Question

为什么功能性＆＃39;：=＆＃39;不使用＆＃39;按＆＃39;汇总唯一行然而LHS：RHS使用＆＃39;进行聚合。下面是一个包含58个变量的20行数据的.csv文件。一个简单的复制，粘贴，delim = .csv工作。我仍在尝试找到将样本数据发布到SO的最佳方法。我的代码的两个变体是：

prodMatrix <- so.sample[, ':=' (Count = .N), by = eval(names(so.sample)[2:28])]

---此版本不使用by ---

聚合rowID

prodMatrix <- so.sample[, (Count = .N), by = eval(names(so.sample)[2:28])]

---这个版本使用---

汇总了rowID

"CID","NetIncome_length_Auto Advantage","NetIncome_length_Certificates","NetIncome_length_Comm. Share Draft","NetIncome_length_Escrow Shares","NetIncome_length_HE Fixed","NetIncome_length_HE Variable","NetIncome_length_Holiday Club","NetIncome_length_IRA Certificates","NetIncome_length_IRA Shares","NetIncome_length_Indirect Balloon","NetIncome_length_Indirect New","NetIncome_length_Indirect RV","NetIncome_length_Indirect Used","NetIncome_length_Loanline/CR","NetIncome_length_New Auto","NetIncome_length_Non-Owner","NetIncome_length_Personal","NetIncome_length_Preferred Plus Shares","NetIncome_length_Preferred Shares","NetIncome_length_RV","NetIncome_length_Regular Shares","NetIncome_length_S/L Fixed","NetIncome_length_S/L Variable","NetIncome_length_SBA","NetIncome_length_Share Draft","NetIncome_length_Share/CD Secured","NetIncome_length_Used Auto","NetIncome_sum_Auto Advantage","NetIncome_sum_Certificates","NetIncome_sum_Comm. Share Draft","NetIncome_sum_Escrow Shares","NetIncome_sum_HE Fixed","NetIncome_sum_HE Variable","NetIncome_sum_Holiday Club","NetIncome_sum_IRA Certificates","NetIncome_sum_IRA Shares","NetIncome_sum_Indirect Balloon","NetIncome_sum_Indirect New","NetIncome_sum_Indirect RV","NetIncome_sum_Indirect Used","NetIncome_sum_Loanline/CR","NetIncome_sum_New Auto","NetIncome_sum_Non-Owner","NetIncome_sum_Personal","NetIncome_sum_Preferred Plus Shares","NetIncome_sum_Preferred Shares","NetIncome_sum_RV","NetIncome_sum_Regular Shares","NetIncome_sum_S/L Fixed","NetIncome_sum_S/L Variable","NetIncome_sum_SBA","NetIncome_sum_Share Draft","NetIncome_sum_Share/CD Secured","NetIncome_sum_Used Auto","totNI","Count","totalNI"
93,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,-123.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,212.97,0,0,0,-71.36,0,0,0,49.01,0,0,67.42,6,404.52
114,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.54,0,0,0,0,0,-285.44,0,0,0,49.01,0,0,-221.89,90,-19970.1
1112,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,60.23,0,0,0,0,-101.55,0,-71.36,0,0,0,98.02,0,0,-14.66,28,-410.48
5366,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-71.36,0,0,0,49.01,0,0,-22.35,77631,-1735052.85
6078,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,7,0,0,0,1,0,0,0,0,0,0,0,0,-17.44,0,0,0,0,0,0,0,14.54,0,0,0,0,0,-499.52,0,0,0,49.01,0,0,-453.41,3,-1360.23
11684,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-71.36,0,0,0,49.01,0,0,-22.35,77631,-1735052.85
47358,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,-14.43,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-71.36,0,0,0,0,0,0,-85.79,3194,-274013.26
193761,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-101.55,0,-71.36,0,0,0,49.01,0,0,-123.9,9973,-1235654.7
232530,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-71.36,0,0,0,49.01,0,0,-22.35,77631,-1735052.85
604897,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-71.36,0,0,0,49.01,0,0,-22.35,77631,-1735052.85
1021309,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-71.36,0,0,0,0,0,0,-71.36,43262,-3087176.32
1023633,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-71.36,0,0,0,0,0,0,-71.36,43262,-3087176.32
1029726,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,60.23,0,0,0,0,0,0,-71.36,0,0,0,49.01,0,0,37.88,8688,329101.44
1040005,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-71.36,0,0,0,49.01,0,0,-22.35,77631,-1735052.85
1040092,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-71.36,0,0,0,49.01,0,0,-22.35,77631,-1735052.85
1064453,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14.54,0,212.97,0,0,0,-142.72,0,0,0,0,0,0,84.79,49,4154.71
1067508,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,-123.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-71.36,0,0,0,0,0,0,-194.56,4162,-809758.72
1080303,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-71.36,0,0,0,0,0,0,-71.36,43262,-3087176.32
1181005,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-101.55,0,-142.72,0,0,0,98.02,0,0,-146.25,614,-89797.5
1200484,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,-101.55,0,-285.44,0,0,0,0,0,0,-386.99,50,-19349.5

Answer 1

因为:=通过引用进行操作。这意味着它不会调用数据集的内存副本，但它会就地更新它对数据集进行聚合是其原始未聚合形式的副本您可以在Reference semantics小插图中了解有关它的更多信息。

这是data.table中的一个设计概念，:=用于按引用更新和其他形式 - .()，list()或直接表达式用于查询数据。并且查询数据不是通过引用操作的。 by reference 操作无法聚合行，它只能计算聚合并将其放入数据集中。查询能够聚合数据集，因为查询结果与原始data.table中的内存对象不同。

LHS：RHS与data.table中的功能

1 个答案: