Visualization techniques: Difference between revisions
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* [https://plot.ly/r/ Plotly for R] - makes graphs available in a cloud and enables interactive collaboration | * [https://plot.ly/r/ Plotly for R] - makes graphs available in a cloud and enables interactive collaboration | ||
* Another way to make the figures interactive [http://shiny.rstudio.com/gallery/ R shiny] | * Another way to make the figures interactive [http://shiny.rstudio.com/gallery/ R shiny] | ||
* Some links to spatial analyses in R: [http://www.rspatial.org/analysis/ Rspatial] or [https://cran.r-project.org/doc/contrib/intro-spatial-rl.pdf Spatial data analysis] | * Some links to spatial analyses in R: [http://www.rspatial.org/analysis/ Rspatial] or [https://cran.r-project.org/doc/contrib/intro-spatial-rl.pdf Spatial data analysis]. A good overview is available in the R help pages for the raster package, a five page summary of the most often used raster functions. | ||
=== Python === | === Python === | ||
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== Tips and tricks for polishing == | == Tips and tricks for polishing == | ||
* Export figures from the original environment in vector graph format ( | * Export figures from the original environment in vector graph format (svg, pdf ...) so that you can customize all elements by hand, which were not accessible via code. This helps also to align multiple figures of different types to produce one overview. An environment to load and adjust vector graphics is [http://wiki.inkscape.org/wiki/index.php/Inkscape inkscape]. | ||
* Particularly for maps, with different types of data and layers, it is suitable to export them individually (vector - svg, raster - png) and reassemble them in inkscape. In this way you can make sure that the files take only the least amount of space, you can still handle them and choose the specific layers for each plot export. | |||
== Infographics == | == Infographics == | ||
Revision as of 13:16, 26 March 2018
This is an open and hopefully ever growing collection of links to favorite tools (libraries) and techniques that help us produce informative figures from complex models.
Plain, useful figures
First you should select the main environment from which you do your analytics and want to produce figures. We have collected a few libraries for each of them.
R
- The main R graph gallery
- GGplot - a famous engine to produce figures in R. Provides the opportunity to customize every detail.
- Plotly for R - makes graphs available in a cloud and enables interactive collaboration
- Another way to make the figures interactive R shiny
- Some links to spatial analyses in R: Rspatial or Spatial data analysis. A good overview is available in the R help pages for the raster package, a five page summary of the most often used raster functions.
Python
- The main python graph gallery
- Seaborn - a widely used library for pretty scientific figures, next to
- Matplotlib - the classical standard for matlab-alike figures + the tutorial
- Plotly for python - makes graphs available in a cloud and enables interactive collaboration + the tutorial to integrate this in a jupyter notebook
- A way for a 'shiny' figure production within notebooks are provided by widgets, and a way showing how to do this is here
Matlab
Tips and tricks for polishing
- Export figures from the original environment in vector graph format (svg, pdf ...) so that you can customize all elements by hand, which were not accessible via code. This helps also to align multiple figures of different types to produce one overview. An environment to load and adjust vector graphics is inkscape.
- Particularly for maps, with different types of data and layers, it is suitable to export them individually (vector - svg, raster - png) and reassemble them in inkscape. In this way you can make sure that the files take only the least amount of space, you can still handle them and choose the specific layers for each plot export.