histogram

I would like to construct a histogram with unequal bins (intervals)..Matlab construct only histograms with equal bins as if it's a diagram..!!! Please help me...thanks a lot!! Here's an example: x = randn(100,1)*3; %# some random data e = [-10 -5 -3 -1 1 2 3 20]; %# edges of intervals: e(i) <= x < end(i+1) c = histc(x,e); %# get count in each interval bar(e, c, 'histc') %# bar plot set(gca, 'xlim',[e(1) e(end)]) 2 solutions: Specify bin centers with the 2nd argument to hist . Specify bin Edges with with the 2nd argument to histc . This function takes some further processing since it does not

I want to create a histogram from a number of observations (i.e. d <- c(1,2.1,3.4,4.5) ) and then highlight the bin that a particular observation falls in, such that I have an output that looks like this: how do I do this in R? Expanding on dangerstat's answer, here is a little function that will automatically find which bin contains the value that you want to highlight: highlight <- function(x, value, col.value, col=NA, ...){ hst <- hist(x, ...) idx <- findInterval(value, hst$breaks) cols <- rep(col, length(hst$counts)) cols[idx] <- col.value hist(x, col=cols, ...) } Now x <- rnorm(100)

I would like some help coloring a ggplot2 histogram generated from already-summarized count data. The data are something like counts of # males and # females living in a number of different areas. It's easy enough to plot the histogram for the total counts (i.e. males + females): set.seed(1) N=100; X=data.frame(C1=rnbinom(N,15,0.1), C2=rnbinom(N,15,0.1),C=rep(0,N)); X$C=X$C1+X$C2; ggplot(X,aes(x=C)) + geom_histogram() However, I'd like to color each bar according to the relative contribution from C1 and C2, so that I get the same histogram (i.e. overall bar heights) as in the above example,

I'm trying to plot a time-series of histograms in Python. There has been a similar question about this, but in R . So, basically, I need the same thing, but I'm really bad in R. There are usually 48 values per day in my dataset. Where - 9999 represents missing data. Here's the sample of the data. I started with reading in the data and constructing a pandas DataFrame . import pandas as pd df = pd.read_csv('sample.csv', parse_dates=True, index_col=0, na_values='-9999') print df <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 336 entries, 2008-07-25 14:00:00 to 2008-08-01 13:30:00 Data

As part of a test bench I'm building, I'm looking for a simple class to calculate a histogram of integer values (number of iterations taken for an algorithm to solve a problem). The answer should be called something like this: Histogram my_hist = new Histogram(); for( uint i = 0; i < NUMBER_OF_RESULTS; i++ ) { myHist.AddValue( some_result ); } for( uint j = 0; j < myHist.NumOfBins; j++ ) { Console.WriteLine( "{0} occurred {1} times", myHist.BinValues[j], myHist.BinCounts[j] ); } I was suprised a bit of googling didn't turn up a neat solution but maybe I didn't search for the right things. Is