<\/p>\n
Funkcji colMeans()<\/strong> w j\u0119zyku R mo\u017cna u\u017cy\u0107 do obliczenia \u015bredniej z wielu kolumn macierzy lub ramki danych w j\u0119zyku R.<\/span><\/p>\n Ta funkcja wykorzystuje nast\u0119puj\u0105c\u0105 podstawow\u0105 sk\u0142adni\u0119:<\/span><\/p>\n Poni\u017csze przyk\u0142ady pokazuj\u0105, jak u\u017cywa\u0107 tej sk\u0142adni w praktyce.<\/span><\/p>\n Poni\u017cszy kod pokazuje, jak obliczy\u0107 \u015bredni\u0105 z ka\u017cdej kolumny w ramce danych:<\/span><\/p>\n Poni\u017cszy kod pokazuje, jak obliczy\u0107 \u015bredni\u0105 z ka\u017cdej kolumny i wykluczy\u0107 warto\u015bci NA:<\/span><\/p>\n Poni\u017cszy kod pokazuje jak obliczy\u0107 \u015brednie warto\u015bci poszczeg\u00f3lnych kolumn w ramce danych:<\/span><\/p>\n Pami\u0119taj, \u017ce warto\u015bci indeks\u00f3w mo\u017cemy wykorzysta\u0107 tak\u017ce do u\u015brednienia konkretnych kolumn:<\/span><\/p>\n Poni\u017csze samouczki wyja\u015bniaj\u0105, jak wykonywa\u0107 inne typowe funkcje w R:<\/span><\/p>\n Jak obliczy\u0107 odchylenie standardowe kolumn w R<\/a> Funkcji colMeans() w j\u0119zyku R mo\u017cna u\u017cy\u0107 do obliczenia \u015bredniej z wielu kolumn macierzy lub ramki danych w j\u0119zyku R. Ta funkcja wykorzystuje nast\u0119puj\u0105c\u0105 podstawow\u0105 sk\u0142adni\u0119: #calculate column means of every column colMeans(df) #calculate column means and exclude NA values colMeans(df, na. rm = T ) #calculate column means of specific columns colMeans(df[c(’ col1 ’, […]<\/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":[],"class_list":["post-2066","post","type-post","status-publish","format-standard","hentry","category-przewodnik"],"yoast_head":"\n #calculate column means of every column<\/span>\ncolMeans(df)\n\n#calculate column means and exclude NA values<\/span>\ncolMeans(df, na. rm<\/span> = T<\/span> )\n\n#calculate column means of specific columns<\/span>\ncolMeans(df[c(' col1<\/span> ', ' col3<\/span> ', ' col4<\/span> ')])<\/strong><\/pre>\n Przyk\u0142ad 1: Oblicz \u015bredni\u0105 z ka\u017cdej kolumny<\/strong><\/span><\/h3>\n
#create data frame\n<\/span>df <- data. frame<\/span> (points=c(99, 91, 86, 88, 95),\n assists=c(33, 28, 31, 39, 34),\n rebounds=c(30, 28, 24, 24, 28),\n blocks=c(1, 4, 11, 0, 2))\n\n#calculate column means\n<\/span>colMeans(df)\n\n points assists rebounds blocks \n 91.8 33.0 26.8 3.6<\/strong><\/pre>\n Przyk\u0142ad 2<\/strong><\/span> : Oblicz \u015bredni\u0105 z ka\u017cdej kolumny i wyklucz NA<\/strong><\/span><\/h3>\n
#create data frame with some NA values\n<\/span>df <- data. frame<\/span> (points=c(99, 91, 86, 88, 95),\n assists=c(33, NA, 31, 39, 34),\n rebounds=c(30, 28, NA, NA, 28),\n blocks=c(1, 4, 11, 0, 2))\n\n#calculate column means\n<\/span>colMeans(df, na. rm<\/span> = T<\/span> )\n\n points assists rebounds blocks \n91.80000 34.25000 28.66667 3.60000<\/strong><\/pre>\n Przyk\u0142ad 3: Oblicz \u015bredni\u0105 z okre\u015blonych kolumn<\/strong><\/h3>\n
#create data frame\n<\/span>df <- data. frame<\/span> (points=c(99, 91, 86, 88, 95),\n assists=c(33, 28, 31, 39, 34),\n rebounds=c(30, 28, 24, 24, 28),\n blocks=c(1, 4, 11, 0, 2))\n\n#calculate column means for 'points' and 'blocks' columns\n<\/span>colMeans(df[c(' points<\/span> ', ' blocks<\/span> ')])\n\npoint blocks \n 91.8 3.6<\/strong><\/pre>\n #create data frame\n<\/span>df <- data. frame<\/span> (points=c(99, 91, 86, 88, 95),\n assists=c(33, 28, 31, 39, 34),\n rebounds=c(30, 28, 24, 24, 28),\n blocks=c(1, 4, 11, 0, 2))\n\n#calculate column means for columns in position 1 and 4\n<\/span>colMeans(df[c(1, 4)])\n\npoint blocks \n 91.8 3.6<\/strong><\/pre>\n Dodatkowe zasoby<\/strong><\/span><\/h3>\n
Jak obliczy\u0107 \u015bredni\u0105 na grup\u0119 w R<\/a>
Jak obliczy\u0107 sum\u0119 wed\u0142ug grupy w R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"