53.940 farkl\u0131 p\u0131rlanta i\u00e7in 10 farkl\u0131 de\u011fi\u015fkene (fiyat, renk, berrakl\u0131k vb.) ili\u015fkin \u00f6l\u00e7\u00fcmler i\u00e7erir.<\/span><\/p>\n Bu e\u011fitimde, R’deki elmas<\/strong> veri k\u00fcmesinin nas\u0131l ke\u015ffedilece\u011fi, \u00f6zetlenece\u011fi ve g\u00f6rselle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r.<\/span><\/p>\n Elmas<\/b> veri k\u00fcmesi ggplot2’de yerle\u015fik bir veri k\u00fcmesi oldu\u011fundan, \u00f6nce (hen\u00fcz de\u011filse) ggplot2 paketini kurmam\u0131z ve y\u00fcklememiz gerekir:<\/span><\/p>\n Ggplot2’yi y\u00fckledikten sonra elmas<\/strong> veri k\u00fcmesini y\u00fcklemek i\u00e7in data()<\/strong> fonksiyonunu kullanabiliriz:<\/span><\/p>\n Head()<\/strong> fonksiyonunu kullanarak veri setinin ilk alt\u0131 sat\u0131r\u0131na g\u00f6z atabiliriz:<\/span><\/p>\n Veri k\u00fcmesindeki her de\u011fi\u015fkeni h\u0131zl\u0131 bir \u015fekilde \u00f6zetlemek i\u00e7in Summary()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n Say\u0131sal de\u011fi\u015fkenlerin her biri i\u00e7in a\u015fa\u011f\u0131daki bilgileri g\u00f6rebiliriz:<\/span><\/p>\n Veri k\u00fcmesindeki kategorik de\u011fi\u015fkenler (kesim, renk ve netlik) i\u00e7in her de\u011ferin frekans say\u0131s\u0131n\u0131 g\u00f6r\u00fcyoruz.<\/span><\/p>\n \u00d6rne\u011fin kesme<\/strong> de\u011fi\u015fkeni i\u00e7in:<\/span><\/p>\n Veri k\u00fcmesinin boyutlar\u0131n\u0131 sat\u0131r ve s\u00fctun say\u0131s\u0131na g\u00f6re elde etmek i\u00e7in dim()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n Veri setinin 53.940<\/b> sat\u0131r ve 10<\/strong> s\u00fctundan olu\u015ftu\u011funu g\u00f6r\u00fcyoruz.<\/span><\/p>\n Veri \u00e7er\u00e7evesinin s\u00fctun adlar\u0131n\u0131 g\u00f6r\u00fcnt\u00fclemek i\u00e7in labels()<\/strong> i\u015flevini de kullanabiliriz:<\/span><\/p>\n Veri k\u00fcmesinin de\u011ferlerini g\u00f6rselle\u015ftirmek i\u00e7in grafikler de olu\u015fturabiliriz.<\/span><\/p>\n \u00d6rne\u011fin, belirli bir de\u011fi\u015fkenin de\u011ferlerinin histogram\u0131n\u0131 olu\u015fturmak i\u00e7in geom_histogram()<\/strong> i\u015flevini kullanabiliriz:<\/span> <\/p>\n De\u011fi\u015fkenlerin ikili birle\u015fiminden olu\u015fan bir nokta bulutu olu\u015fturmak i\u00e7in geom_point()<\/strong> i\u015flevini de kullanabiliriz:<\/span> <\/p>\n Ba\u015fka bir de\u011fi\u015fkene g\u00f6re grupland\u0131r\u0131lm\u0131\u015f bir de\u011fi\u015fkenin kutu grafi\u011fini olu\u015fturmak i\u00e7in geom_boxplot()<\/strong> i\u015flevini de kullanabiliriz:<\/span> <\/p>\n Bu ggplot2 fonksiyonlar\u0131n\u0131 kullanarak elmas<\/strong> veri setindeki de\u011fi\u015fkenler hakk\u0131nda \u00e7ok \u015fey \u00f6\u011frenebiliriz.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde R’deki di\u011fer veri k\u00fcmelerinin nas\u0131l ke\u015ffedilece\u011fi a\u00e7\u0131klanmaktad\u0131r: R’deki \u0130ris Veri K\u00fcmesi \u0130\u00e7in Tam Bir K\u0131lavuz<\/a> Elmas veri k\u00fcmesi, R’deki ggplot2 paketine yerle\u015fik bir veri k\u00fcmesidir. 53.940 farkl\u0131 p\u0131rlanta i\u00e7in 10 farkl\u0131 de\u011fi\u015fkene (fiyat, renk, berrakl\u0131k vb.) ili\u015fkin \u00f6l\u00e7\u00fcmler i\u00e7erir. Bu e\u011fitimde, R’deki elmas veri k\u00fcmesinin nas\u0131l ke\u015ffedilece\u011fi, \u00f6zetlenece\u011fi ve g\u00f6rselle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r. Elmas veri k\u00fcmesini y\u00fckle Elmas veri k\u00fcmesi ggplot2’de yerle\u015fik bir veri k\u00fcmesi oldu\u011fundan, \u00f6nce (hen\u00fcz de\u011filse) ggplot2 paketini kurmam\u0131z […]<\/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-2803","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n Elmas veri k\u00fcmesini y\u00fckle<\/strong><\/span><\/h3>\n
#install ggplot2 if not already installed<\/span>\ninstall. packages<\/span> (' ggplot2<\/span> ')\n\n#load ggplot2\nlibrary<\/span> (ggplot2)<\/span><\/span><\/strong><\/span><\/pre>\n data(diamonds)<\/strong><\/span><\/pre>\n #view first six rows of diamonds dataset<\/span>\nhead(diamonds)\n\n carat cut color clarity depth table price xyz\n1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43\n2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31\n3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31\n4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63\n5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75\n6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48\n<\/strong><\/span><\/pre>\n Elmas veri k\u00fcmesini \u00f6zetleme<\/strong><\/span><\/h3>\n
#summarize diamonds dataset<\/span>\nsummary(diamonds)\n\n carat cut color clarity depth \n Min. :0.2000 Fair: 1610 D: 6775 SI1:13065 Min. :43.00 \n 1st Qu.:0.4000 Good: 4906 E: 9797 VS2:12258 1st Qu.:61.00 \n Median: 0.7000 Very Good: 12082 F: 9542 SI2: 9194 Median: 61.80 \n Mean: 0.7979 Premium: 13791 G: 11292 VS1: 8171 Mean: 61.75 \n 3rd Qu.:1.0400 Ideal:21551 H:8304 VVS2:5066 3rd Qu.:62.50 \n Max. :5.0100 I: 5422 VVS1: 3655 Max. :79.00 \n D: 2808 (Other): 2531 \n table price xyz Min. :43.00 Min. : 326 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 56.00 1st Qu.: 950 1st Qu.: 4.710 1st Qu.: 4.720 1st Qu.: 2.910 \n Median: 57.00 Median: 2401 Median: 5.700 Median: 5.710 Median: 3.530 \n Mean: 57.46 Mean: 3933 Mean: 5.731 Mean: 5.735 Mean: 3.539 \n 3rd Qu.: 59.00 3rd Qu.: 5324 3rd Qu.: 6.540 3rd Qu.: 6.540 3rd Qu.: 4.040 \n Max. :95.00 Max. :18823 Max. :10,740 Max. :58,900 Max. :31,800<\/strong><\/span><\/pre>\n\n
\n
#display rows and columns<\/span>\ndim(diamonds)\n\n[1] 53940 10\n<\/strong><\/span><\/pre>\n #display column names<\/span>\nnames(diamonds)\n\n[1] \"carat\" \"cut\" \"color\" \"clarity\" \"depth\" \"table\" \"price\" \"x\" \n[9] \u201cy\u201d \u201cz\u201d \n<\/strong><\/span><\/pre>\n Elmas Veri K\u00fcmesini G\u00f6rselle\u015ftirin<\/strong><\/span><\/h3>\n
#create histogram of values for price<\/span>\nggplot(data=diamonds, aes<\/span> (x=price)) +\n geom_histogram(fill=\" steelblue<\/span> \", color=\" black<\/span> \") +\n ggtitle(\" Histogram of Price Values<\/span> \")\n<\/strong><\/span><\/pre>\n![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/diamants2.jpg\")
<\/p>\n #create scatterplot of carat vs. price, using cut as color variable<\/span>\nggplot(data=diamonds, aes<\/span> (x=carat, y=price, color=cut)) + \n geom_point()\n<\/strong><\/span><\/pre>\n![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/diamants3.jpg\")
<\/p>\n #create scatterplot of price, grouped by cut<\/span>\nggplot(data=diamonds, aes<\/span> (x=cut, y=price)) + \n geom_boxplot(fill=\" steelblue<\/span> \")\n<\/strong><\/span><\/pre>\n![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/diamants4.jpg\")
<\/p>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
<\/span><\/p>\n
R’deki mtcars veri k\u00fcmesine y\u00f6nelik eksiksiz bir k\u0131lavuz<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"