{"id":50686,"date":"2022-11-19T02:22:51","date_gmt":"2024-03-08T13:24:31","guid":{"rendered":"https:\/\/www.silicloud.com\/zh\/blog\/%e5%9c%a8r%e4%b8%ad%e8%bf%9b%e8%a1%8c%e5%bc%82%e5%b8%b8%e5%80%bc%e5%88%86%e6%9e%90-%e6%a3%80%e6%b5%8b%e5%92%8c%e5%8e%bb%e9%99%a4%e5%bc%82%e5%b8%b8%e5%80%bc\/"},"modified":"2024-04-29T01:23:03","modified_gmt":"2024-04-28T17:23:03","slug":"%e5%9c%a8r%e4%b8%ad%e8%bf%9b%e8%a1%8c%e5%bc%82%e5%b8%b8%e5%80%bc%e5%88%86%e6%9e%90-%e6%a3%80%e6%b5%8b%e5%92%8c%e5%8e%bb%e9%99%a4%e5%bc%82%e5%b8%b8%e5%80%bc","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/zh\/blog\/%e5%9c%a8r%e4%b8%ad%e8%bf%9b%e8%a1%8c%e5%bc%82%e5%b8%b8%e5%80%bc%e5%88%86%e6%9e%90-%e6%a3%80%e6%b5%8b%e5%92%8c%e5%8e%bb%e9%99%a4%e5%bc%82%e5%b8%b8%e5%80%bc\/","title":{"rendered":"\u5728R\u4e2d\u8fdb\u884c\u5f02\u5e38\u503c\u5206\u6790-\u68c0\u6d4b\u548c\u53bb\u9664\u5f02\u5e38\u503c"},"content":{"rendered":"

\u5927\u5bb6\u597d\uff0c\u8bfb\u8005\u4eec\uff01\u5728\u8fd9\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5c06\u8be6\u7ec6\u8ba8\u8bbaR\u7f16\u7a0b\u4e2d\u7684\u5f02\u5e38\u503c\u5206\u6790\u3002<\/p>\n

\u6240\u4ee5\uff0c\u8ba9\u6211\u4eec\u5f00\u59cb\u5427\uff01\uff01<\/p>\n

\n

\u6570\u636e\u4e2d\u7684\u5f02\u5e38\u503c\u662f\u4ec0\u4e48\uff1f<\/h2>\n

\u5728\u6df1\u5165\u63a2\u8ba8\u5f02\u5e38\u503c\u6982\u5ff5\u4e4b\u524d\uff0c\u8ba9\u6211\u4eec\u5148\u4e13\u6ce8\u4e8e\u6570\u636e\u503c\u7684\u9884\u5904\u7406\u3002<\/p>\n

\u5728\u6570\u636e\u79d1\u5b66\u548c\u673a\u5668\u5b66\u4e60\u9886\u57df\uff0c\u6570\u636e\u503c\u7684\u9884\u5904\u7406\u662f\u4e00\u4e2a\u5173\u952e\u6b65\u9aa4\u3002\u6240\u8c13\u9884\u5904\u7406\uff0c\u662f\u6307\u5728\u5efa\u6a21\u4e4b\u524d\u5c06\u6240\u6709\u7684\u9519\u8bef\u548c\u566a\u97f3\u4ece\u6570\u636e\u4e2d\u6e05\u9664\u3002<\/p>\n

\u5728\u6211\u4eec\u7684\u4e0a\u4e00\u7bc7\u6587\u7ae0\u4e2d\uff0c\u6211\u4eec\u5df2\u7ecf\u4e86\u89e3\u4e86\u5728R\u7f16\u7a0b\u4e2d\u7684\u7f3a\u5931\u503c\u5206\u6790\u3002<\/p>\n

\u4eca\u5929\uff0c\u6211\u4eec\u5c06\u96c6\u4e2d\u8ba8\u8bbaR\u4e2d\u7684\u540c\u4e00\u4e2a\u9ad8\u7ea7\u6c34\u5e73 – \u5f02\u5e38\u503c\u68c0\u6d4b\u548c\u53bb\u9664\u3002<\/p>\n

