{"id":393,"date":"2023-10-05T18:18:32","date_gmt":"2022-12-05T14:08:15","guid":{"rendered":"https:\/\/www.silicloud.com\/ja\/blog\/index.php\/2023\/11\/30\/r%e3%81%aesample%e3%82%92%e4%bd%bf%e7%94%a8%e3%81%97%e3%81%a6%e3%80%81%e3%82%b5%e3%83%b3%e3%83%97%e3%83%ab%e3%82%92%e3%81%a9%e3%81%ae%e3%82%88%e3%81%86%e3%81%ab%e5%8f%96%e3%82%8b%e3%81%ae%e3%81%8b\/"},"modified":"2025-07-31T23:14:24","modified_gmt":"2025-07-31T14:14:24","slug":"r%e3%81%aesample%e3%82%92%e4%bd%bf%e7%94%a8%e3%81%97%e3%81%a6%e3%80%81%e3%82%b5%e3%83%b3%e3%83%97%e3%83%ab%e3%82%92%e3%81%a9%e3%81%ae%e3%82%88%e3%81%86%e3%81%ab%e5%8f%96%e3%82%8b%e3%81%ae%e3%81%8b","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/ja\/blog\/r%e3%81%aesample%e3%82%92%e4%bd%bf%e7%94%a8%e3%81%97%e3%81%a6%e3%80%81%e3%82%b5%e3%83%b3%e3%83%97%e3%83%ab%e3%82%92%e3%81%a9%e3%81%ae%e3%82%88%e3%81%86%e3%81%ab%e5%8f%96%e3%82%8b%e3%81%ae%e3%81%8b\/","title":{"rendered":"R\u306esample()\u3092\u4f7f\u7528\u3057\u3066\u3001\u30b5\u30f3\u30d7\u30eb\u3092\u3069\u306e\u3088\u3046\u306b\u53d6\u308b\u306e\u304b\uff1f"},"content":{"rendered":"

R\u306e\u4e2d\u3067\u3088\u304f\u4f7f\u308f\u308c\u308b\u95a2\u6570\u306e\u3072\u3068\u3064\u3001sample()\u3092\u7406\u89e3\u3057\u307e\u3057\u3087\u3046\u3002\u30c7\u30fc\u30bf\u5206\u6790\u3067\u306f\u3001\u30c7\u30fc\u30bf\u306e\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u308b\u3053\u3068\u304c\u30a2\u30ca\u30ea\u30b9\u30c8\u306b\u3088\u3063\u3066\u6700\u3082\u4e00\u822c\u7684\u306a\u30d7\u30ed\u30bb\u30b9\u3067\u3059\u3002\u30c7\u30fc\u30bf\u3092\u7814\u7a76\u3057\u7406\u89e3\u3059\u308b\u305f\u3081\u306b\u3001\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u308b\u3053\u3068\u304c\u6700\u5584\u306e\u65b9\u6cd5\u3067\u3042\u308a\u3001\u7279\u306b\u30d3\u30c3\u30b0\u30c7\u30fc\u30bf\u306e\u5834\u5408\u306b\u306f\u307b\u3068\u3093\u3069\u771f\u5b9f\u3067\u3059\u3002<\/p>\n

R\u3067\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089\u30b5\u30f3\u30d7\u30eb\u3092\u62bd\u51fa\u3059\u308b\u305f\u3081\u306e\u6a19\u6e96\u95a2\u6570sample()\u304c\u63d0\u4f9b\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u591a\u304f\u306e\u30d3\u30b8\u30cd\u30b9\u3084\u30c7\u30fc\u30bf\u5206\u6790\u306e\u554f\u984c\u3067\u306f\u3001\u30c7\u30fc\u30bf\u304b\u3089\u30b5\u30f3\u30d7\u30eb\u3092\u62bd\u51fa\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u306e\u30d7\u30ed\u30bb\u30b9\u3067\u306f\u3001\u7f6e\u63db\u3092\u7528\u3044\u305f\u30e9\u30f3\u30c0\u30e0\u30c7\u30fc\u30bf\u306e\u751f\u6210\u304c\u884c\u308f\u308c\u307e\u3059\u3002\u4ee5\u4e0b\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u8a73\u3057\u304f\u8aac\u660e\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n

\n

\u3055\u3041\u3001\u8a71\u984c\u306b\u5165\u3063\u3066\u3044\u304d\u307e\u3057\u3087\u3046\uff01<\/p>\n

R\u306esample()\u306e\u69cb\u6587<\/h2>\n
sample(x, size, replace = FALSE, prob = NULL)\r\n<\/code><\/pre>\n
    \n
  • x – vector or a data set.<\/li>\n
  • size – sample size.<\/li>\n
  • replace – with or without replacement of values.<\/li>\n
