dotplot

dotPlot.m

function [sh, lh, mp] = dotPlot(X, G, Wid, med, beas)
% [sh, lh, mp] = dotPlot(X, G, Wid, med, beas)
%
% load fisheriris.mat
% [sh, lh, mp] = dotPlot(meas(:,3), species, .25, false, true)
% 
% input: 
%   X: data
%   G: Group
%   Wid: width of dot distribution (default: .35)
%   med: flag of lineplot by median (default: false, mean)
%
% output:
%   sh: handle of scatter plot
%   lh: handle of mean (median) line plot 
%   mp: mean (median) value of each group
%
% Ryosuke F Takeuchi 2017

switch nargin
	case 2
		Wid = .35;
		med = 0;
		beas= 0;
	case 3
		med = 0;
		beas = 0;
	case 4
		beas = 0;
end

xvar = unique(G);
for i = 1:length(xvar)
	if iscell(xvar)
		xx =  strcmp(G, xvar{i});
	else
		xx = G==xvar(i);
	end	
	[xa, Xo(xx)] = dotw(X(xx), Wid);
		
	if med
		mp(i) = nanmedian(X(xx));
	else 
		mp(i) = nanmean(X(xx));
	end
	
	if ~beas
		Xo(xx)=X(xx);		
	end
	
	sh(i) = scatter(i+xa, Xo(xx), '.', 'SizeData', 60); hold on;
	lh(i) = plot(i+[-1.4 1.4]*Wid, mp(i)*[1 1], ...
		'k', 'LineWidth', 1.75);
end

if iscell(xvar)
	set(gca, 'XTick', 1:length(xvar), 'XTickLabel', xvar);
end

end

function [xb, Xo] = dotw(X, Width)
xb = X*0;
Xo = X;
[N, Edge] = histcounts(X, round(length(X)/3)); 
Nn = Width*N/max(N);

for i = 1:length(N)
	poi = X >= Edge(i) & X < Edge(i+1);	
	if i == length(N)
		poi = X >= Edge(i) & X <= Edge(i+1);	
	end	
	xb(poi) = linspace(-Nn(i), Nn(i), N(i));
	if sum(poi) ==1
		xb(poi) = 0;
	end
	tmpX = X(poi);
	tmpX = sort(tmpX);
	Xo(poi) = tmpX(:);

	% Order in bin
	tmpX = X(poi);
	tmpX  = sort(tmpX);
	tmpxb = xb(poi);
	[~, I] = sort(abs(tmpxb));	
	Xo(poi) = tmpX;
	xb(poi) = sign(tmpxb(I)).*sort(abs(tmpxb))...
		-median(sign(tmpxb(I)).*sort(abs(tmpxb)));
end
end