{"id":4200,"date":"2023-07-12T21:55:51","date_gmt":"2023-07-12T21:55:51","guid":{"rendered":"https:\/\/statorials.org\/pl\/ggplot-srednia-linia-na-grupe\/"},"modified":"2023-07-12T21:55:51","modified_gmt":"2023-07-12T21:55:51","slug":"ggplot-srednia-linia-na-grupe","status":"publish","type":"post","link":"https:\/\/statorials.org\/pl\/ggplot-srednia-linia-na-grupe\/","title":{"rendered":"Jak wykre\u015bli\u0107 \u015bredni\u0105 lini\u0119 wed\u0142ug grupy w ggplot2"},"content":{"rendered":"

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Mo\u017cesz u\u017cy\u0107 nast\u0119puj\u0105cej podstawowej sk\u0142adni, aby wykre\u015bli\u0107 \u015bredni\u0105 lini\u0119 na grup\u0119 w ggplot2:<\/span><\/p>\n

 #calculate mean points value by team\n<\/span>mean_team <- df %>% group_by(team) %>% summarise(mean_pts=mean(points))\n \n#create scatterplot of assists vs points with mean line of points by team\n<\/span>ggplot(df, aes(x=assists, y=points)) +\n  geom_point(aes(color=team)) +\n  geom_hline(data=mean_team, aes(yintercept=mean_pts, col=team))\n<\/strong><\/pre>\n
 Ten konkretny przyk\u0142ad tworzy wykres rozrzutu asyst vs. zmienne asysty<\/strong> . punkty<\/strong> , a nast\u0119pnie dodaje lini\u0119 reprezentuj\u0105c\u0105 \u015bredni\u0105 warto\u015b\u0107 punkt\u00f3w<\/strong> pogrupowanych wed\u0142ug zmiennej zespo\u0142u<\/strong> .<\/span><\/p>\n
 Poni\u017cszy przyk\u0142ad pokazuje, jak zastosowa\u0107 t\u0119 sk\u0142adni\u0119 w praktyce.<\/span><\/p>\n
 Przyk\u0142ad: wykre\u015blanie \u015bredniej linii na grup\u0119 w ggplot2<\/strong><\/span><\/h2>\n
 Za\u0142\u00f3\u017cmy, \u017ce mamy nast\u0119puj\u0105c\u0105 ramk\u0119 danych w j\u0119zyku R, kt\u00f3ra zawiera informacje o punktach i asystach koszykarzy z trzech r\u00f3\u017cnych dru\u017cyn:<\/span><\/p>\n
 #create data frame\n<\/span>df <- data. frame<\/span> (team=rep(c(' A<\/span> ', ' B<\/span> ', ' C<\/span> '), each= 5<\/span> ),\n                 assists=c(2, 4, 4, 5, 6, 6, 7, 7,\n                           8, 9, 7, 8, 13, 14, 12),\n                 dots=c(8, 8, 9, 9, 10, 9, 12, 13,\n                          14, 15, 14, 14, 16, 19, 22))\n\n#view data frame\n<\/span>df\n\n   team assists points\n1 to 2 8\n2 to 4 8\n3 to 4 9\n4 to 5 9\n5 to 6 10\n6 B 6 9\n7 B 7 12\n8 B 7 13\n9 B 8 14\n10 B 9 15\n11 C 7 14\n12 C 8 14\n13 C 13 16\n14 C 14 19\n15 C 12 22\n<\/strong><\/span><\/pre>\n
 Mo\u017cemy u\u017cy\u0107 poni\u017cszego kodu, aby utworzy\u0107 wykres rozrzutu asyst<\/strong> vs. zmienne. punkty<\/strong> , a nast\u0119pnie dodaj lini\u0119 przedstawiaj\u0105c\u0105 \u015bredni\u0105 warto\u015b\u0107 punkt\u00f3w<\/strong> pogrupowanych wed\u0142ug zmiennej zespo\u0142u<\/strong> .<\/span> <\/p>\n
 library<\/span> (dplyr)\nlibrary<\/span> (ggplot2)\n\n#calculate mean points value by team\nmean_team <- df %>% group_by(team) %>% summarise(mean_pts=mean(points))\n \n#create scatterplot of assists vs points with mean line of points by team\n<\/span>ggplot(df, aes(x=assists, y=points)) +\n  geom_point(aes(color=team)) +\n  geom_hline(data=mean_team, aes(yintercept=mean_pts, col=team))<\/span><\/span><\/strong><\/span> <\/pre>\n
\"ggplot<\/p>\n
 Trzy linie s\u0105 oznaczone kolorami, aby wy\u015bwietli\u0107 \u015bredni\u0105 warto\u015b\u0107 punktow\u0105 ka\u017cdej dru\u017cyny.<\/span><\/p>\n
 Mo\u017cemy wy\u015bwietli\u0107 utworzon\u0105 przez nas ramk\u0119 danych Mean_Team<\/strong> , aby zobaczy\u0107 rzeczywiste \u015brednie warto\u015bci punktowe dla ka\u017cdego zespo\u0142u:<\/span><\/p>\n
 #view mean points value by team\nmean_team<\/span>\n\n<\/span>`summarise()` ungrouping output (override with `.groups` argument)\n# A tibble: 3 x 2\n  team mean_pts\n      \n1 to 8.8\n2 B 12.6\n3 C 17<\/strong><\/span><\/pre>\n
 Z wyniku mo\u017cemy zobaczy\u0107:<\/span><\/p>\n