  • replace – with or without replacement of values.<\/li>\n
  • prob – probability weights<\/li>\n<\/ul>\n
    \n
    \u65e5\u672c\u8a9e\u3067\u305d\u306e\u8868\u73fe\u306e\u30d1\u30e9\u30d5\u30ec\u30fc\u30ba\u3092\u793a\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u9078\u629e\u80a2\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\uff1a
    \n– \u7f6e\u63db\u3092\u542b\u3080\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u308b<\/h2>\n
    \u300c\u7f6e\u304d\u63db\u3048\u3066\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u308b\u300d\u3068\u306f\u3001\u4f55\u3092\u610f\u5473\u3059\u308b\u306e\u304b\u7591\u554f\u306b\u601d\u3046\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002 (Okikaete sanpuru o toru to wa, nani o imi suru no ka gimon ni omou kamoshiremasen.)<\/p>\n
    \u30ea\u30b9\u30c8\u3084\u30c7\u30fc\u30bf\u304b\u3089\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u308b\u5834\u5408\u3001replace=TRUE\u307e\u305f\u306fT\u3092\u6307\u5b9a\u3059\u308b\u3068\u3001\u95a2\u6570\u306f\u5024\u306e\u7e70\u308a\u8fd4\u3057\u3092\u8a31\u53ef\u3057\u307e\u3059\u3002<\/p>\n
    \u4ee5\u4e0b\u306e\u4f8b\u3092\u53c2\u8003\u306b\u3057\u3066\u3001\u30b1\u30fc\u30b9\u3092\u660e\u78ba\u306b\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n
    #sample range lies between 1 to 5<\/span>\r\nx<-<\/span> sample(<\/span>1<\/span>:<\/span>5<\/span>)<\/span>\r\n#prints the samples<\/span>\r\nx\r\nOutput -><\/span> 3<\/span> 2<\/span> 1<\/span> 5<\/span> 4<\/span>\r\n\r\n\r\n#samples range is 1 to 5 and number of samples is 3<\/span>\r\nx<-<\/span> sample(<\/span>1<\/span>:<\/span>5<\/span>,<\/span> 3<\/span>)<\/span>\r\n#prints the samples (3 samples)<\/span>\r\nx\r\nOutput -><\/span> 2<\/span> 4<\/span> 5<\/span>\r\n\r\n\r\n#sample range is 1 to 5 and the number of samples is 6<\/span>\r\nx<-<\/span> sample(<\/span>1<\/span>:<\/span>5<\/span>,<\/span> 6<\/span>)<\/span>\r\nx\r\n#shows error as the range should include only 5 numbers (1:5)<\/span>\r\nError in<\/span> sample.int(<\/span>length(<\/span>x)<\/span>,<\/span> size,<\/span> replace,<\/span> prob)<\/span> :<\/span> \r\n  cannot take a sample larger than the population when 'replace = FALSE'<\/span>\r\n\r\n#specifing replace=TRUE or T will allow repetition of values so that the function will generate 6 samples in the range 1 to 5. Here 2 is repeated.<\/span>\r\n \r\nx<-<\/span> sample(<\/span>1<\/span>:<\/span>5<\/span>,<\/span> 6<\/span>,<\/span> replace=<\/span>T)<\/span>\r\nOutput -><\/span> 2<\/span> 4<\/span> 2<\/span> 2<\/span> 4<\/span> 3<\/span>\r\n\r\n<\/code><\/pre>\n
    \n
    \u300cR\u3067\u306e\u975e\u5fa9\u5143\u62bd\u51fa\u306e\u30b5\u30f3\u30d7\u30eb\u300d<\/h2>\n
    \u3053\u306e\u5834\u5408\u3001\u7121\u91cd\u8907\u3067\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u308a\u307e\u3059\u3002\u5168\u4f53\u306e\u30b3\u30f3\u30bb\u30d7\u30c8\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n
    \u540c\u69d8\u306e\u5834\u5408\u3001\u7f6e\u63db\u306a\u3057\u306e\u95a2\u6570replace=F\u304c\u4f7f\u7528\u3055\u308c\u3001\u5024\u306e\u7e70\u308a\u8fd4\u3057\u3092\u8a31\u3057\u307e\u305b\u3093\u3002<\/p>\n
    #samples without replacement <\/span>\r\nx<-<\/span>sample(<\/span>1<\/span>:<\/span>8<\/span>,<\/span> 7<\/span>,<\/span> replace=<\/span>F)<\/span>\r\nx\r\nOutput -><\/span> 4<\/span> 1<\/span> 6<\/span> 5<\/span> 3<\/span> 2<\/span> 7<\/span>\r\nx<-<\/span>sample(<\/span>1<\/span>:<\/span>8<\/span>,<\/span> 9<\/span>,<\/span> replace=<\/span>F)<\/span>\r\nError in<\/span> sample.int(<\/span>length(<\/span>x)<\/span>,<\/span> size,<\/span> replace,<\/span> prob)<\/span> :<\/span>\r\ncannot take a sample larger than the population when 'replace = FALSE'<\/span>\r\n\r\n\r\n#here the size of the sample is equal to range 'x'. <\/span>\r\nx<-<\/span> sample(<\/span>1<\/span>:<\/span>5<\/span>,<\/span> 5<\/span>,<\/span> replace=<\/span>F)<\/span>\r\nx\r\nOutput -><\/span> 5<\/span> 4<\/span> 1<\/span> 3<\/span> 2<\/span>\r\n<\/code><\/pre>\n
    \n
    \u95a2\u6570set.seed()\u3092\u4f7f\u7528\u3057\u3066\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u5f97\u3059\u308b\u3002<\/h2>\n
    \u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u308b\u969b\u306b\u306f\u3001\u6bce\u56de\u30e9\u30f3\u30c0\u30e0\u306b\u5909\u308f\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u3053\u3068\u3092\u7d4c\u9a13\u3059\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002\u6bce\u56de\u7570\u306a\u308b\u30b5\u30f3\u30d7\u30eb\u3092\u907f\u3051\u305f\u3044\u5834\u5408\u3001set.seed\uff08\uff09\u95a2\u6570\u3092\u5229\u7528\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
    set.seed() – set.seed\u95a2\u6570\u306f\u3001\u5b9f\u884c\u3059\u308b\u3068\u5e38\u306b\u540c\u3058\u30b7\u30fc\u30b1\u30f3\u30b9\u3092\u751f\u6210\u3057\u307e\u3059\u3002<\/p>\n
    \u4ee5\u4e0b\u306f\u3053\u306e\u5834\u5408\u306e\u5177\u4f53\u4f8b\u3067\u3059\u3002\u6bce\u56de\u540c\u3058\u30e9\u30f3\u30c0\u30e0\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u5f97\u3059\u308b\u305f\u3081\u306b\u3001\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
    #set the index <\/span>\r\nset.seed(<\/span>5<\/span>)<\/span>\r\n#takes the random samples with replacement<\/span>\r\nsample(<\/span>1<\/span>:<\/span>5<\/span>,<\/span> 4<\/span>,<\/span> replace=<\/span>T)<\/span>\r\n2<\/span> 3<\/span> 1<\/span> 3<\/span>\r\n\r\nset.seed(<\/span>5<\/span>)<\/span>\r\nsample(<\/span>1<\/span>:<\/span>5<\/span>,<\/span> 4<\/span>,<\/span> replace=<\/span>T)<\/span>\r\n2<\/span> 3<\/span> 1<\/span> 3<\/span>\r\n\r\nset.seed(<\/span>5<\/span>)<\/span>\r\nsample(<\/span>1<\/span>:<\/span>5<\/span>,<\/span> 4<\/span>,<\/span> replace=<\/span>T)<\/span>\r\n2<\/span> 3<\/span> 1<\/span> 3<\/span>\r\n<\/code><\/pre>\n
    \n
    \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u308b<\/h2>\n
    \u3053\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u306f\u3001Rstudio\u3067\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089\u30b5\u30f3\u30d7\u30eb\u3092\u751f\u6210\u3057\u307e\u3059\u3002<\/p>\n
    \u3053\u306e\u30b3\u30fc\u30c9\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u300cToothGrowth\u300d\u304b\u308910\u884c\u3092\u30b5\u30f3\u30d7\u30eb\u3068\u3057\u3066\u53d6\u308a\u51fa\u3057\u3066\u8868\u793a\u3057\u307e\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089\u5fc5\u8981\u306a\u30b5\u30a4\u30ba\u306e\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
    #reads the dataset 'Toothgrwoth' and take the 10 rows as sample<\/span>\r\ndf<-<\/span> sample(<\/span>1<\/span>:<\/span>nrow(<\/span>ToothGrowth)<\/span>,<\/span> 10<\/span>)<\/span>\r\ndf\r\n-<\/span>-><\/span> 53<\/span> 12<\/span> 16<\/span> 26<\/span> 37<\/span> 27<\/span>  9<\/span> 22<\/span> 28<\/span> 10<\/span>\r\n#sample 10 rows<\/span>\r\nToothGrowth[<\/span>df,<\/span>]<\/span>\r\n\r\n    len supp dose\r\n53<\/span> 22.4<\/span>   OJ  2.0<\/span>\r\n12<\/span> 16.5<\/span>   VC  1.0<\/span>\r\n16<\/span> 17.3<\/span>   VC  1.0<\/span>\r\n26<\/span> 32.5<\/span>   VC  2.0<\/span>\r\n37<\/span>  8.2<\/span>   OJ  0.5<\/span>\r\n27<\/span> 26.7<\/span>   VC  2.0<\/span>\r\n9<\/span>   5.2<\/span>   VC  0.5<\/span>\r\n22<\/span> 18.5<\/span>   VC  2.0<\/span>\r\n28<\/span> 21.5<\/span>   VC  2.0<\/span>\r\n10<\/span>  7.0<\/span>   VC  0.5<\/span>\r\n<\/code><\/pre>\n
    \n
    \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089set.seed()\u95a2\u6570\u3092\u4f7f\u3063\u3066\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u308b\u3002<\/h2>\n
    \u3053\u306e\u30bb\u30af\u30b7\u30e7\u30f3\u3067\u306f\u3001set.seed\uff08\uff09\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u5f97\u3057\u307e\u3059\u3002<\/p>\n
    set.seed()\u3092\u4f7f\u7528\u3057\u3066\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089\u30b5\u30f3\u30d7\u30eb\u3092\u751f\u6210\u3059\u308b\u305f\u3081\u306b\u3001\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
    #set.seed function<\/span>\r\nset.seed(<\/span>10<\/span>)<\/span>\r\n#taking sample of 10 rows from the iris dataset. <\/span>\r\nx<-<\/span> sample(<\/span>1<\/span>:<\/span>nrow(<\/span>iris)<\/span>,<\/span> 10<\/span>)<\/span>\r\nx\r\n-<\/span>-><\/span> 137<\/span>  74<\/span> 112<\/span>  72<\/span>  88<\/span>  15<\/span> 143<\/span> 149<\/span>  24<\/span>  13<\/span>\r\n#displays the 10 rows<\/span>\r\niris[<\/span>x,<\/span> ]<\/span>\r\n    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species\r\n137<\/span>          6.3<\/span>         3.4<\/span>          5.6<\/span>         2.4<\/span>  virginica\r\n74<\/span>           6.1<\/span>         2.8<\/span>          4.7<\/span>         1.2<\/span> versicolor\r\n112<\/span>          6.4<\/span>         2.7<\/span>          5.3<\/span>         1.9<\/span>  virginica\r\n72<\/span>           6.1<\/span>         2.8<\/span>          4.0<\/span>         1.3<\/span> versicolor\r\n88<\/span>           6.3<\/span>         2.3<\/span>          4.4<\/span>         1.3<\/span> versicolor\r\n15<\/span>           5.8<\/span>         4.0<\/span>          1.2<\/span>         0.2<\/span>     