{"id":33263,"date":"2025-10-31T20:42:29","date_gmt":"2025-10-31T17:42:29","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/?p=33263"},"modified":"2025-10-31T20:42:29","modified_gmt":"2025-10-31T17:42:29","slug":"rda-quantile-fonksiyonu-kapsamli-bir-rehber","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/rda-quantile-fonksiyonu-kapsamli-bir-rehber\/","title":{"rendered":"R&#8217;da quantile() Fonksiyonu: Kapsaml\u0131 Bir Rehber"},"content":{"rendered":"<p><body><\/p>\n<h2>R&#8217;da <code>quantile()<\/code> Fonksiyonu: Kapsaml\u0131 Bir Rehber<\/h2>\n<p>Veri analizi, istatistik ve makine \u00f6\u011frenimi gibi alanlarda, veri setlerinin da\u011f\u0131l\u0131m\u0131n\u0131 anlamak kritik \u00f6neme sahiptir. Bir veri setinin merkezi e\u011filimini (ortalama, medyan) ve yay\u0131l\u0131m\u0131n\u0131 (standart sapma, varyans) incelemek yayg\u0131n yakla\u015f\u0131mlard\u0131r. Ancak, verinin nas\u0131l yay\u0131ld\u0131\u011f\u0131n\u0131 ve belirli y\u00fczdelik dilimlerdeki de\u011ferlerini anlamak i\u00e7in kuantiller (y\u00fczdelikler) vazge\u00e7ilmez bir ara\u00e7t\u0131r. R programlama dilinde, bu ihtiyac\u0131 kar\u015f\u0131layan g\u00fc\u00e7l\u00fc ve esnek bir fonksiyon olan <code>quantile()<\/code> fonksiyonu bulunur. Bu rehberde, <code>quantile()<\/code> fonksiyonunun temel kullan\u0131m\u0131ndan geli\u015fmi\u015f \u00f6zelliklerine, farkl\u0131 hesaplama y\u00f6ntemlerinden pratik uygulamalar\u0131na kadar her y\u00f6n\u00fcn\u00fc detayl\u0131 bir \u015fekilde inceleyece\u011fiz.<\/p>\n<h3><code>quantile()<\/code> Fonksiyonunun Temel Kullan\u0131m\u0131<\/h3>\n<p><code>quantile()<\/code> fonksiyonu, bir say\u0131sal vekt\u00f6rdeki belirli olas\u0131l\u0131k (y\u00fczdelik) de\u011ferlerine kar\u015f\u0131l\u0131k gelen kuantilleri hesaplamak i\u00e7in kullan\u0131l\u0131r. Bu fonksiyon, veri setinizin i\u00e7 yap\u0131s\u0131n\u0131 anlaman\u0131za, ayk\u0131r\u0131 de\u011ferleri tespit etmenize ve veri da\u011f\u0131l\u0131m\u0131n\u0131 \u00f6zetlemenize yard\u0131mc\u0131 olur.<\/p>\n<h4>S\u00f6zdizimi<\/h4>\n<p><code>quantile()<\/code> fonksiyonunun genel s\u00f6zdizimi \u015f\u00f6yledir:<\/p>\n<pre><code class=\"language-r\">quantile(x, probs = seq(0, 1, 0.25), na.rm = FALSE, names = TRUE, type = 7, ...)\n<\/code><\/pre>\n<h4>Parametreler<\/h4>\n<ul>\n<li><code>x<\/code>: Kuantillerin hesaplanaca\u011f\u0131 say\u0131sal vekt\u00f6r. Bu, bir veri \u00e7er\u00e7evesinin s\u00fctunu, bir liste eleman\u0131 veya basit bir say\u0131sal vekt\u00f6r olabilir.