Extracting data from histogram plots
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Hello. I'm trying to process some data from some chemical analyses I did a while ago. I have 3 types of data: particle diameter, nitrogen content (%), and sulfur content (%). I've already managed to organize the particle diameter data into a histogram plot with something like 50 bins. Now, I'd like to figure out the average nitrogen and sulfur content of the particles in each bin. I'm not sure how to do this, though, and I haven't found any obvious tutorials to explain how to do this. Any advice?
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3 methods to group data and compute mean for each group
Each method deals with empty bins differently.
discretize + splitapply
Use discretize to group each value into the bins used in histogram and then splitapply to compute the mean for each group. Note that each bin must contain at least one data point.
Example: compute the mean of data in bins defined by edges.
rng default % for reproducibility of this demo
data = rand(1,100)*100;
edges = 0:10:100;
binID = discretize(data,edges)
a = splitapply(@mean,data,binID)
discretize + groupsummary
Use discretize to group each value into the bins and then groupsummary to compute the mean of each group. When working with vectors, the first two arguments must be column vectors.
Note that the output vector skips empty bins. See additional outputs to groupsummary to identify which bins are represented in the first output.
s = groupsummary(data(:),binID(:),'mean')
discretize + accumarray
Use discretize to group each value into the bins and then accumarray to compute the mean of all bins.
Note that empty bins are represented by a 0.
m = accumarray(binID(:),data,[],@mean)
Comparison of these methods when some bins are empty
data = randn(100,1)+10; % expected range: ~6 : ~13
edges = 0:3:15; % 5 bins but the first two will be empty
binID = discretize(data, edges);
m = accumarray(binID,data,[],@mean)
s = groupsummary(data,binID(:),'mean')
a = splitapply(@mean,data,binID)
7 个评论
Haley Royer
2023-3-10
Adam Danz
2023-3-10
Yes, the second input argument for discretize is a vector of bin edges which you can define any way you'd like. If you're happy with your histogram bin edges, you can use the same edges to discretize your data.
Torsten
2023-3-10
If you had read the documentation of "discretize", you would know that the parameter "edges" is exactly what you are asking for.
Haley Royer
2023-3-10
Haley Royer
2023-3-10
Haley Royer
2023-3-11
Adam Danz
2023-3-11
Let's keep it civil here.
As you mentioned, if one of the bins have no values, then splitapply won't work.
I'll add alternatives to my answer.
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