Three months of GSoC 2014 term was over this week and finally I released my product Nyaplot as a gem. This blog post was written to introduce Nyaplot version 0.1.1.
There are various plotting libraries in the world as ggplot2 from R community and Plotrb and Rubyvis from Ruby. The features of Nyaplot compared with those libraries are interactivity, simplicity, and extensibility.
Interactivity is the main theme of my D3 project in GSoC 2014. You can soon find the aforementioned interactivity by clicking plots with your mouse or trackpad. This feature is also available on browsers bundled with Android or iOS thanks to technologies like SVG and WebGL.
However, the word ‘interactivity’ is not limited to situations like the above. You can explore that for yourself by using Nyaplot in IRuby notebook. Various modules prepared by Nyaplot help you to create plots interactively in the notebook. You can also publish the result quickly by uploading the notebook to your Dropbox storage, a gist or pastebin, or somewhere else. Here is an example which Mauricio Giraldo created and published on gist.
Simplicity is also an important element. Many plotting libraries has MATLAB-like function-based API but Nyaplot does not. Nyaplot is designed in a more Ruby-like object-oriented style, and its plots consist of various objects created from different classes. But Nyaplot has simple shortcut methods and users may avoid the more complex API.
You can install Nyaplot by simply running
gem install nyaplot.
After that, find example code from
path_to_gems/nyaplot-0.1.1/examples/rb and run some of them using
ruby command. These scripts will generate some plots with Nyaplot
and export them as html files to current directory.
In addition, I strongly recommend you to install IRuby notebook at the same time. IRuby is a web-based, interactive Ruby environment and is useful for quickly creating plots with Nyaplot. The introduction video embedded at the top of this post was also created on IRuby.
IRuby depends on some software outside of Ruby-ecosystem, so its installation method is a bit complicated. Please read the description in the readme to learn more.
I already wrote tutorials, so I’d like to limit the role of this paragraph to the introduction of the basic usage. You can skip reading this paragraph and find details in the nbviewer tutorial and documentation.
The minimum code to create a scatter plot is as shown below:
Nyaplot::Plot is the base class to create plots and
Nyaplot::Plot#add is the method to add diagram to its instance. The
first argument is to specify the type of diagram, and the second and
third are for data mapped into x and y axes.
Plot#add returns an
Nyaplot::Diagram and you can change attributes like
color and stroke of each diagram component.
For example you can change its color by running the code below.
Nyaplot::Colors is the simple wrapper for
Colorbrewer, and one of its methods
Nyaplot::Colors.qual randomly returns colorset suitable for
If you execute this code in IRuby notebook, you can check if you favor the colorset through html-table based interface. See the tutorial to learn more.
The plot generated from the code can be exported with two lines below.
We cannot explain Nyaplot without
Nyaplot::DataFrame. Example code
above does not contain the word ‘DataFrame’, but the software
internally create an instance of
Nyaplot:DataFrame and creates plots
based on it.
To create the same scatter plot as above, run the code below.
You may not feel any advantage of using
Plot#add_with_df instead of
Plot#add, but the former is useful when creating more complicated plots. Have a look at the tutorial to learn more.
My GSoC term has concluded, but that do not mean the end of development. There are still many things to do for improvement of this project. For example Mapnya and Bionya are both experimental implementation, and the functionality of Nyaplot::DataFrame is relatively limited. If you are interested in this project, please fork either or both of the two repositories below and send me pull-requests.
Any form of contribution is welcome.
At last I’d like to express my gratitude to my mentor Pjotr Prins and other members of SciRuby community. I would not have been able to finish my GSoC term without great help from them. Feedback from people outside of the community also helped me a lot. I had a great summer thanks to all people related to this project.