<\/li>\n<li><code>probs<\/code>: Kuantillerin hesaplanaca\u011f\u0131 olas\u0131l\u0131k de\u011ferlerini (0 ile 1 aras\u0131nda) i\u00e7eren say\u0131sal bir vekt\u00f6r. Varsay\u0131lan olarak, bu parametre <code>seq(0, 1, 0.25)<\/code> olarak ayarlanm\u0131\u015ft\u0131r, yani \u00e7eyreklikler (0%, 25%, 50%, 75%, 100%) hesaplan\u0131r. \u00d6rne\u011fin, %90&#8217;l\u0131k kuantili almak i\u00e7in <code>probs = 0.90<\/code> veya %5 ve %95&#8217;lik kuantilleri almak i\u00e7in <code>probs = c(0.05, 0.95)<\/code> kullanabilirsiniz.<\/li>\n<li><code>na.rm<\/code>: Mant\u0131ksal bir de\u011ferdir (TRUE\/FALSE). E\u011fer <code>TRUE<\/code> ise, <code>x<\/code> vekt\u00f6r\u00fcndeki <code>NA<\/code> (eksik) de\u011ferler hesaplamadan \u00f6nce \u00e7\u0131kar\u0131l\u0131r. Varsay\u0131lan olarak <code>FALSE<\/code>&#8216;dur, yani <code>NA<\/code> de\u011ferleri varsa sonu\u00e7 da <code>NA<\/code> olur.<\/li>\n<li><code>names<\/code>: Mant\u0131ksal bir de\u011ferdir. E\u011fer <code>TRUE<\/code> ise, sonu\u00e7 vekt\u00f6r\u00fcndeki kuantillere ilgili olas\u0131l\u0131k de\u011ferlerini g\u00f6steren isimler atan\u0131r. Varsay\u0131lan olarak <code>TRUE<\/code>&#8216;dur.<\/li>\n<li><code>type<\/code>: Kuantil hesaplama algoritmas\u0131n\u0131 belirten bir tam say\u0131 (1&#8217;den 9&#8217;a kadar). Bu parametre, farkl\u0131 istatistiksel yaz\u0131l\u0131mlar\u0131n veya teorik yakla\u015f\u0131mlar\u0131n kuantil tan\u0131m\u0131ndaki ince farklar\u0131 yans\u0131t\u0131r. Varsay\u0131lan de\u011fer <code>7<\/code>&#8216;dir ve genellikle g\u00fcvenli bir se\u00e7im olarak kabul edilir. Bu parametreye daha sonra detayl\u0131 de\u011finece\u011fiz.<\/li>\n<li><code>...<\/code>: Di\u011fer arg\u00fcmanlar (nadiren kullan\u0131l\u0131r).<\/li>\n<\/ul>\n<h4>Basit \u00d6rnekler<\/h4>\n<p>\u015eimdi birka\u00e7 basit \u00f6rnekle <code>quantile()<\/code> fonksiyonunun nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 g\u00f6relim:<\/p>\n<pre><code class=\"language-r\"># \u00d6rnek bir veri seti olu\u015ftural\u0131m\nset.seed(123) # Tekrarlanabilirlik i\u00e7in\nveri <- c(12, 15, 18, 20, 22, 25, 28, 30, 33, 35, 38, 40, 42, 45, 48, 50)\n\n<h2>Varsay\u0131lan olarak \u00e7eyreklikleri hesapla (0%, 25%, 50%, 75%, 100%)<\/h2>\nquantile(veri)\n<h2>\u00c7\u0131kt\u0131:<\/h2>\n<h2>0%  25%  50%  75% 100% <\/h2>\n<h2>12 21.5 31.5 40.5   50 <\/h2>\n\n<h2>Sadece medyan\u0131 (50. y\u00fczdelik) hesapla<\/h2>\nquantile(veri, probs = 0.5)\n<h2>\u00c7\u0131kt\u0131:<\/h2>\n<h2>50% <\/h2>\n<h2>31.5 <\/h2>\n\n<h2>%10 ve %90'l\u0131k kuantilleri hesapla<\/h2>\nquantile(veri, probs = c(0.10, 0.90))\n<h2>\u00c7\u0131kt\u0131:<\/h2>\n<h2>10%  90% <\/h2>\n<h2>16.5 43.5 <\/h2>\n\n<h2>Veri setinde NA de\u011ferleri varken<\/h2>\nveri_na <- c(veri, NA, 55)\nquantile(veri_na) # NA de\u011ferleri oldu\u011fu i\u00e7in sonu\u00e7 NA olur\n<h2>\u00c7\u0131kt\u0131:<\/h2>\n<h2>0%  25%  50%  75% 100% <\/h2>\n<h2>NA   NA   NA   NA   NA <\/h2>\n\n<h2>NA de\u011ferlerini g\u00f6z ard\u0131 ederek hesapla<\/h2>\nquantile(veri_na, na.rm = TRUE)\n<h2>\u00c7\u0131kt\u0131:<\/h2>\n<h2>0%  25%  50%  75% 100% <\/h2>\n<h2>12 22.5 32.5 41.5   55 <\/h2>\n\n<h2>Sonu\u00e7lara isim atama<\/h2>\nquantile(veri, probs = c(0.25, 0.75), names = FALSE) # \u0130simsiz \u00e7\u0131kt\u0131\n<h2>\u00c7\u0131kt\u0131:<\/h2>\n<h2>21.5 40.5 <\/h2>\n<\/code><\/pre>\n<h3><code>type<\/code> Parametresi ve Farkl\u0131 Kuantil Hesaplama Y\u00f6ntemleri<\/h3>\n<p><code>quantile()<\/code> fonksiyonunun en teknik ve bazen kafa kar\u0131\u015ft\u0131r\u0131c\u0131 olabilen parametresi <code>type<\/code>&#8216;t\u0131r. Bu parametre, bir veri setindeki kuantillerin nas\u0131l tahmin edilece\u011fini belirleyen dokuz farkl\u0131 algoritmay\u0131 temsil eder. Bu algoritmalar, \u00f6zellikle k\u00fc\u00e7\u00fck veri setleri veya kesikli da\u011f\u0131l\u0131mlar i\u00e7in farkl\u0131 sonu\u00e7lar \u00fcretebilir. Farkl\u0131 algoritmalar\u0131n varl\u0131\u011f\u0131, kuantilin matematiksel tan\u0131m\u0131n\u0131n tek bir yorumu olmamas\u0131ndan kaynaklan\u0131r; genellikle s\u00fcrekli bir da\u011f\u0131l\u0131mdan \u00f6rneklenen kesikli verilere uygulan\u0131rken enterpolasyon y\u00f6ntemleri devreye girer.<\/p>\n<p>\u0130\u015fte en yayg\u0131n kullan\u0131lan <code>type<\/code> de\u011ferleri ve k\u0131sa a\u00e7\u0131klamalar\u0131:<\/p>\n<ul>\n<li><code>type = 1<\/code>: Ters empirik da\u011f\u0131l\u0131m fonksiyonu. En basit y\u00f6ntemdir, g\u00f6zlem de\u011ferlerinden birini d\u00f6nd\u00fcr\u00fcr.<\/li>\n<li><code>type = 2<\/code>: <code>type=1<\/code>&#8216;in ortalamas\u0131d\u0131r.<\/li>\n<li><code>type = 3<\/code>: SAS sistemindeki <code>PCTLDEF=3<\/code>&#8216;e benzer. En yak\u0131n tek say\u0131l\u0131 indekse g\u00f6re enterpolasyon yapar.<\/li>\n<li><code>type = 4<\/code>: SAS sistemindeki <code>PCTLDEF=4<\/code>&#8216;e benzer. En yak\u0131n \u00e7ift say\u0131l\u0131 indekse g\u00f6re enterpolasyon yapar.<\/li>\n<li><code>type = 5<\/code>: Do\u011frusal enterpolasyon, g\u00f6zlemlerin orta noktalar\u0131n\u0131 kullan\u0131r.<\/li>\n<li><code>type = 6<\/code>: Do\u011frusal enterpolasyon, Excel&#8217;in <code>PERCENTILE.INC<\/code> fonksiyonuna benzer.<\/li>\n<li><code>type = 7<\/code> (Varsay\u0131lan): S ve R&#8217;\u0131n varsay\u0131lan y\u00f6ntemidir. Empirik da\u011f\u0131l\u0131m fonksiyonunun do\u011frusal enterpolasyonunu kullan\u0131r. S\u00fcrekli veriler i\u00e7in genellikle iyi bir se\u00e7imdir ve en yayg\u0131n olarak kullan\u0131l\u0131r.<\/li>\n<li><code>type = 8<\/code>: Plotting pozisyonlar\u0131na dayal\u0131 do\u011frusal enterpolasyon. Medyan konumlar\u0131n\u0131 kullan\u0131r.<\/li>\n<li><code>type = 9<\/code>: Plotting pozisyonlar\u0131na dayal\u0131 do\u011frusal enterpolasyon. Ortalama konumlar\u0131n\u0131 kullan\u0131r.<\/li>\n<\/ul>\n<p>Bu farkl\u0131 <code>type<\/code> de\u011ferleri, \u00f6zellikle k\u00fc\u00e7\u00fck veri setlerinde veya kuantilin tam olarak bir veri noktas\u0131na denk gelmedi\u011fi durumlarda farkl\u0131 sonu\u00e7lar verebilir. B\u00fcy\u00fck veri setlerinde ise sonu\u00e7lar genellikle birbirine yak\u0131nla\u015f\u0131r.<\/p>\n<h4><code>type<\/code> Parametresinin Farkl\u0131l\u0131klar\u0131n\u0131 G\u00f6steren \u00d6rnek<\/h4>\n<pre><code class=\"language-r\">veri_kucuk <- 1:10 # 1'den 10'a kadar say\u0131lar\n\n<h2>%50'lik kuantil (medyan) i\u00e7in farkl\u0131 tiplerin kar\u015f\u0131la\u015ft\u0131rmas\u0131<\/h2>\ncat(\"Type 1:\", quantile(veri_kucuk, probs = 0.5, type = 1), \"\\n\")\ncat(\"Type 2:\", quantile(veri_kucuk, probs = 0.5, type = 2), \"\\n\")\ncat(\"Type 3:\", quantile(veri_kucuk, probs = 0.5, type = 3), \"\\n\")\ncat(\"Type 4:\", quantile(veri_kucuk, probs = 0.5, type = 4), \"\\n\")\ncat(\"Type 5:\", quantile(veri_kucuk, probs = 0.5, type = 5), \"\\n\")\ncat(\"Type 6:\", quantile(veri_kucuk, probs = 0.5, type = 6), \"\\n\")\ncat(\"Type 7 (Default):\", quantile(veri_kucuk, probs = 0.5, type = 7), \"\\n\")\ncat(\"Type 8:\", quantile(veri_kucuk, probs = 0.5, type = 8), \"\\n\")\ncat(\"Type 9:\", quantile(veri_kucuk, probs = 0.5, type = 9), \"\\n\")\n\n<h2>\u00c7\u0131kt\u0131 \u00f6rnekleri (farkl\u0131 R versiyonlar\u0131nda veya veri setlerinde de\u011fi\u015febilir):<\/h2>\n<h2>Type 1: 5 <\/h2>\n<h2>Type 2: 5 <\/h2>\n<h2>Type 3: 5 <\/h2>\n<h2>Type 4: 6 <\/h2>\n<h2>Type 5: 5.5 <\/h2>\n<h2>Type 6: 5.5 <\/h2>\n<h2>Type 7 (Default): 5.5 <\/h2>\n<h2>Type 8: 5.5 <\/h2>\n<h2>Type 9: 5.5 <\/h2>\n<\/code><\/pre>\n<p>Yukar\u0131daki \u00f6rnekte, 1&#8217;den 10&#8217;a kadar olan say\u0131lar i\u00e7in medyan (50. y\u00fczdelik) hesaplan\u0131rken, baz\u0131 tipler 5 veya 6 gibi tam say\u0131 de\u011ferleri d\u00f6nd\u00fcr\u00fcrken, \u00e7o\u011fu tip 5.5 gibi enterpolasyonlu bir de\u011fer d\u00f6nd\u00fcrm\u00fc\u015ft\u00fcr. Bu farkl\u0131l\u0131klar, her bir tipin kuantili nas\u0131l tan\u0131mlad\u0131\u011f\u0131 ve g\u00f6zlemler aras\u0131nda nas\u0131l enterpolasyon yapt\u0131\u011f\u0131 ile ilgilidir. Genellikle, s\u00fcrekli bir da\u011f\u0131l\u0131mdan geldi\u011fi varsay\u0131lan veriler i\u00e7in <code>type = 7<\/code> (varsay\u0131lan) veya <code>type = 8<\/code>\/<code>type = 9<\/code> tercih edilirken, kesikli veriler veya belirli bir yaz\u0131l\u0131mla uyumluluk gerekti\u011finde di\u011fer tipler kullan\u0131labilir.<\/p>\n<h3>Pratik Uygulamalar ve \u0130pu\u00e7lar\u0131<\/h3>\n<p><code>quantile()<\/code> fonksiyonu, veri analizinde \u00e7ok \u00e7e\u015fitli pratik senaryolarda kullan\u0131labilir. \u0130\u015fte baz\u0131 \u00f6rnekler ve ipu\u00e7lar\u0131:<\/p>\n<h4>Ayk\u0131r\u0131 De\u011fer Tespiti<\/h4>\n<p>Kuantiller, \u00f6zellikle \u00e7eyreklikler aras\u0131 aral\u0131k (IQR &#8211; Interquartile Range) kullanarak ayk\u0131r\u0131 de\u011ferleri tespit etmek i\u00e7in g\u00fc\u00e7l\u00fc bir y\u00f6ntem sa\u011flar. IQR, 75. y\u00fczdelik (Q3) ile 25. y\u00fczdelik (Q1) aras\u0131ndaki farkt\u0131r. Genellikle, bir de\u011ferin Q1 &#8211; 1.5 <em> IQR&#8217;den k\u00fc\u00e7\u00fck veya Q3 + 1.5 <\/em> IQR&#8217;den b\u00fcy\u00fck olmas\u0131 ayk\u0131r\u0131 de\u011fer olarak kabul edilir.<\/p>\n<pre><code class=\"language-r\"># IQR kullanarak ayk\u0131r\u0131 de\u011fer tespiti\nveri_aykiri <- c(10, 12, 15, 18, 20, 22, 25, 28, 30, 80, 85)\n\nkuantiller <- quantile(veri_aykiri, probs = c(0.25, 0.75))\nQ1 <- kuantiller[1]\nQ3 <- kuantiller[2]\nIQR <- Q3 - Q1\n\nalt_sinir <- Q1 - 1.5 * IQR\nust_sinir <- Q3 + 1.5 * IQR\n\naykiri_degerler <- veri_aykiri[veri_aykiri < alt_sinir | veri_aykiri > ust_sinir]\n\ncat(\"Q1:\", Q1, \"\\n\")\ncat(\"Q3:\", Q3, \"\\n\")\ncat(\"IQR:\", IQR, \"\\n\")\ncat(\"Alt S\u0131n\u0131r:\", alt_sinir, \"\\n\")\ncat(\"\u00dcst S\u0131n\u0131r:\", ust_sinir, \"\\n\")\ncat(\"Ayk\u0131r\u0131 De\u011ferler:\", aykiri_degerler, \"\\n\")\n<h2>\u00c7\u0131kt\u0131: Ayk\u0131r\u0131 De\u011ferler: 80 85 <\/h2>\n<\/code><\/pre>\n<h4>Veri Da\u011f\u0131l\u0131m\u0131n\u0131 Anlama<\/h4>\n<p>Kuantiller, bir veri setinin merkezi e\u011filimini, yay\u0131l\u0131m\u0131n\u0131 ve \u00e7arp\u0131kl\u0131\u011f\u0131n\u0131 h\u0131zl\u0131ca \u00f6zetlemenize olanak tan\u0131r. \u00d6rne\u011fin, medyan\u0131n (50. y\u00fczdelik) ortalamadan ne kadar farkl\u0131 oldu\u011funu veya Q1 ile medyan aras\u0131ndaki mesafenin medyan ile Q3 aras\u0131ndaki mesafeden ne kadar farkl\u0131 oldu\u011funu inceleyerek da\u011f\u0131l\u0131m\u0131n \u00e7arp\u0131k olup olmad\u0131\u011f\u0131n\u0131 anlayabilirsiniz.<\/p>\n<h4>Kar\u015f\u0131la\u015ft\u0131rmal\u0131 Analiz<\/h4>\n<p>Farkl\u0131 gruplar veya zaman dilimleri aras\u0131ndaki verileri kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in kuantilleri kullanabilirsiniz. \u00d6rne\u011fin, iki farkl\u0131 \u00fcr\u00fcn\u00fcn sat\u0131\u015f fiyatlar\u0131n\u0131n %90&#8217;l\u0131k kuantillerini kar\u015f\u0131la\u015ft\u0131rarak hangi \u00fcr\u00fcn\u00fcn daha y\u00fcksek fiyatl\u0131 segmentte yer ald\u0131\u011f\u0131n\u0131 g\u00f6rebilirsiniz.<\/p>\n<pre><code class=\"language-r\">urun_A_fiyatlar <- c(100, 120, 150, 130, 110, 180, 140, 160)\nurun_B_fiyatlar <- c(90, 110, 100, 120, 130, 105, 115, 125)\n\nquantile(urun_A_fiyatlar, probs = 0.90)\nquantile(urun_B_fiyatlar, probs = 0.90)\n<h2>Bu \u00f6rnekte, A \u00fcr\u00fcn\u00fcn\u00fcn %90'l\u0131k fiyat\u0131 (174) B \u00fcr\u00fcn\u00fcn\u00fcn %90'l\u0131k fiyat\u0131ndan (126.5) daha y\u00fcksek.<\/h2>\n<\/code><\/pre>\n<h4>Performans Metrikleri<\/h4>\n<p>Sistem performans\u0131, a\u011f gecikmesi veya servis yan\u0131t s\u00fcreleri gibi metriklerde, ortalama yerine y\u00fcksek y\u00fczdelikler (\u00f6rne\u011fin, %95 veya %99) kullanmak daha anlaml\u0131 olabilir. Bu, en k\u00f6t\u00fc durum senaryolar\u0131nda dahi sistemin ne kadar iyi performans g\u00f6sterdi\u011fini anlaman\u0131za yard\u0131mc\u0131 olur.<\/p>\n<h4>Dikkat Edilmesi Gerekenler<\/h4>\n<ul>\n<li><strong>\u00d6rneklem Boyutu:<\/strong> K\u00fc\u00e7\u00fck \u00f6rneklemlerde, <code>type<\/code> parametresinin se\u00e7imi sonu\u00e7lar\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde etkileyebilir. B\u00fcy\u00fck \u00f6rneklemlerde bu fark genellikle azal\u0131r.<\/li>\n<li><strong>Veri Tipi:<\/strong> Verinizin s\u00fcrekli mi yoksa kesikli mi oldu\u011funu g\u00f6z \u00f6n\u00fcnde bulundurun. S\u00fcrekli veriler i\u00e7in enterpolasyonlu tipler (varsay\u0131lan <code>type = 7<\/code>) daha uygunken, kesikli veriler i\u00e7in bazen enterpolasyon yapmayan tipler daha anlaml\u0131 olabilir.<\/li>\n<li><strong>Yorumlama:<\/strong> Elde etti\u011finiz kuantil de\u011ferlerini her zaman verinizin ba\u011flam\u0131nda yorumlay\u0131n. \u00d6rne\u011fin, %90&#8217;l\u0131k kuantil, verilerin %90&#8217;\u0131n\u0131n bu de\u011ferin alt\u0131nda oldu\u011funu g\u00f6sterir.<\/li>\n<\/ul>\n<h3>Sonu\u00e7 ve S\u0131k\u00e7a Sorulan Sorular (SSS)<\/h3>\n<p><code>quantile()<\/code> fonksiyonu, R&#8217;daki en temel ancak en g\u00fc\u00e7l\u00fc istatistiksel ara\u00e7lardan biridir. Veri setlerinin da\u011f\u0131l\u0131m\u0131n\u0131 h\u0131zl\u0131ca anlamak, ayk\u0131r\u0131 de\u011ferleri tespit etmek ve farkl\u0131 gruplar\u0131 kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in vazge\u00e7ilmezdir. <code>type<\/code> parametresinin n\u00fcanslar\u0131n\u0131 anlamak, analizinizi daha do\u011fru ve g\u00fcvenilir hale getirecektir.<\/p>\n<h4>S\u0131k\u00e7a Sorulan Sorular (SSS)<\/h4>\n<p><strong>S1: <code>quantile()<\/code> ile <code>summary()<\/code> aras\u0131ndaki fark nedir?<\/strong><br \/>\nC1: <code>summary()<\/code> fonksiyonu, bir say\u0131sal vekt\u00f6r i\u00e7in minimum, maksimum, medyan, Q1 ve Q3 gibi sabit istatistikleri d\u00f6nd\u00fcr\u00fcr. <code>quantile()<\/code> ise, istedi\u011finiz herhangi bir y\u00fczdelik dilimini (olas\u0131l\u0131\u011f\u0131) belirterek daha esnek bir \u015fekilde kuantilleri hesaplaman\u0131za olanak tan\u0131r. <code>summary()<\/code> asl\u0131nda <code>quantile()<\/code> fonksiyonunu belirli <code>probs<\/code> de\u011ferleriyle \u00e7a\u011f\u0131r\u0131r.<\/p>\n<p><strong>S2: Hangi <code>type<\/code> de\u011ferini kullanmal\u0131y\u0131m?<\/strong><br \/>\nC2: \u00c7o\u011fu durumda ve s\u00fcrekli veriler i\u00e7in varsay\u0131lan <code>type = 7<\/code> iyi bir se\u00e7imdir ve genellikle di\u011fer istatistiksel yaz\u0131l\u0131mlar\u0131n sonu\u00e7lar\u0131yla uyumludur. Ancak, belirli bir yaz\u0131l\u0131mla (\u00f6rne\u011fin SAS veya Excel) uyumluluk sa\u011flaman\u0131z gerekiyorsa veya verinizin do\u011fas\u0131 (kesikli olmas\u0131 gibi) belirli bir <code>type<\/code> de\u011ferini gerektiriyorsa, di\u011fer tipleri deneyebilirsiniz. K\u00fc\u00e7\u00fck \u00f6rneklemler i\u00e7in farkl\u0131 tipler aras\u0131ndaki farklar daha belirgin olabilir.<\/p>\n<p><strong>S3: Kuantil hesaplarken <code>NA<\/code> de\u011ferlerini nas\u0131l y\u00f6netmeliyim?<\/strong><br \/>\nC3: <code>na.rm = TRUE<\/code> arg\u00fcman\u0131n\u0131 kullanarak <code>NA<\/code> de\u011ferlerini hesaplamadan \u00e7\u0131karabilirsiniz. E\u011fer <code>na.rm = FALSE<\/code> (varsay\u0131lan) b\u0131rak\u0131l\u0131rsa ve vekt\u00f6rde <code>NA<\/code> de\u011ferleri varsa, sonu\u00e7 da <code>NA<\/code> olacakt\u0131r. Hangi yakla\u015f\u0131m\u0131n do\u011fru oldu\u011fu, eksik verilerle ilgili analitik stratejinize ba\u011fl\u0131d\u0131r (\u00f6rne\u011fin, eksik verileri impute etmek veya tamamen g\u00f6z ard\u0131 etmek).<\/p>\n<p><strong>S4: \u00c7ok b\u00fcy\u00fck veri setlerinde <code>quantile()<\/code> performans\u0131 nas\u0131ld\u0131r?<\/strong><br \/>\nC4: R&#8217;daki <code>quantile()<\/code> fonksiyonu, s\u0131ralama algoritmalar\u0131na dayand\u0131\u011f\u0131 i\u00e7in genellikle olduk\u00e7a verimlidir. Ancak, milyarlarca eleman\u0131 olan a\u015f\u0131r\u0131 b\u00fcy\u00fck veri setlerinde performans sorunlar\u0131 ya\u015fanabilir. Bu t\u00fcr durumlarda, <code>data.table<\/code> veya <code>dplyr<\/code> gibi paketlerin optimize edilmi\u015f fonksiyonlar\u0131n\u0131 kullanmak veya b\u00fcy\u00fck veri i\u015fleme tekniklerini (\u00f6rne\u011fin, \u00f6rnekleme veya da\u011f\u0131t\u0131lm\u0131\u015f hesaplama) d\u00fc\u015f\u00fcnmek faydal\u0131 olabilir.<\/p>\n<p><strong>S5: Kuantiller ve y\u00fczdelikler ayn\u0131 \u015fey midir?<\/strong><br \/>\nC5: Evet, temelde ayn\u0131 kavram\u0131 ifade ederler. Kuantil, bir veri setini e\u015fit boyutlu dilimlere b\u00f6len de\u011ferlerdir. Y\u00fczdelikler (percentiles) ise, kuantillerin %0 ile %100 aras\u0131nda ifade edilen \u00f6zel bir bi\u00e7imidir. \u00d6rne\u011fin, 0.25 kuantili %25&#8217;lik y\u00fczdeli\u011fe e\u015fittir.<\/p>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"R&#8217;da quantile() Fonksiyonu: Kapsaml\u0131 Bir Rehber\nVeri analizi, istatistik ve makine \u00f6\u011frenimi gibi alanlarda, veri setlerinin da\u011f\u0131l\u0131m\u0131n\u0131 anla","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-33263","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.5 (Yoast SEO v25.3.1) - 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