{"id":4176,"date":"2023-07-13T02:02:15","date_gmt":"2023-07-13T02:02:15","guid":{"rendered":"https:\/\/statorials.org\/ru\/%d0%be%d1%87%d0%b8%d1%81%d1%82%d0%ba%d0%b0-%d0%b4%d0%b0%d0%bd%d0%bd%d1%8b%d1%85-%d0%b2-r\/"},"modified":"2023-07-13T02:02:15","modified_gmt":"2023-07-13T02:02:15","slug":"%d0%be%d1%87%d0%b8%d1%81%d1%82%d0%ba%d0%b0-%d0%b4%d0%b0%d0%bd%d0%bd%d1%8b%d1%85-%d0%b2-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/ru\/%d0%be%d1%87%d0%b8%d1%81%d1%82%d0%ba%d0%b0-%d0%b4%d0%b0%d0%bd%d0%bd%d1%8b%d1%85-%d0%b2-r\/","title":{"rendered":"\u041a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043e\u0447\u0438\u0441\u0442\u043a\u0443 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 r (\u0441 \u043f\u0440\u0438\u043c\u0435\u0440\u043e\u043c)"},"content":{"rendered":"

<\/p>\n

\n

\u041e\u0447\u0438\u0441\u0442\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445<\/strong> \u2014 \u044d\u0442\u043e \u043f\u0440\u043e\u0446\u0435\u0441\u0441 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u043d\u0435\u043e\u0431\u0440\u0430\u0431\u043e\u0442\u0430\u043d\u043d\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445<\/a> \u0432 \u0434\u0430\u043d\u043d\u044b\u0435, \u043f\u0440\u0438\u0433\u043e\u0434\u043d\u044b\u0435 \u0434\u043b\u044f \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u0438\u043b\u0438 \u043f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0435\u0439.<\/span><\/p>\n

\u0412 \u0431\u043e\u043b\u044c\u0448\u0438\u043d\u0441\u0442\u0432\u0435 \u0441\u043b\u0443\u0447\u0430\u0435\u0432 \u00ab\u043e\u0447\u0438\u0441\u0442\u043a\u0430\u00bb \u043d\u0430\u0431\u043e\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u043f\u0440\u0435\u0434\u043f\u043e\u043b\u0430\u0433\u0430\u0435\u0442 \u0440\u0430\u0431\u043e\u0442\u0443 \u0441 \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u043c\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f\u043c\u0438 \u0438 \u043f\u043e\u0432\u0442\u043e\u0440\u044f\u044e\u0449\u0438\u043c\u0438\u0441\u044f \u0434\u0430\u043d\u043d\u044b\u043c\u0438.<\/span><\/p>\n

\u0412\u043e\u0442 \u043d\u0430\u0438\u0431\u043e\u043b\u0435\u0435 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u043d\u044b\u0435 \u043c\u0435\u0442\u043e\u0434\u044b \u00ab\u043e\u0447\u0438\u0441\u0442\u043a\u0438\u00bb \u043d\u0430\u0431\u043e\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 R:<\/span><\/p>\n

\u0421\u043f\u043e\u0441\u043e\u0431 1. \u0423\u0434\u0430\u043b\u0435\u043d\u0438\u0435 \u0441\u0442\u0440\u043e\u043a \u0441 \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u043c\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f\u043c\u0438<\/strong><\/span><\/p>\n

 library<\/span> (dplyr)\n\n#remove rows with any missing values\n<\/span>df %>% na. omit<\/span> ()\n<\/strong><\/pre>\n
 \u0421\u043f\u043e\u0441\u043e\u0431 2: \u0437\u0430\u043c\u0435\u043d\u0438\u0442\u044c \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0434\u0440\u0443\u0433\u0438\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\u043c<\/strong><\/span><\/p>\n
 library<\/span> (dplyr)\nlibrary<\/span> (tidyr)\n\n#replace missing values in each numeric column with median value of column\n<\/span>df %>% mutate(across(where(is. numeric<\/span> ), ~replace_na(., median(., na. rm<\/span> = TRUE<\/span> ))))\n<\/strong><\/pre>\n
 \u0421\u043f\u043e\u0441\u043e\u0431 3: \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u043f\u043e\u0432\u0442\u043e\u0440\u044f\u044e\u0449\u0438\u0435\u0441\u044f \u0441\u0442\u0440\u043e\u043a\u0438<\/strong><\/span><\/p>\n
 library<\/span> (dplyr)\n\ndf %>% distinct(. keep_all<\/span> = TRUE<\/span> )\n<\/strong><\/pre>\n
 \u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u043f\u0440\u0438\u043c\u0435\u0440\u0430\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u043a\u0430\u043a \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043a\u0430\u0436\u0434\u044b\u0439 \u0438\u0437 \u044d\u0442\u0438\u0445 \u043c\u0435\u0442\u043e\u0434\u043e\u0432 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435 \u0441\u043e \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c \u0444\u0440\u0435\u0439\u043c\u043e\u043c \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 R, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u0431\u0430\u0441\u043a\u0435\u0442\u0431\u043e\u043b\u0438\u0441\u0442\u0430\u0445:<\/span><\/p>\n
 #create data frame\n<\/span>df <- data. frame<\/span> (team=c('A', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'),\n                 points=c(4, 4, NA, 8, 6, 12, 14, 86, 13, 8),\n                 rebounds=c(9, 9, 7, 6, 8, NA, 9, 14, 12, 11),\n                 assists=c(2, 2, NA, 7, 6, 6, 9, 10, NA, 14))\n\n#view data frame\n<\/span>df\n\n   team points rebound assists\n1 to 4 9 2\n2 to 4 9 2\n3 B NA 7 NA\n4 C 8 6 7\n5 D 6 8 6\n6 E 12 NA 6\n7 F 14 9 9\n8 G 86 14 10\n9:13:12 NA\n10 I 8 11 14\n<\/strong><\/pre>\n
 \u041f\u0440\u0438\u043c\u0435\u0440 1. \u0423\u0434\u0430\u043b\u0435\u043d\u0438\u0435 \u0441\u0442\u0440\u043e\u043a \u0441 \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u043c\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f\u043c\u0438<\/strong><\/span><\/h2>\n
 \u041c\u044b \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441 \u0434\u043b\u044f \u0443\u0434\u0430\u043b\u0435\u043d\u0438\u044f \u0441\u0442\u0440\u043e\u043a \u0441 \u043e\u0442\u0441\u0443\u0442\u0441\u0442\u0432\u0443\u044e\u0449\u0438\u043c\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f\u043c\u0438 \u0432 \u043b\u044e\u0431\u043e\u043c \u0441\u0442\u043e\u043b\u0431\u0446\u0435:<\/span><\/p>\n
 library<\/span> (dplyr)\n\n#remove rows with missing values\n<\/span>new_df <- df %>% na. omit<\/span> ()\n\n#view new data frame\n<\/span>new_df\n\n   team points rebound assists\n1 to 4 9 2\n2 to 4 9 2\n4 C 8 6 7\n5 D 6 8 6\n7 F 14 9 9\n8 G 86 14 10\n10 I 8 11 14<\/strong><\/pre>\n
