r - 根据R中的条件计算日期之间的平均差

Question

我有这个数据集:

Date New_Renew
 2019-01-10 22:11:16  Renewing
 2019-02-23 00:21:48  Renewing
 2019-03-05 05:26:17  Renewing
 2019-04-18 15:05:10       NEW
 2019-04-18 15:07:52       NEW
 2019-04-26 11:32:25  Renewing
 2019-05-03 14:15:25  Renewing
 2019-05-08 21:10:08       NEW
 2019-05-16 13:35:57  Renewing
 2019-05-24 13:18:23  Renewing
 2019-06-01 12:42:21  Renewing
 2019-06-17 18:08:09  Renewing
 2019-06-26 13:40:29  Renewing
 2019-12-13 17:57:43  Renewing
 2020-01-03 11:49:14  Renewing
 2020-01-11 11:46:51  Renewing
 2020-01-14 21:08:08       NEW
 2020-01-18 21:14:30       NEW
 2020-01-21 16:08:37       NEW
 2020-01-28 11:41:44  Renewing
 2020-01-30 13:34:21  Renewing
 2020-02-03 13:29:37  Renewing
 2020-02-18 17:15:52  Renewing
 2020-02-20 13:37:52  Renewing
 2020-02-24 12:55:25  Renewing
 2020-02-26 21:13:38       NEW
 2020-03-04 13:23:41  Renewing
 2020-03-09 16:48:36  Renewing

我想要的是，当 New_Renew 变量等于 NEW 时，计算与 NEW 相关联的日期之间差异的平均值。简而言之，用户进行新交易的频率。

Answer 1

使用 aggregate和 diff . 60*24将产生的秒数转换为天数。

aggregate(Date ~ New_Renew, dat, function(x) mean(diff(x))/(60*24))
#  New_Renew         Date
# 1       NEW 52.38292438 
# 2  Renewing  0.01471444 

或与 tapply .

with(dat, tapply(Date, New_Renew, function(x) mean(diff(x))/(60*24)))
#         NEW    Renewing 
# 52.38292438  0.01471444 

数据

dat <- structure(list(Date = structure(c(1547154676, 1550877708, 1551759977, 
1555592710, 1555592872, 1556271145, 1556885725, 1557342608, 1558006557, 
1558696703, 1559385741, 1560787689, 1561549229, 1576256263, 1578048554, 
1578739611, 1579032488, 1579378470, 1579619317, 1580208104, 1580387661, 
1580732977, 1582042552, 1582202272, 1582545325, 1582748018, 1583324621, 
1583768916), class = c("POSIXct", "POSIXt"), tzone = ""), New_Renew = c("Renewing", 
"Renewing", "Renewing", "NEW", "NEW", "Renewing", "Renewing", 
"NEW", "Renewing", "Renewing", "Renewing", "Renewing", "Renewing", 
"Renewing", "Renewing", "Renewing", "NEW", "NEW", "NEW", "Renewing", 
"Renewing", "Renewing", "Renewing", "Renewing", "Renewing", "NEW", 
"Renewing", "Renewing")), row.names = c(NA, -28L), class = "data.frame")