Geni\u015f<\/strong> format, ilk s\u00fctunda tekrarlanmayan<\/em> de\u011ferleri i\u00e7erir.<\/span><\/p>\n Uzun<\/strong> format, ilk s\u00fctunda tekrarlanan<\/em> de\u011ferleri i\u00e7erir.<\/span><\/p>\n \u00d6rne\u011fin, farkl\u0131 formatlarda ifade edilen tamamen ayn\u0131 verileri i\u00e7eren a\u015fa\u011f\u0131daki iki veri k\u00fcmesini d\u00fc\u015f\u00fcn\u00fcn:<\/span> <\/p>\n melt()<\/strong> i\u015flevi, geni\u015f formatl\u0131 bir veri \u00e7er\u00e7evesini uzun formata d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in a\u015fa\u011f\u0131daki temel s\u00f6zdizimini kullan\u0131r:<\/span><\/p>\n Kimlik<\/strong> arg\u00fcman\u0131, de\u011ferleri tekrarlanacak olan veri \u00e7er\u00e7evesinin ilk s\u00fctunu olarak hangi de\u011fi\u015fkenin kullan\u0131laca\u011f\u0131n\u0131 belirtir.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekte bu fonksiyonun pratikte nas\u0131l kullan\u0131laca\u011f\u0131 g\u00f6sterilmektedir.<\/span><\/p>\n R’de \u015fu anda geni\u015f formatta olan a\u015fa\u011f\u0131daki veri \u00e7er\u00e7evesine sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n Veri \u00e7er\u00e7evesini h\u0131zl\u0131 bir \u015fekilde uzun formata d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in melt()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n Veri \u00e7er\u00e7evesinin art\u0131k uzun formatta oldu\u011funu unutmay\u0131n.<\/span><\/span><\/p>\n Say\u0131lar<\/strong> , asistler<\/strong> ve ribaundlar<\/strong> s\u00fctunlar\u0131n\u0131n t\u00fcm\u00fc de\u011fi\u015fken<\/strong> ad\u0131 verilen tek bir s\u00fctunda s\u0131k\u0131\u015ft\u0131r\u0131lm\u0131\u015f, de\u011ferleri ise de\u011ferler<\/strong> ad\u0131 verilen tek bir s\u00fctunda s\u0131k\u0131\u015ft\u0131r\u0131lm\u0131\u015ft\u0131r.<\/span><\/p>\n Sonu\u00e7ta ortaya \u00e7\u0131kan veri \u00e7er\u00e7evesinin s\u00fctunlar\u0131n\u0131 , labels()<\/strong> i\u015flevini kullanarak yeniden adland\u0131rmaktan \u00e7ekinmeyin:<\/span><\/p>\n S\u00fctunlar\u0131n yeniden adland\u0131r\u0131ld\u0131\u011f\u0131n\u0131 unutmay\u0131n.<\/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’deki sat\u0131rlar nas\u0131l filtrelenir<\/a> Bir veri \u00e7er\u00e7evesini geni\u015f formattan uzun formata d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in R’deki reshape2 paketinin melt() fonksiyonunu kullanabilirsiniz. Geni\u015f format, ilk s\u00fctunda tekrarlanmayan de\u011ferleri i\u00e7erir. Uzun format, ilk s\u00fctunda tekrarlanan de\u011ferleri i\u00e7erir. \u00d6rne\u011fin, farkl\u0131 formatlarda ifade edilen tamamen ayn\u0131 verileri i\u00e7eren a\u015fa\u011f\u0131daki iki veri k\u00fcmesini d\u00fc\u015f\u00fcn\u00fcn: melt() i\u015flevi, geni\u015f formatl\u0131 bir veri \u00e7er\u00e7evesini uzun formata d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in a\u015fa\u011f\u0131daki […]<\/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-3905","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n![\"Geni\u015f](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/largelong1-1.png\")
<\/p>\n melt(df, id=' team<\/span> ')<\/strong><\/pre>\n \u00d6rnek: R’de melt() nas\u0131l kullan\u0131l\u0131r?<\/strong><\/span><\/h2>\n
#create data frame in wide format\n<\/span>df <- data. frame<\/span> (team=c('A', 'B', 'C', 'D'),\n dots=c(88, 91, 99, 94),\n assists=c(12, 17, 24, 28),\n rebounds=c(22, 28, 30, 31))\n\n#view data frame\n<\/span>df\n\n team points assists rebounds\n1 A 88 12 22\n2 B 91 17 28\n3 C 99 24 30\n4 D 94 28 31<\/strong><\/pre>\n library<\/span> (reshape2)\n\n#use melt() to convert data frame from wide to long format\n<\/span>long_df <- melt(df, id=' team<\/span> ')\n\n#view long data frame<\/span>\nlong_df\n\n team variable value\n1 A points 88\n2 B points 91\n3 C points 99\n4 D points 94\n5 A assists 12\n6 B assists 17\n7 C assists 24\n8 D assists 28\n9 A rebounds 22\n10 B rebounds 28\n11 C rebounds 30\n12 D rebounds 31\n<\/strong><\/pre>\n #rename columns in long_df\n<\/span>names(long_df) <- c(' team<\/span> ', ' metric<\/span> ', ' amount<\/span> ')\n\n#view updated data frame\n<\/span>long_df\n\n team metric amount\n1 A points 88\n2 B points 91\n3 C points 99\n4 D points 94\n5 A assists 12\n6 B assists 17\n7 C assists 24\n8 D assists 28\n9 A rebounds 22\n10 B rebounds 28\n11 C rebounds 30\n12 D rebounds 31<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Bir veri \u00e7er\u00e7evesi R’ye nas\u0131l aktar\u0131l\u0131r<\/a>
R’de veri \u00e7er\u00e7evesi s\u00fctunlar\u0131 nas\u0131l yeniden adland\u0131r\u0131l\u0131r<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"