data-visualization

I would like to make a graph with ggplot as shown below. The idea is to plot "percentage matches" between two categorical variables. It is easy to come close by altering the size of points, but I wondered if it is possible to make these small pie charts... An example code for plotting this with the size of points as a measure of the score. temp <- data.frame(Exercise=c(1, 1, 1, 2, 2, 2), Name=c(1, 2, 3, 1, 2, 3), Score=c(0.2, 0.5, 0.3, 0.9, 1.0, 0.6)) ggplot(temp) + geom_point(aes(Exercise, Name, size=Score)) How can this code be altered to give something close to the figure below? Using a

I'm using this function for some quick and easy sparklines with R but I can't seem to work out how to change the font size to avoid ugly overlaps of the y-axis tick labels. Here's my code (see below for a reproducible example): sparklines(gamma.df, sub=c(1:23),outer.margin = unit(c(2, 2, 2, 2), "cm")) and the resulting plot: I seem to be able to completely suppress the y-axis with sparklines(gamma.df, sub=c(1:23),yaxis=FALSE,outer.margin = unit(c(2, 2, 2, 2), "cm")) But what I really want is just shrink the numbers at the tick marks (and add grey fill under the line, but it looks like I'd have

I've got an input file with a list of ~50000 clusters and presence of a number of factors in each of them (~10 million entries in total), see a smaller example below: set.seed(1) x = paste("cluster-",sample(c(1:100),500,replace=TRUE),sep="") y = c( paste("factor-",sample(c(letters[1:3]),300, replace=TRUE),sep=""), paste("factor-",sample(c(letters[1]),100, replace=TRUE),sep=""), paste("factor-",sample(c(letters[2]),50, replace=TRUE),sep=""), paste("factor-",sample(c(letters[3]),50, replace=TRUE),sep="") ) data = data.frame(cluster=x,factor=y) With a bit of help from another question, I got it

I have a collection of 3D points. These points are sampled at constant levels (z=0,1,...,7). An image should make it clear: These points are in a numpy ndarray of shape (N, 3) called X . The above plot is created using: import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D X = load('points.npy') fig = plt.figure() ax = fig.gca(projection='3d') ax.plot_wireframe(X[:,0], X[:,1], X[:,2]) ax.scatter(X[:,0], X[:,1], X[:,2]) plt.draw() I'd like to instead triangulate only the surface of this object, and plot the surface. I do not want the convex hull of this object, however,

Creating heatmaps in R has been a topic of many posts, discussions and iterations. My main problem is that it's tricky to combine visual flexibility of solutions available in lattice levelplot() or basic graphics image() , with effortless clustering of basic's heatmap() , pheatmap's pheatmap() or gplots' heatmap.2() . It's a tiny detail I want to change - diagonal orientation of labels on x-axis. Let me show you my point in the code. #example data d <- matrix(rnorm(25), 5, 5) colnames(d) = paste("bip", 1:5, sep = "") rownames(d) = paste("blob", 1:5, sep = "") You can change orientation to

Our software produces a lot of data that is georeferenced and recorded over time. We are considering ways to improve the visualisation, and showing the (processed) data in a 3D view, given it's georeferenced, seems a good idea. I am looking for SO's recommendations for what 3D libraries are best to use as a base when building these kind of visualisations in a Delphi- / C++Builder-based Windows application. I'll add a bounty when I can. The data Is recorded over time (hours to days) and is GPS-tagged. So, we have a lot of data following a path over time. Is spatial: it represents real 3D

I have a very simple Shiny app, wherein I have a set of data on past customers, and a set of data on 3 new customers. All my data consists only of 2 variables: age and score. The purpose is to select one of the 3 new customers, and see how the past customers of similar ages scored. We do this with a simple scatterplot. For example, since new customer #1 is 30 years old, we get to see how all the past customers of ages 25 - 35 scored: (my apologies for the small image) Everything works fine. The trouble begins when I add an age slider with the intentions of allowing the user to override the

See the example code: var data = new google.visualization.DataTable(); data.addColumn('string', 'Mac'); data.addColumn('number', 'Score'); data.addColumn({ type: 'string', role: 'style' }); data.addRows([ ['Mac model 12', 200, 'color: #8bba30; opacity: 0.75;'], ['Another Mac Model', 110, 'color: #ffcc33; opacity: 0.75;'], ]); var options = { title: '', width: 500, height: data.getNumberOfRows() * 40 + 100, hAxis: { minValue: 0, maxValue: 255, ticks: [0, 75, 150, 255], textPosition: 'out', side: 'top' }, series: { 0: { axis: 'Mac' }, 1: { axis: 'Score' } }, chartArea: { top: 0, bottom: 50,

I am new to D3.js and trying to make a bar chart with GDP on Y axis and date on X axis. I am trying to make a xScale with d3.timeScale() but for some reason the code keeps returning undefined when using it. Below is my code, what am I doing wrong? var data = [ [ "2011-01-01", 15238.4 ] , ["2015-07-01", 18064.7 ] ]; var w = 1000; var h = 300; var barPadding = 1; var svg = d3.select("#chart") .append("svg") .attr("height", h); var maxDate = d3.max(data, function(d){ return d[0]; }); var minDate = d3.min(data, function(d){ return d[0]; }); var maxGDP = d3.max(data, function(d){ return d[1]; });