{"id":489,"date":"2023-07-29T17:59:32","date_gmt":"2023-07-29T17:59:32","guid":{"rendered":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84%e5%b9%b4%e9%be%84%e9%87%91%e5%ad%97%e5%a1%94\/"},"modified":"2023-07-29T17:59:32","modified_gmt":"2023-07-29T17:59:32","slug":"r%e4%b8%ad%e7%9a%84%e5%b9%b4%e9%be%84%e9%87%91%e5%ad%97%e5%a1%94","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84%e5%b9%b4%e9%be%84%e9%87%91%e5%ad%97%e5%a1%94\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u521b\u5efa\u4eba\u53e3\u91d1\u5b57\u5854"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u4eba\u53e3\u91d1\u5b57\u5854<\/strong>\u662f\u663e\u793a\u7ed9\u5b9a\u4eba\u53e3\u7684\u5e74\u9f84\u548c\u6027\u522b\u5206\u5e03\u7684\u56fe\u8868\u3002\u8fd9\u662f\u4e00\u4e2a\u6709\u7528\u7684\u56fe\u8868\uff0c\u53ef\u4ee5\u8f7b\u677e\u4e86\u89e3\u4eba\u53e3\u6784\u6210\u4ee5\u53ca\u5f53\u524d\u7684\u4eba\u53e3\u589e\u957f\u8d8b\u52bf\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5982\u679c\u4eba\u53e3\u91d1\u5b57\u5854\u5448\u77e9\u5f62\uff0c\u5219\u8868\u660e\u4eba\u53e3\u589e\u957f\u901f\u5ea6\u8f83\u6162\uff1b\u8001\u4e00\u4ee3\u88ab\u5927\u5c0f\u5927\u81f4\u76f8\u540c\u7684\u65b0\u4e00\u4ee3\u6240\u53d6\u4ee3\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5982\u679c\u4eba\u53e3\u91d1\u5b57\u5854\u7684\u5f62\u72b6\u50cf\u91d1\u5b57\u5854\uff0c\u5219\u8868\u660e\u4eba\u53e3\u589e\u957f\u901f\u5ea6\u8f83\u5feb\uff1b\u8001\u4e00\u4ee3\u4ea7\u751f\u4e86\u65b0\u7684\u3001\u66f4\u5927\u7684\u4e00\u4ee3\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5728\u8be5\u56fe\u4e2d\uff0c\u6027\u522b\u663e\u793a\u5728\u5de6\u4fa7\u548c\u53f3\u4fa7\uff0c\u5e74\u9f84\u663e\u793a\u5728y\u8f74\u4e0a\uff0c\u4eba\u53e3\u767e\u5206\u6bd4\u6216\u6570\u91cf\u663e\u793a\u5728x\u8f74\u4e0a\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5728 R \u4e2d\u521b\u5efa\u4eba\u53e3\u91d1\u5b57\u5854\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u5728 R \u4e2d\u521b\u5efa\u4eba\u53e3\u91d1\u5b57\u5854<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b\u6570\u636e\u96c6\uff0c\u663e\u793a\u57fa\u4e8e\u5e74\u9f84\uff080 \u5230 100 \u5c81\uff09\u548c\u6027\u522b\uff08M =\u201c\u7537\u6027\u201d\uff0cF =\u201c\u5973\u6027\u201d\uff09\u7684\u4eba\u53e3\u767e\u5206\u6bd4\u6784\u6210\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#make this example reproducible<\/span>\nset.seed(1)\n\n<span style=\"color: #008080;\">#create data frame\n<\/span>data &lt;- data.frame(age = rep(1:100, 2), gender = rep(c(\"M\", \"F\"), each = 100))\n\n<span style=\"color: #008080;\">#add variable population\n<\/span>data$population &lt;- 1\/sqrt(data$age) * runif(200, 10000, 15000)\n\n<span style=\"color: #008080;\">#convert population variable to percentage\n<\/span>data$population &lt;- data$population \/ sum(data$population) * 100\n\n<span style=\"color: #008080;\">#view first six rows of dataset\n<\/span>head(data)\n\n# age gender population\n#1 1M 2.424362\n#2 2M 1.794957\n#3 3M 1.589594\n#4 4M 1.556063\n#5 5M 1.053662\n#6 6M 1.266231\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>ggplot2<\/strong>\u5e93\u4e3a\u6b64\u6570\u636e\u96c6\u521b\u5efa\u4e00\u4e2a\u57fa\u672c\u7684\u4eba\u53e3\u91d1\u5b57\u5854\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load <em>ggplot2\n<\/em><\/span>library(ggplot2)\n\n<span style=\"color: #008080;\">#create population pyramid<\/span>\nggplot(data, aes(x = age, fill = gender,\n                 y = ifelse(test = gender == \"M\",\n                            yes = -population, no = population))) + \n  geom_bar(stat = \"identity\") +\n  scale_y_continuous(labels = abs, limits = max(data$population) * c(-1,1)) +\n  coordinate_flip()<\/strong><\/pre>\n<h3><strong>\u6dfb\u52a0\u6807\u9898\u548c\u6807\u7b7e<\/strong><\/h3>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>labs()<\/strong>\u53c2\u6570\u5c06\u6807\u9898\u548c\u8f74\u6807\u7b7e\u6dfb\u52a0\u5230\u4eba\u53e3\u91d1\u5b57\u5854\u4e2d\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong>ggplot(data, aes(x = age, fill = gender,\n                 y = ifelse(test = gender == \"M\",\n                            yes = -population, no = population))) + \n  geom_bar(stat = \"identity\") +\n  scale_y_continuous(labels = abs, limits = max(data$population) * c(-1,1)) <span style=\"color: #800080;\">+\n  labs(title = \"Population Pyramid\", x = \"Age\", y = \"Percent of population\")<\/span> +\n  coordinate_flip()<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u6539\u53d8\u989c\u8272<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>scale_color_manual()<\/strong>\u53c2\u6570\u66f4\u6539\u7528\u4e8e\u8868\u793a\u6027\u522b\u7684\u4e24\u79cd\u989c\u8272\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong>ggplot(data, aes(x = age, fill = gender,\n                 y = ifelse(test = gender == \"M\",\n                            yes = -population, no = population))) + \n  geom_bar(stat = \"identity\") +\n  scale_y_continuous(labels = abs, limits = max(data$population) * c(-1,1)) +\n  labs(title = \"Population Pyramid\", x = \"Age\", y = \"Percent of population\") <span style=\"color: #800080;\">+\n  scale_color_manual(values = c(\"pink\", \"steelblue\"),<\/span>\n<span style=\"color: #800080;\">aesthetics = c(\"color\", \"fill\"))<\/span> +\n  coordinate_flip()<\/strong><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u591a\u65f6\u4ee3\u91d1\u5b57\u5854<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u8fd8\u53ef\u4ee5\u4f7f\u7528<strong>facet_wrap()<\/strong>\u53c2\u6570\u5c06\u591a\u4e2a\u4eba\u53e3\u91d1\u5b57\u5854\u7ed8\u5236\u5728\u4e00\u8d77\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u6211\u4eec\u6709<em>A\u3001B\u3001<\/em> <em>C<\/em>\u4e09\u4e2a\u56fd\u5bb6\u7684\u4eba\u53e3\u6570\u636e\u3002\u4ee5\u4e0b\u4ee3\u7801\u8bf4\u660e\u4e86\u5982\u4f55\u4e3a\u6bcf\u4e2a\u56fd\u5bb6\u521b\u5efa\u4eba\u53e3\u91d1\u5b57\u5854\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#make this example reproducible<\/span>\nset.seed(1)\n\n<span style=\"color: #008080;\">#create data frame\n<\/span>data_multiple &lt;- data.frame(age = rep(1:100, 6),\n                   gender = rep(c(\"M\", \"F\"), each = 300),\n                   country = rep(c(\"A\", \"B\", \"C\"), each = 100, times = 2))\n\n<span style=\"color: #008080;\">#add variable population\n<\/span>data_multiple$population &lt;- round(1\/sqrt(data_multiple$age)*runif(200, 10000, 15000), 0)\n\n<span style=\"color: #008080;\">#view first six rows of dataset\n<\/span>head(data_multiple)\n\n# age gender country population\n#1 1 MA 11328\n#2 2 MA 8387\n#3 3 MA 7427\n#4 4 MA 7271\n#5 5 MA 4923\n#6 6 MA 5916\n\n<span style=\"color: #008080;\">#create one population pyramid per country\n<\/span>ggplot(data_multiple, aes(x = age, fill = gender,\n                          y = ifelse(test = gender == \"M\",\n                                     yes = -population, no = population))) + \n  geom_bar(stat = \"identity\") +\n  scale_y_continuous(labels = abs, limits = max(data_multiple$population) * c(-1,1)) +\n  labs(y = \"Population Amount\") + \n  coordinate_flip() <span style=\"color: #800080;\">+\n  facet_wrap(~country) <span style=\"color: #000000;\">+<\/span>\n<span style=\"color: #000000;\">theme(axis.text.x = element_text(angle = 90, hjust = 1))<\/span> <span style=\"color: #008080;\">#rotate x-axis labels<\/span><\/span><\/strong><\/pre>\n<h2><span style=\"color: #000000;\"><strong>\u6539\u53d8\u4e3b\u9898<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u6539\u56fe\u5f62\u7684\u4e3b\u9898\u3002\u4f8b\u5982\uff0c\u4ee5\u4e0b\u4ee3\u7801\u4f7f\u7528<strong>theme_classic()<\/strong>\u4f7f\u56fe\u5f62\u770b\u8d77\u6765\u66f4\u52a0\u7b80\u7ea6\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong>ggplot(data_multiple, aes(x = age, fill = gender,\n                          y = ifelse(test = gender == \"M\",\n                                     yes = -population, no = population))) + \n  geom_bar(stat = \"identity\") +\n  scale_y_continuous(labels = abs, limits = max(data_multiple$population) * c(-1,1)) +\n  labs(y = \"Population Amount\") + \n  coordinate_flip() +\n  facet_wrap(~country) <span style=\"color: #800080;\">+<\/span>\n<span style=\"color: #800080;\">theme_classic()<\/span> + \n  theme(axis.text.x = element_text(angle = 90, hjust = 1))\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6216\u8005\u60a8\u53ef\u4ee5\u4f7f\u7528\u81ea\u5b9a\u4e49 ggthemes\u3002\u6709\u5173 ggthemes \u7684\u5b8c\u6574\u5217\u8868\uff0c\u8bf7\u53c2\u9605<\/span><a href=\"https:\/\/yutannihilation.github.io\/allYourFigureAreBelongToUs\/ggthemes\/\" target=\"_blank\" rel=\"noopener\">\u6587\u6863\u9875\u9762<\/a><span style=\"color: #000000;\">\u3002<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u4eba\u53e3\u91d1\u5b57\u5854\u662f\u663e\u793a\u7ed9\u5b9a\u4eba\u53e3\u7684\u5e74\u9f84\u548c\u6027\u522b\u5206\u5e03\u7684\u56fe\u8868\u3002\u8fd9\u662f\u4e00\u4e2a\u6709\u7528\u7684\u56fe\u8868\uff0c\u53ef\u4ee5\u8f7b\u677e\u4e86\u89e3\u4eba\u53e3\u6784\u6210\u4ee5\u53ca\u5f53\u524d\u7684\u4eba\u53e3\u589e\u957f\u8d8b\u52bf [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-489","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u5982\u4f55\u5728 R \u4e2d\u521b\u5efa\u4eba\u53e3\u91d1\u5b57\u5854 - Statorials<\/title>\n<meta name=\"description\" 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