{"id":105335,"date":"2026-09-15T10:00:22","date_gmt":"2026-09-15T02:00:22","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/105335.html"},"modified":"2026-09-15T10:00:22","modified_gmt":"2026-09-15T02:00:22","slug":"r-%e7%bb%98%e5%9b%be-%e9%a5%bc%e5%9b%be%ef%bc%9a%e4%bb%8e%e5%9f%ba%e7%a1%80%e5%88%b0%e8%bf%9b%e9%98%b6%e7%9a%84%e5%ae%8c%e6%95%b4%e6%8c%87%e5%8d%97","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/105335.html","title":{"rendered":"R \u7ed8\u56fe - \u997c\u56fe\uff1a\u4ece\u57fa\u7840\u5230\u8fdb\u9636\u7684\u5b8c\u6574\u6307\u5357"},"content":{"rendered":"<h3>1. \u5f15\u8a00<\/h3>\n<p>\u997c\u56fe&#xff08;Pie Chart&#xff09;\u662f\u6570\u636e\u53ef\u89c6\u5316\u4e2d\u6700\u76f4\u89c2\u7684\u56fe\u8868\u7c7b\u578b\u4e4b\u4e00&#xff0c;\u5b83\u901a\u8fc7\u6247\u5f62\u7684\u89d2\u5ea6\u6216\u9762\u79ef\u6765\u5c55\u793a\u5404\u90e8\u5206\u5728\u6574\u4f53\u4e2d\u6240\u5360\u7684\u6bd4\u4f8b\u3002\u5728 R \u8bed\u8a00\u4e2d&#xff0c;\u997c\u56fe\u7684\u7ed8\u5236\u65b9\u6cd5\u591a\u6837&#xff0c;\u65e2\u53ef\u4ee5\u4f7f\u7528\u57fa\u7840\u7ed8\u56fe\u7cfb\u7edf&#xff0c;\u4e5f\u53ef\u4ee5\u501f\u52a9 ggplot2 \u7b49\u9ad8\u7ea7\u7ed8\u56fe\u5305\u5b9e\u73b0\u66f4\u7f8e\u89c2\u3001\u66f4\u7075\u6d3b\u7684\u5b9a\u5236\u3002\u672c\u6587\u5c06\u7cfb\u7edf\u4ecb\u7ecd R \u4e2d\u7ed8\u5236\u997c\u56fe\u7684\u591a\u79cd\u65b9\u6cd5&#xff0c;\u5e76\u63d0\u4f9b\u4e30\u5bcc\u7684\u53ef\u8fd0\u884c\u4ee3\u7801\u5b9e\u4f8b\u3002<\/p>\n<h3>2. \u57fa\u7840\u997c\u56fe&#xff1a;pie() \u51fd\u6570<\/h3>\n<p>R \u57fa\u7840\u7ed8\u56fe\u7cfb\u7edf\u63d0\u4f9b\u4e86 pie() \u51fd\u6570&#xff0c;\u7528\u4e8e\u5feb\u901f\u7ed8\u5236\u997c\u56fe\u3002\u5176\u57fa\u672c\u8bed\u6cd5\u5982\u4e0b&#xff1a;<\/p>\n<p>pie(x, labels &#061; names(x), edges &#061; 200, radius &#061; 0.8,<br \/>\n    clockwise &#061; FALSE, init.angle &#061; if (clockwise) 90 else 0,<br \/>\n    col &#061; NULL, border &#061; NULL, main &#061; NULL)<\/p>\n<p>\u5176\u4e2d&#xff0c;x \u662f\u4e00\u4e2a\u6570\u503c\u5411\u91cf&#xff0c;labels \u7528\u4e8e\u6307\u5b9a\u6bcf\u4e2a\u6247\u533a\u7684\u6807\u7b7e&#xff0c;col \u7528\u4e8e\u8bbe\u7f6e\u989c\u8272&#xff0c;main \u7528\u4e8e\u6dfb\u52a0\u6807\u9898\u3002\u4e0b\u9762\u662f\u4e00\u4e2a\u6700\u7b80\u5355\u7684\u997c\u56fe\u793a\u4f8b&#xff1a;<\/p>\n<p># \u793a\u4f8b\u6570\u636e&#xff1a;\u67d0\u73ed\u7ea7\u5b66\u751f\u6700\u559c\u6b22\u7684\u79d1\u76ee<br \/>\nsubjects &lt;- c(\u6570\u5b66 &#061; 12, \u8bed\u6587 &#061; 8, \u82f1\u8bed &#061; 10, \u7269\u7406 &#061; 6, \u5316\u5b66 &#061; 4)<br \/>\n\u7ed8\u5236\u57fa\u7840\u997c\u56fe<br \/>\npie(subjects,<br \/>\nmain &#061; &#034;\u5b66\u751f\u6700\u559c\u6b22\u7684\u79d1\u76ee\u5206\u5e03&#034;,<br \/>\ncol &#061; c(&#034;lightblue&#034;, &#034;lightgreen&#034;, &#034;lightyellow&#034;, &#034;lightpink&#034;, &#034;lightcyan&#034;))<\/p>\n<p>\u8fd0\u884c\u4e0a\u8ff0\u4ee3\u7801\u540e&#xff0c;R \u4f1a\u751f\u6210\u4e00\u4e2a\u5e26\u6709\u9ed8\u8ba4\u6807\u7b7e\u548c\u6807\u9898\u7684\u997c\u56fe\u3002\u9700\u8981\u6ce8\u610f\u7684\u662f&#xff0c;pie() \u51fd\u6570\u9ed8\u8ba4\u6309\u6570\u636e\u5411\u91cf\u7684\u987a\u5e8f\u7ed8\u5236\u6247\u533a&#xff0c;\u4ece 3 \u70b9\u949f\u65b9\u5411\u5f00\u59cb\u9006\u65f6\u9488\u6392\u5217\u3002<\/p>\n<h3>3. \u81ea\u5b9a\u4e49\u997c\u56fe\u5916\u89c2<\/h3>\n<p>\u57fa\u7840\u997c\u56fe\u867d\u7136\u7b80\u5355&#xff0c;\u4f46\u5916\u89c2\u8f83\u4e3a\u6734\u7d20\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8c03\u6574\u53c2\u6570\u6765\u7f8e\u5316\u56fe\u8868&#xff0c;\u4f8b\u5982\u8bbe\u7f6e\u6247\u533a\u8fb9\u6846\u989c\u8272\u3001\u8c03\u6574\u8d77\u59cb\u89d2\u5ea6\u3001\u63a7\u5236\u7ed8\u5236\u65b9\u5411\u7b49\u3002<\/p>\n<h4>3.1 \u8bbe\u7f6e\u8fb9\u6846\u4e0e\u8d77\u59cb\u89d2\u5ea6<\/h4>\n<p># \u8bbe\u7f6e\u8fb9\u6846\u989c\u8272\u548c\u8d77\u59cb\u89d2\u5ea6<br \/>\npie(subjects,<br \/>\n    main &#061; &#034;\u5e26\u8fb9\u6846\u7684\u997c\u56fe&#034;,<br \/>\n    col &#061; c(&#034;steelblue&#034;, &#034;tomato&#034;, &#034;gold&#034;, &#034;mediumseagreen&#034;, &#034;orchid&#034;),<br \/>\n    border &#061; &#034;white&#034;,<br \/>\n    init.angle &#061; 90,   # \u4ece 12 \u70b9\u949f\u65b9\u5411\u5f00\u59cb<br \/>\n    clockwise &#061; TRUE)  # \u987a\u65f6\u9488\u6392\u5217<\/p>\n<h4>3.2 \u6dfb\u52a0\u767e\u5206\u6bd4\u6807\u7b7e<\/h4>\n<p>\u9ed8\u8ba4\u7684\u997c\u56fe\u6807\u7b7e\u53ea\u663e\u793a\u540d\u79f0&#xff0c;\u4e0d\u663e\u793a\u6bd4\u4f8b\u3002\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7\u8ba1\u7b97\u767e\u5206\u6bd4\u6765\u751f\u6210\u66f4\u4e30\u5bcc\u7684\u6807\u7b7e&#xff1a;<\/p>\n<p># \u8ba1\u7b97\u767e\u5206\u6bd4<br \/>\npct &lt;- round(subjects \/ sum(subjects) * 100, 1)<br \/>\nlabels &lt;- paste(names(subjects), &#034;(&#034;, pct, &#034;%)&#034;, sep &#061; &#034;&#034;)<br \/>\n\u7ed8\u5236\u5e26\u767e\u5206\u6bd4\u6807\u7b7e\u7684\u997c\u56fe<br \/>\npie(subjects,<br \/>\nlabels &#061; labels,<br \/>\nmain &#061; &#034;\u5b66\u751f\u6700\u559c\u6b22\u7684\u79d1\u76ee\u5206\u5e03&#xff08;\u542b\u767e\u5206\u6bd4&#xff09;&#034;,<br \/>\ncol &#061; c(&#034;steelblue&#034;, &#034;tomato&#034;, &#034;gold&#034;, &#034;mediumseagreen&#034;, &#034;orchid&#034;),<br \/>\nborder &#061; &#034;white&#034;,<br \/>\ncex &#061; 0.9)  # \u8c03\u6574\u6807\u7b7e\u5b57\u4f53\u5927\u5c0f<\/p>\n<h3>4. \u4f7f\u7528 ggplot2 \u7ed8\u5236\u997c\u56fe<\/h3>\n<p>ggplot2 \u662f R \u4e2d\u6700\u6d41\u884c\u7684\u6570\u636e\u53ef\u89c6\u5316\u5305&#xff0c;\u867d\u7136\u5b83\u6ca1\u6709\u76f4\u63a5\u63d0\u4f9b geom_pie() \u51fd\u6570&#xff0c;\u4f46\u53ef\u4ee5\u901a\u8fc7 coord_polar() \u5c06\u6761\u5f62\u56fe\u8f6c\u6362\u4e3a\u997c\u56fe\u3002\u8fd9\u79cd\u65b9\u6cd5\u66f4\u52a0\u7075\u6d3b&#xff0c;\u4fbf\u4e8e\u540e\u7eed\u7684\u7f8e\u5316\u548c\u5b9a\u5236\u3002<\/p>\n<h4>4.1 \u5b89\u88c5\u5e76\u52a0\u8f7d ggplot2<\/h4>\n<p># \u5b89\u88c5 ggplot2&#xff08;\u5982\u679c\u5c1a\u672a\u5b89\u88c5&#xff09;<br \/>\n# install.packages(&#034;ggplot2&#034;)<br \/>\n\u52a0\u8f7d ggplot2<br \/>\nlibrary(ggplot2)<\/p>\n<h4>4.2 \u57fa\u7840 ggplot2 \u997c\u56fe<\/h4>\n<p># \u51c6\u5907\u6570\u636e\u6846<br \/>\ndf &lt;- data.frame(<br \/>\n  subject &#061; c(&#034;\u6570\u5b66&#034;, &#034;\u8bed\u6587&#034;, &#034;\u82f1\u8bed&#034;, &#034;\u7269\u7406&#034;, &#034;\u5316\u5b66&#034;),<br \/>\n  count &#061; c(12, 8, 10, 6, 4)<br \/>\n)<br \/>\n\u7ed8\u5236\u997c\u56fe<br \/>\nggplot(df, aes(x &#061; &#034;&#034;, y &#061; count, fill &#061; subject)) &#043;<br \/>\ngeom_bar(stat &#061; &#034;identity&#034;, width &#061; 1) &#043;<br \/>\ncoord_polar(theta &#061; &#034;y&#034;) &#043;<br \/>\nlabs(title &#061; &#034;\u5b66\u751f\u6700\u559c\u6b22\u7684\u79d1\u76ee\u5206\u5e03&#xff08;ggplot2&#xff09;&#034;) &#043;<br \/>\ntheme_void()  # \u53bb\u9664\u80cc\u666f\u548c\u5750\u6807\u8f74<\/p>\n<h4>4.3 \u6dfb\u52a0\u767e\u5206\u6bd4\u6807\u7b7e<\/h4>\n<p># \u8ba1\u7b97\u767e\u5206\u6bd4<br \/>\ndf$pct &lt;- round(df$count \/ sum(df$count) * 100, 1)<br \/>\ndf$label &lt;- paste(df$subject, &#034;(&#034;, df$pct, &#034;%)&#034;, sep &#061; &#034;&#034;)<br \/>\n\u7ed8\u5236\u5e26\u6807\u7b7e\u7684\u997c\u56fe<br \/>\nggplot(df, aes(x &#061; &#034;&#034;, y &#061; count, fill &#061; subject)) &#043;<br \/>\ngeom_bar(stat &#061; &#034;identity&#034;, width &#061; 1) &#043;<br \/>\ncoord_polar(theta &#061; &#034;y&#034;) &#043;<br \/>\ngeom_text(aes(label &#061; label),<br \/>\nposition &#061; position_stack(vjust &#061; 0.5),<br \/>\nsize &#061; 4) &#043;<br \/>\nlabs(title &#061; &#034;\u5b66\u751f\u6700\u559c\u6b22\u7684\u79d1\u76ee\u5206\u5e03&#xff08;\u542b\u767e\u5206\u6bd4\u6807\u7b7e&#xff09;&#034;) &#043;<br \/>\ntheme_void()<\/p>\n<h3>5. \u73af\u5f62\u56fe&#xff08;Donut Chart&#xff09;<\/h3>\n<p>\u73af\u5f62\u56fe\u662f\u997c\u56fe\u7684\u4e00\u79cd\u53d8\u4f53&#xff0c;\u4e2d\u95f4\u6709\u4e00\u4e2a\u7a7a\u5fc3\u5706&#xff0c;\u89c6\u89c9\u6548\u679c\u66f4\u52a0\u73b0\u4ee3\u3002\u5728 ggplot2 \u4e2d&#xff0c;\u53ea\u9700\u5728\u997c\u56fe\u7684\u57fa\u7840\u4e0a\u6dfb\u52a0\u4e00\u4e2a\u7a7a\u767d\u7684\u4e2d\u5fc3\u5706\u5373\u53ef\u5b9e\u73b0\u3002<\/p>\n<p># \u7ed8\u5236\u73af\u5f62\u56fe<br \/>\nggplot(df, aes(x &#061; 2, y &#061; count, fill &#061; subject)) &#043;<br \/>\n  geom_bar(stat &#061; &#034;identity&#034;, width &#061; 1) &#043;<br \/>\n  coord_polar(theta &#061; &#034;y&#034;) &#043;<br \/>\n  geom_text(aes(label &#061; label),<br \/>\n            position &#061; position_stack(vjust &#061; 0.5),<br \/>\n            size &#061; 4) &#043;<br \/>\n  xlim(0.5, 2.5) &#043;  # \u901a\u8fc7\u9650\u5236 x \u8f74\u8303\u56f4\u5236\u9020\u7a7a\u5fc3\u6548\u679c<br \/>\n  labs(title &#061; &#034;\u5b66\u751f\u6700\u559c\u6b22\u7684\u79d1\u76ee\u5206\u5e03&#xff08;\u73af\u5f62\u56fe&#xff09;&#034;) &#043;<br \/>\n  theme_void()<\/p>\n<p>\u5728\u4e0a\u9762\u7684\u4ee3\u7801\u4e2d&#xff0c;xlim(0.5, 2.5) \u5c06 x \u8f74\u7684\u8303\u56f4\u9650\u5236\u5728 0.5 \u5230 2.5 \u4e4b\u95f4&#xff0c;\u4f7f\u5f97\u6761\u5f62\u56fe\u53ea\u5360\u636e\u5916\u5708\u90e8\u5206&#xff0c;\u4ece\u800c\u5f62\u6210\u73af\u5f62\u6548\u679c\u3002<\/p>\n<h3>6. 3D \u997c\u56fe<\/h3>\n<p>\u867d\u7136 3D \u997c\u56fe\u5728\u6570\u636e\u53ef\u89c6\u5316\u9886\u57df\u5b58\u5728\u4e00\u5b9a\u4e89\u8bae&#xff08;\u56e0\u4e3a 3D \u900f\u89c6\u4f1a\u626d\u66f2\u6bd4\u4f8b\u611f\u77e5&#xff09;&#xff0c;\u4f46\u5728\u67d0\u4e9b\u573a\u666f\u4e0b\u4ecd\u7136\u6709\u5c55\u793a\u9700\u6c42\u3002R \u4e2d\u53ef\u4ee5\u4f7f\u7528 plotrix \u5305\u7684 pie3D() \u51fd\u6570\u7ed8\u5236 3D \u997c\u56fe\u3002<\/p>\n<p># \u5b89\u88c5\u5e76\u52a0\u8f7d plotrix \u5305<br \/>\n# install.packages(&#034;plotrix&#034;)<br \/>\nlibrary(plotrix)<br \/>\n\u7ed8\u5236 3D \u997c\u56fe<br \/>\npie3D(subjects,<br \/>\nmain &#061; &#034;\u5b66\u751f\u6700\u559c\u6b22\u7684\u79d1\u76ee\u5206\u5e03&#xff08;3D \u997c\u56fe&#xff09;&#034;,<br \/>\ncol &#061; c(&#034;steelblue&#034;, &#034;tomato&#034;, &#034;gold&#034;, &#034;mediumseagreen&#034;, &#034;orchid&#034;),<br \/>\nexplode &#061; 0.1,   # \u6247\u533a\u5206\u79bb\u7a0b\u5ea6<br \/>\nlabels &#061; paste(names(subjects), pct, &#034;%&#034;, sep &#061; &#034; &#034;),<br \/>\nlabelcex &#061; 0.8)<\/p>\n<p>\u5176\u4e2d&#xff0c;explode \u53c2\u6570\u63a7\u5236\u6247\u533a\u4e4b\u95f4\u7684\u5206\u79bb\u8ddd\u79bb&#xff0c;labelcex \u7528\u4e8e\u8c03\u6574\u6807\u7b7e\u5b57\u4f53\u5927\u5c0f\u3002<\/p>\n<h3>7. \u591a\u9762\u677f\u997c\u56fe<\/h3>\n<p>\u5f53\u9700\u8981\u5bf9\u6bd4\u591a\u7ec4\u6570\u636e\u7684\u6784\u6210\u65f6&#xff0c;\u53ef\u4ee5\u4f7f\u7528 par(mfrow) \u5c06\u591a\u4e2a\u997c\u56fe\u6392\u5217\u5728\u540c\u4e00\u753b\u5e03\u4e0a\u3002<\/p>\n<p># \u4e24\u7ec4\u793a\u4f8b\u6570\u636e<br \/>\nclass_a &lt;- c(\u6570\u5b66 &#061; 12, \u8bed\u6587 &#061; 8, \u82f1\u8bed &#061; 10, \u7269\u7406 &#061; 6, \u5316\u5b66 &#061; 4)<br \/>\nclass_b &lt;- c(\u6570\u5b66 &#061; 10, \u8bed\u6587 &#061; 9, \u82f1\u8bed &#061; 12, \u7269\u7406 &#061; 5, \u5316\u5b66 &#061; 6)<br \/>\n\u8bbe\u7f6e 1 \u884c 2 \u5217\u7684\u5e03\u5c40<br \/>\npar(mfrow &#061; c(1, 2))<br \/>\n\u7ed8\u5236\u4e24\u4e2a\u997c\u56fe<br \/>\npie(class_a, main &#061; &#034;A \u73ed\u79d1\u76ee\u5206\u5e03&#034;,<br \/>\ncol &#061; c(&#034;steelblue&#034;, &#034;tomato&#034;, &#034;gold&#034;, &#034;mediumseagreen&#034;, &#034;orchid&#034;))<br \/>\npie(class_b, main &#061; &#034;B \u73ed\u79d1\u76ee\u5206\u5e03&#034;,<br \/>\ncol &#061; c(&#034;steelblue&#034;, &#034;tomato&#034;, &#034;gold&#034;, &#034;mediumseagreen&#034;, &#034;orchid&#034;))<br \/>\n\u6062\u590d\u9ed8\u8ba4\u5e03\u5c40<br \/>\npar(mfrow &#061; c(1, 1))<\/p>\n<h3>8. \u4f7f\u7528 plotly \u7ed8\u5236\u4ea4\u4e92\u5f0f\u997c\u56fe<\/h3>\n<p>\u5982\u679c\u5e0c\u671b\u997c\u56fe\u5177\u5907\u4ea4\u4e92\u80fd\u529b&#xff08;\u5982\u60ac\u505c\u663e\u793a\u6570\u503c\u3001\u70b9\u51fb\u9ad8\u4eae\u7b49&#xff09;&#xff0c;\u53ef\u4ee5\u4f7f\u7528 plotly \u5305\u3002<\/p>\n<p># \u5b89\u88c5\u5e76\u52a0\u8f7d plotly \u5305<br \/>\n# install.packages(&#034;plotly&#034;)<br \/>\nlibrary(plotly)<br \/>\n\u7ed8\u5236\u4ea4\u4e92\u5f0f\u997c\u56fe<br \/>\nplot_ly(df,<br \/>\nlabels &#061; ~subject,<br \/>\nvalues &#061; ~count,<br \/>\ntype &#061; &#034;pie&#034;,<br \/>\ntextinfo &#061; &#034;label&#043;percent&#034;,<br \/>\ntitle &#061; &#034;\u5b66\u751f\u6700\u559c\u6b22\u7684\u79d1\u76ee\u5206\u5e03&#xff08;\u4ea4\u4e92\u5f0f&#xff09;&#034;)<\/p>\n<p>\u8fd0\u884c\u4e0a\u8ff0\u4ee3\u7801\u540e&#xff0c;\u4f1a\u5728 RStudio \u7684 Viewer \u7a97\u53e3\u6216\u6d4f\u89c8\u5668\u4e2d\u6253\u5f00\u4e00\u4e2a\u53ef\u4ea4\u4e92\u7684\u997c\u56fe&#xff0c;\u9f20\u6807\u60ac\u505c\u5728\u6247\u533a\u4e0a\u5373\u53ef\u67e5\u770b\u8be6\u7ec6\u6570\u503c\u548c\u767e\u5206\u6bd4\u3002<\/p>\n<h3>9. \u997c\u56fe\u4f7f\u7528\u5efa\u8bae<\/h3>\n<p>\u997c\u56fe\u867d\u7136\u76f4\u89c2&#xff0c;\u4f46\u5728\u5b9e\u9645\u4f7f\u7528\u4e2d\u9700\u8981\u6ce8\u610f\u4ee5\u4e0b\u51e0\u70b9&#xff1a;<\/p>\n<ul>\n<li>\u6247\u533a\u6570\u91cf\u4e0d\u5b9c\u8fc7\u591a&#xff1a;\u5f53\u7c7b\u522b\u8d85\u8fc7 6 \u4e2a\u65f6&#xff0c;\u997c\u56fe\u7684\u53ef\u8bfb\u6027\u4f1a\u663e\u8457\u4e0b\u964d&#xff0c;\u5efa\u8bae\u6539\u7528\u6761\u5f62\u56fe\u3002<\/li>\n<li>\u907f\u514d 3D \u6548\u679c&#xff1a;3D \u900f\u89c6\u4f1a\u626d\u66f2\u6247\u533a\u7684\u89c6\u89c9\u9762\u79ef&#xff0c;\u5f71\u54cd\u8bfb\u8005\u5bf9\u6bd4\u4f8b\u7684\u51c6\u786e\u5224\u65ad\u3002<\/li>\n<li>\u6807\u6ce8\u767e\u5206\u6bd4&#xff1a;\u5728\u6247\u533a\u4e0a\u6807\u6ce8\u5177\u4f53\u6570\u503c\u6216\u767e\u5206\u6bd4&#xff0c;\u53ef\u4ee5\u5e2e\u52a9\u8bfb\u8005\u66f4\u51c6\u786e\u5730\u7406\u89e3\u6570\u636e\u3002<\/li>\n<li>\u5bf9\u6bd4\u591a\u7ec4\u6570\u636e\u65f6\u614e\u7528&#xff1a;\u5982\u679c\u9700\u8981\u5bf9\u6bd4\u591a\u7ec4\u6570\u636e\u7684\u6784\u6210&#xff0c;\u4f7f\u7528\u5806\u53e0\u6761\u5f62\u56fe\u6216\u5206\u9762\u6761\u5f62\u56fe\u901a\u5e38\u66f4\u6e05\u6670\u3002<\/li>\n<\/ul>\n<h3>10. \u603b\u7ed3<\/h3>\n<p>\u672c\u6587\u7cfb\u7edf\u4ecb\u7ecd\u4e86 R \u4e2d\u7ed8\u5236\u997c\u56fe\u7684\u591a\u79cd\u65b9\u6cd5&#xff0c;\u5305\u62ec\u57fa\u7840 pie() \u51fd\u6570\u3001ggplot2 \u997c\u56fe\u4e0e\u73af\u5f62\u56fe\u30013D \u997c\u56fe\u3001\u591a\u9762\u677f\u997c\u56fe\u4ee5\u53ca plotly 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