James Black epijim

Studied at @cambridgeuniversity, working at @Novartis after almost a decade at @Roche, living in @swiss

epijim / bubbletable.r

Last active December 28, 2015 01:28

bubble tables

	# ggplot2 base layer
	g <- ggplot(table)

	# Bubble plots - edit limits and seq based on your data
	(g + geom_point(aes(x = XAxis, y = YAxis, size = Percent, colour = total),shape=16, alpha=0.80) +
	scale_colour_gradient(limits = c(0, 1400), low="blue", high="red", breaks= seq(0, 1400, by = 200)) +
	scale_x_continuous(breaks = 1:4, labels=c("Category1", "Category2", "Category3","Category4")) +
	scale_y_continuous(trans = "reverse") + coord_fixed(ratio=0.2)
	)

epijim / parsets.r

Last active December 27, 2015 22:09

Parallel Sets. Based off a comment by Aaron Rendahl on CrossValidated.

	parallelset <- function(..., freq, col="gray", border=0, layer,
	alpha=0.5, gap.width=0.05) {
	p <- data.frame(..., freq, col, border, alpha, stringsAsFactors=FALSE)
	n <- nrow(p)
	if(missing(layer)) { layer <- 1:n }
	p$layer <- layer
	np <- ncol(p) - 5
	d <- p[ , 1:np, drop=FALSE]
	p <- p[ , -c(1:np), drop=FALSE]
	p$freq <- with(p, freq/sum(freq))

epijim / trains.js

Created November 5, 2013 21:12

Trains - from https://gist.github.com/mbostock

epijim / collegewealth.coffee

Created November 5, 2013 12:50

D3.js bubble plot see vlandham/vlandham.github.com for source.


	class BubbleChart
	constructor: (data) ->
	@data = data
	@width = 940
	@height = 600

	# locations the nodes will move towards
	# depending on which view is currently being
	# used

epijim / beanplot.r

Last active December 27, 2015 06:29

Bean plot in R

	library("beanplot")

	#To make it do the split (male/female in my plot), I set a variable up as "MainGrouping" then
	# a space followed by BinaryGrouping encoded as a numeric value (i.e. 1 or 2).

	beanplot(ContinuousVariable ~ MainGrouping_BinaryGrouping, data = DATA, what=c(1,1,1,0), log="",
	ylab = "Continous variable label", side = "both",
	border = NA, beanlinewd = 0.5, overallline = "median",
	col = list( "brown2", "cadetblue3"))
	legend("topright", fill = c("brown2", "cadetblue3"), c("Binary 1", "Binary 2"))

epijim / scrape.r

Last active December 27, 2015 04:09

Scrape google scholar. Need to edit the links at the bottom to the persons google scholar page.

	getPckg <- function(pckg) install.packages(pckg, repos = "http://cran.r-project.org")

	pckg = try(require(wordcloud))
	if(!pckg) {
	cat("Installing 'wordcloud' from CRAN\n")
	getPckg("wordcloud")
	require("wordcloud")
	}
	pckg = try(require(tm))
	if(!pckg) {

epijim / stacked_histogram.r

Created October 1, 2013 13:58

stacked histogram

	plot using ggplot2
	require(ggplot2)
	#data
	set.seed(1233)
	data1 <- data.frame(pop =c(rep("A x B", 200), rep("A x C", 200), rep("B x C", 200) ) , var1 = c(rnorm(1000, 90,10), rnorm(1000, 50, 10), rnorm(1000, 20, 30)))

	qplot( var1, data = data1, geom = "histogram" , group = pop, fill = pop, alpha=.3) + theme_bw( )

epijim / histwithboxplot.r

Created October 1, 2013 13:43

a histograph with a bar chart overlaid

	# data
	set.seed(4566)
	data <- rnorm(100)

	# Added to the plot:
	par(mar=c(3.1, 3.1, 1.1, 2.1))
	hist(data,xlim=c(-4,4), col = "pink")
	boxplot(data, horizontal=TRUE, outline=TRUE, ylim=c(-4,4), frame=F, col = "green1", add = TRUE)

epijim / swarmplot.r

Created October 1, 2013 13:26

swarmplot, visualise continuous distribution by a catergorical stratification variable.

	# data
	set.seed(1234)
	bimodal <- c(rnorm(250, -2, 0.6), rnorm(250, 2, 0.6))
	uniform <- runif(500, -4, 4)
	normal <- rnorm(500, 0, 1.5)
	dataf <- data.frame (group = rep(c("bimodal","uniform", "normal"), each = 500), xv = c(bimodal, uniform, normal), cg = rep( c("A","B"), 750))

	require(beeswarm)

	# hexagon

epijim / Proportional twoway (or more, in a mosiac plot).r

Last active December 23, 2015 03:39

Simple example - a two way table of categorical variables, with size proportional to number

	library(vcd)
	# create a matrix with two categorical variables.
	VECTOR <- xtabs(~ Var1 + Var2, data = Dataframe)
	# create mosiac
	mosaic(VECTOR, gp = shading_max, split_vertical = TRUE)