Question

我有一个像这样的数据框

product_id view_count purchase_count
1           11         1   
2           20         3
3           5          2
...

我想将此转换为一个按view_count分组的表格，并为一个时间间隔汇总purchase_count。

view_count_range total_purchase_count
0-10                 45
10-20                65

这些view_count_ranges的大小固定。我很感激有关如何对这样的范围进行分组的任何建议。

Answer 1

cut是一种方便的工具。这是一种方式：

#First make some data to work with 
#I suggest you do this in the future as it makes it 
#easier to provide you with assistance.
set.seed(10)
dat <- data.frame(product_id=1:15, view_count=sample(1:20, 15, replace=T), 
    purchase_count=sample(1:8, 15, replace=T))
dat   #look at the data

#now we can use cut and aggregate by this new variable we just created
dat$view_count_range <- with(dat, cut(view_count, c(0, 10, 20)))
aggregate(purchase_count~view_count_range, dat, sum)

哪个收益：

  view_count_range purchase_count
1           (0,10]             39
2          (10,20]             31

Answer 2

扩展Tyler的答案并从他的示例dat开始，你可能会发现在data.table中编写这样的查询更容易，更快捷：

> require(data.table)
> DT = as.data.table(dat)

> DT[, sum(purchase_count), by=cut(view_count,c(0,10,20))]
         cut V1
[1,] (10,20] 31
[2,]  (0,10] 39

就是这样。只需一行。易于编写，易于阅读。

注意它将（10,20）组放在第一位。这是因为默认情况下它保留了每个组首次出现在数据中的顺序（此数据集中第一个view_count为11）。相反，请将by更改为keyby：

> DT[, sum(purchase_count), keyby=cut(view_count,c(0,10,20))]
         cut V1
[1,]  (0,10] 39
[2,] (10,20] 31

并命名结果列：

> DT[,list( purchase_count = sum(purchase_count) ),
     keyby=list( view_count_range = cut(view_count,c(0,10,20) ))]
     view_count_range purchase_count
[1,]           (0,10]             39
[2,]          (10,20]             31

在如何组合数据框中的行组时遇到困难

2 个答案: