This plot can be created through the networkD3 package. It allows you to create interactive sankey diagrams. Here you can find an example. I also added a screenshot so you have an idea what it looks like. # Load package library(networkD3) # Load energy projection data # Load energy projection data URL <- paste0( “https://cdn.rawgit.com/christophergandrud/networkD3/”, … Read more
Figure size (figsize) determines the size of the figure in inches. This gives the amount of space the axes (and other elements) have inside the figure. The default figure size is (6.4, 4.8) inches in matplotlib 2. A larger figure size will allow for longer texts, more axes or more ticklabels to be shown. Dots … Read more
What you really have is Volumetric data. I guess that you have is a value of C for every X,Y,Z. And it actually depends a lot in what type of data you have. You’d need to give more information for an specific answer. In general have a look to Volumetric data visualization techniques, but there … Read more
TL;DR With latest versions of D3, you can use selection.raise() as explained by tmpearce in its answer. Please scroll down and upvote! Original answer You will have to change the order of object and make the circle you mouseover being the last element added. As you can see here: https://gist.github.com/3922684 and as suggested by nautat, … Read more
Sure! To set the ticks, just, well… Set the ticks (see matplotlib.pyplot.xticks or ax.set_xticks). (Also, you don’t need to manually set the facecolor of the patches. You can just pass in a keyword argument.) For the rest, you’ll need to do some slightly more fancy things with the labeling, but matplotlib makes it fairly easy. … Read more
glReadPixels runnable PBO example The example below generates either: one ppm per frame at 200 FPS and no extra dependencies, one png per frame at 600 FPS with libpng one mpg for all frames at 1200 FPS with FFmpeg on a ramfs. The better the compression, the larger the FPS, so we must be memory … Read more
I agree with Cade about the limitations of Venn diagrams here. A more apposite visual representation might be this. Tables SELECT A.Colour, B.Colour FROM A CROSS JOIN B SQL Fiddle The cross join (or cartesian product) produces a result with every combination of the rows from the two tables. Each table has 4 rows so … Read more
NEW ANSWER: With the introduction of ggplot2 v2.2.0, position_stack() can be used to position the labels without the need to calculate a position variable first. The following code will give you the same result as the old answer: ggplot(data = dat, aes(x = “”, y = Cnt, fill = Volume)) + geom_bar(stat = “identity”) + … Read more
Imports and Sample DataFrame import matplotlib.pyplot as plt import pandas as pd import seaborn as sns # for sample data from matplotlib.lines import Line2D # for legend handle # DataFrame used for all options df = sns.load_dataset(‘diamonds’) carat cut color clarity depth table price x y z 0 0.23 Ideal E SI2 61.5 55.0 326 … Read more
Any ggplots side-by-side (or n plots on a grid) The function grid.arrange() in the gridExtra package will combine multiple plots; this is how you put two side by side. require(gridExtra) plot1 <- qplot(1) plot2 <- qplot(1) grid.arrange(plot1, plot2, ncol=2) This is useful when the two plots are not based on the same data, for example … Read more