{"id":4176,"date":"2023-07-13T02:10:38","date_gmt":"2023-07-13T02:10:38","guid":{"rendered":"https:\/\/statorials.org\/de\/boston-r-datensatz\/"},"modified":"2023-07-13T02:10:38","modified_gmt":"2023-07-13T02:10:38","slug":"boston-r-datensatz","status":"publish","type":"post","link":"https:\/\/statorials.org\/de\/boston-r-datensatz\/","title":{"rendered":"Ein vollst\u00e4ndiger leitfaden zum boston-datensatz in r"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Der <b>Boston-<\/b> Datensatz aus dem <strong>MASS-<\/strong> Paket in R enth\u00e4lt Informationen zu verschiedenen Attributen der Vororte von Boston, Massachusetts.<\/span><\/p>\n<p> <span style=\"color: #000000;\">In diesem Tutorial wird erl\u00e4utert, wie Sie den <b>Boston-<\/b> Datensatz in R erkunden, zusammenfassen und visualisieren.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Laden Sie den Boston-Datensatz<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Bevor wir den <strong>Boston-<\/strong> Datensatz anzeigen k\u00f6nnen, m\u00fcssen wir zun\u00e4chst das <strong>MASS-<\/strong> Paket laden:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">library<\/span> (MASS)<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Anschlie\u00dfend k\u00f6nnen wir mit der Funktion <strong>head()<\/strong> die ersten sechs Zeilen des Datensatzes anzeigen:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#view first six rows of Boston dataset<\/span>\nhead(Boston)\n\n     crim zn indus chas nox rm age dis rad tax ptratio black lstat\n1 0.00632 18 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98\n2 0.02731 0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14\n3 0.02729 0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03\n4 0.03237 0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94\n5 0.06905 0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33\n6 0.02985 0 2.18 0 0.458 6.430 58.7 6.0622 3 222 18.7 394.12 5.21\n  medv\n1 24.0\n2 21.6\n3 34.7\n4 33.4\n5 36.2\n6 28.7\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Um eine Beschreibung jeder Variablen im Datensatz anzuzeigen, k\u00f6nnen wir Folgendes eingeben:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#view description of each variable in dataset<\/span>\n?Boston\n\n     This data frame contains the following columns:\n\n     'crime' per capita crime rate by town.\n\n     'zn' proportion of residential land zoned for lots over 25,000\n          sq.ft.\n\n     'industrial' proportion of non-retail business acres per town.\n\n     'chas' Charles River dummy variable (= 1 if tract bounds river; 0\n          otherwise).\n\n     'nox' nitrogen oxides concentration (parts per 10 million).\n\n     'rm' average number of rooms per dwelling.\n\n     'age' proportion of owner-occupied units built prior to 1940.\n\n     'dis' weighted mean of distances to five Boston employment\n          centers.\n\n     'rad' index of accessibility to radial highways.\n\n     'tax' full-value property-tax rate per $10,000.\n\n     'ptratio' pupil-teacher ratio by town.\n\n     'black' 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by\n          town.\n\n     'lstat' lower status of the population (percent).\n\n     'medv' median value of owner-occupied homes in $1000s.\n<\/strong><\/span><\/pre>\n<h2> <span style=\"color: #000000;\"><strong>Fassen Sie den Boston-Datensatz zusammen<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Mit der Funktion <strong>summary()<\/strong> k\u00f6nnen wir jede Variable im Datensatz schnell zusammenfassen:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#summarize Boston dataset<\/span>\nsummary(Boston)\n\n      crim zn indus chas        \n Min. : 0.00632 Min. : 0.00 Min. : 0.46 Min. :0.00000  \n 1st Q: 0.08205 1st Q: 0.00 1st Q: 5.19 1st Q: 0.00000  \n Median: 0.25651 Median: 0.00 Median: 9.69 Median: 0.00000  \n Mean: 3.61352 Mean: 11.36 Mean: 11.14 Mean: 0.06917  \n 3rd Qu.: 3.67708 3rd Qu.: 12.50 3rd Qu.: 18.10 3rd Qu.: 0.00000  \n Max. :88.97620 Max. :100.00 Max. :27.74 Max. :1.00000  \n      nox rm age dis        \n Min. :0.3850 Min. :3.561 Min. : 2.90 Min. : 1,130  \n 1st Qu.: 0.4490 1st Qu.: 5.886 1st Qu.: 45.02 1st Qu.: 2.100  \n Median: 0.5380 Median: 6.208 Median: 77.50 Median: 3.207  \n Mean: 0.5547 Mean: 6.285 Mean: 68.57 Mean: 3.795  \n 3rd Qu.: 0.6240 3rd Qu.: 6.623 3rd