{"id":486,"date":"2023-07-29T17:59:32","date_gmt":"2023-07-29T17:59:32","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d9%87%d8%b1%d9%85-%d8%a7%d9%84%d8%b9%d9%85%d8%b1%d9%8a-%d9%81%d9%8a-%d8%b5\/"},"modified":"2023-07-29T17:59:32","modified_gmt":"2023-07-29T17:59:32","slug":"%d8%a7%d9%84%d9%87%d8%b1%d9%85-%d8%a7%d9%84%d8%b9%d9%85%d8%b1%d9%8a-%d9%81%d9%8a-%d8%b5","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d9%87%d8%b1%d9%85-%d8%a7%d9%84%d8%b9%d9%85%d8%b1%d9%8a-%d9%81%d9%8a-%d8%b5\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 \u0627\u0644\u0647\u0631\u0645 \u0627\u0644\u0633\u0643\u0627\u0646\u064a \u0641\u064a r"},"content":{"rendered":"

<\/p>\n

\n

\u0627\u0644\u0647\u0631\u0645 \u0627\u0644\u0633\u0643\u0627\u0646\u064a<\/strong> \u0647\u0648 \u0631\u0633\u0645 \u0628\u064a\u0627\u0646\u064a \u064a\u0648\u0636\u062d \u0627\u0644\u0639\u0645\u0631 \u0648\u0627\u0644\u062a\u0648\u0632\u064a\u0639 \u0627\u0644\u062c\u0646\u0633\u064a \u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0633\u0643\u0627\u0646\u064a\u0629 \u0645\u0639\u064a\u0646\u0629. \u064a\u0639\u062f \u0647\u0630\u0627 \u0645\u062e\u0637\u0637\u064b\u0627 \u0645\u0641\u064a\u062f\u064b\u0627 \u0644\u0641\u0647\u0645 \u062a\u0643\u0648\u064a\u0646 \u0627\u0644\u0633\u0643\u0627\u0646 \u0628\u0627\u0644\u0625\u0636\u0627\u0641\u0629 \u0625\u0644\u0649 \u0627\u062a\u062c\u0627\u0647 \u0627\u0644\u0646\u0645\u0648 \u0627\u0644\u0633\u0643\u0627\u0646\u064a \u0627\u0644\u062d\u0627\u0644\u064a \u0628\u0633\u0647\u0648\u0644\u0629.<\/span><\/p>\n

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\u0625\u0630\u0627 \u0643\u0627\u0646 \u0627\u0644\u0647\u0631\u0645 \u0627\u0644\u0633\u0643\u0627\u0646\u064a \u0639\u0644\u0649 \u0634\u0643\u0644 \u0647\u0631\u0645\u060c \u0641\u0647\u0630\u0627 \u064a\u0634\u064a\u0631 \u0625\u0644\u0649 \u0623\u0646 \u0639\u062f\u062f \u0627\u0644\u0633\u0643\u0627\u0646 \u064a\u0646\u0645\u0648 \u0628\u0645\u0639\u062f\u0644 \u0623\u0633\u0631\u0639\u061b \u0627\u0644\u0623\u062c\u064a\u0627\u0644 \u0627\u0644\u0623\u0643\u0628\u0631 \u0633\u0646\u0627 \u062a\u0646\u062a\u062c \u0623\u062c\u064a\u0627\u0644\u0627 \u062c\u062f\u064a\u062f\u0629 \u0623\u0643\u0628\u0631.<\/span><\/p>\n

\u0641\u064a \u0627\u0644\u0631\u0633\u0645 \u0627\u0644\u0628\u064a\u0627\u0646\u064a\u060c \u064a\u0638\u0647\u0631 \u0627\u0644\u062c\u0646\u0633 \u0639\u0644\u0649 \u0627\u0644\u062c\u0627\u0646\u0628\u064a\u0646 \u0627\u0644\u0623\u064a\u0633\u0631 \u0648\u0627\u0644\u0623\u064a\u0645\u0646\u060c \u0648\u064a\u0638\u0647\u0631 \u0627\u0644\u0639\u0645\u0631 \u0639\u0644\u0649 \u0627\u0644\u0645\u062d\u0648\u0631 \u0635\u060c \u0648\u0627\u0644\u0646\u0633\u0628\u0629 \u0627\u0644\u0645\u0626\u0648\u064a\u0629 \u0623\u0648 \u0639\u062f\u062f \u0627\u0644\u0633\u0643\u0627\u0646 \u0639\u0644\u0649 \u0627\u0644\u0645\u062d\u0648\u0631 \u0633.<\/span><\/p>\n

\u064a\u0634\u0631\u062d \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 \u0627\u0644\u0647\u0631\u0645 \u0627\u0644\u0633\u0643\u0627\u0646\u064a \u0641\u064a R.<\/span><\/p>\n

\u0625\u0646\u0634\u0627\u0621 \u0627\u0644\u0647\u0631\u0645 \u0627\u0644\u0633\u0643\u0627\u0646\u064a \u0641\u064a R<\/strong><\/span><\/h3>\n

