{"id":486,"date":"2023-07-29T17:59:32","date_gmt":"2023-07-29T17:59:32","guid":{"rendered":"https:\/\/statorials.org\/ja\/r%e3%81%ae%e5%b9%b4%e9%bd%a2%e3%83%92%e3%82%9a%e3%83%a9%e3%83%9f%e3%83%83%e3%83%88%e3%82%99\/"},"modified":"2023-07-29T17:59:32","modified_gmt":"2023-07-29T17:59:32","slug":"r%e3%81%ae%e5%b9%b4%e9%bd%a2%e3%83%92%e3%82%9a%e3%83%a9%e3%83%9f%e3%83%83%e3%83%88%e3%82%99","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r%e3%81%ae%e5%b9%b4%e9%bd%a2%e3%83%92%e3%82%9a%e3%83%a9%e3%83%9f%e3%83%83%e3%83%88%e3%82%99\/","title":{"rendered":"R \u3067\u4eba\u53e3\u30d4\u30e9\u30df\u30c3\u30c9\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

<\/p>\n

\n

\u4eba\u53e3\u30d4\u30e9\u30df\u30c3\u30c9\u306f\u3001<\/strong>\u7279\u5b9a\u306e\u4eba\u53e3\u306e\u5e74\u9f62\u3068\u6027\u5225\u306e\u5206\u5e03\u3092\u793a\u3059\u30b0\u30e9\u30d5\u3067\u3059\u3002\u4eba\u53e3\u69cb\u6210\u3084\u73fe\u5728\u306e\u4eba\u53e3\u5897\u52a0\u50be\u5411\u3092\u7c21\u5358\u306b\u628a\u63e1\u3059\u308b\u306e\u306b\u4fbf\u5229\u306a\u30b0\u30e9\u30d5\u3067\u3059\u3002<\/span><\/p>\n

\u4eba\u53e3\u30d4\u30e9\u30df\u30c3\u30c9\u304c\u9577\u65b9\u5f62\u306e\u5834\u5408\u3001\u4eba\u53e3\u306e\u5897\u52a0\u901f\u5ea6\u304c\u9045\u3044\u3053\u3068\u3092\u793a\u3057\u307e\u3059\u3002\u53e4\u3044\u4e16\u4ee3\u306f\u3001\u307b\u307c\u540c\u3058\u30b5\u30a4\u30ba\u306e\u65b0\u3057\u3044\u4e16\u4ee3\u306b\u7f6e\u304d\u63db\u3048\u3089\u308c\u307e\u3059\u3002<\/span><\/p>\n

\u4eba\u53e3\u30d4\u30e9\u30df\u30c3\u30c9\u304c\u30d4\u30e9\u30df\u30c3\u30c9\u306e\u3088\u3046\u306a\u5f62\u3092\u3057\u3066\u3044\u308b\u5834\u5408\u3001\u305d\u308c\u306f\u4eba\u53e3\u304c\u3088\u308a\u901f\u3044\u901f\u5ea6\u3067\u5897\u52a0\u3057\u3066\u3044\u308b\u3053\u3068\u3092\u793a\u3057\u307e\u3059\u3002\u53e4\u3044\u4e16\u4ee3\u306f\u3001\u3088\u308a\u5927\u304d\u306a\u65b0\u3057\u3044\u4e16\u4ee3\u3092\u751f\u307f\u51fa\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u30b0\u30e9\u30d5\u3067\u306f\u3001\u5de6\u5074\u3068\u53f3\u5074\u306b\u6027\u5225\u3001Y \u8ef8\u306b\u5e74\u9f62\u3001X \u8ef8\u306b\u4eba\u53e3\u306e\u5272\u5408\u307e\u305f\u306f\u4eba\u53e3\u304c\u8868\u793a\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u4eba\u53e3\u30d4\u30e9\u30df\u30c3\u30c9\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n

R \u3067\u4eba\u53e3\u30d4\u30e9\u30df\u30c3\u30c9\u3092\u4f5c\u6210\u3059\u308b<\/strong><\/span><\/h3>\n

\u5e74\u9f62 (0 \uff5e 100 \u6b73) \u3068\u6027\u5225 (M = \u300c\u7537\u6027\u300d\u3001F = \u300c\u5973\u6027\u300d) \u306b\u57fa\u3065\u3044\u305f\u4eba\u53e3\u306e\u69cb\u6210\u5272\u5408\u3092\u793a\u3059\u6b21\u306e\u30c7\u30fc\u30bf \u30bb\u30c3\u30c8\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n

 #make this example reproducible<\/span>\nset.seed(1)\n\n#create data frame\n<\/span>data <- data.frame(age = rep(1:100, 2), gender = rep(c(\"M\", \"F\"), each = 100))\n\n#add variable population\n<\/span>data$population <- 1\/sqrt(data$age) * runif(200, 10000, 15000)\n\n#convert population variable to percentage\n<\/span>data$population <- data$population \/ sum(data$population) * 100\n\n#view first six rows of dataset\n<\/span>head(data)\n\n# age gender population\n#1 1M 2.424362\n#2 2M 1.794957\n#3 3M 1.589594\n#4 4M 1.556063\n#5 5M 1.053662\n#6 6M 1.266231\n<\/strong><\/pre>\n
 ggplot2<\/strong>\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u7528\u3057\u3066\u3001\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u57fa\u672c\u7684\u306a\u4eba\u53e3\u30d4\u30e9\u30df\u30c3\u30c9\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #load ggplot2\n<\/em><\/span>library(ggplot2)\n\n#create population pyramid<\/span>\nggplot(data, aes(x = age, fill = gender,\n                 y = ifelse(test = gender == \"M\",\n                            yes = -population, no = population))) + \n  geom_bar(stat = \"identity\") +\n  scale_y_continuous(labels = abs, limits = max(data$population) * c(-1,1)) +\n  coordinate_flip()<\/strong><\/pre>\n
\u30bf\u30a4\u30c8\u30eb\u3068\u30bf\u30b0\u3092\u8ffd\u52a0\u3059\u308b<\/strong><\/h3>\n
labs()<\/strong>\u5f15\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30bf\u30a4\u30c8\u30eb\u3068\u8ef8\u30e9\u30d9\u30eb\u306e\u4e21\u65b9\u3092\u4eba\u53e3\u30d4\u30e9\u30df\u30c3\u30c9\u306b\u8ffd\u52a0\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 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$population) * c(-1,1)) +\n  labs(title = \"Population Pyramid\", x = \"Age\", y = \"Percent of population\")<\/span> +\n  coordinate_flip()<\/strong><\/pre>\n
\u8272\u306e\u5909\u66f4<\/strong><\/span><\/h3>\n
scale_color_manual()<\/strong>\u5f15\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u6027\u5225\u3092\u8868\u3059\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u308b 2 \u3064\u306e\u8272\u3092\u5909\u66f4\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 ggplot(data, aes(x = age, fill = gender,\n                 y = ifelse(test = gender == \"M\",\n                            yes = -population, no = population))) + \n  geom_bar(stat = \"identity\") +\n  scale_y_continuous(labels = abs, limits = max(data$population) * c(-1,1)) +\n  labs(title = \"Population Pyramid\", x = \"Age\", y = \"Percent of population\") +\n  scale_color_manual(values = c(\"pink\", \"steelblue\"),<\/span>\naesthetics = c(\"color\", \"fill\"))<\/span> +\n  coordinate_flip()<\/strong><\/pre>\n
\u8907\u6570\u306e\u5e74\u9f62\u30d4\u30e9\u30df\u30c3\u30c9<\/strong><\/span><\/h2>\n
facet_wrap()<\/strong>\u5f15\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u8907\u6570\u306e\u4eba\u53e3\u30d4\u30e9\u30df\u30c3\u30c9\u3092\u4e00\u7dd2\u306b\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002\u305f\u3068\u3048\u3070\u3001 A\u3001B\u3001<\/em> C<\/em>\u56fd\u306e\u4eba\u53e3\u30c7\u30fc\u30bf\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u5404\u56fd\u306e\u4eba\u53e3\u30d4\u30e9\u30df\u30c3\u30c9\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 #make this example reproducible<\/span>\nset.seed(1)\n\n#create data frame\n<\/span>data_multiple <- 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#add variable population\n<\/span>data_multiple$population <- round(1\/sqrt(data_multiple$age)*runif(200, 10000, 15000), 0)\n\n#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#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$population) * c(-1,1)) +\n  labs(y = \"Population Amount\") + \n  coordinate_flip() +\n  facet_wrap(~country) +<\/span>\ntheme(axis.text.x = element_text(angle = 90, hjust = 1))<\/span> #rotate x-axis labels<\/span><\/span><\/strong><\/pre>\n
\u30c6\u30fc\u30de\u306e\u5909\u66f4<\/strong><\/span><\/h2>\n
\u6700\u5f8c\u306b\u3001\u30b0\u30e9\u30d5\u30a3\u30c3\u30af\u306e\u30c6\u30fc\u30de\u3092\u5909\u66f4\u3067\u304d\u307e\u3059\u3002\u305f\u3068\u3048\u3070\u3001\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001 theme_classic()<\/strong>\u3092\u4f7f\u7528\u3057\u3066\u3001\u30b0\u30e9\u30d5\u30a3\u30c3\u30af\u3092\u3088\u308a\u30b7\u30f3\u30d7\u30eb\u306b\u898b\u305b\u307e\u3059\u3002<\/span><\/p>\n
 ggplot(data_multiple, aes(x = age, fill = gender,\n                          y = ifelse(test = gender == \"M\",\n                                     yes = -population, no = population))) + \n  geom_bar(stat = \"identity\") +\n  scale_y_continuous(labels = abs, limits = max(data_multiple$population) * c(-1,1)) +\n  labs(y = \"Population Amount\") + \n  coordinate_flip() +\n  facet_wrap(~country) +<\/span>\ntheme_classic()<\/span> + \n  theme(axis.text.x = element_text(angle = 90, hjust = 1))\n<\/strong><\/pre>\n
\u307e\u305f\u306f\u3001\u30ab\u30b9\u30bf\u30e0 gg \u30c6\u30fc\u30de\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002 ggtheme \u306e\u5b8c\u5168\u306a\u30ea\u30b9\u30c8\u306b\u3064\u3044\u3066\u306f\u3001<\/span> \u30c9\u30ad\u30e5\u30e1\u30f3\u30c8 \u30da\u30fc\u30b8\u3092<\/a>\u53c2\u7167\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
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