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? 内容介绍
气泡饼图是数据可视化中一种独特而有趣的图表类型。它将饼图和散点图的特点结合在一起，通过展示数据的比例和分布情况，使得观察者能够更直观地理解数据背后的含义。在本文中，我们将深入探讨气泡饼图的特点、应用场景以及如何创建和解读这种图表。
首先，让我们来了解一下气泡饼图的基本构成。与传统的饼图相比，气泡饼图在每个扇形区域内添加了一个气泡，气泡的大小代表了该区域所占比例的大小。这种设计使得气泡饼图能够同时展示数据的相对比例和绝对数值，提供了更全面的信息。
气泡饼图的应用场景非常广泛。首先，它常用于展示不同类别或组织之间的比例关系。例如，在市场调研中，我们可以使用气泡饼图来展示各个品牌的市场份额，气泡的大小代表了各个品牌的销售额。这样一来，我们可以直观地看到不同品牌之间的相对比例以及整个市场的规模。
此外，气泡饼图还可以用于展示多维数据之间的关系。例如，在人口统计学中，我们可以使用气泡饼图来展示不同年龄段的人口比例，气泡的大小可以表示该年龄段的人口数量。这样一来，我们可以通过观察气泡饼图，快速了解不同年龄段的人口分布情况。
在创建气泡饼图时，我们需要注意一些关键的步骤。首先，我们需要确定要展示的数据集，并将其按照比例转换为角度。然后，我们需要计算每个扇形区域的起始角度和结束角度。接下来，我们可以根据数据的绝对数值确定每个扇形区域内气泡的大小。最后，我们可以使用数据可视化工具或编程语言来创建气泡饼图，并添加必要的标签和图例，使得图表更易于理解和解读。
解读气泡饼图时，我们需要注意一些关键的要点。首先，我们应该关注气泡的大小，以了解不同区域所占比例的大小。其次，我们可以比较不同区域之间的气泡大小，以了解它们之间的相对比例。此外，我们还可以观察气泡的位置和分布情况，以获取更多关于数据的洞察。
总结一下，气泡饼图是一种独特而有趣的数据可视化工具，它能够同时展示数据的比例和分布情况。通过使用气泡饼图，我们可以更直观地理解数据背后的含义，并从中获取有价值的洞察。在今天数据驱动的世界中，气泡饼图无疑是一种强大的工具，帮助我们更好地理解和传达数据。无论是市场调研、人口统计学还是其他领域，气泡饼图都将成为我们分析和展示数据的重要利器。
? 部分代码
BubbleChart ExamplesA BubblePie chart visualizing the revenue of an imaginary store that sells pizza ingredients.XData = 2015:2017;YData = [50 80 65];PieData = [12,26,12; 40,15,25; 10,10,45];b = bubblePieChart(XData, YData, PieData);b.LineStyle = ‘none’;b.Labels = {‘Basil’,’Tomatoes’,’Cheese’};colororder([.47 .67 .19; .85 .33 .1; 1 1 .1]);xlabel(“Year”)ylabel(“Revenue (thousands)”)title(“Pizza Ingredient Sales”)A BubblePie chart plotting 25 pies with random data and sizes.XData = 10 * rand(1,25);YData = 10 * rand(1,25);PieData = rand(25,5);SizeData = 40 * rand(1,25) + 20;b = bubblePieChart(XData, YData, PieData, SizeData);colororder(cool(5));b.LegendVisible = ‘off’; classdef bubblePieChart < matlab.graphics.chartcontainer.ChartContainer & … matlab.graphics.chartcontainer.mixin.Legend% bubblePieChart Creates a bubble pie chart. % bubblePieChart(x,y,p) create a bubble pie chart with the specified% pie locations and data. The sizes of the pies are determined % automatically based on the pie data. %% bubblePieChart(x,y,p,s) create a bubble pie chart with % the specified size for each pie in points, where one point equals % 1/72of an inch. s can be a scalar or a vector the same length as x % and y. Ifs is a scalar, the same size is usedfor all pies. % % bubblePieChart() create a bubble pie chart using only name-value % pairs. %% bubblePieChart(___,Name,Value) specifies additional optionsfor% the bubble pie chart using one or more name-value pair arguments. % Specify the options after all other input arguments. %% bubblePieChart(parent,___) creates the bubble pie chartin the % specified parent. %% b = bubblePieChart(___) returns the bubblePieChart object. Use b % to modify properties of the plot after creating it. % Copyright 2020–2021The MathWorks, Inc. properties % x-axis locations of the pies XData (1,:) = []% y-axis locations of the pies YData (1,:) = [] % Pie data, specified as a matrix, where each row corresponds to % the data forone pie PieData {mustBeNumeric} = [] % Pie sizes, diameters in points (1/72 inch) SizeData (1,:) {mustBeNumeric} =50 % Line style to use for drawing pies LineStyle {mustBeMember(LineStyle,{‘-‘, ‘–‘,‘:’,‘-.’,‘none’})} =‘-‘ % Names of the pie categories Labels (:,1) categorical = categorical.empty(0,1)% Title of the plot Title (:,1) string = “” % Subtitle of the plot Subtitle (:,1) string = “” % x-label of the plotXLabel (:,1) string = “” % y-label of the plot YLabel (:,1) string = “” % Mode for the x-limits.% Note that it is not a dependent property since auto limits are % set by the chart and not the axes XLimitsMode (1,:) char {mustBeAutoManual} =‘auto’ % Mode for the y-limits. YLimitsMode (1,:) char {mustBeAutoManual} = ‘auto’ end properties (Access = protected) % Used for saving to .fig files ChartState = [] endproperties(Access = private,Transient,NonCopyable) % Array of parent transforms for the pies PieChartArray (1,:) matlab.graphics.primitive.Transform % Boolean specifying if PieData was changed since the previous call% to update.If true, all pies need to be redrawn. PieDataChanged logical = true end properties (Dependent)% List of colors to usefor pie categories ColorOrder {validatecolor(ColorOrder, ‘multiple’)} = get(groot, ‘factoryAxesColorOrder’) % x-limits of the plot XLimits (1,2) double {mustBeLimits} = [0 1]% y-limits of the plot YLimits (1,2) double {mustBeLimits} = [0 1] end methods function obj = bubblePieChart(varargin)% Initialize list of arguments args = varargin; leadingArgs = cell(0); % Check ifthe first input argument is a graphics object to use as parent. if ~isempty(args) && isa(args{1},‘matlab.graphics.Graphics’)% bubblePieChart(parent, ___) leadingArgs = args(1); args = args(2:end); end % Check foroptional positional arguments. if ~isempty(args) && numel(args) >= 3 && isnumeric(args{1}) … && isnumeric(args{2}) && isnumeric(args{3}) if mod(numel(args),2) == 1 % bubblePieChart(x,y,p) % bubblePieChart(x,y,p,Name,Value)x = args{1}; y = args{2}; p = args{3}; % set size automatically. The largest pie has size 50% and the others are scaled relative to it totals = sum(p,2); ratios = totals/max(totals); s = 50*ratios;leadingArgs = [leadingArgs {‘XData’, x, ‘YData’, y, ‘PieData’, p, ‘SizeData’, s}]; args = args(4:end); elseifmod(numel(args),1) == 0 % bubblePieChart(x,y,p,s) % bubblePieChart(x,y,p,s,Name,Value) x = args{1}; y = args{2};p = args{3}; s = args{4}; leadingArgs = [leadingArgs {‘XData’, x, ‘YData’, y, ‘PieData’, p, ‘SizeData’, s}];args = args(5:end); else error(‘bubblePieChart:InvalidSyntax’, … ‘Specify x locations, y locations, pie data, and optionally size data.’); end end % Combine positional arguments with name/value pairs.args = [leadingArgs args]; % Call superclass constructor methodobj@matlab.graphics.chartcontainer.ChartContainer(args{:}); end end methods(Access = protected) function setup(obj) % Create the axes ax = getAxes(obj);ax.Units =‘points’; % make limit mode manual so that we can control the limits ax.XLimMode = ‘manual’;ax.YLimMode =‘manual’; % Set axes interactions ax.Interactions = [ panInteraction; zoomInteraction;rulerPanInteraction]; % Set restoreview button callback btn = axtoolbarbtn(axtoolbar(ax), ‘icon’, ‘restoreview’); btn.ButtonPushedFcn = @(~,~) update(obj); % Call the load method in case of loading from a fig fileloadstate(obj); end function update(obj) ax = getAxes(obj); % Verify that the dataproperties are consistent with one % another. showChart = verifyDataProperties(obj); set(obj.PieChartArray,‘Visible’, showChart);% Abort earlyif not visible due to invalid data. if ~showChart return end % If pie datais changed, delete and recreate all pies if obj.PieDataChanged delete(obj.PieChartArray); hold(ax,‘on’); for r = 1:size(obj.PieData,1) % Create new Transform t = hgtransform(‘Parent’,ax); obj.PieChartArray(r) = t;% Create new pie with transform as parent x = obj.PieData(r,:); myPie(t,x); end hold(ax,‘off’) obj.PieDataChanged = false; end% Set only the first pie chart to showin the legend obj.PieChartArray(1).Annotation.LegendInformation.IconDisplayStyle =‘children’; % Update legend labels if ~isempty(obj.Labels) lgd = getLegend(obj);lgd.String = obj.Labels; end % Set Colormap based on ColorOrder ax.Colormap = obj.ColorOrder(mod(0:size(obj.PieData,2)-1,size(obj.ColorOrder,1))+1,:); % Automatically set axes limits if strcmp(obj.XLimitsMode,‘auto’) obj.setAutoXLimits(); end if strcmp(obj.YLimitsMode,‘auto’) obj.setAutoYLimits(); end% Set position, scale, and style of each pie for i = 1:length(obj.PieChartArray) % move and scale pies txy = makehgtform(‘translate’, … [obj.XData(i), obj.YData(i), 0]); % Determine scale to use % divide by 2since SizeData corresponds to diameter, and % default pies have radius of 1 if isscalar(obj.SizeData)scale = obj.SizeData/2; else scale = obj.SizeData(i)/2; end % Convert scale from axis units to data units sx = (ax.XLim(2) – ax.XLim(1))*scale/ax.Position(3); sy = (ax.YLim(2) – ax.YLim(1))*scale/ax.Position(4);sxy = makehgtform(‘scale’,[sx, sy, 1]); obj.PieChartArray(i).Matrix = txy * sxy;patches = findall(obj.PieChartArray(i),‘Type’, ‘Patch’); set(patches,‘LineStyle’,obj.LineStyle); end % Set titletitle(ax, obj.Title, obj.Subtitle); % Set axis labels xlabel(ax, obj.XLabel); ylabel(ax, obj.YLabel); end functionshowChart = verifyDataProperties(obj) % x and y must be the same length. showChart = numel(obj.XData) == numel(obj.YData); if ~showChart warning(‘bubblePieChart:DataLengthMismatch’,… ‘XData and YData must be the same legnth’); return end % PieData must have the same number of rows as the length of xshowChart = size(obj.PieData,1) == numel(obj.XData); if ~showChart warning(‘bubblePieChart:DataLengthMismatch’,… ‘PieData must have the same number of rows as XData.’); return end% SizeData must be a scalar or be the same length as x showChart = isscalar(obj.SizeData) || numel(obj.SizeData) == numel(obj.XData); if ~showChart warning(‘bubblePieChart:DataLengthMismatch’,… ‘SizeData must be a scalar or have the same number of rows as XData.’); return end end end methods function data = get.ChartState(obj)% This method gets called when a .fig file is saved isLoadedStateAvailable = ~isempty(obj.ChartState); if isLoadedStateAvailable data= obj.ChartState; else data = struct; ax = getAxes(obj); % Get axis limits only if mode is