如何使计算/插入日期差异列更快？
你能让这个 R 代码更快吗？不知道如何对其进行矢量化。 我有一个数据框如下（下面的示例行）：
> str(tt)
'data.frame': 1008142 obs. of 4 variables:
$ customer_id: int, visit_date : Date, format: "2010-04-04", ...
我想计算客户的visit_dates 之间的差异。 因此，我执行 diff(tt$visit_date)，但必须在 customer_id 发生变化的地方强制执行不连续性 (NA)，并且 diff 毫无意义，例如下面的第 74 行。 底部的代码执行此操作，但在 1M 行数据集上需要 15 分钟以上。 我还尝试了分段计算并绑定每个 customer_id 的子结果（使用 which()），这也很慢。 有什么建议吗？谢谢。我确实搜索了 SO、R-intro、R 手册页等。
customer_id visit_date visit_spend ivi
72 40 2011-03-15 18.38 5
73 40 2011-03-20 23.45 5
74 79 2010-04-07 150.87 NA
75 79 2010-04-17 101.90 10
76 79 2010-05-02 111.90 15
代码：（
all_tt_cids <- unique(tt$customer_id)
# Append ivi (Intervisit interval) column
tt$ivi <- c(NA,diff(tt$visit_date))
for (cid in all_tt_cids) {
# ivi has a discontinuity when customer_id changes
tt$ivi[min(which(tt$customer_id==cid))] <- NA
}
想知道我们是否可以创建一个逻辑索引，其中 customer_id 与上面的行不同？）
Can you make this R code faster? Can't see how to vectorize it.
I have a data-frame as follows (sample rows below):
> str(tt)
'data.frame': 1008142 obs. of 4 variables:
$ customer_id: int, visit_date : Date, format: "2010-04-04", ...
I want to compute the diff between visit_dates for a customer.
So I do diff(tt$visit_date), but have to enforce a discontinuity (NA) everywhere customer_id changes and the diff is meaningless, e.g. row 74 below.
The code at bottom does this, but takes >15 min on the 1M row dataset.
I also tried piecewise computing and cbind'ing the subresult per customer_id (using which()), that was also slow.
Any suggestions? Thanks. I did search SO, R-intro, R manpages, etc.
customer_id visit_date visit_spend ivi
72 40 2011-03-15 18.38 5
73 40 2011-03-20 23.45 5
74 79 2010-04-07 150.87 NA
75 79 2010-04-17 101.90 10
76 79 2010-05-02 111.90 15
Code:
all_tt_cids <- unique(tt$customer_id)
# Append ivi (Intervisit interval) column
tt$ivi <- c(NA,diff(tt$visit_date))
for (cid in all_tt_cids) {
# ivi has a discontinuity when customer_id changes
tt$ivi[min(which(tt$customer_id==cid))] <- NA
}
(Wondering if we can create a logical index where customer_id differs to the row above?)
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要将
NA设置到适当的位置，您可以再次使用diff()和一行技巧：解释
让我们采用一些向量
x我们想要提取这样的索引，它以新数字开头，即（0,5,8,11,12）。我们可以使用 diff() 来实现这一点。
并取那些不等于零的索引：
to set
NAto appropriate places, you again can usediff()and one-line trick:explanation
let's take some vector
xwe want to extract such indexes, which start with new number, i.e. (0,5,8,11,12). We can use
diff()for that.and take those indexes, that are not equal to zero: