{"id":2215,"date":"2023-07-23T05:12:02","date_gmt":"2023-07-23T05:12:02","guid":{"rendered":"https:\/\/statorials.org\/uk\/r-%d0%bf%d1%96%d0%b4%d1%81%d1%83%d0%bc%d1%83%d0%b2%d0%b0%d1%82%d0%b8-%d1%81%d1%82%d0%b0%d1%82%d0%b8%d1%81%d1%82%d0%b8%d1%87%d0%bd%d1%96-%d0%b4%d0%b0%d0%bd%d1%96-%d0%bf%d0%be-%d0%b3%d1%80%d1%83%d0%bf\/"},"modified":"2023-07-23T05:12:02","modified_gmt":"2023-07-23T05:12:02","slug":"r-%d0%bf%d1%96%d0%b4%d1%81%d1%83%d0%bc%d1%83%d0%b2%d0%b0%d1%82%d0%b8-%d1%81%d1%82%d0%b0%d1%82%d0%b8%d1%81%d1%82%d0%b8%d1%87%d0%bd%d1%96-%d0%b4%d0%b0%d0%bd%d1%96-%d0%bf%d0%be-%d0%b3%d1%80%d1%83%d0%bf","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/r-%d0%bf%d1%96%d0%b4%d1%81%d1%83%d0%bc%d1%83%d0%b2%d0%b0%d1%82%d0%b8-%d1%81%d1%82%d0%b0%d1%82%d0%b8%d1%81%d1%82%d0%b8%d1%87%d0%bd%d1%96-%d0%b4%d0%b0%d0%bd%d1%96-%d0%bf%d0%be-%d0%b3%d1%80%d1%83%d0%bf\/","title":{"rendered":"\u042f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 \u043f\u0456\u0434\u0441\u0443\u043c\u043a\u043e\u0432\u0443 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0443 \u043f\u043e \u0433\u0440\u0443\u043f\u0430\u0445 \u0443 r"},"content":{"rendered":"

<\/p>\n

\n

\u0406\u0441\u043d\u0443\u0454 \u0434\u0432\u0430 \u043e\u0441\u043d\u043e\u0432\u043d\u0438\u0445 \u0441\u043f\u043e\u0441\u043e\u0431\u0438 \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f \u043f\u0456\u0434\u0441\u0443\u043c\u043a\u043e\u0432\u043e\u0457 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0438 \u0433\u0440\u0443\u043f\u0438 \u0432 R:<\/span><\/p>\n

\u0421\u043f\u043e\u0441\u0456\u0431 1. \u0412\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 tapply() \u0456\u0437 Base R<\/strong><\/span><\/p>\n

 tapply(df$value_col, df$group_col, summary)\n<\/strong><\/pre>\n
 \u0421\u043f\u043e\u0441\u0456\u0431 2: \u0412\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 group_by() \u0437 \u043f\u0430\u043a\u0435\u0442\u0430 dplyr<\/strong><\/span><\/p>\n
 library<\/span> (dplyr)\n\ndf %>%\n  group_by<\/span> (group_col) %>% \n  summarize<\/span> (min = min(value_col),\n            q1 = quantile(value_col, 0.25<\/span> ),\n            median = median(value_col),\n            mean = mean(value_col),\n            q3 = quantile(value_col, 0.75<\/span> ),\n            max = max(value_col))<\/strong><\/span><\/pre>\n
 \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0456 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0438 \u043f\u043e\u043a\u0430\u0437\u0443\u044e\u0442\u044c, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u043a\u043e\u0436\u0435\u043d \u043c\u0435\u0442\u043e\u0434 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u0446\u0456.<\/span><\/p>\n
 \u0421\u043f\u043e\u0441\u0456\u0431 1. \u0412\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 tapply() \u0456\u0437 Base R<\/strong><\/span><\/h3>\n
 \u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u043c\u0443 \u043a\u043e\u0434\u0456 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u044e tapply()<\/strong> \u0443 R \u0434\u043b\u044f \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f \u043f\u0456\u0434\u0441\u0443\u043c\u043a\u043e\u0432\u043e\u0457 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0438 \u0437\u0430 \u0433\u0440\u0443\u043f\u043e\u044e:<\/span><\/p>\n
 #create data frame\n<\/span>df <- data. frame<\/span> (team=c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'),\n                 points=c(99, 68, 86, 88, 95, 74, 78, 93),\n                 assists=c(22, 28, 31, 35, 34, 45, 28, 31),\n                 rebounds=c(30, 28, 24, 24, 30, 36, 30, 29))\n\n#calculate summary statistics of 'points' grouped by 'team'\n<\/span>tapply(df$points, df$team, summary)\n\n$A\n   Min. 1st Qu. Median Mean 3rd Qu. Max. \n  68.00 81.50 87.00 85.25 90.75 99.00 \n\n$B\n   Min. 1st Qu. Median Mean 3rd Qu. Max. \n   74.0 77.0 85.5 85.0 93.5 95.0 \n<\/span><\/span><\/strong><\/pre>\n
 \u0421\u043f\u043e\u0441\u0456\u0431 2: \u0412\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 group_by() \u0437 \u043f\u0430\u043a\u0435\u0442\u0430 dplyr<\/strong><\/span><\/h3>\n
 \u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u043e\u043c\u0443 \u043a\u043e\u0434\u0456 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u044f\u043a \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u0442\u0438 \u0444\u0443\u043d\u043a\u0446\u0456\u0457 group_by()<\/strong> \u0456 summary()<\/strong> \u0443 \u043f\u0430\u043a\u0435\u0442\u0456 dplyr<\/a> \u0434\u043b\u044f \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f \u043f\u0456\u0434\u0441\u0443\u043c\u043a\u043e\u0432\u043e\u0457 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0438 \u0437\u0430 \u0433\u0440\u0443\u043f\u0430\u043c\u0438:<\/span><\/p>\n
 library<\/span> (dplyr)<\/span>\n\n#create data frame\n<\/span>df <- data. frame<\/span> (team=c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'),\n                 points=c(99, 68, 86, 88, 95, 74, 78, 93),\n                 assists=c(22, 28, 31, 35, 34, 45, 28, 31),\n                 rebounds=c(30, 28, 24, 24, 30, 36, 30, 29))\n\n#calculate summary statistics of 'points' grouped by 'team'\n<\/span>df %>%\n  group_by<\/span> (team) %>% \n  summarize<\/span> (min = min(points),\n            q1 = quantile(points, 0.25<\/span> ),\n            median = median(points),\n            mean = mean(points),\n            q3 = quantile(points, 0.75<\/span> ),\n            max = max(points))\n\n# A tibble: 2 x 7\n  team min q1 median mean q3 max\n         \n1 A 68 81.5 87 85.2 90.8 99\n2 B 74 77 85.5 85 93.5 95\n<\/span><\/span><\/strong><\/pre>\n