manual. ifstrcmp(ax.XLimMode,‘manual’) data.XLim = ax.XLim; end if strcmp(ax.YLimMode,‘manual’) data.YLim = ax.YLim; end end end function loadstate(obj) % Call this method from setup to handle loading of .fig files data=obj.ChartState;ax = getAxes(obj); % Look for states that changed if isfield(data, ‘XLim’) ax.XLim=data.XLim; end if isfield(data, ‘YLim’) ax.YLim=data.YLim; end end function set.PieData(obj,val) obj.PieData = val;obj.PieDataChanged = true; end function updateNow(obj) update(obj); end function set.ColorOrder(obj, map) ax = getAxes(obj);ax.ColorOrder = validatecolor(map,‘multiple’); end function map = get.ColorOrder(obj) ax = getAxes(obj);map = ax.ColorOrder; end % xlim method function varargout = xlim(obj,varargin) ax = getAxes(obj); [varargout{1:nargout}] = xlim(ax,varargin{:}); end % ylim method function varargout = ylim(obj,varargin)ax = getAxes(obj); [varargout{1:nargout}] = ylim(ax,varargin{:}); end % set and get methods for XLimits functionset.XLimits(obj,xlm) ax = getAxes(obj); ax.XLim = xlm; end function xlm = get.XLimits(obj) ax = getAxes(obj);xlm = ax.XLim; end % set and get methods for YLimits function set.YLimits(obj,ylm) ax = getAxes(obj);ax.YLim = ylm; end function ylm = get.YLimits(obj) ax = getAxes(obj); ylm = ax.YLim; end end methods(Access=private)% Helperfunction for auotmatically setting x-limits function setAutoXLimits(obj) ax = getAxes(obj);minX = min(obj.XData); maxX = max(obj.XData); if(minX==maxX) minX = minX-1; maxX = maxX+1; endmaxRadius = min(max(obj.SizeData)/2, ax.Position(3)/3); A = [-maxRadius+ax.Position(3) maxRadius; maxRadius ax.Position(3)-maxRadius]; b = [minX*ax.Position(3); maxX*ax.Position(3)]; xlimits = Ab; obj.XLimits = xlimits; end% Helperfunction for auotmatically setting y-limits function setAutoYLimits(obj) ax = getAxes(obj);minY = min(obj.YData); maxY = max(obj.YData); if(minY==maxY) minY = minY-1; maxY = maxY+1; endmaxRadius = min(max(obj.SizeData)/2, ax.Position(4)/3); A = [-maxRadius+ax.Position(4) maxRadius;maxRadius ax.Position(4)-maxRadius]; b = [minY*ax.Position(4); maxY*ax.Position(4)]; ylimits = Ab; obj.YLimits = ylimits; end endendfunction mustBeLimits(a) if numel(a) ~= 2 || a(2) <= a(1) throwAsCaller(MException(‘densityScatterChart:InvalidLimits’, ‘Specify limits as two increasing values.’)) endendfunction mustBeAutoManual(mode)mustBeMember(mode, {‘auto’,‘manual’})end% Helper function for creating pies baesd on MATLAB‘s pie functionfunction h = myPie(ax,x) % Normalize input data x = x/sum(x); h = []; theta0 = pi/2; maxpts = 100;for i=1:length(x) n = max(1,ceil(maxpts*x(i))); r = [0;ones(n+1,1);0]; theta = theta0 + [0;x(i)*(0:n)’/n;0]*2*pi; [xx,yy] = pol2cart(theta,r); theta0 = max(theta); h = [h,… patch(‘XData’,xx,‘YData’,yy,‘CData’,i*ones(size(xx)), … ‘FaceColor’,‘Flat’,‘parent’,ax)]; %#ok<AGROW> endend⛳️ 运行结果
? 参考文献
[1] 张蕾.基于神经网络的地区配变重过载预测研究[J].陕西理工大学, 2019.
[2] 俞建荣,卜凡亮,曹建树,等.基于Matlab的流化床气泡运动的图像识别与处理[J].仪器仪表学报, 2006(z3):2.DOI:10.3321/j.issn:0254-3087.2006.z3.215.
[3] 王寻,王宏哲,张泽坤,等.基于Matlab GUI的气泡动力学仿真系统设计[J].实验室研究与探索, 2022(004):041.
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