\u5f02\u5e38\u503c\uff0c\u987e\u540d\u601d\u4e49\uff0c\u662f\u6570\u636e\u96c6\u4e2d\u79bb\u5176\u4ed6\u6570\u636e\u70b9\u8f83\u8fdc\u7684\u6570\u636e\u70b9\u3002\u4e5f\u5c31\u662f\u8bf4\uff0c\u5b83\u4eec\u662f\u4e0e\u5176\u4ed6\u6570\u636e\u503c\u76f8\u8ddd\u8f83\u8fdc\uff0c\u4ece\u800c\u6270\u4e71\u4e86\u6570\u636e\u96c6\u7684\u6574\u4f53\u5206\u5e03\u3002<\/p>\n

\u8fd9\u901a\u5e38\u88ab\u8ba4\u4e3a\u662f\u6570\u636e\u503c\u7684\u5f02\u5e38\u5206\u5e03\u3002<\/p>\n

\u5f02\u5e38\u503c\u5bf9\u6a21\u578b\u7684\u5f71\u54cd<\/p>\n

    \n
  1. \n
      \u6570\u636e\u7684\u683c\u5f0f\u53d1\u73b0\u51fa\u73b0\u4e86\u504f\u659c\u3002<\/ol>\n<\/li>\n<\/ol>\n

       <\/p>\n

        \n
      1. \n
          \u6539\u53d8\u4e86\u6570\u636e\u7684\u6574\u4f53\u7edf\u8ba1\u5206\u5e03\uff0c\u5305\u62ec\u5747\u503c\u3001\u65b9\u5dee\u7b49\u3002<\/ol>\n<\/li>\n<\/ol>\n

           <\/p>\n

            \u5bfc\u81f4\u6a21\u578b\u7684\u51c6\u786e\u6027\u6c34\u5e73\u51fa\u73b0\u504f\u5dee\u3002<\/ol>\n

            \u7406\u89e3\u4e86\u5f02\u5e38\u503c\u7684\u5f71\u54cd\u4e4b\u540e\uff0c\u73b0\u5728\u662f\u65f6\u5019\u7740\u624b\u5b9e\u65bd\u4e86\u3002<\/p>\n

            \n

            \u5f02\u5e38\u503c\u5206\u6790 – \u5f00\u59cb\u5427!<\/h2>\n

            \u9996\u5148\uff0c\u6211\u4eec\u975e\u5e38\u91cd\u89c6\u5728\u6570\u636e\u96c6\u4e2d\u68c0\u6d4b\u79bb\u7fa4\u503c\u7684\u5b58\u5728\u3002<\/p>\n

            \u90a3\u4e48\uff0c\u8ba9\u6211\u4eec\u5f00\u59cb\u5427\u3002\u6211\u4eec\u4f7f\u7528\u4e86\u81ea\u884c\u8f66\u79df\u8d41\u6b21\u6570\u9884\u6d4b\u6570\u636e\u96c6\u3002\u4f60\u53ef\u4ee5\u5728\u8fd9\u91cc\u627e\u5230\u6570\u636e\u96c6\uff01<\/p>\n

            1. \u8f7d\u5165\u6570\u636e\u96c6<\/h3>\n

            \u8d77\u521d\uff0c\u6211\u4eec\u4f7f\u7528read.csv()\u51fd\u6570\u5c06\u6570\u636e\u96c6\u52a0\u8f7d\u5230R\u73af\u5883\u4e2d\u3002<\/p>\n