 \u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e \u043d\u043e\u0432\u044b\u0439 \u0444\u0440\u0435\u0439\u043c \u0434\u0430\u043d\u043d\u044b\u0445 \u043d\u0435 \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442 \u0441\u0442\u0440\u043e\u043a \u0441 \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u043c\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f\u043c\u0438.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0435\u0440 2: \u0417\u0430\u043c\u0435\u043d\u0438\u0442\u044c \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0434\u0440\u0443\u0433\u0438\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\u043c<\/strong><\/span><\/h2>\n
 \u041c\u044b \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0447\u0442\u043e\u0431\u044b \u0437\u0430\u043c\u0435\u043d\u0438\u0442\u044c \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u043c\u0435\u0434\u0438\u0430\u043d\u043d\u044b\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\u043c \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u0430:<\/span><\/p>\n
 library<\/span> (dplyr)\nlibrary<\/span> (tidyr)\n\n#replace missing values in each numeric column with median value of column\n<\/span>new_df <-df %>% mutate(across(where(is. numeric<\/span> ),~replace_na(.,median(.,na. rm<\/span> = TRUE<\/span> )))) \n\n#view new data frame\n<\/span>new_df\n\n   team points rebound assists\n1 to 4 9 2.0\n2 to 4 9 2.0\n3 B 8 7 6.5\n4 C 8 6 7.0\n5 D 6 8 6.0\n6 E 12 9 6.0\n7 F 14 9 9.0\n8 G 86 14 10.0\n9:13 12 6.5\n10 I 8 11 14.0<\/strong><\/pre>\n
 \u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0432 \u043a\u0430\u0436\u0434\u043e\u043c \u0447\u0438\u0441\u043b\u043e\u0432\u043e\u043c \u0441\u0442\u043e\u043b\u0431\u0446\u0435 \u0437\u0430\u043c\u0435\u043d\u0435\u043d\u044b \u043c\u0435\u0434\u0438\u0430\u043d\u043d\u044b\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\u043c \u0441\u0442\u043e\u043b\u0431\u0446\u0430.<\/span><\/p>\n
 \u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e \u0432\u044b \u0442\u0430\u043a\u0436\u0435 \u043c\u043e\u0436\u0435\u0442\u0435 \u0437\u0430\u043c\u0435\u043d\u0438\u0442\u044c \u043c\u0435\u0434\u0438\u0430\u043d\u0443<\/strong> \u0432 \u0444\u043e\u0440\u043c\u0443\u043b\u0435 \u0441\u0440\u0435\u0434\u043d\u0438\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\u043c<\/strong> , \u0447\u0442\u043e\u0431\u044b \u0437\u0430\u043c\u0435\u043d\u0438\u0442\u044c \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0441\u0440\u0435\u0434\u043d\u0438\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\u043c \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u0430.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0435\u0447\u0430\u043d\u0438\u0435<\/strong> . \u0412 \u044d\u0442\u043e\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u0435 \u043d\u0430\u043c \u0442\u0430\u043a\u0436\u0435 \u043f\u0440\u0438\u0448\u043b\u043e\u0441\u044c \u0437\u0430\u0433\u0440\u0443\u0437\u0438\u0442\u044c \u043f\u0430\u043a\u0435\u0442 Tidyr<\/strong> , \u043f\u043e\u0441\u043a\u043e\u043b\u044c\u043a\u0443 \u0444\u0443\u043d\u043a\u0446\u0438\u044f drop_na()<\/strong> \u043f\u0440\u043e\u0438\u0441\u0445\u043e\u0434\u0438\u0442 \u0438\u0437 \u044d\u0442\u043e\u0433\u043e \u043f\u0430\u043a\u0435\u0442\u0430.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0435\u0440 3. \u0423\u0434\u0430\u043b\u0435\u043d\u0438\u0435 \u043f\u043e\u0432\u0442\u043e\u0440\u044f\u044e\u0449\u0438\u0445\u0441\u044f \u0441\u0442\u0440\u043e\u043a<\/strong><\/span><\/h2>\n
 \u041c\u044b \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u0441\u0438\u043d\u0442\u0430\u043a\u0441\u0438\u0441, \u0447\u0442\u043e\u0431\u044b \u0437\u0430\u043c\u0435\u043d\u0438\u0442\u044c \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u043c\u0435\u0434\u0438\u0430\u043d\u043d\u044b\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435\u043c \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u0430:<\/span><\/p>\n
 library<\/span> (dplyr)\n\n#remove duplicate rows\n<\/span>new_df <- df %>% distinct(. keep_all<\/span> = TRUE<\/span> )\n\n#view new data frame\n<\/span>new_df\n\n  team points rebound assists\n1 to 4 9 2\n2 B NA 7 NA\n3 C 8 6 7\n4 D 6 8 6\n5 E 12 NA 6\n6 F 14 9 9\n7 G 86 14 10\n8:13:12 NA\n9 I 8 11 14<\/strong><\/pre>\n
 \u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e \u0432\u0442\u043e\u0440\u0430\u044f \u0441\u0442\u0440\u043e\u043a\u0430 \u0431\u044b\u043b\u0430 \u0443\u0434\u0430\u043b\u0435\u043d\u0430 \u0438\u0437 \u0444\u0440\u0435\u0439\u043c\u0430 \u0434\u0430\u043d\u043d\u044b\u0445, \u043f\u043e\u0441\u043a\u043e\u043b\u044c\u043a\u0443 \u043a\u0430\u0436\u0434\u043e\u0435 \u0438\u0437 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432\u043e \u0432\u0442\u043e\u0440\u043e\u0439 \u0441\u0442\u0440\u043e\u043a\u0435 \u0431\u044b\u043b\u043e \u0434\u0443\u0431\u043b\u0438\u043a\u0430\u0442\u043e\u043c \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432 \u043f\u0435\u0440\u0432\u043e\u0439 \u0441\u0442\u0440\u043e\u043a\u0435.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0435\u0447\u0430\u043d\u0438\u0435<\/strong> . \u041f\u043e\u043b\u043d\u0443\u044e \u0434\u043e\u043a\u0443\u043c\u0435\u043d\u0442\u0430\u0446\u0438\u044e \u043f\u043e \u0444\u0443\u043d\u043a\u0446\u0438\u0438 dplyrdependent()<\/strong> \u043c\u043e\u0436\u043d\u043e \u043d\u0430\u0439\u0442\u0438 \u0437\u0434\u0435\u0441\u044c<\/a> .<\/span><\/p>\n
 \u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0440\u0435\u0441\u0443\u0440\u0441\u044b<\/strong><\/span><\/h2>\n
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 \u041a\u0430\u043a \u0433\u0440\u0443\u043f\u043f\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0438 \u0441\u0443\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0434\u0430\u043d\u043d\u044b\u0435 \u0432 R<\/a>
 \u041a\u0430\u043a \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u0441\u0432\u043e\u0434\u043d\u044b\u0435 \u0442\u0430\u0431\u043b\u0438\u0446\u044b \u0432 R<\/a>
 \u041a\u0430\u043a \u0443\u0434\u0430\u043b\u0438\u0442\u044c \u0441\u0442\u0440\u043e\u043a\u0438 \u0441 \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u043c\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f\u043c\u0438 \u0432 R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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\u0441 \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u043c\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f\u043c\u0438 \u0438 \u043f\u043e\u0432\u0442\u043e\u0440\u044f\u044e\u0449\u0438\u043c\u0438\u0441\u044f \u0434\u0430\u043d\u043d\u044b\u043c\u0438. \u0412\u043e\u0442 \u043d\u0430\u0438\u0431\u043e\u043b\u0435\u0435 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u043d\u044b\u0435 \u043c\u0435\u0442\u043e\u0434\u044b \u00ab\u043e\u0447\u0438\u0441\u0442\u043a\u0438\u00bb \u043d\u0430\u0431\u043e\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 R: \u0421\u043f\u043e\u0441\u043e\u0431 1. \u0423\u0434\u0430\u043b\u0435\u043d\u0438\u0435 \u0441\u0442\u0440\u043e\u043a \u0441 \u043f\u0440\u043e\u043f\u0443\u0449\u0435\u043d\u043d\u044b\u043c\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f\u043c\u0438 library (dplyr) #remove rows with any missing values df 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