Qu.: 94.08 3rd Qu.: 5.188  \n Max. :0.8710 Max. :8,780 Max. :100.00 Max. :12,127  \n      rad tax ptratio black       \n Min. : 1,000 Min. :187.0 Min. :12.60 Min. : 0.32  \n 1st Qu.: 4,000 1st Qu.:279.0 1st Qu.:17.40 1st Qu.:375.38  \n Median: 5,000 Median: 330.0 Median: 19.05 Median: 391.44  \n Mean: 9.549 Mean: 408.2 Mean: 18.46 Mean: 356.67  \n 3rd Qu.:24,000 3rd Qu.:666.0 3rd Qu.:20.20 3rd Qu.:396.23  \n Max. :24,000 Max. :711.0 Max. :22.00 Max. :396.90  \n     lstat medv      \n Min. : 1.73 Min. : 5.00  \n 1st Q: 6.95 1st Q: 17.02  \n Median: 11.36 Median: 21.20  \n Mean:12.65 Mean:22.53  \n 3rd Qu.:16.95 3rd Qu.:25.00  \n Max. :37.97 Max. :50.00<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">F\u00fcr jede der numerischen Variablen k\u00f6nnen wir die folgenden Informationen sehen:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>Min<\/strong> : Der Mindestwert.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>1. Qu<\/strong> : Der Wert des ersten Quartils (25. Perzentil).<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>Median<\/strong> : Der Medianwert.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>Durchschnitt<\/strong> : Der Durchschnittswert.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>3. Qu<\/strong> : Der Wert des dritten Quartils (75. Perzentil).<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>Max<\/strong> : Der Maximalwert.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Mit der Funktion <strong>dim()<\/strong> k\u00f6nnen wir die Dimensionen des Datensatzes in Bezug auf die Anzahl der Zeilen und Spalten ermitteln:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#display rows and columns<\/span>\nsun(Boston)\n\n[1] 506 14\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Wir k\u00f6nnen sehen, dass der Datensatz <b>506<\/b> Zeilen und <strong>14<\/strong> Spalten hat.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Visualisieren Sie den Boston-Datensatz<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Wir k\u00f6nnen auch Diagramme erstellen, um die Werte des Datensatzes zu visualisieren.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Beispielsweise k\u00f6nnen wir mit der Funktion <strong>hist()<\/strong> ein Histogramm der Werte einer bestimmten Variablen erstellen:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#create histogram of values for 'rm' column<\/span>\nhist(Boston$rm,\n     col=' <span style=\"color: #ff0000;\">steelblue<\/span> ',\n     main=' <span style=\"color: #ff0000;\">Histogram of Rooms per Dwelling<\/span> ',\n     xlab=' <span style=\"color: #ff0000;\">Rooms<\/span> ',\n     ylab=' <span style=\"color: #ff0000;\">Frequency<\/span> ')\n<\/strong><\/span><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-33091 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/boston1.png\" alt=\"\" width=\"492\" height=\"486\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Wir k\u00f6nnen auch die Funktion <strong>plot()<\/strong> verwenden, um ein Streudiagramm einer beliebigen paarweisen Kombination von Variablen zu erstellen:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#create scatterplot of median home value vs crime rate\n<\/span><span style=\"color: #000000;\">plot(Boston$medv, Boston$crime,<\/span>\n     col=' <span style=\"color: #ff0000;\">steelblue<\/span> ',\n     main=' <span style=\"color: #ff0000;\">Median Home Value vs. Crime Rate<\/span> ',\n     xlab=' <span style=\"color: #ff0000;\">Median Home Value<\/span> ',\n     ylab=' <span style=\"color: #ff0000;\">Crime Rate<\/span> ',\n     pch= <span style=\"color: #008000;\">19<\/span> )<\/strong><\/span> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-33092 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/boston2.png\" alt=\"\" width=\"500\" height=\"474\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Wir k\u00f6nnen ein \u00e4hnliches Streudiagramm erstellen, um die Beziehung zwischen zwei beliebigen Variablen im Datensatz zu visualisieren.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Zus\u00e4tzliche Ressourcen<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Die folgenden Tutorials bieten eine umfassende Anleitung zu anderen beliebten Datens\u00e4tzen in R:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/de\/iris-r-datensatz\/\" target=\"_blank\" rel=\"noopener\">Eine vollst\u00e4ndige Anleitung zum Iris-Datensatz in R<\/a><br \/> <a href=\"https:\/\/statorials.org\/de\/mtcars-r-datensatz\/\" target=\"_blank\" rel=\"noopener\">Eine vollst\u00e4ndige Anleitung zum mtcars-Datensatz in R<\/a><br \/> <a href=\"https:\/\/statorials.org\/de\/r-diamantdatensatz\/\" target=\"_blank\" rel=\"noopener\">Eine vollst\u00e4ndige Anleitung zum Diamantdatensatz in R<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Der Boston- Datensatz aus dem MASS- Paket in R enth\u00e4lt Informationen zu verschiedenen Attributen der Vororte von Boston, Massachusetts. In diesem Tutorial wird erl\u00e4utert, wie Sie den Boston- Datensatz in R erkunden, zusammenfassen und visualisieren. Laden Sie den Boston-Datensatz Bevor wir den Boston- Datensatz anzeigen k\u00f6nnen, m\u00fcssen wir zun\u00e4chst das MASS- Paket laden: library (MASS) [&hellip;]<\/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":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Ein vollst\u00e4ndiger Leitfaden zum Boston-Datensatz in R \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"Dieses Tutorial bietet eine vollst\u00e4ndige Anleitung zum Boston-Datensatz in R, einschlie\u00dflich Beispielen zur Analyse des Datensatzes.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/de\/boston-r-datensatz\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Ein vollst\u00e4ndiger Leitfaden zum Boston-Datensatz in R \u2013 Statorials\" \/>\n<meta property=\"og:description\" content=\"Dieses Tutorial bietet eine vollst\u00e4ndige Anleitung zum Boston-Datensatz in R, einschlie\u00dflich Beispielen zur Analyse des Datensatzes.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/de\/boston-r-datensatz\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-13T02:10:38+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/boston1.png\" \/>\n<meta name=\"author\" content=\"Dr. Benjamin Anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Verfasst von\" \/>\n\t<meta name=\"twitter:data1\" content=\"Dr. Benjamin Anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Gesch\u00e4tzte Lesezeit\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 Minuten\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/de\/boston-r-datensatz\/\",\"url\":\"https:\/\/statorials.org\/de\/boston-r-datensatz\/\",\"name\":\"Ein vollst\u00e4ndiger Leitfaden zum Boston-Datensatz in R \u2013 Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/de\/#website\"},\"datePublished\":\"2023-07-13T02:10:38+00:00\",\"dateModified\":\"2023-07-13T02:10:38+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/de\/#\/schema\/person\/ec75c4d6365f2708f8a0ad3a42121aa0\"},\"description\":\"Dieses Tutorial bietet eine vollst\u00e4ndige Anleitung zum Boston-Datensatz in R, einschlie\u00dflich Beispielen zur Analyse des Datensatzes.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/de\/boston-r-datensatz\/#breadcrumb\"},\"inLanguage\":\"de-DE\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/de\/boston-r-datensatz\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/de\/boston-r-datensatz\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Heim\",\"item\":\"https:\/\/statorials.org\/de\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Ein vollst\u00e4ndiger leitfaden zum boston-datensatz in r\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/de\/#website\",\"url\":\"https:\/\/statorials.org\/de\/\",\"name\":\"Statorials\",\"description\":\"Ihr Leitfaden f\u00fcr statistische Kompetenz !\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/de\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"de-DE\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/de\/#\/schema\/person\/ec75c4d6365f2708f8a0ad3a42121aa0\",\"name\":\"Dr. Benjamin Anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"de-DE\",\"@id\":\"https:\/\/statorials.org\/de\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/statorials.org\/de\/wp-content\/uploads\/2023\/11\/Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"https:\/\/statorials.org\/de\/wp-content\/uploads\/2023\/11\/Benjamin-Anderson-96x96.jpg\",\"caption\":\"Dr. Benjamin Anderson\"},\"description\":\"Hallo, ich bin Benjamin, ein pensionierter Statistikprofessor, der sich zum engagierten Statorials-Lehrer entwickelt hat. 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