{"id":3283,"date":"2023-07-18T07:13:50","date_gmt":"2023-07-18T07:13:50","guid":{"rendered":"https:\/\/statorials.org\/ko\/dplyr%e1%84%8b%e1%85%b3%e1%86%ab-%e1%84%8b%e1%85%a7%e1%84%85%e1%85%a5-%e1%84%8b%e1%85%a7%e1%86%af%e1%84%8b%e1%85%b3%e1%86%af-%e1%84%8b%e1%85%ad%e1%84%8b%e1%85%a3%e1%86%a8%e1%84%92%e1%85%a1%e1%86%b8\/"},"modified":"2023-07-18T07:13:50","modified_gmt":"2023-07-18T07:13:50","slug":"dplyr%e1%84%8b%e1%85%b3%e1%86%ab-%e1%84%8b%e1%85%a7%e1%84%85%e1%85%a5-%e1%84%8b%e1%85%a7%e1%86%af%e1%84%8b%e1%85%b3%e1%86%af-%e1%84%8b%e1%85%ad%e1%84%8b%e1%85%a3%e1%86%a8%e1%84%92%e1%85%a1%e1%86%b8","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/dplyr%e1%84%8b%e1%85%b3%e1%86%ab-%e1%84%8b%e1%85%a7%e1%84%85%e1%85%a5-%e1%84%8b%e1%85%a7%e1%86%af%e1%84%8b%e1%85%b3%e1%86%af-%e1%84%8b%e1%85%ad%e1%84%8b%e1%85%a3%e1%86%a8%e1%84%92%e1%85%a1%e1%86%b8\/","title":{"rendered":"Dplyr\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc5ec\ub7ec \uc5f4\uc744 \uc694\uc57d\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

dplyr\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130 \ud504\ub808\uc784\uc758 \uc5ec\ub7ec \uc5f4\uc744 \uc694\uc57d\ud558\ub824\uba74 \ub2e4\uc74c \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ubc29\ubc95 1: \ubaa8\ub4e0 \uc5f4 \uc694\uc57d<\/strong><\/span><\/p>\n

 #summarize mean of all columns<\/span>\ndf %>%\n  group_by(group_var) %>%\n  summarise(across(everything(), mean, na. rm<\/span> = TRUE<\/span> ))\n<\/strong><\/pre>\n
 \ubc29\ubc95 2: \ud2b9\uc815 \uc5f4 \uc694\uc57d<\/strong><\/span><\/p>\n
 #summarize mean of col1 and col2 only<\/span>\ndf %>%\n  group_by(group_var) %>%\n  summarise(across(c(col1, col2), mean, na. rm<\/span> = TRUE<\/span> ))\n<\/strong><\/pre>\n
 \ubc29\ubc95 3: \ubaa8\ub4e0 \uc22b\uc790 \uc5f4 \uc694\uc57d<\/strong><\/span><\/p>\n
 #summarize mean and standard deviation of all numeric columns\n<\/span>df %>%\n  group_by(group_var) %>%\n  summarise(across(where(is. numeric<\/span> ), list(mean=mean, sd=sd), na. rm<\/span> = TRUE<\/span> ))\n<\/strong><\/pre>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \ub2e4\uc74c \ub370\uc774\ud130 \ud504\ub808\uc784\uc5d0\uc11c \uac01 \uba54\uc11c\ub4dc\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 #create data frame\n<\/span>df <- data. frame<\/span> (team=c('A', 'A', 'A', 'B', 'B', 'B'),\n                 points=c(99, 90, 86, 88, 95, 90),\n                 assists=c(33, 28, 31, 39, 34, 25),\n                 rebounds=c(NA, 28, 24, 24, 28, 19))\n\n#view data frame\n<\/span>df\n\n  team points assists rebounds\n1 A 99 33 NA\n2 A 90 28 28\n3 A 86 31 24\n4 B 88 39 24\n5 B 95 34 28\n6 B 90 25 19\n<\/strong><\/pre>\n
 \uc608\uc2dc 1: \ubaa8\ub4e0 \uc5f4 \uc694\uc57d<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \ubaa8\ub4e0 \uc5f4\uc758 \ud3c9\uade0\uc744 \uc694\uc57d\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 library<\/span> (dplyr)<\/span>\n\n#summarize mean of all columns, grouped by team<\/span>\ndf %>%\n  group_by(team) %>%\n  summarise(across(everything(), mean, na. rm<\/span> = TRUE<\/span> ))\n\n# A tibble: 2 x 4\n  team points assists rebounds\n           \n1 A 91.7 30.7 26  \n2 B 91 32.7 23.7\n<\/strong><\/pre>\n
 \uc608\uc2dc 2: \ud2b9\uc815 \uc5f4 \uc694\uc57d<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \ud3ec\uc778\ud2b8<\/strong> \ubc0f \ub9ac\ubc14\uc6b4\ub4dc<\/strong> \uc5f4\uc758 \ud3c9\uade0\uc744 \uc694\uc57d\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 library<\/span> (dplyr)<\/span>\n\n#summarize mean of points and rebounds, grouped by team<\/span>\ndf %>%\n  group_by(team) %>%\n  summarise(across(c(points, rebounds), mean, na. rm<\/span> = TRUE<\/span> ))\n\n# A tibble: 2 x 3\n  team points rebounds\n        \n1 A 91.7 26  \n2 B 91 23.7<\/strong><\/pre>\n
