<\/p>\n
Veri \u00e7er\u00e7evesi nesnelerinin, veri \u00e7er\u00e7evesi ad\u0131n\u0131 yazmaya gerek kalmadan eri\u015filebilir olmas\u0131n\u0131 sa\u011flamak i\u00e7in R’dekiattach ()<\/b> i\u015flevini kullanabilirsiniz.<\/span><\/p>\n Bu i\u015flev a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullan\u0131r:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler, bu i\u015flevin a\u015fa\u011f\u0131daki veri \u00e7er\u00e7evesiyle farkl\u0131 senaryolarda nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Normalde ortalamay\u0131, medyan\u0131, aral\u0131\u011f\u0131 vb. hesaplamak istiyorsak. bir veri \u00e7er\u00e7evesindeki bir s\u00fctun i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanaca\u011f\u0131z:<\/span><\/span><\/p>\n Ancak, attach()<\/strong> kullan\u0131rsak, bu hesaplamalar\u0131 ger\u00e7ekle\u015ftirmek i\u00e7in veri \u00e7er\u00e7evesi ad\u0131n\u0131 girmemize bile gerek kalmaz:<\/span><\/p>\n attach()<\/strong> kullanarak do\u011frudan s\u00fctun ad\u0131na ba\u015fvurabiliriz ve R, hangi veri \u00e7er\u00e7evesini kullanmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131m\u0131z\u0131 bilir.<\/span><\/span><\/p>\n Normalde, R’ye do\u011frusal bir regresyon modeli s\u0131\u011fd\u0131rmak istiyorsak a\u015fa\u011f\u0131daki s\u00f6zdizimini kullan\u0131r\u0131z:<\/span><\/p>\n Bununla birlikte, e\u011ferattach ()<\/strong> kullan\u0131rsak, regresyon modeline uyum sa\u011flamak i\u00e7in lm()<\/strong> fonksiyonundaki veri<\/strong> arg\u00fcman\u0131n\u0131 kullanmam\u0131za bile gerek kalmaz:<\/span><\/p>\n Regresyon sonu\u00e7lar\u0131n\u0131n tamamen ayn\u0131 oldu\u011funu unutmay\u0131n.<\/span><\/span><\/p>\n Ge\u00e7erli R ortam\u0131ndaki t\u00fcm ekli nesneleri g\u00f6r\u00fcnt\u00fclemek i\u00e7in search()<\/strong> i\u015flevini kullanabilirsiniz:<\/span><\/p>\n \u015eu anda ayr\u0131lm\u0131\u015f bir nesneyi ay\u0131rmak i\u00e7in detach()<\/strong> i\u015flevini kullanabilirsiniz:<\/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> Veri \u00e7er\u00e7evesi nesnelerinin, veri \u00e7er\u00e7evesi ad\u0131n\u0131 yazmaya gerek kalmadan eri\u015filebilir olmas\u0131n\u0131 sa\u011flamak i\u00e7in R’dekiattach () i\u015flevini kullanabilirsiniz. Bu i\u015flev a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullan\u0131r: attach(data) A\u015fa\u011f\u0131daki \u00f6rnekler, bu i\u015flevin a\u015fa\u011f\u0131daki veri \u00e7er\u00e7evesiyle farkl\u0131 senaryolarda nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir: #create data frame df <- data. frame (team=c(‘A’, ‘B’, ‘C’, ‘D’, ‘E’), points=c(99, 90, 86, 88, 95), assists=c(33, 28, […]<\/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-3087","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n attach(data)\n<\/strong><\/pre>\n
#create data frame\n<\/span>df <- data. frame<\/span> (team=c('A', 'B', 'C', 'D', 'E'),\n points=c(99, 90, 86, 88, 95),\n assists=c(33, 28, 31, 39, 34),\n rebounds=c(30, 28, 24, 24, 28))\n\n#view data frame\n<\/span>df\n\n team points assists rebounds\n1 A 99 33 30\n2 B 90 28 28\n3 C 86 31 24\n4 D 88 39 24\n5 E 95 34 28\n<\/strong><\/pre>\n \u00d6rnek 1: Hesaplamalar\u0131 ger\u00e7ekle\u015ftirmek i\u00e7inattach() i\u015flevini kullan\u0131n<\/strong><\/span><\/h3>\n
#calculate mean of rebounds column\n<\/span>mean(df$rebounds)\n\n[1] 26.8\n\n#calculate median of rebounds column<\/span>\nmedian(df$rebounds)\n\n[1] 28\n\n#calculate range of rebounds column<\/span>\nrange(df$rebounds)\n\n[1] 24 30\n<\/span><\/span><\/strong><\/pre>\n attach(df)<\/span>\n\n#calculate mean of rebounds column\n<\/span>mean(rebounds)\n\n[1] 26.8\n#calculate median of rebounds column<\/span>\nmedian(rebounds)\n\n[1] 28\n#calculate range of rebounds column<\/span>\nrange(rebounds)\n\n[1] 24 30\n<\/span><\/span><\/strong><\/pre>\n \u00d6rnek 2: Regresyon modellerine uyacak \u015fekildeattach() i\u015flevini kullan\u0131n<\/strong><\/span><\/h3>\n
#fit regression model\n<\/span>fit <- lm(points ~ assists + rebounds, data=df)\n\n#view coefficients of regression model\n<\/span>summary(fit)$coef\n\n Estimate Std. Error t value Pr(>|t|)\n(Intercept) 18.7071984 13.2030474 1.416885 0.29222633\nassists 0.5194553 0.2162095 2.402555 0.13821408\nrebounds 2.0802529 0.3273034 6.355733 0.02387244<\/span><\/span><\/strong><\/pre>\n #fit regression model\n<\/span>fit <- lm(points ~ assists + rebounds)\n\n#view coefficients of regression model\n<\/span>summary(fit)$coef\n\n Estimate Std. Error t value Pr(>|t|)\n(Intercept) 18.7071984 13.2030474 1.416885 0.29222633\nassists 0.5194553 0.2162095 2.402555 0.13821408\nrebounds 2.0802529 0.3273034 6.355733 0.02387244<\/span><\/span><\/strong><\/pre>\n Bonus: detach() ve search() i\u015flevini kullan\u0131n<\/strong><\/span><\/h3>\n
#show all attached objects<\/span>\nsearch()\n\n [1] \".GlobalEnv\" \"df\" \"package:stats\" \n [4] \"package:graphics\" \"package:grDevices\" \"package:utils\" \n [7] \"package:datasets\" \"package:methods\" \"Autoloads\" \n[10] \"package:base\" \n<\/span><\/span><\/strong><\/pre>\n #detach data frame<\/span>\ndetach(df)<\/span><\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
RStudio’daki t\u00fcm grafikler nas\u0131l temizlenir?<\/a>
R’de ayn\u0131 sat\u0131ra birden fazla de\u011fi\u015fken nas\u0131l yazd\u0131r\u0131l\u0131r<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"