我有一个Sql表并使用R与odbcConnect()连接到它。我使用的数据是在这里找到：http://robjhyndman.com/tsdldata/data/nybirths.dat
我用
加载了一个查询bstring <- sqlQuery(dbcon, "SELECT maptostring(__raw__) FROM births")
其中dbcon是与我的数据库的连接。返回的数据是以下字符串：
bstring
$maptostring
[1] {\n "26.663;23.598;26.931;24.74;25.806" : "24.364;24.477;23.901;23.175;23.227"\n}\n
[2] {\n "26.663;23.598;26.931;24.74;25.806" : "21.672;21.87;21.439;21.089;23.709"\n}\n
[3] {\n "26.663;23.598;26.931;24.74;25.806" : "21.669;21.752;20.761;23.479;23.824"\n}\n
[4] {\n "26.663;23.598;26.931;24.74;25.806" : "23.105;23.11;21.759;22.073;21.937"\n}\n
[5] {\n "26.663;23.598;26.931;24.74;25.806" : "20.035;23.59;21.672;22.222;22.123"\n}\n
[6] {\n "26.663;23.598;26.931;24.74;25.806" : "23.95;23.504;22.238;23.142;21.059"\n}\n
[7] {\n "26.663;23.598;26.931;24.74;25.806" : "21.573;21.548;20;22.424;20.615"\n}\n
[8] {\n "26.663;23.598;26.931;24.74;25.806" : "21.761;22.874;24.104;23.748;23.262"\n}\n
[9] {\n "26.663;23.598;26.931;24.74;25.806" : "22.907;21.519;22.025;22.604;20.894"\n}\n
[10] {\n "26.663;23.598;26.931;24.74;25.806" : "24.677;23.673;25.32;23.583;24.671"\n}\n
[11] {\n "26.663;23.598;26.931;24.74;25.806" : "24.454;24.122;24.252;22.084;22.991"\n}\n
[12] {\n "26.663;23.598;26.931;24.74;25.806" : "23.287;23.049;25.076;24.037;24.43"\n}\n
[13] {\n "26.663;23.598;26.931;24.74;25.806" : "24.667;26.451;25.618;25.014;25.11"\n}\n
[14] {\n "26.663;23.598;26.931;24.74;25.806" : "22.964;23.981;23.798;22.27;24.775"\n}\n
[15] {\n "26.663;23.598;26.931;24.74;25.806" : "22.646;23.988;24.737;26.276;25.816"\n}\n
[16] {\n "26.663;23.598;26.931;24.74;25.806" : "25.21;25.199;23.162;24.707;24.364"\n}\n
[17]{\n "26.663;23.598;26.931;24.74;25.806" : "22.644;25.565;24.062;25.431;24.635"\n}\n
[18] {\n "26.663;23.598;26.931;24.74;25.806" : "27.009;26.606;26.268;26.462;25.246"\n}\n
[19] {\n "26.663;23.598;26.931;24.74;25.806" : "25.18;24.657;23.304;26.982;26.199"\n}\n
[20] {\n "26.663;23.598;26.931;24.74;25.806" : "27.21;26.122;26.706;26.878;26.152"\n}\n
[21] {\n "26.663;23.598;26.931;24.74;25.806" : "26.379;24.712;25.688;24.99;24.239"\n}\n
[22] {\n "26.663;23.598;26.931;24.74;25.806" : "26.721;23.475;24.767;26.219;28.361"\n}\n
[23] {\n "26.663;23.598;26.931;24.74;25.806" : "28.599;27.914;27.784;25.693;26.881"\n}\n
[24] {\n "26.663;23.598;26.931;24.74;25.806" : "26.217;24.218;27.914;26.975;28.527"\n}\n
[25] {\n "26.663;23.598;26.931;24.74;25.806" : "27.139;28.982;28.169;28.056;29.136"\n}\n
[26] {\n "26.663;23.598;26.931;24.74;25.806" : "26.291;26.987;26.589;24.848;27.543"\n}\n
[27] {\n "26.663;23.598;26.931;24.74;25.806" : "26.896;28.878;27.39;28.065;28.141"\n}\n
[28] {\n "26.663;23.598;26.931;24.74;25.806" : "29.048;28.484;26.634;27.735;27.132"\n}\n
[29] {\n "26.663;23.598;26.931;24.74;25.806" : "24.924;28.963;26.589;27.931;28.009"\n}\n
[30] {\n "26.663;23.598;26.931;24.74;25.806" : "29.229;28.759;28.405;27.945;25.912"\n}\n
[31] {\n "26.663;23.598;26.931;24.74;25.806" : "26.619;26.076;25.286;27.66;25.951"\n}\n
[32] {\n "26.663;23.598;26.931;24.74;25.806" : "26.398;25.565;28.865;30;29.261"\n}\n
[33] {\n "26.663;23.598;26.931;24.74;25.806" : "29.012;26.992;27.897"\n}\n
