Question

我是UCI机器学习资源库数据集的新手

我试图将数据下载到R中，但我不能这样做。

有人可以帮忙吗？

注意，我使用的是MacBook Pro。

data capture

This is the data I want to use

Answer 1

您需要首先查看数据以了解其排列以及是否存在任何元数据（如标题）。您的浏览器应该足够了。 ionosphere.data文件的前两行是：

1,0,0.99539,-0.05889,0.85243,0.02306,0.83398,-0.37708,1,0.03760,0.85243,-0.17755,0.59755,-0.44945,0.60536,-0.38223,0.84356,-0.38542,0.58212,-0.32192,0.56971,-0.29674,0.36946,-0.47357,0.56811,-0.51171,0.41078,-0.46168,0.21266,-0.34090,0.42267,-0.54487,0.18641,-0.45300,g
1,0,1,-0.18829,0.93035,-0.36156,-0.10868,-0.93597,1,-0.04549,0.50874,-0.67743,0.34432,-0.69707,-0.51685,-0.97515,0.05499,-0.62237,0.33109,-1,-0.13151,-0.45300,-0.18056,-0.35734,-0.20332,-0.26569,-0.20468,-0.18401,-0.19040,-0.11593,-0.16626,-0.06288,-0.13738,-0.02447,b

所以，没有标题，但是一个CSV文件。可以使用read.table和sep =＆＃34;，＆＃34;或read.csv标题= FALSE。您可能（我的确错误）假设列名在另一个文件中，但这是一个没有标签的机器学习任务，因此read.*函数会将通用名称分配给创建的数据帧的列

您将浏览器的链接地址复制到数据文件，然后将其粘贴到引号中的read.table并添加分隔符参数（因为read.table的默认分隔符值（空格）不包括逗号：

ionosphere <- read.table( "https://archive.ics.uci.edu/ml/machine-learning-databases/ionosphere/ionosphere.data",
                           sep=",")  # header=FALSE is default for read.table

> str(ionosphere)
'data.frame':   351 obs. of  35 variables:
 $ V1 : int  1 1 1 1 1 1 1 0 1 1 ...
 $ V2 : int  0 0 0 0 0 0 0 0 0 0 ...
 $ V3 : num  0.995 1 1 1 1 ...
 $ V4 : num  -0.0589 -0.1883 -0.0336 -0.4516 -0.024 ...
 $ V5 : num  0.852 0.93 1 1 0.941 ...
 $ V6 : num  0.02306 -0.36156 0.00485 1 0.06531 ...
 $ V7 : num  0.834 -0.109 1 0.712 0.921 ...
 $ V8 : num  -0.377 -0.936 -0.121 -1 -0.233 ...
 $ V9 : num  1 1 0.89 0 0.772 ...
 $ V10: num  0.0376 -0.0455 0.012 0 -0.164 ...
 $ V11: num  0.852 0.509 0.731 0 0.528 ...
 $ V12: num  -0.1776 -0.6774 0.0535 0 -0.2028 ...
 $ V13: num  0.598 0.344 0.854 0 0.564 ...
 $ V14: num  -0.44945 -0.69707 0.00827 0 -0.00712 ...
 $ V15: num  0.605 -0.517 0.546 -1 0.344 ...
 $ V16: num  -0.38223 -0.97515 0.00299 0.14516 -0.27457 ...
 $ V17: num  0.844 0.055 0.838 0.541 0.529 ...
 $ V18: num  -0.385 -0.622 -0.136 -0.393 -0.218 ...
 $ V19: num  0.582 0.331 0.755 -1 0.451 ...
 $ V20: num  -0.3219 -1 -0.0854 -0.5447 -0.1781 ...
 $ V21: num  0.5697 -0.1315 0.7089 -0.6997 0.0598 ...
 $ V22: num  -0.297 -0.453 -0.275 1 -0.356 ...
 $ V23: num  0.3695 -0.1806 0.4339 0 0.0231 ...
 $ V24: num  -0.474 -0.357 -0.121 0 -0.529 ...
 $ V25: num  0.5681 -0.2033 0.5753 1 0.0329 ...
 $ V26: num  -0.512 -0.266 -0.402 0.907 -0.652 ...
 $ V27: num  0.411 -0.205 0.59 0.516 0.133 ...
 $ V28: num  -0.462 -0.184 -0.221 1 -0.532 ...
 $ V29: num  0.2127 -0.1904 0.431 1 0.0243 ...
 $ V30: num  -0.341 -0.116 -0.174 -0.201 -0.622 ...
 $ V31: num  0.4227 -0.1663 0.6044 0.2568 -0.0571 ...
 $ V32: num  -0.5449 -0.0629 -0.2418 1 -0.5957 ...
 $ V33: num  0.1864 -0.1374 0.5605 -0.3238 -0.0461 ...
 $ V34: num  -0.453 -0.0245 -0.3824 1 -0.657 ...
 $ V35: Factor w/ 2 levels "b","g": 2 1 2 1 2 1 2 1 2 1 ...

UCI机器学习资源库数据集

1 个答案: