<\/p>\n
Dplyr kullanarak bir veri \u00e7er\u00e7evesindeki birden \u00e7ok s\u00fctunu \u00f6zetlemek i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz:<\/span><\/p>\n Y\u00f6ntem 1: T\u00fcm s\u00fctunlar\u0131 \u00f6zetle<\/strong><\/span><\/p>\n Y\u00f6ntem 2: Belirli S\u00fctunlar\u0131 \u00d6zetleyin<\/strong><\/span><\/p>\n Y\u00f6ntem 3: T\u00fcm Say\u0131sal S\u00fctunlar\u0131 \u00d6zetle<\/strong><\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler, her y\u00f6ntemin a\u015fa\u011f\u0131daki veri \u00e7er\u00e7evesiyle nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, t\u00fcm s\u00fctunlar\u0131n ortalamas\u0131n\u0131n nas\u0131l \u00f6zetlenece\u011fini g\u00f6sterir:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, yaln\u0131zca puan<\/strong> ve ribaund<\/strong> s\u00fctunlar\u0131n\u0131n ortalamas\u0131n\u0131n nas\u0131l \u00f6zetlenece\u011fini g\u00f6sterir:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, veri \u00e7er\u00e7evesindeki t\u00fcm say\u0131sal s\u00fctunlar\u0131n ortalama ve standart sapmas\u0131n\u0131n nas\u0131l \u00f6zetlenece\u011fini g\u00f6sterir:<\/span><\/p>\n \u00c7\u0131kt\u0131, veri \u00e7er\u00e7evesindeki t\u00fcm say\u0131sal de\u011fi\u015fkenlerin ortalamas\u0131n\u0131 ve standart sapmas\u0131n\u0131 g\u00f6r\u00fcnt\u00fcler.<\/span><\/p>\n Bu \u00f6rnekte hesaplamak istedi\u011fimiz \u00e7e\u015fitli \u00f6zet istatistikleri listelemek i\u00e7in list()<\/strong> i\u015flevini kulland\u0131\u011f\u0131m\u0131z\u0131 unutmay\u0131n.<\/span><\/p>\n Not<\/strong> : Her \u00f6rnekte dplyr cross()<\/strong> fonksiyonunu kulland\u0131k. Bu i\u015fleve ili\u015fkin t\u00fcm belgeleri burada<\/a> bulabilirsiniz.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde dplyr kullan\u0131larak di\u011fer yayg\u0131n i\u015flevlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Dplyr kullanarak sat\u0131rlar nas\u0131l silinir<\/a> Dplyr kullanarak bir veri \u00e7er\u00e7evesindeki birden \u00e7ok s\u00fctunu \u00f6zetlemek i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz: Y\u00f6ntem 1: T\u00fcm s\u00fctunlar\u0131 \u00f6zetle #summarize mean of all columns df %>% group_by(group_var) %>% summarise(across(everything(), mean, na. rm = TRUE )) Y\u00f6ntem 2: Belirli S\u00fctunlar\u0131 \u00d6zetleyin #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":[11],"tags":[],"class_list":["post-3285","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\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 #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 #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 #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 \u00d6rnek 1: T\u00fcm s\u00fctunlar\u0131 \u00f6zetleme<\/strong><\/span><\/h2>\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 \u00d6rnek 2: Belirli s\u00fctunlar\u0131 \u00f6zetleme<\/strong><\/span><\/h2>\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 \u00d6rnek 3: T\u00fcm say\u0131sal s\u00fctunlar\u0131 \u00f6zetleyin<\/strong><\/span><\/h2>\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 Ek kaynaklar<\/strong><\/span><\/h2>\n
Dplyr kullanarak sat\u0131rlar nas\u0131l d\u00fczenlenir<\/a>
Dplyr kullanarak birden \u00e7ok ko\u015fula g\u00f6re filtreleme nas\u0131l yap\u0131l\u0131r<\/p>\n","protected":false},"excerpt":{"rendered":"