setosa\r\n143<\/span>          5.8<\/span>         2.7<\/span>          5.1<\/span>         1.9<\/span>  virginica\r\n149<\/span>          6.2<\/span>         3.4<\/span>          5.4<\/span>         2.3<\/span>  virginica\r\n24<\/span>           5.1<\/span>         3.3<\/span>          1.7<\/span>         0.5<\/span>     setosa\r\n13<\/span>           4.8<\/span>         3.0<\/span>          1.4<\/span>         0.1<\/span>     setosa\r\n<\/code><\/pre>\n
    \u540c\u3058\u30b3\u30fc\u30c9\u3092\u8907\u6570\u56de\u5b9f\u884c\u3059\u308b\u3068\u3001\u540c\u3058\u884c\u304c\u5f97\u3089\u308c\u307e\u3059\u3002\u5024\u306fset.seed()\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3044\u308b\u305f\u3081\u5909\u308f\u308a\u307e\u305b\u3093\u3002<\/p>\n
    \n
    R\u3067sample()\u3092\u4f7f\u3063\u3066\u30e9\u30f3\u30c0\u30e0\u306a\u30b5\u30f3\u30d7\u30eb\u3092\u751f\u6210\u3059\u308b\u3002<\/h2>\n
    \u305d\u308c\u3067\u306f\u3001\u3053\u306e\u6982\u5ff5\u304c\u308f\u304b\u308a\u3084\u3059\u3044\u3088\u3046\u306b\u3001\u554f\u984c\u3092\u4f7f\u3063\u3066\u8aac\u660e\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n
    \u554f\u984c\uff1a\u3042\u308b\u96d1\u8ca8\u5e97\u306f\u3001\u304a\u5ba2\u69d8\u306e\u4e2d\u306e\u4e00\u4eba\u306b\u30b5\u30d7\u30e9\u30a4\u30ba\u306e\u8d08\u308a\u7269\u3092\u3059\u308b\u3053\u3068\u306b\u6c7a\u5b9a\u3057\u307e\u3057\u305f\u3002\u305d\u306e\u305f\u3081\u306b\u3001\u3044\u304f\u3064\u304b\u306e\u540d\u524d\u3092\u96c6\u3081\u307e\u3057\u305f\u3002\u554f\u984c\u306f\u3001\u30ea\u30b9\u30c8\u306e\u4e2d\u304b\u3089\u30e9\u30f3\u30c0\u30e0\u306a\u540d\u524d\u3092\u9078\u3076\u3053\u3068\u3067\u3059\u3002<\/p>\n
    \u4ee5\u4e0b\u306e\u6587\u7ae0\u3092\u65e5\u672c\u8a9e\u3067\u81ea\u7136\u306a\u8a00\u8449\u3067\u8a00\u3044\u63db\u3048\u3066\u304f\u3060\u3055\u3044\u3002\u30aa\u30d7\u30b7\u30e7\u30f3\u306f\u4e00\u3064\u3060\u3051\u3067\u5927\u4e08\u592b\u3067\u3059\u3002
    \n\u30d2\u30f3\u30c8\uff1asample() \u95a2\u6570\u3092\u4f7f\u3063\u3066\u30e9\u30f3\u30c0\u30e0\u306a\u30b5\u30f3\u30d7\u30eb\u3092\u751f\u6210\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
    \u4e0b\u8a18\u306e\u901a\u308a\u3001\u3053\u306e\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3059\u308b\u305f\u3073\u306b\u53c2\u52a0\u8005\u540d\u306e\u30e9\u30f3\u30c0\u30e0\u306a\u30b5\u30f3\u30d7\u30eb\u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002<\/p>\n
    #creates a list of names and generates one sample from this list<\/span>\r\nsample(<\/span>c(<\/span>'jack'<\/span>,<\/span>'Rossie'<\/span>,<\/span>'Kyle'<\/span>,<\/span>'Edwards'<\/span>,<\/span>'Joseph'<\/span>,<\/span>'Paloma'<\/span>,<\/span>'Kelly'<\/span>,<\/span>'Alok'<\/span>,<\/span>'Jolie'<\/span>)<\/span>,<\/span>1<\/span>)<\/span>\r\n-<\/span>-><\/span> \"Rossie\"<\/span>\r\n sample(<\/span>c(<\/span>'jack'<\/span>,<\/span>'Rossie'<\/span>,<\/span>'Kyle'<\/span>,<\/span>'Edwards'<\/span>,<\/span>'Joseph'<\/span>,<\/span>'Paloma'<\/span>,<\/span>'Kelly'<\/span>,<\/span>'Alok'<\/span>,<\/span>'Jolie'<\/span>)<\/span>,<\/span>1<\/span>)<\/span>\r\n-<\/span>-><\/span> \"Jolie\"<\/span>\r\n\r\nsample(<\/span>c(<\/span>'jack'<\/span>,<\/span>'Rossie'<\/span>,<\/span>'Kyle'<\/span>,<\/span>'Edwards'<\/span>,<\/span>'Joseph'<\/span>,<\/span>'Paloma'<\/span>,<\/span>'Kelly'<\/span>,<\/span>'Alok'<\/span>,<\/span>'Jolie'<\/span>)<\/span>,<\/span>1<\/span>)<\/span>\r\n-<\/span>-><\/span> \"jack\"<\/span>\r\n\r\nsample(<\/span>c(<\/span>'jack'<\/span>,<\/span>'Rossie'<\/span>,<\/span>'Kyle'<\/span>,<\/span>'Edwards'<\/span>,<\/span>'Joseph'<\/span>,<\/span>'Paloma'<\/span>,<\/span>'Kelly'<\/span>,<\/span>'Alok'<\/span>,<\/span>'Jolie'<\/span>)<\/span>,<\/span>1<\/span>)<\/span>\r\n-<\/span>-><\/span> \"Edwards\"<\/span>\r\n\r\nsample(<\/span>c(<\/span>'jack'<\/span>,<\/span>'Rossie'<\/span>,<\/span>'Kyle'<\/span>,<\/span>'Edwards'<\/span>,<\/span>'Joseph'<\/span>,<\/span>'Paloma'<\/span>,<\/span>'Kelly'<\/span>,<\/span>'Alok'<\/span>,<\/span>'Jolie'<\/span>)<\/span>,<\/span>1<\/span>)<\/span>\r\n-<\/span>-><\/span> \"Kyle\"<\/span>\r\n<\/code><\/pre>\n
    \n
    \u78ba\u7387\u3092\u8a2d\u5b9a\u3057\u3066\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u308b\u3053\u3068\u3002<\/h2>\n
    \u4e0a\u8a18\u306e\u4f8b\u3068\u6982\u5ff5\u306e\u52a9\u3051\u3092\u501f\u308a\u3066\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089\u30e9\u30f3\u30c0\u30e0\u306a\u30b5\u30f3\u30d7\u30eb\u3092\u751f\u6210\u3057\u3001\u7279\u5b9a\u306e\u30c7\u30fc\u30bf\u3092\u62bd\u51fa\u3059\u308b\u65b9\u6cd5\u3092\u7406\u89e3\u3057\u307e\u3057\u305f\u3002<\/p>\n
    \u7c21\u5358\u306a\u4f8b\u3092\u7528\u3044\u3066\u3001R\u3092\u5229\u7528\u3059\u308b\u3053\u3068\u3067\u78ba\u7387\u3092\u8a2d\u5b9a\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u305f\u3081\u3001\u591a\u304f\u306e\u554f\u984c\u304c\u89e3\u6c7a\u3067\u304d\u308b\u3068\u8a00\u3048\u3070\u3001\u7686\u3055\u3093\u306e\u4e2d\u306b\u306f\u30ea\u30e9\u30c3\u30af\u30b9\u3067\u304d\u308b\u65b9\u3082\u3044\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002\u305d\u308c\u3067\u306f\u3001\u5177\u4f53\u7684\u306a\u4f8b\u3092\u901a\u3057\u3066R\u304c\u3069\u306e\u3088\u3046\u306b\u6a5f\u80fd\u3059\u308b\u306e\u304b\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n
    10\u500b\u306e\u6642\u8a08\u3092\u88fd\u9020\u3059\u308b\u4f1a\u793e\u306b\u3064\u3044\u3066\u8003\u3048\u307e\u3057\u3087\u3046\u3002\u3053\u306e10\u500b\u306e\u6642\u8a08\u306e\u3046\u3061\u300120\uff05\u304c\u4e0d\u826f\u54c1\u3068\u5224\u660e\u3057\u307e\u3057\u305f\u3002\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u3063\u3066\u3053\u308c\u3092\u8aac\u660e\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n
    #creates a probability of 80% good watches an 20% effective watches.<\/span>\r\n sample (<\/span>c(<\/span>'Good'<\/span>,<\/span>'Defective'<\/span>)<\/span>,<\/span> size=<\/span>10<\/span>,<\/span> replace=<\/span>T,<\/span> prob=<\/span>c(<\/span>.80<\/span>,<\/span>.20<\/span>)<\/span>)<\/span>\r\n \r\n\"Good\"<\/span>      \"Good\"<\/span>      \"Good\"<\/span>      \"Defective\"<\/span> \"Good\"<\/span>      \"Good\"<\/span>     \r\n\"Good\"<\/span>      \"Good\"<\/span>      \"Defective\"<\/span> \"Good\"<\/span>  \r\n<\/code><\/pre>\n
    \u4ee5\u4e0b\u306b\u793a\u3059\u3088\u3046\u306b\u3001\u7570\u306a\u308b\u78ba\u7387\u306e\u8abf\u6574\u3082\u8a66\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n
     sample (<\/span>c(<\/span>'Good'<\/span>,<\/span>'Defective'<\/span>)<\/span>,<\/span> size=<\/span>10<\/span>,<\/span> replace=<\/span>T,<\/span> prob=<\/span>c(<\/span>.60<\/span>,<\/span>.40<\/span>)<\/span>)<\/span>\r\n \r\n-<\/span>-><\/span> \"Good\"<\/span>      \"Defective\"<\/span> \"Good\"<\/span>      \"Defective\"<\/span> \"Defective\"<\/span> \"Good\"<\/span>     \r\n \"Good\"<\/span>      \"Good\"<\/span>      \"Defective\"<\/span> \"Good\"<\/span>\r\n<\/code><\/pre>\n
    \n
    \u307e\u3068\u3081\u308b (Matomeru)<\/h2>\n
    \u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3001\u30d9\u30af\u30c8\u30eb\u3001\u307e\u305f\u306f\u30ea\u30b9\u30c8\u3092\u4f7f\u3063\u3066\u3001\u7f6e\u63db\u306e\u6709\u7121\u306b\u304b\u304b\u308f\u3089\u305a\u3001\u30b5\u30f3\u30d7\u30eb\u3092\u751f\u6210\u3059\u308b\u65b9\u6cd5\u3092\u5b66\u3073\u307e\u3057\u305f\u3002 set.seed()\u95a2\u6570\u306f\u3001\u540c\u3058\u30b5\u30f3\u30d7\u30eb\u306e\u30b7\u30fc\u30b1\u30f3\u30b9\u3092\u751f\u6210\u3059\u308b\u969b\u306b\u5f79\u7acb\u3061\u307e\u3059\u3002<\/p>\n
    R\u3067\u5229\u7528\u53ef\u80fd\u306a\u3055\u307e\u3056\u307e\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304b\u3089\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u5f97\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002\u307e\u305f\u3001CSV\u30d5\u30a1\u30a4\u30eb\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3066\u78ba\u7387\u8abf\u6574\u3092\u884c\u3044\u306a\u304c\u3089\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/p>\n
    \u3082\u3063\u3068\u52c9\u5f37\uff1aR\u306e\u30c9\u30ad\u30e5\u30e1\u30f3\u30c8<\/p>\n","protected":false},"excerpt":{"rendered":"
    R\u306e\u4e2d\u3067\u3088\u304f\u4f7f\u308f\u308c\u308b\u95a2\u6570\u306e\u3072\u3068\u3064\u3001sample()\u3092\u7406\u89e3\u3057\u307e\u3057\u3087\u3046\u3002\u30c7\u30fc\u30bf\u5206\u6790\u3067\u306f\u3001\u30c7\u30fc\u30bf\u306e\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u308b\u3053\u3068\u304c\u30a2\u30ca\u30ea\u30b9\u30c8\u306b\u3088\u3063\u3066\u6700\u3082\u4e00\u822c\u7684\u306a\u30d7\u30ed\u30bb\u30b9\u3067\u3059\u3002\u30c7\u30fc\u30bf\u3092\u7814\u7a76\u3057\u7406\u89e3\u3059\u308b\u305f\u3081\u306b\u3001\u30b5\u30f3\u30d7\u30eb\u3092\u53d6\u308b\u3053\u3068\u304c\u6700\u5584\u306e\u65b9\u6cd5\u3067\u3042 […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[26,61],"class_list":["post-393","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-26","tag-61"],"yoast_head":"\nR\u306esample()\u3092\u4f7f\u7528\u3057\u3066\u3001\u30b5\u30f3\u30d7\u30eb\u3092\u3069\u306e\u3088\u3046\u306b\u53d6\u308b\u306e\u304b\uff1f - 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