\u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646 \u0644\u062f\u064a\u0646\u0627 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0627\u0644\u062a\u064a \u062a\u0648\u0636\u062d \u0627\u0644\u0646\u0633\u0628\u0629 \u0627\u0644\u0645\u0626\u0648\u064a\u0629 \u0644\u062a\u0643\u0648\u064a\u0646 \u0627\u0644\u0633\u0643\u0627\u0646 \u0628\u0646\u0627\u0621\u064b \u0639\u0644\u0649 \u0627\u0644\u0639\u0645\u0631 (\u0645\u0646 0 \u0625\u0644\u0649 100 \u0639\u0627\u0645) \u0648\u0627\u0644\u062c\u0646\u0633 (M = “\u0630\u0643\u0631”\u060c F = “\u0623\u0646\u062b\u0649”):<\/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$population) * 100\n\n#view first six rows of dataset\n<\/span>head(data)\n\n# age gender population\n#1 1M 2.424362\n#2 2M 1.794957\n#3 3M 1.589594\n#4 4M 1.556063\n#5 5M 1.053662\n#6 6M 1.266231\n<\/strong><\/pre>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0625\u0646\u0634\u0627\u0621 \u0647\u0631\u0645 \u0633\u0643\u0627\u0646\u064a \u0623\u0633\u0627\u0633\u064a \u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0647\u0630\u0647 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0645\u0643\u062a\u0628\u0629 ggplot2<\/strong> :<\/span><\/p>\n
 #load ggplot2\n<\/em><\/span>library(ggplot2)\n\n#create population pyramid<\/span>\nggplot(data, aes(x = age, fill = gender,\n                 y = ifelse(test = gender == \"M\",\n                            yes = -population, no = population))) + \n  geom_bar(stat = \"identity\") +\n  scale_y_continuous(labels = abs, limits = max(data$population) * c(-1,1)) +\n  coordinate_flip()<\/strong><\/pre>\n
 \u0625\u0636\u0627\u0641\u0629 \u0627\u0644\u0639\u0646\u0627\u0648\u064a\u0646 \u0648\u0627\u0644\u0639\u0644\u0627\u0645\u0627\u062a<\/strong><\/h3>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u0625\u0636\u0627\u0641\u0629 \u0643\u0644 \u0645\u0646 \u0627\u0644\u0639\u0646\u0627\u0648\u064a\u0646 \u0648\u062a\u0633\u0645\u064a\u0627\u062a \u0627\u0644\u0645\u062d\u0627\u0648\u0631 \u0625\u0644\u0649 \u0627\u0644\u0647\u0631\u0645 \u0627\u0644\u0633\u0643\u0627\u0646\u064a \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0648\u0633\u064a\u0637\u0629 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$population) * c(-1,1)) +\n  labs(title = \"Population Pyramid\", x = \"Age\", y = \"Percent of population\")<\/span> +\n  coordinate_flip()<\/strong><\/pre>\n
 \u062a\u063a\u064a\u064a\u0631 \u0627\u0644\u0623\u0644\u0648\u0627\u0646<\/strong><\/span><\/h3>\n
 \u064a\u0645\u0643\u0646\u0646\u0627 \u062a\u063a\u064a\u064a\u0631 \u0627\u0644\u0644\u0648\u0646\u064a\u0646 \u0627\u0644\u0645\u0633\u062a\u062e\u062f\u0645\u064a\u0646 \u0644\u062a\u0645\u062b\u064a\u0644 \u0627\u0644\u0623\u062c\u0646\u0627\u0633 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0648\u0633\u064a\u0637\u0629 scale_color_manual()<\/strong> :<\/span><\/p>\n
 ggplot(data, aes(x = age, fill = gender,\n                 y = ifelse(test = gender == \"M\",\n                            yes = -population, no = population))) + \n  geom_bar(stat = \"identity\") +\n  scale_y_continuous(labels = abs, limits = max(data$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
 \u0623\u0647\u0631\u0627\u0645\u0627\u062a \u0639\u0645\u0631\u064a\u0629 \u0645\u062a\u0639\u062f\u062f\u0629<\/strong><\/span><\/h2>\n
 \u0645\u0646 \u0627\u0644\u0645\u0645\u0643\u0646 \u0623\u064a\u0636\u064b\u0627 \u0631\u0633\u0645 \u0623\u0647\u0631\u0627\u0645\u0627\u062a \u0633\u0643\u0627\u0646\u064a\u0629 \u0645\u062a\u0639\u062f\u062f\u0629 \u0645\u0639\u064b\u0627 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0648\u0633\u064a\u0637\u0629 facet_wrap()<\/strong> . \u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u0644\u0646\u0641\u062a\u0631\u0636 \u0623\u0646 \u0644\u062f\u064a\u0646\u0627 \u0628\u064a\u0627\u0646\u0627\u062a \u0633\u0643\u0627\u0646\u064a\u0629 \u0644\u0644\u0628\u0644\u062f\u0627\u0646 \u0623\u060c \u0628\u060c<\/em> \u062c.<\/em> \u064a\u0648\u0636\u062d \u0627\u0644\u0643\u0648\u062f \u0627\u0644\u062a\u0627\u0644\u064a \u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 \u0647\u0631\u0645 \u0633\u0643\u0627\u0646\u064a \u0644\u0643\u0644 \u0628\u0644\u062f:<\/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$population) * c(-1,1)) +\n  labs(y = \"Population Amount\") + \n  coordinate_flip() +\n  facet_wrap(~country) +<\/span>\ntheme(axis.text.x = element_text(angle = 90, hjust = 1))<\/span> #rotate x-axis labels<\/span><\/span><\/strong><\/pre>\n
 \u063a\u064a\u0631 \u0627\u0644\u062e\u0644\u0641\u064a\u0629<\/strong><\/span><\/h2>\n
 \u0648\u0623\u062e\u064a\u0631\u0627\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u062a\u063a\u064a\u064a\u0631 \u0645\u0648\u0636\u0648\u0639 \u0627\u0644\u0631\u0633\u0648\u0645\u0627\u062a. \u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u062a\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u0629 \u0627\u0644\u0628\u0631\u0645\u062c\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 theme_classic()<\/strong> \u200e \u0644\u062c\u0639\u0644 \u0627\u0644\u0631\u0633\u0648\u0645\u0627\u062a \u062a\u0628\u062f\u0648 \u0623\u0643\u062b\u0631 \u0628\u0633\u0627\u0637\u0629:<\/span><\/p>\n
 ggplot(data_multiple, aes(x = age, fill = gender,\n                          y = ifelse(test = gender == \"M\",\n                                     yes = -population, no = population))) + \n  geom_bar(stat = \"identity\") +\n  scale_y_continuous(labels = abs, limits = max(data_multiple$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
 \u0623\u0648 \u064a\u0645\u0643\u0646\u0643 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0633\u0645\u0627\u062a gg \u0627\u0644\u0645\u062e\u0635\u0635\u0629. \u0644\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u0642\u0627\u0626\u0645\u0629 \u0643\u0627\u0645\u0644\u0629 \u0628\u0645\u0648\u0636\u0648\u0639\u0627\u062a gg\u060c \u0631\u0627\u062c\u0639<\/span> \u0635\u0641\u062d\u0629 \u0627\u0644\u0648\u062b\u0627\u0626\u0642<\/a> .<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0627\u0644\u0647\u0631\u0645 \u0627\u0644\u0633\u0643\u0627\u0646\u064a \u0647\u0648 \u0631\u0633\u0645 \u0628\u064a\u0627\u0646\u064a \u064a\u0648\u0636\u062d \u0627\u0644\u0639\u0645\u0631 \u0648\u0627\u0644\u062a\u0648\u0632\u064a\u0639 \u0627\u0644\u062c\u0646\u0633\u064a \u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0633\u0643\u0627\u0646\u064a\u0629 \u0645\u0639\u064a\u0646\u0629. \u064a\u0639\u062f \u0647\u0630\u0627 \u0645\u062e\u0637\u0637\u064b\u0627 \u0645\u0641\u064a\u062f\u064b\u0627 \u0644\u0641\u0647\u0645 \u062a\u0643\u0648\u064a\u0646 \u0627\u0644\u0633\u0643\u0627\u0646 \u0628\u0627\u0644\u0625\u0636\u0627\u0641\u0629 \u0625\u0644\u0649 \u0627\u062a\u062c\u0627\u0647 \u0627\u0644\u0646\u0645\u0648 \u0627\u0644\u0633\u0643\u0627\u0646\u064a \u0627\u0644\u062d\u0627\u0644\u064a \u0628\u0633\u0647\u0648\u0644\u0629. \u0625\u0630\u0627 \u0643\u0627\u0646 \u0644\u0644\u0647\u0631\u0645 \u0627\u0644\u0633\u0643\u0627\u0646\u064a \u0634\u0643\u0644 \u0645\u0633\u062a\u0637\u064a\u0644\u060c \u0641\u0647\u0630\u0627 \u064a\u0634\u064a\u0631 \u0625\u0644\u0649 \u0623\u0646 \u0639\u062f\u062f \u0627\u0644\u0633\u0643\u0627\u0646 \u064a\u0646\u0645\u0648 \u0628\u0645\u0639\u062f\u0644 \u0623\u0628\u0637\u0623\u061b \u064a\u062a\u0645 \u0627\u0633\u062a\u0628\u062f\u0627\u0644 \u0627\u0644\u0623\u062c\u064a\u0627\u0644 \u0627\u0644\u0623\u0642\u062f\u0645 \u0628\u0623\u062c\u064a\u0627\u0644 \u062c\u062f\u064a\u062f\u0629 \u0628\u0646\u0641\u0633 \u0627\u0644\u062d\u062c\u0645 \u062a\u0642\u0631\u064a\u0628\u064b\u0627. \u0625\u0630\u0627 \u0643\u0627\u0646 \u0627\u0644\u0647\u0631\u0645 \u0627\u0644\u0633\u0643\u0627\u0646\u064a \u0639\u0644\u0649 […]<\/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":[],"yoast_head":"\n\u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 \u0627\u0644\u0647\u0631\u0645 \u0627\u0644\u0633\u0643\u0627\u0646\u064a \u0641\u064a R - 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