data-visualization

I'd like to use ggplot2's stat_binhex() to simultaneously plot two independent variables on the same chart, each with its own color gradient using scale_colour_gradientn(). If we disregard the fact that the x-axis units do not match, a reproducible example would be to plot the following in the same image while maintaining separate fill gradients. d <- ggplot(diamonds, aes(x=carat,y=price))+ stat_binhex(colour="white",na.rm=TRUE)+ scale_fill_gradientn(colours=c("white","blue"),name = "Frequency",na.value=NA) try(ggsave(plot=d,filename=<some file>,height=6,width=8)) d <- ggplot(diamonds, aes(x

Looking for an efficient way to plot trees in rstudio, H2O's Flow or in local html page from h2o's RF and GBM models similar to the one in the image in link below. Specifically, how do you plot trees for the objects, (fitted models) rf1 and gbm2 produced by code below perhaps by parsing h2o.download_pojo(rf1) or h2o.download_pojo(gbm1)? # # The following two commands remove any previously installed H2O packages for R. # if ("package:h2o" %in% search()) { detach("package:h2o", unload=TRUE) } # if ("h2o" %in% rownames(installed.packages())) { remove.packages("h2o") } # # Next, we download

What is the best way to graph scatter plots in C++? Do you write data to a file and use another tool? Is there a library like matplotlib in Python? I always write out data and then using gnuplot to create my graphs. It is by far the best way I have found of producing graphs in a variety of formats: eps, png, jpeg, xpm, you name it. gnuplot will do scatter plot very easily. Provided the x and y values are in 2 space-separated columnss, then plot "data.txt" using 1:2 Will give you a quick scatter plot. Then you can adjust it and what not using other gnuplot commands. If you are involved in

We currently have a dynamically updated network graph with around 1,500 nodes and 2,000 edges . It's ever-growing. Our current layout engine uses Prefuse - the force directed layout in particular - and it takes about 10 minutes with a hefty server to get a nice, stable layout. I've looked a little GraphViz 's sfpd algorithm, but haven't tested it yet... Are there faster alternatives I should look at? I don't care about the visual appearance of the nodes and edges - we process that separately - just putting x, y on the nodes. We do need to be able to tinker with the layout properties for

I have a data.frame with multiple time series vectors against a date:time vector. I would like to plot all of the relevant vectors, vertically stacked on separate graphs with the same X axis but unique Y axes. A graph similar to this one: my data looks like this: dt <- structure(list(DEPTH = c(156, 156.5, 157.4, 158.15, 158.8, 159.2, 159.75, 160.35, 160.85, 161.1, 161.6, 162.05, 162.5, 162.65, 163.15, 163.45, 163.55, 163.8, 163.65, 163.75, 163.8, 163.8, 163.75, 164.45, 164.8, 165.35, 165.65, 165.75, 166.1, 166.75, 167, 167.2, 167.65, 168, 168.8, 169.3, 169.7, 170.2, 170.65, 170.9, 171.45, 171

Looking for software that will analyze php code (i.e. all of wordpress or the thematic theme) and show me pretty pictures (perhaps a block diagram ) of all the connections to help me more quickly get an understanding of where things are and what's connected to what. Ideally, this software would run on a Mac, but I'll take anything: Windows, Linux, web-based, etc. KCachegrind - With Xdebug you can profile the execution of your scripts, KCachegrind can generate some pretty awesome call graphs from this nwire for Eclipse [UPDATE: This answer does not handle namespaces, so is basically obsolete. I

I'm interested in drawing a treemap : What is the easiest way to make one in Python? Is there a library that could produce such a graphic, given the proper input data? nkint The SciPy cookbook includes an example using matplotlib , but without labels . IA link: https://web.archive.org/web/20150324163314/http://wiki.scipy.org/Cookbook/Matplotlib/TreeMap You can use Pygal library, this is so simple http://pygal.org/en/stable/documentation/types/treemap.html Another solution is you can use squarify library, here's a code that I was used import matplotlib import matplotlib.pyplot as plt import