 \u0417\u0430\u0443\u0432\u0430\u0436\u0442\u0435, \u0449\u043e \u043e\u0431\u0438\u0434\u0432\u0430 \u043c\u0435\u0442\u043e\u0434\u0438 \u043f\u043e\u0432\u0435\u0440\u0442\u0430\u044e\u0442\u044c \u0430\u0431\u0441\u043e\u043b\u044e\u0442\u043d\u043e \u043e\u0434\u043d\u0430\u043a\u043e\u0432\u0456 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0438.<\/span><\/p>\n
 \u0412\u0430\u0440\u0442\u043e \u0437\u0430\u0437\u043d\u0430\u0447\u0438\u0442\u0438, \u0449\u043e \u043f\u0456\u0434\u0445\u0456\u0434 dplyr, \u0448\u0432\u0438\u0434\u0448\u0435 \u0437\u0430 \u0432\u0441\u0435, \u0431\u0443\u0434\u0435 \u0448\u0432\u0438\u0434\u0448\u0438\u043c \u0434\u043b\u044f \u0432\u0435\u043b\u0438\u043a\u0438\u0445 \u043a\u0430\u0434\u0440\u0456\u0432 \u0434\u0430\u043d\u0438\u0445, \u0430\u043b\u0435 \u043e\u0431\u0438\u0434\u0432\u0430 \u043c\u0435\u0442\u043e\u0434\u0438 \u043f\u0440\u0430\u0446\u044e\u0432\u0430\u0442\u0438\u043c\u0443\u0442\u044c \u043e\u0434\u043d\u0430\u043a\u043e\u0432\u043e \u043d\u0430 \u043c\u0435\u043d\u0448\u0438\u0445 \u043a\u0430\u0434\u0440\u0430\u0445 \u0434\u0430\u043d\u0438\u0445.<\/span><\/p>\n
 \u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h3>\n
 \u0423 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0445 \u043f\u043e\u0441\u0456\u0431\u043d\u0438\u043a\u0430\u0445 \u043f\u043e\u044f\u0441\u043d\u044e\u0454\u0442\u044c\u0441\u044f, \u044f\u043a \u0432\u0438\u043a\u043e\u043d\u0443\u0432\u0430\u0442\u0438 \u0456\u043d\u0448\u0456 \u043f\u043e\u0448\u0438\u0440\u0435\u043d\u0456 \u0444\u0443\u043d\u043a\u0446\u0456\u0457 \u0433\u0440\u0443\u043f\u0443\u0432\u0430\u043d\u043d\u044f \u0432 R:<\/span><\/p>\n
 \u042f\u043a \u0441\u0442\u0432\u043e\u0440\u0438\u0442\u0438 \u0442\u0430\u0431\u043b\u0438\u0446\u044e \u0447\u0430\u0441\u0442\u043e\u0442 \u0437\u0430 \u0433\u0440\u0443\u043f\u0430\u043c\u0438 \u0432 R<\/a>
\u042f\u043a \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0441\u0443\u043c\u0443 \u0437\u0430 \u0433\u0440\u0443\u043f\u043e\u044e \u0432 R<\/a>
 \u042f\u043a \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u043d\u0430 \u0433\u0440\u0443\u043f\u0443 \u0432 R<\/a>
\u042f\u043a \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0441\u0443\u043c\u0443 \u0437\u0430 \u0433\u0440\u0443\u043f\u043e\u044e \u0432 R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0406\u0441\u043d\u0443\u0454 \u0434\u0432\u0430 \u043e\u0441\u043d\u043e\u0432\u043d\u0438\u0445 \u0441\u043f\u043e\u0441\u043e\u0431\u0438 \u043e\u0431\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044f \u043f\u0456\u0434\u0441\u0443\u043c\u043a\u043e\u0432\u043e\u0457 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0438 \u0433\u0440\u0443\u043f\u0438 \u0432 R: \u0421\u043f\u043e\u0441\u0456\u0431 1. \u0412\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 tapply() \u0456\u0437 Base R tapply(df$value_col, df$group_col, summary) \u0421\u043f\u043e\u0441\u0456\u0431 2: \u0412\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 group_by() \u0437 \u043f\u0430\u043a\u0435\u0442\u0430 dplyr library (dplyr) df %>% group_by (group_col) %>% summarize (min = min(value_col), q1 = quantile(value_col, 0.25 ), median = median(value_col), mean = mean(value_col), q3 = quantile(value_col, 0.75 ), max […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"yoast_head":"\n\u042f\u043a \u0440\u043e\u0437\u0440\u0430\u0445\u0443\u0432\u0430\u0442\u0438 \u0437\u0432\u0435\u0434\u0435\u043d\u0443 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0443 \u043f\u043e \u0433\u0440\u0443\u043f\u0430\u0445 \u0443 R - Statology<\/title>\n<meta name=\"description\" content=\"\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043f\u043e\u044f\u0441\u043d\u044e\u0454, \u044f\u043a \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u043f\u0456\u0434\u0441\u0443\u043c\u043a\u043e\u0432\u0443 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0443 \u0433\u0440\u0443\u043f\u0438 \u0432 R, \u0437 \u043a\u0456\u043b\u044c\u043a\u043e\u043c\u0430 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u043c\u0438.\" 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