            \u5728\u5f02\u5e38\u503c\u68c0\u6d4b\u4e4b\u524d\uff0c\u6211\u4eec\u8fdb\u884c\u4e86\u7f3a\u5931\u503c\u5206\u6790\uff0c\u4ec5\u4ec5\u662f\u4e3a\u4e86\u68c0\u67e5\u662f\u5426\u5b58\u5728\u4efb\u4f55NULL\u6216\u7f3a\u5931\u503c\u3002\u4e3a\u6b64\uff0c\u6211\u4eec\u4f7f\u7528\u4e86sum(is.na(data))\u51fd\u6570\u3002<\/p>\n

            #Removed all the existing objects<\/span>\r\nrm(<\/span>list =<\/span> ls(<\/span>)<\/span>)<\/span>\r\n\r\n#Setting the working directory<\/span>\r\nsetwd(<\/span>\"D:\/Ediwsor_Project - Bike_Rental_Count\/\"<\/span>)<\/span>\r\ngetwd(<\/span>)<\/span>\r\n\r\n#Load the dataset<\/span>\r\nbike_data =<\/span> read.csv(<\/span>\"day.csv\"<\/span>,<\/span>header=<\/span>TRUE<\/span>)<\/span>\r\n\r\n### Missing Value Analysis ###<\/span>\r\nsum(<\/span>is.na(<\/span>bike_data)<\/span>)<\/span>\r\nsummary(<\/span>is.na(<\/span>bike_data)<\/span>)<\/span>\r\n\r\n#From the above result, it is clear that the dataset contains NO Missing Values.<\/span>\r\n\r\n<\/code><\/pre>\n
            \u8fd9\u91cc\u7684\u6570\u636e\u6ca1\u6709\u7f3a\u5931\u503c\u3002<\/p>\n
            \u4f7f\u7528\u7bb1\u7ebf\u56fe\u51fd\u6570\u68c0\u6d4b\u5f02\u5e38\u503c\u3002<\/h3>\n
            \u8bf4\u5230\u8fd9\u4e00\u70b9\uff0c\u73b0\u5728\u662f\u65f6\u5019\u68c0\u6d4b\u6570\u636e\u96c6\u4e2d\u7684\u5f02\u5e38\u503c\u3002\u4e3a\u4e86\u8fbe\u5230\u8fd9\u4e2a\u76ee\u7684\uff0c\u6211\u4eec\u4f7f\u7528c()\u51fd\u6570\u5c06\u6570\u503c\u6570\u636e\u5217\u4fdd\u5b58\u5230\u4e00\u4e2a\u5355\u72ec\u7684\u6570\u636e\u7ed3\u6784\/\u53d8\u91cf\u4e2d\u3002<\/p>\n
            \u6b64\u5916\uff0c\u6211\u4eec\u4f7f\u7528\u4e86boxplot()\u51fd\u6570\u6765\u68c0\u6d4b\u6570\u503c\u53d8\u91cf\u4e2d\u662f\u5426\u5b58\u5728\u5f02\u5e38\u503c\u3002<\/p>\n
            \u76d2\u987b\u56fe (H\u00e9 X\u016b T\u00fa)<\/p>\n
            \"Outlier<\/div>\n
            \u4ece\u56fe\u8868\u53ef\u89c1\uff0c\u53d8\u91cf’hum’\u548c’windspeed’\u7684\u6570\u636e\u503c\u4e2d\u660e\u663e\u5b58\u5728\u5f02\u5e38\u503c\u3002<\/p>\n
            3. \u7528NULL\u503c\u66ff\u6362\u79bb\u7fa4\u503c<\/h3>\n
            \u73b0\u5728\uff0c\u5728\u4f7f\u7528R\u8fdb\u884c\u5f02\u5e38\u503c\u5206\u6790\u4e4b\u540e\uff0c\u6211\u4eec\u5c06\u901a\u8fc7boxplot\uff08\uff09\u65b9\u6cd5\u8bc6\u522b\u51fa\u7684\u5f02\u5e38\u503c\u66ff\u6362\u4e3aNULL\u503c\uff0c\u4ee5\u4fbf\u50cf\u4e0b\u9762\u6240\u793a\u5bf9\u5176\u8fdb\u884c\u5904\u7406\u3002<\/p>\n
            ##############################Outlier Analysis -- DETECTION###########################<\/span>\r\n\r\n# 1. Outliers in the data values exists only in continuous\/numeric form of data variables. Thus, we need to store all the numeric and categorical independent variables into a separate array structure.<\/span>\r\ncol =<\/span> c(<\/span>'temp'<\/span>,<\/span>'cnt'<\/span>,<\/span>'hum'<\/span>,<\/span>'windspeed'<\/span>)<\/span>\r\ncategorical_col =<\/span> c(<\/span>\"season\"<\/span>,<\/span>\"yr\"<\/span>,<\/span>\"mnth\"<\/span>,<\/span>\"holiday\"<\/span>,<\/span>\"weekday\"<\/span>,<\/span>\"workingday\"<\/span>,<\/span>\"weathersit\"<\/span>)<\/span>\r\n\r\n# 2. Using BoxPlot to detect the presence of outliers in the numeric\/continuous data columns.<\/span>\r\nboxplot(<\/span>bike_data[<\/span>,<\/span>c(<\/span>'temp'<\/span>,<\/span>'atemp'<\/span>,<\/span>'hum'<\/span>,<\/span>'windspeed'<\/span>)<\/span>]<\/span>)<\/span>\r\n\r\n# From the above visualization, it is clear that the data variables 'hum' and 'windspeed' contains outliers in the data values.<\/span>\r\n#OUTLIER ANALYSIS -- Removal of Outliers<\/span>\r\n# 1. From the boxplot, we have identified the presence of outliers. That is, the data values that are present above the upper quartile and below the lower quartile can be considered as the outlier data values.<\/span>\r\n# 2. Now, we will replace the outlier data values with NULL.<\/span>\r\n\r\nfor<\/span> (<\/span>x in<\/span> c(<\/span>'hum'<\/span>,<\/span>'windspeed'<\/span>)<\/span>)<\/span>\r\n{<\/span>\r\n  value =<\/span> bike_data[<\/span>,<\/span>x]<\/span>[<\/span>bike_data[<\/span>,<\/span>x]<\/span> %in%<\/span> boxplot.stats(<\/span>bike_data[<\/span>,<\/span>x]<\/span>)<\/span>$<\/span>out]<\/span>\r\n  bike_data[<\/span>,<\/span>x]<\/span>[<\/span>bike_data[<\/span>,<\/span>x]<\/span> %in%<\/span> value]<\/span> =<\/span> NA<\/span>\r\n}<\/span> \r\n\r\n#Checking whether the outliers in the above defined columns are replaced by NULL or not<\/span>\r\nsum(<\/span>is.na(<\/span>bike_data$<\/span>hum)<\/span>)<\/span>\r\nsum(<\/span>is.na(<\/span>bike_data$<\/span>windspeed)<\/span>)<\/span>\r\nas.data.frame(<\/span>colSums(<\/span>is.na(<\/span>bike_data)<\/span>)<\/span>)<\/span>\r\n<\/code><\/pre>\n
            4. \u786e\u8ba4\u6240\u6709\u5f02\u5e38\u503c\u5df2\u88ab\u66ff\u6362\u4e3aNULL<\/h3>\n
            \u73b0\u5728\uff0c\u6211\u4eec\u4f7f\u7528sum(is.na())\u51fd\u6570\u68c0\u67e5\u7f3a\u5931\u6570\u636e\u7684\u5b58\u5728\uff0c\u5373\u5f02\u5e38\u503c\u662f\u5426\u5df2\u6b63\u786e\u8f6c\u6362\u4e3a\u7f3a\u5931\u503c\u3002<\/p>\n
            \u8f93\u51fa\uff1a<\/p>\n
            ><\/span> sum(<\/span>is.na(<\/span>bike_data$<\/span>hum)<\/span>)<\/span>\r\n[<\/span>1<\/span>]<\/span> 2<\/span>\r\n><\/span> sum(<\/span>is.na(<\/span>bike_data$<\/span>windspeed)<\/span>)<\/span>\r\n[<\/span>1<\/span>]<\/span> 13<\/span>\r\n><\/span> as.data.frame(<\/span>colSums(<\/span>is.na(<\/span>bike_data)<\/span>)<\/span>)<\/span>\r\n           colSums(<\/span>is.na(<\/span>bike_data)<\/span>)<\/span>\r\ninstant                            0<\/span>\r\ndteday                             0<\/span>\r\nseason                             0<\/span>\r\nyr                                 