{"id":3099,"date":"2023-07-19T05:18:21","date_gmt":"2023-07-19T05:18:21","guid":{"rendered":"https:\/\/statorials.org\/ru\/r-%d0%b2%d1%8b%d0%b1%d1%80%d0%b0%d1%82%d1%8c-%d1%81%d1%82%d1%80%d0%be%d0%ba%d0%b8-%d0%b2%d0%be-%d1%84%d1%80%d0%b5%d0%b8%d0%bc%d0%b5-%d0%b4%d0%b0%d0%bd%d0%bd%d1%8b%d1%85-%d0%bd%d0%b0-%d0%be%d1%81%d0%bd\/"},"modified":"2023-07-19T05:18:21","modified_gmt":"2023-07-19T05:18:21","slug":"r-%d0%b2%d1%8b%d0%b1%d1%80%d0%b0%d1%82%d1%8c-%d1%81%d1%82%d1%80%d0%be%d0%ba%d0%b8-%d0%b2%d0%be-%d1%84%d1%80%d0%b5%d0%b8%d0%bc%d0%b5-%d0%b4%d0%b0%d0%bd%d0%bd%d1%8b%d1%85-%d0%bd%d0%b0-%d0%be%d1%81%d0%bd","status":"publish","type":"post","link":"https:\/\/statorials.org\/ru\/r-%d0%b2%d1%8b%d0%b1%d1%80%d0%b0%d1%82%d1%8c-%d1%81%d1%82%d1%80%d0%be%d0%ba%d0%b8-%d0%b2%d0%be-%d1%84%d1%80%d0%b5%d0%b8%d0%bc%d0%b5-%d0%b4%d0%b0%d0%bd%d0%bd%d1%8b%d1%85-%d0%bd%d0%b0-%d0%be%d1%81%d0%bd\/","title":{"rendered":"A: \u043a\u0430\u043a \u0432\u044b\u0431\u0440\u0430\u0442\u044c \u0441\u0442\u0440\u043e\u043a\u0438 \u0432\u043e \u0444\u0440\u0435\u0439\u043c\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u0432\u0435\u043a\u0442\u043e\u0440\u043d\u044b\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439"},"content":{"rendered":"

<\/p>\n

\n

\u0412\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043b\u044e\u0431\u043e\u0439 \u0438\u0437 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u043c\u0435\u0442\u043e\u0434\u043e\u0432 \u0434\u043b\u044f \u0432\u044b\u0431\u043e\u0440\u0430 \u0441\u0442\u0440\u043e\u043a \u0438\u0437 \u0444\u0440\u0435\u0439\u043c\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 R \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432\u0435\u043a\u0442\u043e\u0440\u0430:<\/span><\/p>\n

\u041c\u0435\u0442\u043e\u0434 1: \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 \u0431\u0430\u0437\u0443 R<\/strong><\/span><\/p>\n

 new_df <- df[df$column_name %in%<\/span> values_vector, ]\n<\/strong><\/span><\/pre>\n
 \u0421\u043f\u043e\u0441\u043e\u0431 2. \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 \u043f\u0430\u043a\u0435\u0442 dplyr.<\/strong><\/span><\/p>\n
 library<\/span> (dplyr)\n\nnew_df <- df %>% filter(column_name %in%<\/span> values_vector)\n<\/strong><\/span><\/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 \u043c\u0435\u0442\u043e\u0434 \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:<\/span><\/p>\n
 #create data frame\n<\/span>df <- data. frame<\/span> (division=c('West', 'West', 'East', 'East', 'North'),\n                 points=c(120, 100, 104, 98, 105),\n                 assists=c(30, 35, 64, 28, 23))\n\n#view data frame\n<\/span>df\n\n  assists points division\n1 West 120 30\n2 West 100 35\n3 East 104 64\n4 East 98 28\n5 North 105 23\n<\/strong><\/span><\/pre>\n
 \u041f\u0440\u0438\u043c\u0435\u0440 1. \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 Base R \u0434\u043b\u044f \u0432\u044b\u0431\u043e\u0440\u0430 \u0441\u0442\u0440\u043e\u043a \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432\u0435\u043a\u0442\u043e\u0440\u0430<\/span><\/strong><\/h3>\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 \u043a\u043e\u0434, \u0447\u0442\u043e\u0431\u044b \u0432\u044b\u0431\u0440\u0430\u0442\u044c \u0442\u043e\u043b\u044c\u043a\u043e \u0442\u0435 \u0441\u0442\u0440\u043e\u043a\u0438 \u0438\u0437 \u0438\u0441\u0445\u043e\u0434\u043d\u043e\u0433\u043e \u0444\u0440\u0435\u0439\u043c\u0430 \u0434\u0430\u043d\u043d\u044b\u0445, \u0433\u0434\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 \u0434\u0435\u043b\u0435\u043d\u0438\u044f<\/strong> \u0440\u0430\u0432\u043d\u043e \u00ab\u0417\u0430\u043f\u0430\u0434\u00bb \u0438\u043b\u0438 \u00ab\u0421\u0435\u0432\u0435\u0440\u00bb.<\/span><\/p>\n
 #define values of interest\nmy_values <- c(' West<\/span> ', ' North<\/span> ')\n\n#select rows that contain 'West' or 'North' in division column\n<\/span>new_df <- df[df$division %in% my_values, ]\n\n#view results\n<\/span>new_df\n\n  assists points division\n1 West 120 30\n2 West 100 35\n5 North 105 23\n<\/span><\/span><\/strong><\/span><\/pre>\n
 \u041d\u043e\u0432\u044b\u0439 \u0444\u0440\u0435\u0439\u043c \u0434\u0430\u043d\u043d\u044b\u0445 \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442 \u0442\u043e\u043b\u044c\u043a\u043e \u0441\u0442\u0440\u043e\u043a\u0438, \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043a\u043e\u0442\u043e\u0440\u044b\u0445 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 \u0434\u0435\u043b\u0435\u043d\u0438\u044f<\/strong> \u0440\u0430\u0432\u043d\u043e \u00ab\u0417\u0430\u043f\u0430\u0434\u00bb \u0438\u043b\u0438 \u00ab\u0421\u0435\u0432\u0435\u0440\u00bb.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0435\u0440 2. \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 dplyr \u0434\u043b\u044f \u0432\u044b\u0431\u043e\u0440\u0430 \u0441\u0442\u0440\u043e\u043a \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u0432\u0435\u043a\u0442\u043e\u0440\u043d\u044b\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439.<\/span><\/strong><\/h3>\n
 \u041c\u044b \u0442\u0430\u043a\u0436\u0435 \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0444\u0443\u043d\u043a\u0446\u0438\u044e filter()<\/strong> \u0438\u0437 \u043f\u0430\u043a\u0435\u0442\u0430 dplyr<\/a> \u0432 R, \u0447\u0442\u043e\u0431\u044b \u0432\u044b\u0431\u0440\u0430\u0442\u044c \u0442\u043e\u043b\u044c\u043a\u043e \u0442\u0435 \u0441\u0442\u0440\u043e\u043a\u0438 \u0438\u0437 \u0438\u0441\u0445\u043e\u0434\u043d\u043e\u0433\u043e \u0444\u0440\u0435\u0439\u043c\u0430 \u0434\u0430\u043d\u043d\u044b\u0445, \u0433\u0434\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 \u0434\u0435\u043b\u0435\u043d\u0438\u044f<\/strong> \u0440\u0430\u0432\u043d\u043e \u00ab\u0417\u0430\u043f\u0430\u0434\u00bb \u0438\u043b\u0438 \u00ab\u0421\u0435\u0432\u0435\u0440\u00bb.<\/span><\/p>\n
 library<\/span> (dplyr)<\/span>\n\n#define values of interest\nmy_values <- c(' West<\/span> ', ' North<\/span> ')\n\n#select rows that contain 'West' or 'North' in division column\n<\/span>new_df <- df %>% filter(division %in%<\/span> my_values)\n\n#view results\n<\/span>new_df\n\n  assists points division\n1 West 120 30\n2 West 100 35\n3 North 105 23\n<\/span><\/span><\/strong><\/span><\/pre>\n
 \u041d\u043e\u0432\u044b\u0439 \u0444\u0440\u0435\u0439\u043c \u0434\u0430\u043d\u043d\u044b\u0445 \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442 \u0442\u043e\u043b\u044c\u043a\u043e \u0441\u0442\u0440\u043e\u043a\u0438, \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043a\u043e\u0442\u043e\u0440\u044b\u0445 \u0432 \u0441\u0442\u043e\u043b\u0431\u0446\u0435 \u0434\u0435\u043b\u0435\u043d\u0438\u044f<\/strong> \u0440\u0430\u0432\u043d\u043e \u00ab\u0417\u0430\u043f\u0430\u0434\u00bb \u0438\u043b\u0438 \u00ab\u0421\u0435\u0432\u0435\u0440\u00bb.<\/span><\/p>\n
 \u041f\u0440\u0438\u043c\u0435\u0447\u0430\u043d\u0438\u0435<\/strong> . \u0411\u0430\u0437\u043e\u0432\u044b\u0435 \u043c\u0435\u0442\u043e\u0434\u044b R \u0438 dplyr \u0434\u0430\u044e\u0442 \u043e\u0434\u0438\u043d\u0430\u043a\u043e\u0432\u044b\u0435 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b. \u041e\u0434\u043d\u0430\u043a\u043e \u043c\u0435\u0442\u043e\u0434 dplyr \u0431\u0443\u0434\u0435\u0442 \u0440\u0430\u0431\u043e\u0442\u0430\u0442\u044c \u0431\u044b\u0441\u0442\u0440\u0435\u0435 \u043f\u0440\u0438 \u0440\u0430\u0431\u043e\u0442\u0435 \u0441 \u043e\u0447\u0435\u043d\u044c \u0431\u043e\u043b\u044c\u0448\u0438\u043c\u0438 \u043a\u0430\u0434\u0440\u0430\u043c\u0438 \u0434\u0430\u043d\u043d\u044b\u0445.<\/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><\/h3>\n
 \u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0430\u0445 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0441\u044f, \u043a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0442\u044c \u0434\u0440\u0443\u0433\u0438\u0435 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u043d\u044b\u0435 \u0437\u0430\u0434\u0430\u0447\u0438 \u0432 R:<\/span><\/p>\n
 \u041a\u0430\u043a \u0432\u044b\u0431\u0440\u0430\u0442\u044c \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u044b\u0435 \u0441\u0442\u0440\u043e\u043a\u0438 \u0432 R \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e dplyr<\/a>
 \u041a\u0430\u043a \u0432\u044b\u0431\u0440\u0430\u0442\u044c \u0441\u0442\u0440\u043e\u043a\u0438 \u043f\u043e \u0443\u0441\u043b\u043e\u0432\u0438\u044e \u0432 R<\/a>
 \u041a\u0430\u043a \u0432\u044b\u0431\u0440\u0430\u0442\u044c \u0441\u0442\u0440\u043e\u043a\u0438, \u0432 \u043a\u043e\u0442\u043e\u0440\u044b\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u043e\u0442\u043e\u0431\u0440\u0430\u0436\u0430\u0435\u0442\u0441\u044f \u0432 \u043b\u044e\u0431\u043e\u043c \u0441\u0442\u043e\u043b\u0431\u0446\u0435 \u0432 R<\/p>\n","protected":false},"excerpt":{"rendered":"
\u0412\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043b\u044e\u0431\u043e\u0439 \u0438\u0437 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u043c\u0435\u0442\u043e\u0434\u043e\u0432 \u0434\u043b\u044f \u0432\u044b\u0431\u043e\u0440\u0430 \u0441\u0442\u0440\u043e\u043a \u0438\u0437 \u0444\u0440\u0435\u0439\u043c\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 R \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432\u0435\u043a\u0442\u043e\u0440\u0430: \u041c\u0435\u0442\u043e\u0434 1: \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 \u0431\u0430\u0437\u0443 R new_df <- df[df$column_name %in% values_vector, ] \u0421\u043f\u043e\u0441\u043e\u0431 2. \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 \u043f\u0430\u043a\u0435\u0442 dplyr. library (dplyr) new_df <- df %>% filter(column_name %in% values_vector) \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 \u043c\u0435\u0442\u043e\u0434 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435 \u0441\u043e \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c […]<\/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":[],"class_list":["post-3099","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"\nA: \u041a\u0430\u043a \u0432\u044b\u0431\u0440\u0430\u0442\u044c \u0441\u0442\u0440\u043e\u043a\u0438 \u0432\u043e \u0444\u0440\u0435\u0439\u043c\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432\u0435\u043a\u0442\u043e\u0440\u0430 \u2014 \u0421\u0442\u0430\u0442\u043e\u0440\u0438\u0430\u043b\u044b<\/title>\n<meta name=\"description\" content=\"\u0412 \u044d\u0442\u043e\u043c \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0435 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0441\u044f, \u043a\u0430\u043a \u0432\u044b\u0431\u0438\u0440\u0430\u0442\u044c \u0441\u0442\u0440\u043e\u043a\u0438 \u0432\u043e \u0444\u0440\u0435\u0439\u043c\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 R \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0432\u0435\u043a\u0442\u043e\u0440\u0430, \u0441 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u043c\u0438 \u043f\u0440\u0438\u043c\u0435\u0440\u0430\u043c\u0438.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, 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