<\/p>\n
R programlama dilinde data.frame<\/strong> , R veritaban\u0131n\u0131n bir par\u00e7as\u0131d\u0131r.<\/span><\/p>\n Herhangi bir data.frame,<\/strong> data.table<\/strong> paketinin setDF<\/strong> i\u015flevi kullan\u0131larak data.table’a<\/strong> d\u00f6n\u00fc\u015ft\u00fcr\u00fclebilir.<\/span><\/p>\n Bir data.table, R’deki bir data.frame’e g\u00f6re a\u015fa\u011f\u0131daki avantajlar\u0131 sunar:<\/span><\/p>\n 1.<\/strong> Bir dosyay\u0131 data.table’a okumak i\u00e7in data.table paketindeki fread<\/a> i\u015flevini, dosyalar\u0131 data.frame’e okuyan read.csv<\/a> gibi temel R i\u015flevlerinden \u00e7ok<\/em> daha h\u0131zl\u0131 kullanabilirsiniz.<\/span><\/p>\n 2.<\/strong> Data.table \u00fczerinde i\u015flemleri (gruplama ve toplama gibi) data.frame’den \u00e7ok<\/em> daha h\u0131zl\u0131 ger\u00e7ekle\u015ftirebilirsiniz.<\/span><\/p>\n 3.<\/strong> Bir konsola bir veri \u00e7er\u00e7evesi yazd\u0131r\u0131rken, R, veri \u00e7er\u00e7evesi i\u00e7indeki her sat\u0131r\u0131 yazd\u0131rmaya \u00e7al\u0131\u015facakt\u0131r. Ancak bir data.table yaln\u0131zca ilk 100 sat\u0131r\u0131 g\u00f6r\u00fcnt\u00fcler; bu, b\u00fcy\u00fck bir veri k\u00fcmesiyle \u00e7al\u0131\u015f\u0131yorsan\u0131z oturumunuzun ask\u0131da kalmas\u0131n\u0131 veya \u00e7\u00f6kmesini engelleyebilir.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekler, veri.\u00e7er\u00e7eveleri ve veri.tablolar\u0131 aras\u0131ndaki bu farkl\u0131l\u0131klar\u0131 pratikte g\u00f6stermektedir.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, data.table paketindeki fread<\/strong> i\u015flevini ve R veritaban\u0131ndaki read.csv<\/strong> i\u015flevini kullanarak 10.000 sat\u0131r ve 100 s\u00fctundan olu\u015fan bir veri \u00e7er\u00e7evesinin nas\u0131l i\u00e7e aktar\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Sonu\u00e7lardan fread’in<\/strong> bu CSV dosyas\u0131n\u0131 i\u00e7e aktarman\u0131n read.csv<\/strong> i\u015flevine k\u0131yasla yakla\u015f\u0131k 10 kat daha h\u0131zl\u0131 oldu\u011funu g\u00f6rebiliriz.<\/span><\/p>\n Daha b\u00fcy\u00fck veri k\u00fcmeleri i\u00e7in bu fark\u0131n daha da b\u00fcy\u00fck olaca\u011f\u0131n\u0131 unutmay\u0131n.<\/span><\/p>\n Genel olarak data.table<\/strong> , herhangi bir veri i\u015fleme g\u00f6revini data.frame’den<\/strong> \u00e7ok daha h\u0131zl\u0131 ger\u00e7ekle\u015ftirebilir.<\/span><\/p>\n \u00d6rne\u011fin, a\u015fa\u011f\u0131daki kod hem data.table hem de data.frame’de ba\u015fka bir de\u011fi\u015fkene g\u00f6re grupland\u0131r\u0131lm\u0131\u015f bir de\u011fi\u015fkenin ortalamas\u0131n\u0131n nas\u0131l hesaplanaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Sonu\u00e7lardan data.table’\u0131n<\/strong> data.frame’den<\/strong> yakla\u015f\u0131k \u00fc\u00e7 kat daha h\u0131zl\u0131 oldu\u011funu g\u00f6rebiliriz.<\/span><\/p>\n Daha b\u00fcy\u00fck veri k\u00fcmeleri i\u00e7in bu fark daha da b\u00fcy\u00fck olacakt\u0131r.<\/span><\/p>\n Bir data.frame’i<\/strong> bir konsola yazd\u0131r\u0131rken, R, data.frame’deki her sat\u0131r\u0131 yazd\u0131rmaya \u00e7al\u0131\u015facakt\u0131r.<\/span><\/p>\n Ancak bir data.table<\/strong> yaln\u0131zca ilk 100 sat\u0131r\u0131 g\u00f6r\u00fcnt\u00fcler; bu, b\u00fcy\u00fck bir veri k\u00fcmesiyle \u00e7al\u0131\u015f\u0131yorsan\u0131z oturumunuzun ask\u0131da kalmas\u0131n\u0131 veya \u00e7\u00f6kmesini engelleyebilir.<\/span><\/p>\n \u00d6rne\u011fin a\u015fa\u011f\u0131daki kodda hem veri \u00e7er\u00e7evesi hem de 200 sat\u0131rl\u0131k data.table olu\u015fturuyoruz.<\/span><\/p>\n data.frame’i yazd\u0131r\u0131rken R, data.table’\u0131 yazd\u0131r\u0131rken her sat\u0131r\u0131 yazd\u0131rmaya \u00e7al\u0131\u015facak, yaln\u0131zca ilk be\u015f sat\u0131r\u0131 ve son be\u015f sat\u0131r\u0131 g\u00f6r\u00fcnt\u00fcleyecektir:<\/span><\/p>\n Bu, \u00f6zellikle yanl\u0131\u015fl\u0131kla konsola yazd\u0131rmak istemedi\u011finiz b\u00fcy\u00fck veri k\u00fcmeleriyle \u00e7al\u0131\u015f\u0131rken data.table’\u0131n<\/strong> data.frame’e<\/strong> g\u00f6re sundu\u011fu bir avantajd\u0131r.<\/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 bir veri \u00e7er\u00e7evesine sat\u0131rlar nas\u0131l eklenir<\/a> R programlama dilinde data.frame , R veritaban\u0131n\u0131n bir par\u00e7as\u0131d\u0131r. Herhangi bir data.frame, data.table paketinin setDF i\u015flevi kullan\u0131larak data.table’a d\u00f6n\u00fc\u015ft\u00fcr\u00fclebilir. Bir data.table, R’deki bir data.frame’e g\u00f6re a\u015fa\u011f\u0131daki avantajlar\u0131 sunar: 1. Bir dosyay\u0131 data.table’a okumak i\u00e7in data.table paketindeki fread i\u015flevini, dosyalar\u0131 data.frame’e okuyan read.csv gibi temel R i\u015flevlerinden \u00e7ok daha h\u0131zl\u0131 kullanabilirsiniz. 2. Data.table \u00fczerinde i\u015flemleri (gruplama […]<\/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-3618","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n Fark #1: Fread ile daha h\u0131zl\u0131 i\u00e7e aktarma<\/strong><\/span><\/h2>\n
library<\/span> (microbenchmark)\nlibrary<\/span> (data.table)\n\n#make this example reproducible\n<\/span>set. seeds<\/span> (1)\n\n#create data frame with 10,000 rows and 100 columns\n<\/span>df <- as. data<\/span> . frame<\/span> (matrix(runif(10^4 * 100), nrow = 10^4))\n\n#export CSV to current working directory\n<\/span>write.write. csv<\/span> (df, \" test.csv<\/span> \", quote = FALSE<\/span> )\n\n#import CSV file using fread and read.csv and time how long it takes\n<\/span>results <- microbenchmark(\n read.csv = read. csv<\/span> (\" test.csv<\/span> \", header = TRUE<\/span> , stringsAsFactors = FALSE<\/span> ),\n fread = fread(\" test.csv<\/span> \", sep = \",\", stringsAsFactors = FALSE<\/span> ),\n times = 10)\n\n#view results\n<\/span>results\n\nUnit: milliseconds\n expr min lq mean median uq max neval cld\n read.csv 817.1867 892.8748 1026.7071 899.5755 926.9120 1964.0540 10 b\n fread 113.5889 116.2735 136.4079 124.3816 136.0534 211.7484 10 a<\/strong><\/pre>\n Fark #2: data.table ile daha h\u0131zl\u0131 veri i\u015fleme<\/strong><\/span><\/h2>\n
library<\/span> (microbenchmark)\nlibrary<\/span> (data.table)\n\n#make this example reproducible\n<\/span>set.seed(1)\n\n#create data frame with 10,000 rows and 100 columns\nd_frame <- data. frame<\/span> (team=rep(c(' A<\/span> ', ' B<\/span> '), each=5000),\n points=c(rnorm(10000, mean=20, sd=3)))\n\n#create data.table from data.frame\n<\/span>d_table <- setDT(d_frame)\n\n#calculate mean of points grouped by team in data.frame and data.table\n<\/span>results <- microbenchmark(\n mean_d_frame = aggregate(d_frame$points, list(d_frame$team), FUN=mean),\n mean_d_table = d_table[ ,list(mean=mean(points)), by=team],\n times = 10)\n\n#view results\n<\/span>results\n\nUnit: milliseconds\n expr min lq mean median uq max neval cld\n mean_d_frame 2.9045 3.0077 3.11683 3.1074 3.1654 3.4824 10 b\n mean_d_table 1.0539 1.1140 1.52002 1.2075 1.2786 3.6084 10 a<\/strong><\/span><\/pre>\n Fark #3: data.table ile daha az sat\u0131r yazd\u0131r\u0131l\u0131yor<\/strong><\/span><\/h2>\n
library<\/span> (data.table)\n\n#make this example reproducible\n<\/span>set. seeds<\/span> (1)\n\n#create data frame\n<\/span>d_frame <- data. frame<\/span> (x=rnorm(200),\n y=rnorm(200),\n z=rnorm(200))\n#view data frame\n<\/span>d_frame\n\n X Y Z\n1 -0.055303118 1.54858564 -2.065337e-02\n2 0.354143920 0.36706204 -3.743962e-01\n3 -0.999823809 -1.57842544 4.392027e-01\n4 2.586214840 0.17383147 -2.081125e+00\n5 -1.917692199 -2.11487401 4.073522e-01\n6 0.039614766 2.21644236 1.869164e+00\n7 -1.942259548 0.81566443 4.740712e-01\n8 -0.424913746 1.01081030 4.996065e-01\n9 -1.753210825 -0.98893038 -6.290307e-01\n10 0.232382655 -1.25229873 -1.324883e+00\n11 0.027278832 0.44209325 -3.221920e-01\n...\n#create data table\n<\/span>d_table <- setDT(d_frame)\n\n#view data table\n<\/span>d_table\n\n X Y Z\n 1: -0.05530312 1.54858564 -0.02065337\n 2: 0.35414392 0.36706204 -0.37439617\n 3: -0.99982381 -1.57842544 0.43920275\n 4: 2.58621484 0.17383147 -2.08112491\n 5: -1.91769220 -2.11487401 0.40735218\n --- \n196: -0.06196178 1.08164065 0.58609090\n197: 0.34160667 -0.01886703 1.61296255\n198: -0.38361957 -0.03890329 0.71377217\n199: -0.80719743 -0.89674205 -0.49615702\n200: -0.26502679 -0.15887435 -1.73781026<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
R’de belirli s\u00fctunlar nas\u0131l korunur?<\/a>
R’de yaln\u0131zca say\u0131sal s\u00fctunlar nas\u0131l se\u00e7ilir<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"