[34] {\n "26.663;23.598;26.931;24.74;25.806" : "24.364;24.477;23.901;23.175;23.227"\n}\n
[35] {\n "26.663;23.598;26.931;24.74;25.806" : "21.672;21.87;21.439;21.089;23.709"\n}\n
[36] {\n "26.663;23.598;26.931;24.74;25.806" : "21.669;21.752;20.761;23.479;23.824"\n}\n
[37] {\n "26.663;23.598;26.931;24.74;25.806" : "23.105;23.11;21.759;22.073;21.937"\n}\n
[38] {\n "26.663;23.598;26.931;24.74;25.806" : "20.035;23.59;21.672;22.222;22.123"\n}\n
[39] {\n "26.663;23.598;26.931;24.74;25.806" : "23.95;23.504;22.238;23.142;21.059"\n}\n
[40] {\n "26.663;23.598;26.931;24.74;25.806" : "21.573;21.548;20;22.424;20.615"\n}\n
[41] {\n "26.663;23.598;26.931;24.74;25.806" : "21.761;22.874;24.104;23.748;23.262"\n}\n
[42] {\n "26.663;23.598;26.931;24.74;25.806" : "22.907;21.519;22.025;22.604;20.894"\n}\n
[43] {\n "26.663;23.598;26.931;24.74;25.806" : "24.677;23.673;25.32;23.583;24.671"\n}\n
[44] {\n "26.663;23.598;26.931;24.74;25.806" : "24.454;24.122;24.252;22.084;22.991"\n}\n
[45] {\n "26.663;23.598;26.931;24.74;25.806" : "23.287;23.049;25.076;24.037;24.43"\n}\n
[46] {\n "26.663;23.598;26.931;24.74;25.806" : "24.667;26.451;25.618;25.014;25.11"\n}\n
[47] {\n "26.663;23.598;26.931;24.74;25.806" : "22.964;23.981;23.798;22.27;24.775"\n}\n
[48] {\n "26.663;23.598;26.931;24.74;25.806" : "22.646;23.988;24.737;26.276;25.816"\n}\n
[49] {\n "26.663;23.598;26.931;24.74;25.806" : "25.21;25.199;23.162;24.707;24.364"\n}\n
[50] {\n "26.663;23.598;26.931;24.74;25.806" : "22.644;25.565;24.062;25.431;24.635"\n}\n
[51] {\n "26.663;23.598;26.931;24.74;25.806" : "27.009;26.606;26.268;26.462;25.246"\n}\n
[52] {\n "26.663;23.598;26.931;24.74;25.806" : "25.18;24.657;23.304;26.982;26.199"\n}\n
[53] {\n "26.663;23.598;26.931;24.74;25.806" : "27.21;26.122;26.706;26.878;26.152"\n}\n
[54] {\n "26.663;23.598;26.931;24.74;25.806" : "26.379;24.712;25.688;24.99;24.239"\n}\n
[55] {\n "26.663;23.598;26.931;24.74;25.806" : "26.721;23.475;24.767;26.219;28.361"\n}\n
[56] {\n "26.663;23.598;26.931;24.74;25.806" : "28.599;27.914;27.784;25.693;26.881"\n}\n
[57] {\n "26.663;23.598;26.931;24.74;25.806" : "26.217;24.218;27.914;26.975;28.527"\n}\n
[58] {\n "26.663;23.598;26.931;24.74;25.806" : "27.139;28.982;28.169;28.056;29.136"\n}\n
[59] {\n "26.663;23.598;26.931;24.74;25.806" : "26.291;26.987;26.589;24.848;27.543"\n}\n
[60] {\n "26.663;23.598;26.931;24.74;25.806" : "26.896;28.878;27.39;28.065;28.141"\n}\n
[61] {\n "26.663;23.598;26.931;24.74;25.806" : "29.048;28.484;26.634;27.735;27.132"\n}\n
[62] {\n "26.663;23.598;26.931;24.74;25.806" : "24.924;28.963;26.589;27.931;28.009"\n}\n
[63] {\n "26.663;23.598;26.931;24.74;25.806" : "29.229;28.759;28.405;27.945;25.912"\n}\n
[64] {\n "26.663;23.598;26.931;24.74;25.806" : "26.619;26.076;25.286;27.66;25.951"\n}\n
[65] {\n "26.663;23.598;26.931;24.74;25.806" : "26.398;25.565;28.865;30;29.261"\n}\n
[66] {\n "26.663;23.598;26.931;24.74;25.806" : "29.012;26.992;27.897"\n}\n
33 Levels: {\n "26.663;23.598;26.931;24.74;25.806" : "20.035;23.59;21.672;22.222;22.123"\n}\n ...
我想使用ARIMA使用此数据创建预测。为此，我尝试使用
将此数据解析为数字bnumeric <- as.numeric(unlist(bstring))
这不行，我得到了一个相当无用的数字向量。如何从此数据中提取数字以创建预测？