 \uc608 3: \ubaa8\ub4e0 \uc22b\uc790 \uc5f4 \uc694\uc57d<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub294 \ub370\uc774\ud130 \ud504\ub808\uc784\uc5d0 \uc788\ub294 \ubaa8\ub4e0 \uc22b\uc790 \uc5f4\uc758 \ud3c9\uade0 \ubc0f \ud45c\uc900 \ud3b8\ucc28\ub97c \uc694\uc57d\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 library<\/span> (dplyr)<\/span>\n\n#summarize mean and standard deviation of all numeric columns<\/span>\ndf %>%\n  group_by(team) %>%\n  summarise(across(where(is. numeric<\/span> ), list(mean=mean, sd=sd), na. rm<\/span> = TRUE<\/span> ))\n\n# A tibble: 2 x 7\n  team points_mean points_sd assists_mean assists_sd rebounds_mean rebounds_sd\n                                            \n1 A 91.7 6.66 30.7 2.52 26 2.83\n2 B 91 3.61 32.7 7.09 23.7 4.51<\/strong><\/pre>\n
 \ucd9c\ub825\uc5d0\ub294 \ub370\uc774\ud130 \ud504\ub808\uc784\uc5d0 \uc788\ub294 \ubaa8\ub4e0 \uc22b\uc790 \ubcc0\uc218\uc758 \ud3c9\uade0 \ubc0f \ud45c\uc900 \ud3b8\ucc28\uac00 \ud45c\uc2dc\ub429\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774 \uc608\uc5d0\uc11c\ub294 list()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uacc4\uc0b0\ud558\ub824\ub294 \uc5ec\ub7ec \uc694\uc57d \ud1b5\uacc4\ub97c \ub098\uc5f4\ud588\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucc38\uace0<\/strong> : \uac01 \uc608\uc5d0\uc11c\ub294 dplyr across()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud588\uc2b5\ub2c8\ub2e4. \uc774 \uae30\ub2a5\uc5d0 \ub300\ud55c \uc804\uccb4 \ubb38\uc11c\ub294 \uc5ec\uae30\uc5d0\uc11c<\/a> \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\n
 \ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 dplyr\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uae30\ub2a5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 dplyr\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud589\uc744 \uc0ad\uc81c\ud558\ub294 \ubc29\ubc95<\/a>
 dplyr\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud589\uc744 \uc815\ub82c\ud558\ub294 \ubc29\ubc95<\/a>
 dplyr\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc5ec\ub7ec \uc870\uac74\uc73c\ub85c \ud544\ud130\ub9c1\ud558\ub294 \ubc29\ubc95<\/p>\n","protected":false},"excerpt":{"rendered":"
dplyr\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130 \ud504\ub808\uc784\uc758 \uc5ec\ub7ec \uc5f4\uc744 \uc694\uc57d\ud558\ub824\uba74 \ub2e4\uc74c \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: \ubaa8\ub4e0 \uc5f4 \uc694\uc57d #summarize mean of all columns df %>% group_by(group_var) %>% summarise(across(everything(), mean, na. rm = TRUE )) \ubc29\ubc95 2: \ud2b9\uc815 \uc5f4 \uc694\uc57d #summarize mean of col1 and col2 only df %>% group_by(group_var) %>% summarise(across(c(col1, col2), mean, na. rm = […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-3283","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\ndplyr\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc5ec\ub7ec \uc5f4\uc744 \uc694\uc57d\ud558\ub294 \ubc29\ubc95 - Statorials<\/title>\n<meta name=\"description\" content=\"\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc5ec\ub7ec \uc608\uc640 \ud568\uaed8 dplyr\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130 \ud504\ub808\uc784\uc758 \uc5ec\ub7ec \uc5f4\uc744 \uc694\uc57d\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/ko\/dplyr\u110b\u1173\u11ab-\u110b\u1167\u1105\u1165-\u110b\u1167\u11af\u110b\u1173\u11af-\u110b\u116d\u110b\u1163\u11a8\u1112\u1161\u11b8\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"dplyr\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc5ec\ub7ec \uc5f4\uc744 \uc694\uc57d\ud558\ub294 \ubc29\ubc95 - 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