0<\/span>\r\nmnth                               0<\/span>\r\nholiday                            0<\/span>\r\nweekday                            0<\/span>\r\nworkingday                         0<\/span>\r\nweathersit                         0<\/span>\r\ntemp                               0<\/span>\r\natemp                              0<\/span>\r\nhum                                2<\/span>\r\nwindspeed                         13<\/span>\r\ncasual                             0<\/span>\r\nregistered                         0<\/span>\r\ncnt                                0<\/span>\r\n<\/code><\/pre>\n
            \u56e0\u6b64\uff0c\u6211\u4eec\u5c06\u201c\u55e1\u55e1\u58f0\u201d\u5217\u4e2d\u76842\u4e2a\u5f02\u5e38\u70b9\u548c\u201c\u98ce\u901f\u201d\u5217\u4e2d\u768413\u4e2a\u5f02\u5e38\u70b9\u8f6c\u6362\u4e3a\u7f3a\u5931\u503c\uff08NA\uff09\u3002<\/p>\n
            5. \u4e22\u5f03\u5177\u6709\u7f3a\u5931\u503c\u7684\u5217<\/h3>\n
            \u6700\u540e\uff0c\u6211\u4eec\u4f7f\u7528\u2018tidyr\u2019\u5e93\u4e2d\u7684drop_na()\u51fd\u6570\u5220\u9664\u7f3a\u5931\u503c\uff0c\u5904\u7406\u7f3a\u5931\u503c\u3002<\/p>\n
            #Removing the null values<\/span>\r\nlibrary(<\/span>tidyr)<\/span>\r\nbike_data =<\/span> drop_na(<\/span>bike_data)<\/span>\r\nas.data.frame(<\/span>colSums(<\/span>is.na(<\/span>bike_data)<\/span>)<\/span>)<\/span>\r\n\r\n<\/code><\/pre>\n
            \u8f93\u51fa\u7ed3\u679c\uff1a<\/p>\n
            \u56e0\u6b64\uff0c\u73b0\u5728\u6240\u6709\u7684\u5f02\u5e38\u503c\u90fd\u5df2\u88ab\u6709\u6548\u53bb\u9664\uff01<\/p>\n
            ><\/span> as.data.frame(<\/span>colSums(<\/span>is.na(<\/span>bike_data)<\/span>)<\/span>)<\/span>\r\n           colSums(<\/span>is.na(<\/span>bike_data)<\/span>)<\/span>\r\ninstant                            0<\/span>\r\ndteday                             0<\/span>\r\nseason                             0<\/span>\r\nyr                                 0<\/span>\r\nmnth                               0<\/span>\r\nholiday                            0<\/span>\r\nweekday                            0<\/span>\r\nworkingday                         0<\/span>\r\nweathersit                         0<\/span>\r\ntemp                               0<\/span>\r\natemp                              0<\/span>\r\nhum                                0<\/span>\r\nwindspeed                          0<\/span>\r\ncasual                             0<\/span>\r\nregistered                         0<\/span>\r\ncnt                                0<\/span>\r\n<\/code><\/pre>\n
            \n
            \u7ed3\u8bba<\/h2>\n
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            \u76f4\u5230\u90a3\u65f6\uff0c\u795d\u4f60\u5b66\u4e60\u6109\u5feb\uff01:)<\/p>\n","protected":false},"excerpt":{"rendered":"
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