Bunu yapmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n R’de \u00e7e\u015fitli basketbol tak\u0131mlar\u0131ndaki oyuncular\u0131n att\u0131\u011f\u0131 puanlar hakk\u0131nda bilgi i\u00e7eren a\u015fa\u011f\u0131daki veri \u00e7er\u00e7evesine sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n \u015eimdi her tak\u0131mdaki oyuncular\u0131n att\u0131\u011f\u0131 ortalama puanlar\u0131 iki farkl\u0131 y\u00f6ntem kullanarak hesaplamak istedi\u011fimizi varsayal\u0131m:<\/span><\/p>\n Bu ifadelerin her birinin y\u00fcr\u00fct\u00fclmesi i\u00e7in gereken s\u00fcreyi \u00f6l\u00e7mek i\u00e7in microbenchmark()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n microbenchmark()<\/strong> i\u015flevi her ifadeyi 100 kez \u00e7al\u0131\u015ft\u0131r\u0131r ve a\u015fa\u011f\u0131daki \u00f6l\u00e7\u00fcmleri \u00f6l\u00e7er:<\/span><\/p>\n Genellikle her ifadeyi y\u00fcr\u00fctmek i\u00e7in yaln\u0131zca ortalama veya medyan s\u00fcreye bakar\u0131z.<\/span><\/p>\n Sonu\u00e7tan \u015funu g\u00f6rebiliriz:<\/span><\/p>\n Bu sonu\u00e7lara dayanarak temel R y\u00f6nteminin \u00f6nemli \u00f6l\u00e7\u00fcde daha h\u0131zl\u0131 oldu\u011fu sonucuna vard\u0131k.<\/span><\/p>\n Her ifadeyi y\u00fcr\u00fctmek i\u00e7in gereken s\u00fcrelerin da\u011f\u0131l\u0131m\u0131n\u0131 g\u00f6rselle\u015ftirmek i\u00e7in boxplot()<\/strong> i\u015flevini de kullanabiliriz:<\/span> <\/p>\n Kutu grafiklerinden dplyr y\u00f6nteminin tak\u0131m ba\u015f\u0131na ortalama puan de\u011ferini hesaplamas\u0131n\u0131n ortalama olarak daha uzun s\u00fcrd\u00fc\u011f\u00fcn\u00fc g\u00f6rebiliriz.<\/span><\/p>\n Not<\/strong> : Bu \u00f6rnekte iki farkl\u0131 ifadenin y\u00fcr\u00fctme s\u00fcrelerini kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in microbenchmark()<\/strong> fonksiyonunu kulland\u0131k ancak pratikte istedi\u011finiz kadar ifadeyi kar\u015f\u0131la\u015ft\u0131rabilirsiniz.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde R’de di\u011fer ortak g\u00f6revlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n R’de ortam nas\u0131l temizlenir<\/a> Farkl\u0131 ifadelerin y\u00fcr\u00fctme s\u00fcrelerini kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in R’deki microbenchmark paketini kullanabilirsiniz. Bunu yapmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabilirsiniz: library (microbenchmark) #compare execution time of two different expressions microbenchmark( expression1, expression2) ) A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir. \u00d6rnek: R’de microbenchmark() i\u015flevini kullanma R’de \u00e7e\u015fitli basketbol tak\u0131mlar\u0131ndaki oyuncular\u0131n att\u0131\u011f\u0131 puanlar hakk\u0131nda bilgi i\u00e7eren a\u015fa\u011f\u0131daki veri \u00e7er\u00e7evesine […]<\/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-4029","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n library<\/span> (microbenchmark)\n\n#compare execution time of two different expressions\n<\/span>microbenchmark(\n expression1,\n expression2)\n)\n<\/strong><\/pre>\n \u00d6rnek: R’de microbenchmark() i\u015flevini kullanma<\/strong><\/span><\/h2>\n
#make this example reproducible\n<\/span>set. seed<\/span> ( 1<\/span> )\n\n#create data frame\n<\/span>df <- data. frame<\/span> (team=rep(c(' A<\/span> ', ' B<\/span> '), each= 500<\/span> ),\n points=rnorm( 1000<\/span> , mean= 20<\/span> ))\n\n#view data frame\n<\/span>head(df)\n\n team points\n1 A 19.37355\n2 A 20.18364\n3 A 19.16437\n4 A 21.59528\n5 A 20.32951\n6 A 19.17953\n<\/span><\/span><\/strong><\/pre>\n\n
library<\/span> (microbenchmark)\nlibrary<\/span> (dplyr)\n\n#time how long it takes to calculate mean value of points by team\n<\/span>microbenchmark(\n aggregate(df$points, list(df$team), FUN=mean),\n df %>% group_by(team) %>% summarise_at(vars(points), list(name = mean))\n)\n\nUnit: milliseconds\n express\n aggregate(df$points, list(df$team), FUN = mean)\n df %>% group_by(team) %>% summarise_at(vars(points), list(name = mean))\n min lq mean median uq max neval cld\n 1.307908 1.524078 1.852167 1.743568 2.093813 4.67408 100 a \n 6.788584 7.810932 9.946286 8.914692 10.239904 56.20928 100 b\n<\/strong><\/pre>\n\n
\n
library<\/span> (microbenchmark)\nlibrary<\/span> (dplyr)\n\n#time how long it takes to calculate mean value of points by team\n<\/span>results <- microbenchmark(\n aggregate(df$points, list(df$team), FUN=mean),\n df %>% group_by(team) %>% summarise_at(vars(points), list(name = mean))\n)\n\n#create boxplot to visualize results\n<\/span>boxplot(results, names=c(' Base R<\/span> ', ' dplyr<\/span> '))\n<\/strong><\/pre>\n![\"Mikro](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/micro1.jpg\")
<\/p>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
RStudio’daki t\u00fcm grafikler nas\u0131l temizlenir?<\/a>
R’ye birden fazla paket nas\u0131l y\u00fcklenir<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"