Question

如何为red_wine_data（下面定义的变量）中的每个条件创建lapply()列的直方图？ 这可以在不编写循环的情况下完成吗？我认为我应该可以使用red_wine_data <- structure(list(subject = 1:400, condition = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Argentina", "Australia", "France", "USA"), class = "factor"), Ratings = c(77L, 82L, 75L, 92L, 83L, 75L, 84L, 86L, 85L, 79L, 92L, 84L, 77L, 65L, 89L, 81L, 81L, 88L, 87L, 85L, 87L, 86L, 82L, 67L, 85L, 81L, 80L, 71L, 78L, 84L, 91L, 80L, 84L, 81L, 71L, 78L, 78L, 81L, 89L, 86L, 80L, 79L, 86L, 85L, 76L, 76L, 84L, 86L, 80L, 87L, 84L, 77L, 83L, 73L, 91L, 95L, 78L, 74L, 85L, 80L, 98L, 81L, 86L, 81L, 76L, 82L, 68L, 91L, 82L, 96L, 84L, 76L, 85L, 74L, 72L, 83L, 78L, 81L, 82L, 77L, 77L, 80L, 89L, 70L, 85L, 83L, 88L, 79L, 84L, 83L, 77L, 89L, 89L, 86L, 92L, 85L, 72L, 77L, 72L, 78L, 70L, 91L, 95L, 89L, 76L, 87L, 75L, 86L, 73L, 85L, 73L, 79L, 82L, 73L, 80L, 84L, 93L, 91L, 77L, 86L, 65L, 74L, 77L, 73L, 82L, 69L, 89L, 84L, 72L, 63L, 63L, 73L, 79L, 82L, 80L, 73L, 79L, 74L, 88L, 76L, 72L, 79L, 76L, 75L, 64L, 57L, 68L, 82L, 81L, 76L, 59L, 92L, 67L, 63L, 76L, 81L, 69L, 73L, 86L, 75L, 74L, 70L, 76L, 66L, 69L, 68L, 77L, 69L, 92L, 78L, 83L, 76L, 80L, 79L, 77L, 86L, 71L, 81L, 76L, 71L, 70L, 87L, 79L, 71L, 70L, 91L, 74L, 67L, 76L, 61L, 83L, 66L, 67L, 86L, 70L, 73L, 77L, 70L, 79L, 69L, 71L, 81L, 67L, 66L, 80L, 71L, 70L, 60L, 39L, 65L, 64L, 75L, 77L, 58L, 73L, 63L, 89L, 69L, 89L, 69L, 86L, 72L, 68L, 72L, 91L, 60L, 60L, 93L, 79L, 50L, 89L, 83L, 55L, 63L, 86L, 77L, 81L, 64L, 71L, 77L, 76L, 65L, 75L, 69L, 79L, 50L, 65L, 75L, 75L, 65L, 84L, 68L, 78L, 71L, 83L, 78L, 63L, 65L, 56L, 80L, 78L, 73L, 52L, 60L, 69L, 60L, 67L, 90L, 76L, 54L, 56L, 83L, 81L, 67L, 73L, 79L, 40L, 78L, 98L, 65L, 75L, 63L, 60L, 94L, 54L, 85L, 71L, 62L, 79L, 39L, 80L, 89L, 66L, 65L, 57L, 80L, 76L, 72L, 65L, 71L, 63L, 63L, 66L, 66L, 69L, 61L, 73L, 67L, 66L, 65L, 73L, 65L, 67L, 66L, 76L, 63L, 67L, 54L, 71L, 63L, 76L, 68L, 66L, 72L, 64L, 80L, 68L, 63L, 69L, 69L, 62L, 65L, 72L, 68L, 67L, 62L, 69L, 63L, 69L, 67L, 63L, 57L, 63L, 69L, 76L, 66L, 62L, 60L, 62L, 64L, 76L, 64L, 63L, 67L, 66L, 61L, 68L, 69L, 78L, 73L, 68L, 61L, 69L, 69L, 64L, 63L, 66L, 75L, 70L, 75L, 68L, 57L, 63L, 65L, 69L, 66L, 74L, 71L, 62L, 67L, 68L, 62L, 68L, 74L, 61L, 68L, 71L, 63L, 59L, 71L, 65L, 63L, 62L, 71L, 65L, 66L, 64L, 71L, 60L, 69L)), .Names = c("subject", "condition", "Ratings"), class = "data.frame", row.names = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", "60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70", "71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94", "95", "96", "97", "98", "99", "100", "101", "102", "103", "104", "105", "106", "107", "108", "109", "110", "111", "112", "113", "114", "115", "116", "117", "118", "119", "120", "121", "122", "123", "124", "125", "126", "127", "128", "129", "130", "131", "132", "133", "134", "135", "136", "137", "138", "139", "140", "141", "142", "143", "144", "145", "146", "147", "148", "149", "150", "151", "152", "153", "154", "155", "156", "157", "158", "159", "160", "161", "162", "163", "164", "165", "166", "167", "168", "169", "170", "171", "172", "173", "174", "175", "176", "177", "178", "179", "180", "181", "182", "183", "184", "185", "186", "187", "188", "189", "190", "191", "192", "193", "194", "195", "196", "197", "198", "199", "200", "201", "202", "203", "204", "205", "206", "207", "208", "209", "210", "211", "212", "213", "214", "215", "216", "217", "218", "219", "220", "221", "222", "223", "224", "225", "226", "227", "228", "229", "230", "231", "232", "233", "234", "235", "236", "237", "238", "239", "240", "241", "242", "243", "244", "245", "246", "247", "248", "249", "250", "251", "252", "253", "254", "255", "256", "257", "258", "259", "260", "261", "262", "263", "264", "265", "266", "267", "268", "269", "270", "271", "272", "273", "274", "275", "276", "277", "278", "279", "280", "281", "282", "283", "284", "285", "286", "287", "288", "289", "290", "291", "292", "293", "294", "295", "296", "297", "298", "299", "300", "301", "302", "303", "304", "305", "306", "307", "308", "309", "310", "311", "312", "313", "314", "315", "316", "317", "318", "319", "320", "321", "322", "323", "324", "325", "326", "327", "328", "329", "330", "331", "332", "333", "334", "335", "336", "337", "338", "339", "340", "341", "342", "343", "344", "345", "346", "347", "348", "349", "350", "351", "352", "353", "354", "355", "356", "357", "358", "359", "360", "361", "362", "363", "364", "365", "366", "367", "368", "369", "370", "371", "372", "373", "374", "375", "376", "377", "378", "379", "380", "381", "382", "383", "384", "385", "386", "387", "388", "389", "390", "391", "392", "393", "394", "395", "396", "397", "398", "399", "400"))。

l <- split(red_wine_data, red_wine_data$condition)

hist(l[["Australia"]][["Ratings"]], main = l[["Australia"]][["condition"]][1], xlab = "score")
hist(l[["USA"]][["Ratings"]], main = l[["USA"]][["condition"]][1], xlab = "score")
hist(l[["France"]][["Ratings"]], main = l[["France"]][["condition"]][1], xlab = "score")
hist(l[["Argentina"]][["Ratings"]], main = l[["Argentina"]][["condition"]][1], xlab = "score")

以下是我用硬编码的值完成的方法：

for(i in 1:length(l)){
  hist(l[[i]][["Ratings"]], main = l[[i]][["condition"]][1], xlab = "score")  
}

这是一个实现相同的循环：

{{1}}

拆分和申请的参考：

Answer 1

我想回答我自己的问题，因为我经常查看lapply()的示例，这些示例似乎很强大，但无法在我的代码中应用:)。最近，它只是点击了。

对于初学者来说，编写循环是了解lapply()必要输入的良好开端。它可以直接转换为使用lapply()而无需循环。完成这样的一些示例后，您可以尝试从一开始就使用lapply()。

for(i in 1:length(l)){
  hist(l[[i]][["Ratings"]], main = l[[i]][["condition"]][1], xlab = "score")  
}

请注意，hist的第一个参数是数据框（Ratings）的单个列（l[[i]]）。

l[[i]][["condition"]]也是数据框架的单个列，l[[i]][["condition"]][1]是该列中的第一个条目。

因此，我们需要一个接受数据框的函数，提取Ratings列，并提取condition列的第一个条目。注意它与for循环体的相似程度。

wineHist <- function(inputDF){
  hist(inputDF[["Ratings"]], main = inputDF[["condition"]][1], xlab = "score")  
}

由于l是一个包含多个数据框的列表，我们希望使用lapply()对每个数据框执行函数：

resultList <- lapply(X = l, FUN = wineHist)

这会将l设置为输入列表，并将wineList()应用于列表的每个元素。

输出resultList是一个列表，其中包含每个直方图的项目，并包含每个直方图的参数。运行此代码时，将创建所有4个图，如果原始输入数据框中有更多条件，则会自动绘制更多直方图。

这是一个情节：

使用lapply（）在没有for（）循环的情况下自动执行重复代码

1 个答案: