在 `df1` 中添加一个新变量（标准偏差），用于依赖于 `df2` 中的多行并以 `Datetime` 和其他两个变量为条件的行

Question

我有数据框 df1，随着时间的推移，每隔一小时 df1$DateTime 总结不同的人 df$Person。

此外，我还有另一个数据框 df2，其中包含有关这些人随时间在 "time spent on the phone" 或 "money spent in a purchases" 列中所做的事情的信息 Data_Type。 Value 列中显示 phone 花费的分钟数或这些特定时间花费的金钱。

举个例子：

df1<- data.frame(DateTime=c("2016-09-27 11:00:00","2016-09-27 11:00:00","2016-09-27 12:00:00","2016-09-27 12:00:00","2016-09-27 13:00:00","2016-09-27 13:00:00"),
                 Person= c(11,12,11,12,11,12))

df2<- data.frame(DateTime= c("2016-09-27 11:03:40","2016-09-27 11:07:40","2016-09-27 11:34:53","2016-09-27 11:48:32","2016-09-27 12:01:40","2016-09-27 12:09:40","2016-09-27 12:21:40","2016-09-27 12:29:40","2016-09-27 12:35:40","2016-09-27 12:41:40","2016-09-27 12:53:26","2016-09-27 13:05:40","2016-09-27 13:24:14","2016-09-27 13:32:50","2016-09-27 13:47:19"),
                 Person= c(11,11,12,11,12,11,11,11,11,12,12,12,11,12,11),
                 Data_Type=c("Call","Call","Call","Call","Purchase","Call","Call","Call","Call","Purchase","Call","Call","Call","Call","Purchase"),
                 Value=c(2.7,5.4,3.2,1.7,300,4.6,2.3,5.1,2.9,100,0.6,6.2,1.8,7.6,380))

> df1
             DateTime Person
1 2016-09-27 11:00:00     11
2 2016-09-27 11:00:00     12
3 2016-09-27 12:00:00     11
4 2016-09-27 12:00:00     12
5 2016-09-27 13:00:00     11
6 2016-09-27 13:00:00     12

> df2
              DateTime Person Data_Type Value
1  2016-09-27 11:03:40     11      Call   2.7
2  2016-09-27 11:07:40     11      Call   5.4
3  2016-09-27 11:34:53     12      Call   3.2
4  2016-09-27 11:48:32     11      Call   1.7
5  2016-09-27 12:01:40     12  Purchase 300.0
6  2016-09-27 12:09:40     11      Call   4.6
7  2016-09-27 12:21:40     11      Call   2.3
8  2016-09-27 12:29:40     11      Call   5.1
9  2016-09-27 12:35:40     11      Call   2.9
10 2016-09-27 12:41:40     12  Purchase 100.0
11 2016-09-27 12:53:26     12      Call   0.6
12 2016-09-27 13:05:40     12      Call   6.2
13 2016-09-27 13:24:14     11      Call   1.8
14 2016-09-27 13:32:50     12      Call   7.6
15 2016-09-27 13:47:19     11  Purchase 380.0

我想在 df1 中添加两个新变量，它们总结了 Calls 和 Purchases 的标准偏差，具体取决于人和指定的一小时间隔。

我想得到这个（也许我在计算 sd 时犯了一些错误）：

> df1
             DateTime Person   sdCalls sdPurchases
1 2016-09-27 11:00:00     11 1.9139836          NA
2 2016-09-27 11:00:00     12 0.0000000          NA
3 2016-09-27 12:00:00     11 1.3375973          NA
4 2016-09-27 12:00:00     12 0.0000000    141.4214
5 2016-09-27 13:00:00     11 0.0000000      0.0000
6 2016-09-27 13:00:00     12 0.9899495          NA

有人知道怎么做吗？

Answer 1

一个选项是 floor 第二个数据集中的 'DateTime' 列，并将 on 与 'Person'、'DateTime' 子集连接 [=19] =] 对应 'Call', 'Purchase' in 'Data_Type' 得到 sd

library(lubridate)
library(data.table)
setDT(df1)[, DateTime := ymd_hms(DateTime)]
setDT(df2)[, dt_floor := floor_date(ymd_hms(DateTime), unit = "hour")]
df2[df1, .(sdsCalls = sd(Value[Data_Type == "Call"]), 
  sdPurchases = sd(Value[Data_Type == 'Purchase'])),
          on = .(Person, dt_floor = DateTime), by = .EACHI]

在 `df1` 中添加一个新变量（标准偏差），用于依赖于 `df2` 中的多行并以 `Datetime` 和其他两个变量为条件的行

Add a new variable (Standard deviation) in `df1` for row dependinig on multiple rows from `df2` and conditioned to `Datetime` and other two variables

r

lubridate

dplyr

tidyverse