological Fields (by eliocamp)
ggtree
:christmas_tree:Visualization and annotation of phylogenetic trees (by YuLab-SMU)
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
metR
Posts with mentions or reviews of metR.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-06-19.
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GIS in R
https://github.com/eliocamp/metR in case you are not aware of Elio's work he has written multiple ggplot2 packages around meteorological data.
ggtree
Posts with mentions or reviews of ggtree.
We have used some of these posts to build our list of alternatives
and similar projects.
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Advice about R for bioinformatics (ggtree and metadata)
I don’t have a lot of experience with this particular package, but if a package provides vignettes I like to look through those. ggtree seems to provide an entire book, which may be helpful to you. You could also look at the examples in the docs, or other scripts people have written. If you find some, read through them line by line and try to understand what they’re doing. Run each line and look at what the output is. See if you can reproduce the example analyses on your own, maybe with different data. That’ll help you learn the packages with more training wheels than just striking out on your own, and then once you get more comfortable you should be able to branch out more.
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Exhaustively list all of a function's aes params?
I'd suggest digging into the code, if you're comfortable with it. The package is available up on github and also links to a reference book from the package author about how to use it. Looks like it has a few more examples than the tutorial you linked in another comment.
What are some alternatives?
When comparing metR and ggtree you can also consider the following projects:
terra - R package for spatial data handling https://rspatial.github.io/terra/reference/terra-package.html
ggthemr - Themes for ggplot2.
healthyR.ts - A time-series companion package to healthyR
DiagrammeR - Graph and network visualization using tabular data in R
ggthemes - Additional themes, scales, and geoms for ggplot2
ggpointdensity - :chart_with_upwards_trend: :bar_chart: Introduces geom_pointdensity(): A Cross Between a Scatter Plot and a 2D Density Plot.
esquisse - RStudio add-in to make plots interactively with ggplot2
geomnet - Examples and data for geom_net
patchwork - The Composer of ggplots