<\/p>\n
Verileri analiz ederken ger\u00e7ekle\u015ftirece\u011finiz en yayg\u0131n g\u00f6revlerden ikisi, verileri gruplamak ve \u00f6zetlemektir.<\/span><\/p>\n Neyse ki, R’deki dplyr<\/a> paketi verileri h\u0131zl\u0131 bir \u015fekilde gruplaman\u0131za ve \u00f6zetlemenize olanak tan\u0131r.<\/span><\/p>\n Bu e\u011fitim, dplyr’\u0131 kullanmaya ba\u015flaman\u0131z i\u00e7in h\u0131zl\u0131 bir k\u0131lavuz sa\u011flar.<\/span><\/p>\n Dplyr paketindeki i\u015flevleri kullanabilmeniz i\u00e7in \u00f6ncelikle paketi y\u00fcklemeniz gerekir:<\/span><\/p>\n Daha sonra, mtcars<\/strong> ad\u0131 verilen yerle\u015fik R veri k\u00fcmesini kullanarak verileri gruplamak ve \u00f6zetlemek i\u00e7in dplyr i\u015flevlerinin kullan\u0131m\u0131na ili\u015fkin birka\u00e7 \u00f6rnek g\u00f6sterece\u011fiz:<\/span><\/p>\n Verileri gruplamak ve \u00f6zetlemek i\u00e7in kullanaca\u011f\u0131m\u0131z temel s\u00f6zdizimi \u015f\u00f6yledir:<\/span><\/p>\n Not:<\/strong> Summary() ve summarise() i\u015flevleri e\u015fde\u011ferdir.<\/em><\/span><\/p>\n A\u015fa\u011f\u0131daki kod, ortalama ve medyan da dahil olmak \u00fczere gruba g\u00f6re merkezi e\u011filim \u00f6l\u00e7\u00fclerinin<\/a> nas\u0131l hesaplanaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, standart sapma, \u00e7eyrekler aras\u0131 aral\u0131k ve mutlak medyan sapma dahil olmak \u00fczere gruba g\u00f6re da\u011f\u0131l\u0131m \u00f6l\u00e7\u00fclerinin nas\u0131l hesaplanaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, R’de numaran\u0131n ve benzersiz numaran\u0131n gruba g\u00f6re nas\u0131l bulunaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, silindir grubuna g\u00f6re mpg de\u011ferlerinin 90. y\u00fczdelik diliminin nas\u0131l bulunaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Tam dplyr paketi belgelerinin yan\u0131 s\u0131ra faydal\u0131 g\u00f6rselle\u015ftirme hile sayfalar\u0131n\u0131 da burada<\/a> bulabilirsiniz.<\/span><\/p>\n group_by()<\/strong> ve Summary()<\/strong> ile kullanabilece\u011finiz di\u011fer yararl\u0131 i\u015flevler aras\u0131nda, veri \u00e7er\u00e7evesinin sat\u0131rlar\u0131n\u0131 filtrelemek<\/a> ve bunlar\u0131 belirli s\u0131ralara g\u00f6re d\u00fczenlemek<\/a> i\u00e7in kullan\u0131lan i\u015flevler bulunur.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":" Verileri analiz ederken ger\u00e7ekle\u015ftirece\u011finiz en yayg\u0131n g\u00f6revlerden ikisi, verileri gruplamak ve \u00f6zetlemektir. Neyse ki, R’deki dplyr paketi verileri h\u0131zl\u0131 bir \u015fekilde gruplaman\u0131za ve \u00f6zetlemenize olanak tan\u0131r. Bu e\u011fitim, dplyr’\u0131 kullanmaya ba\u015flaman\u0131z i\u00e7in h\u0131zl\u0131 bir k\u0131lavuz sa\u011flar. Dplyr paketini kurun ve y\u00fckleyin Dplyr paketindeki i\u015flevleri kullanabilmeniz i\u00e7in \u00f6ncelikle paketi y\u00fcklemeniz gerekir: #install dplyr (if not already […]<\/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-948","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n Dplyr paketini kurun ve y\u00fckleyin<\/strong><\/span><\/h2>\n
#install dplyr (if not already installed)\ninstall.packages(' dplyr<\/span> ')<\/span>\n\n#load dplyr<\/span> \nlibrary(dplyr)<\/strong><\/pre>\n #obtain rows and columns of mtcars<\/em><\/span>\ndim(mtcars)\n\n[1] 32 11\n\n#view first six rows of mtcars<\/em>\n<\/span>head(mtcars)\n\n mpg cyl disp hp drat wt qsec vs am gear carb\nMazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4\nMazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4\nDatsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1\nHornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1\nHornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2\nValiant 18.1 6 225 105 2.76 3,460 20.22 1 0 3 1<\/strong><\/pre>\n data %>%\n group_by<\/span> (col_name) %>%\n summarize<\/span> (summary_name = summary_function)\n<\/strong><\/pre>\n \u00d6rnek 1: Gruba g\u00f6re ortalamay\u0131 ve medyan\u0131 bulun<\/strong><\/h2>\n
#find mean mpg by cylinder<\/span>\nmtcars %>%\n group_by<\/span> (cyl) %>%\n summarize<\/span> (mean_mpg = mean(mpg, na.rm = TRUE<\/span> ))\n\n# A tibble: 3 x 2\n cyl mean_mpg\n \n1 4 26.7\n2 6 19.7\n3 8 15.1\n\n#find median mpg by cylinder\n<\/span>mtcars %>%\n group_by<\/span> (cyl) %>%\n summarize<\/span> (median_mpg = median(mpg, na.rm = TRUE<\/span> ))\n\n# A tibble: 3 x 2\n cyl median_mpg\n \n1 4 26 \n2 6 19.7\n3 8 15.2<\/strong><\/pre>\n \u00d6rnek 2: Gruba g\u00f6re yay\u0131lma \u00f6l\u00e7\u00fclerini bulma<\/strong><\/h2>\n
#find sd, IQR, and mad by cylinder<\/span>\nmtcars %>%\ngroup_by<\/span> (cyl) %>%\nsummarize<\/span> (sd_mpg = sd(mpg, na.rm = TRUE<\/span> ),\n iqr_mpg = IQR(mpg, na.rm = TRUE<\/span> ),\n mad_mpg = mad(mpg, na.rm = TRUE<\/span> ))\n\n# A tibble: 3 x 4\n cyl sd_mpg iqr_mpg mad_mpg\n \n1 4 4.51 7.60 6.52\n2 6 1.45 2.35 1.93\n3 8 2.56 1.85 1.56<\/strong><\/pre>\n \u00d6rnek 3: Numaray\u0131 gruba g\u00f6re bulma<\/strong><\/h2>\n
#find row count and unique row count by cylinder<\/span>\nmtcars %>%\ngroup_by<\/span> (cyl) %>%\nsummarize<\/span> (count_mpg = n(),\n u_count_mpg = n_distinct(mpg))\n\n# A tibble: 3 x 3\n cyl count_mpg u_count_mpg\n \n1 4 11 9\n2 6 7 6\n3 8 14 12\n<\/strong><\/pre>\n \u00d6rnek 4: Gruba g\u00f6re y\u00fczdelik dilimin bulunmas\u0131<\/strong><\/h2>\n
#find 90th percentile of mpg for each cylinder group<\/span>\nmtcars %>%\ngroup_by<\/span> (cyl) %>%\nsummarize<\/span> (quant90 = quantile(mpg, probs = .9))\n\n# A tibble: 3 x 2\n cyl quant90\n \n1 4 32.4\n2 6 21.2\n3 8 18.3<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n