Hi,
How do I plot the histogram for each list with bin width equal 20 in the enclosed notebook?
I appreciate your help.
Regards,
Histogram.nb (53.1 KB)
Turkey0.xlsx (532 KB)
I could not get the second line in your code to combine the data columns so I’m using just the data in the first column.
To get a bin width of 20, use {20} in the Histogram command:
Histogram[data, {20}, "PDF", PlotRange -> {{0, 500}, {0, 0.016}}]
I’ve used the “PDF” option so that your histograms from datasets with differing numbers of observations can be compared.
With large datasets such as yours there is no reason to display a histogram. Try a more modern nonparametric density estimate.
skd = SmoothKernelDistribution[data, Automatic, {"Bounded", {0, \[Infinity]}, "Gaussian"}];
Plot[PDF[skd, x], {x, 0, 500}, PlotRange -> {{0, 500}, {0, 0.016}}]
One of the many advantages is the ability to superimpose several nonparametric density estimates and clearly see differences and similarities of different datasets. If you overlay histograms, you get a mess.
Thank you Jim for the useful explain.
I included the data in Mathematica. I would like to plot four histograms for four lists with a bin width of 20 using MapIndex command.Is it possible?
Data.nb (53 KB)
I’m sure it can be done with MapIndexed and lots of other ways. Here’s one of those ways given that you’ve provided the 4 lists as S1, S2, S3, and S4:
data = {S1, S2, S3, S4};
alldata = Transpose[{data, {"1", "2", "3", "4"}}];
h = Histogram[#[[1]], {20}, "PDF", PlotRange -> {{0, 1300}, {0, 0.016}}, PlotLabel -> #[[2]]] & /@ alldata;
GraphicsRow[h, ImageSize -> 1000]
I’ve given all of the histogram the same PlotRange values to allow a comparison. However, because these are histograms and because of most of the data being mashed up near 0, comparisons are at best difficult. One approach is to overlay nonparametric densities and use a transformation of the data such as a square root transformation.
k = 0.5;
skd = SmoothKernelDistribution[#[[1]]^k, Automatic, {"Bounded", {0, \[Infinity]}, "Gaussian"}] & /@ alldata;
Plot[Evaluate[PDF[#, x] & /@ skd], {x, 0, 1300^k}, PlotRange -> {{0, 1300^k}, {0, Automatic}},
PlotStyle -> {Red, Blue, Green, Cyan}, PlotLegends -> {1, 2, 3, 4}]
This makes it easier to see the similarities and differences among the distributions of the 4 datasets.