{"id":484,"date":"2023-07-29T17:59:32","date_gmt":"2023-07-29T17:59:32","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%b2%d1%96%d0%ba%d0%be%d0%b2%d0%b0-%d0%bf%d1%96%d1%80%d0%b0%d0%bc%d1%96%d0%b4%d0%b0-%d0%b2-%d1%80\/"},"modified":"2023-07-29T17:59:32","modified_gmt":"2023-07-29T17:59:32","slug":"%d0%b2%d1%96%d0%ba%d0%be%d0%b2%d0%b0-%d0%bf%d1%96%d1%80%d0%b0%d0%bc%d1%96%d0%b4%d0%b0-%d0%b2-%d1%80","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%b2%d1%96%d0%ba%d0%be%d0%b2%d0%b0-%d0%bf%d1%96%d1%80%d0%b0%d0%bc%d1%96%d0%b4%d0%b0-%d0%b2-%d1%80\/","title":{"rendered":"\u042f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u043f\u0456\u0440\u0430\u043c\u0456\u0434\u0443 \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f \u0432 r"},"content":{"rendered":"

<\/p>\n

\n

\u041f\u043e\u043f\u0443\u043b\u044f\u0446\u0456\u0439\u043d\u0430 \u043f\u0456\u0440\u0430\u043c\u0456\u0434\u0430<\/strong> \u2014 \u0446\u0435 \u0433\u0440\u0430\u0444\u0456\u043a, \u044f\u043a\u0438\u0439 \u043f\u043e\u043a\u0430\u0437\u0443\u0454 \u0440\u043e\u0437\u043f\u043e\u0434\u0456\u043b \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f \u0437\u0430 \u0432\u0456\u043a\u043e\u043c \u0456 \u0441\u0442\u0430\u0442\u0442\u044e. \u0426\u0435 \u043a\u043e\u0440\u0438\u0441\u043d\u0430 \u0434\u0456\u0430\u0433\u0440\u0430\u043c\u0430, \u0449\u043e\u0431 \u043b\u0435\u0433\u043a\u043e \u0437\u0440\u043e\u0437\u0443\u043c\u0456\u0442\u0438 \u0441\u043a\u043b\u0430\u0434 \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f, \u0430 \u0442\u0430\u043a\u043e\u0436 \u043f\u043e\u0442\u043e\u0447\u043d\u0443 \u0442\u0435\u043d\u0434\u0435\u043d\u0446\u0456\u044e \u0437\u0440\u043e\u0441\u0442\u0430\u043d\u043d\u044f \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f.<\/span><\/p>\n

\u042f\u043a\u0449\u043e \u043f\u0456\u0440\u0430\u043c\u0456\u0434\u0430 \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f \u043c\u0430\u0454 \u043f\u0440\u044f\u043c\u043e\u043a\u0443\u0442\u043d\u0443 \u0444\u043e\u0440\u043c\u0443, \u0446\u0435 \u043e\u0437\u043d\u0430\u0447\u0430\u0454, \u0449\u043e \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f \u0437\u0440\u043e\u0441\u0442\u0430\u0454 \u043f\u043e\u0432\u0456\u043b\u044c\u043d\u0456\u0448\u0435; \u043d\u0430 \u0437\u043c\u0456\u043d\u0443 \u0441\u0442\u0430\u0440\u0438\u043c \u043f\u043e\u043a\u043e\u043b\u0456\u043d\u043d\u044f\u043c \u043f\u0440\u0438\u0445\u043e\u0434\u044f\u0442\u044c \u043d\u043e\u0432\u0456 \u043f\u043e\u043a\u043e\u043b\u0456\u043d\u043d\u044f \u043f\u0440\u0438\u0431\u043b\u0438\u0437\u043d\u043e \u0442\u0430\u043a\u043e\u0433\u043e \u0436 \u0440\u043e\u0437\u043c\u0456\u0440\u0443.<\/span><\/p>\n

\u042f\u043a\u0449\u043e \u043f\u0456\u0440\u0430\u043c\u0456\u0434\u0430 \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f \u043c\u0430\u0454 \u0444\u043e\u0440\u043c\u0443 \u043f\u0456\u0440\u0430\u043c\u0456\u0434\u0438, \u0446\u0435 \u043e\u0437\u043d\u0430\u0447\u0430\u0454, \u0449\u043e \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f \u0437\u0440\u043e\u0441\u0442\u0430\u0454 \u0448\u0432\u0438\u0434\u0448\u0435; \u0441\u0442\u0430\u0440\u0456 \u043f\u043e\u043a\u043e\u043b\u0456\u043d\u043d\u044f \u043f\u043e\u0440\u043e\u0434\u0436\u0443\u044e\u0442\u044c \u043d\u043e\u0432\u0456, \u0431\u0456\u043b\u044c\u0448\u0456 \u043f\u043e\u043a\u043e\u043b\u0456\u043d\u043d\u044f.<\/span><\/p>\n

\u041d\u0430 \u0433\u0440\u0430\u0444\u0456\u043a\u0443 \u0441\u0442\u0430\u0442\u044c \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u0430 \u043b\u0456\u0432\u043e\u0440\u0443\u0447 \u0456 \u043f\u0440\u0430\u0432\u043e\u0440\u0443\u0447, \u0432\u0456\u043a \u2013 \u043d\u0430 \u043e\u0441\u0456 y, \u0430 \u0432\u0456\u0434\u0441\u043e\u0442\u043e\u043a \u0430\u0431\u043e \u043a\u0456\u043b\u044c\u043a\u0456\u0441\u0442\u044c \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f \u2013 \u043d\u0430 \u043e\u0441\u0456 x.<\/span><\/p>\n

\u0426\u0435\u0439 \u043f\u043e\u0441\u0456\u0431\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u043f\u0456\u0440\u0430\u043c\u0456\u0434\u0443 \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f \u0432 R.<\/span><\/p>\n

\u0421\u0442\u0432\u043e\u0440\u0456\u0442\u044c \u043f\u0456\u0440\u0430\u043c\u0456\u0434\u0443 \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f \u0432 R<\/strong><\/span><\/h3>\n

\u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u0449\u043e \u043c\u0438 \u043c\u0430\u0454\u043c\u043e \u0442\u0430\u043a\u0438\u0439 \u043d\u0430\u0431\u0456\u0440 \u0434\u0430\u043d\u0438\u0445, \u044f\u043a\u0438\u0439 \u043f\u043e\u043a\u0430\u0437\u0443\u0454 \u0432\u0456\u0434\u0441\u043e\u0442\u043a\u043e\u0432\u0438\u0439 \u0441\u043a\u043b\u0430\u0434 \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f \u0437\u0430 \u0432\u0456\u043a\u043e\u043c (\u0432\u0456\u0434 0 \u0434\u043e 100 \u0440\u043e\u043a\u0456\u0432) \u0456 \u0441\u0442\u0430\u0442\u0442\u044e (M = \u00ab\u0427\u043e\u043b\u043e\u0432\u0456\u043a\u00bb, F = \u00ab\u0416\u0456\u043d\u043a\u0430\u00bb):<\/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$population) * 100\n\n#view first six rows of dataset\n<\/span>head(data)\n\n# age gender population\n#1 1M 2.424362\n#2 2M 1.794957\n#3 3M 1.589594\n#4 4M 1.556063\n#5 5M 1.053662\n#6 6M 1.266231\n<\/strong><\/pre>\n
 \u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0431\u0430\u0437\u043e\u0432\u0443 \u043f\u0456\u0440\u0430\u043c\u0456\u0434\u0443 \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f \u0434\u043b\u044f \u0446\u044c\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445 \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u0431\u0456\u0431\u043b\u0456\u043e\u0442\u0435\u043a\u0438 ggplot2<\/strong> :<\/span><\/p>\n
 #load ggplot2\n<\/em><\/span>library(ggplot2)\n\n#create population pyramid<\/span>\nggplot(data, aes(x = age, fill = gender,\n                 y = ifelse(test = gender == \"M\",\n                            yes = -population, no = population))) + \n  geom_bar(stat = \"identity\") +\n  scale_y_continuous(labels = abs, limits = max(data$population) * c(-1,1)) +\n  coordinate_flip()<\/strong><\/pre>\n
 \u0414\u043e\u0434\u0430\u0432\u0430\u043d\u043d\u044f \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0456\u0432 \u0456 \u0442\u0435\u0433\u0456\u0432<\/strong><\/h3>\n
 \u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0434\u043e\u0434\u0430\u0442\u0438 \u044f\u043a \u0437\u0430\u0433\u043e\u043b\u043e\u0432\u043a\u0438, \u0442\u0430\u043a \u0456 \u043c\u0456\u0442\u043a\u0438 \u043e\u0441\u0456 \u0434\u043e \u043f\u0456\u0440\u0430\u043c\u0456\u0434\u0438 \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0443 labs()<\/strong> :<\/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$population) * c(-1,1)) +\n  labs(title = \"Population Pyramid\", x = \"Age\", y = \"Percent of population\")<\/span> +\n  coordinate_flip()<\/strong><\/pre>\n
 \u0417\u043c\u0456\u043d\u0430 \u043a\u043e\u043b\u044c\u043e\u0440\u0456\u0432<\/strong><\/span><\/h3>\n
 \u041c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0437\u043c\u0456\u043d\u0438\u0442\u0438 \u0434\u0432\u0430 \u043a\u043e\u043b\u044c\u043e\u0440\u0438, \u044f\u043a\u0456 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0442\u044c\u0441\u044f \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u043d\u044f \u0441\u0442\u0430\u0442\u0456, \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0447\u0438 \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442 scale_color_manual()<\/strong> :<\/span><\/p>\n
 ggplot(data, aes(x = age, fill = gender,\n                 y = ifelse(test = gender == \"M\",\n                            yes = -population, no = population))) + \n  geom_bar(stat = \"identity\") +\n  scale_y_continuous(labels = abs, limits = max(data$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
 \u041a\u0456\u043b\u044c\u043a\u0430 \u0432\u0456\u043a\u043e\u0432\u0438\u0445 \u043f\u0456\u0440\u0430\u043c\u0456\u0434<\/strong><\/span><\/h2>\n
 \u0422\u0430\u043a\u043e\u0436 \u043c\u043e\u0436\u043d\u0430 \u043f\u043e\u0431\u0443\u0434\u0443\u0432\u0430\u0442\u0438 \u0440\u0430\u0437\u043e\u043c \u043a\u0456\u043b\u044c\u043a\u0430 \u043f\u0456\u0440\u0430\u043c\u0456\u0434 \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e \u0430\u0440\u0433\u0443\u043c\u0435\u043d\u0442\u0443 facet_wrap()<\/strong> . \u041d\u0430\u043f\u0440\u0438\u043a\u043b\u0430\u0434, \u043f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u0449\u043e \u043c\u0438 \u043c\u0430\u0454\u043c\u043e \u0434\u0430\u043d\u0456 \u043f\u0440\u043e \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f \u0434\u043b\u044f \u043a\u0440\u0430\u0457\u043d A, B<\/em> \u0456 C.<\/em> \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u0456\u043b\u044e\u0441\u0442\u0440\u0443\u0454, \u044f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u043f\u0456\u0440\u0430\u043c\u0456\u0434\u0443 \u043d\u0430\u0441\u0435\u043b\u0435\u043d\u043d\u044f \u0434\u043b\u044f \u043a\u043e\u0436\u043d\u043e\u0457 \u043a\u0440\u0430\u0457\u043d\u0438:<\/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$population) * c(-1,1)) +\n  labs(y = \"Population Amount\") + \n  coordinate_flip() +\n  facet_wrap(~country) +<\/span>\ntheme(axis.text.x = element_text(angle = 90, hjust = 1))<\/span> #rotate x-axis labels<\/span><\/span><\/strong><\/pre>\n
 \u0417\u043c\u0456\u043d\u0438\u0442\u0438 \u0442\u0435\u043c\u0443<\/strong><\/span><\/h2>\n
 \u041d\u0430\u0440\u0435\u0448\u0442\u0456, \u043c\u0438 \u043c\u043e\u0436\u0435\u043c\u043e \u0437\u043c\u0456\u043d\u0438\u0442\u0438 \u0442\u0435\u043c\u0443 \u0433\u0440\u0430\u0444\u0456\u043a\u0438. \u041d\u0430\u043f\u0440\u0438\u043a\u043b\u0430\u0434, \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454 theme_classic()<\/strong> , \u0449\u043e\u0431 \u0437\u0440\u043e\u0431\u0438\u0442\u0438 \u0433\u0440\u0430\u0444\u0456\u043a\u0443 \u0431\u0456\u043b\u044c\u0448 \u043c\u0456\u043d\u0456\u043c\u0430\u043b\u0456\u0441\u0442\u0438\u0447\u043d\u043e\u044e:<\/span><\/p>\n
 ggplot(data_multiple, aes(x = age, fill = gender,\n                          y = ifelse(test = gender == \"M\",\n                                     yes = -population, no = population))) + \n  geom_bar(stat = \"identity\") +\n  scale_y_continuous(labels = abs, limits = max(data_multiple$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
 \u0410\u0431\u043e \u0432\u0438 \u043c\u043e\u0436\u0435\u0442\u0435 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0441\u043f\u0435\u0446\u0456\u0430\u043b\u044c\u043d\u0456 \u0442\u0435\u043c\u0438 ggthemes. \u041f\u043e\u0432\u043d\u0438\u0439 \u0441\u043f\u0438\u0441\u043e\u043a \u0442\u0435\u043c gg \u0434\u0438\u0432. \u043d\u0430<\/span> \u0441\u0442\u043e\u0440\u0456\u043d\u0446\u0456 \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0456\u0457<\/a> .<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
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