Question

我有一个如下所示的数据框：

user_id        date             price
2375 2012/12/12 00:00:00.000  47.900000
2375 2013/01/16 00:00:00.000  47.900000
2375 2013/01/16 00:00:00.000  47.900000
2375 2013/05/08 00:00:00.000  47.900000
2375 2013/06/01 00:00:00.000  47.900000
2375 2013/10/02 00:00:00.000  26.500000
2375 2014/01/22 00:00:00.000  47.900000
2375 2014/03/21 00:00:00.000  47.900000
2375 2014/05/24 00:00:00.000  47.900000
2375 2015/04/11 00:00:00.000  47.900000
7419 2012/12/12 00:00:00.000   7.174977
7419 2013/01/02 00:00:00.000  27.500000
7419 2013/01/18 00:00:00.000  22.901482
7419 2013/02/08 00:00:00.000  27.500000
7419 2013/03/06 00:00:00.000   8.200000
7419 2013/04/03 00:00:00.000  22.901482
7419 2013/04/03 00:00:00.000   8.200000
7419 2013/04/03 00:00:00.000   6.900000
7419 2013/04/17 00:00:00.000   7.500000
7419 2013/04/17 00:00:00.000   7.500000
7419 2013/05/23 00:00:00.000   7.500000
7419 2013/06/07 00:00:00.000  27.500000
7419 2013/06/07 00:00:00.000   7.500000
7419 2013/06/07 00:00:00.000   7.500000
7419 2013/06/07 00:00:00.000   5.829188
7419 2013/07/10 00:00:00.000  27.500000
7419 2013/08/21 00:00:00.000   7.500000
7419 2013/08/21 00:00:00.000  27.500000
7419 2013/09/06 00:00:00.000  27.500000
7419 2013/12/27 00:00:00.000   7.500000
7419 2014/01/10 00:00:00.000  27.500000
7419 2014/02/16 00:00:00.000  27.500000
7419 2014/05/14 00:00:00.000  41.900000
7419 2014/07/03 00:00:00.000  26.500000
7419 2014/09/26 00:00:00.000  26.500000
7419 2014/09/26 00:00:00.000   7.500000
7419 2014/10/22 00:00:00.000  27.500000
7419 2014/11/15 00:00:00.000   6.900000
7419 2014/11/27 00:00:00.000  26.500000
7419 2014/12/12 00:00:00.000  40.900000
7419 2015/01/14 00:00:00.000  27.200000
7419 2015/02/24 00:00:00.000  26.500000
7419 2015/03/17 00:00:00.000  40.900000
7419 2015/05/02 00:00:00.000  27.200000
7419 2015/05/02 00:00:00.000  26.500000
7419 2015/05/15 00:00:00.000   7.900000
7419 2015/05/20 00:00:00.000  27.500000
7419 2015/06/20 00:00:00.000   7.500000
7419 2015/06/26 00:00:00.000   7.500000
7419 2015/06/30 00:00:00.000  41.900000
7419 2015/07/16 00:00:00.000  78.500000
11860 2012/12/12 00:00:00.000   7.174977
11860 2012/12/12 00:00:00.000  21.500000
11860 2013/03/02 00:00:00.000  22.901482
11860 2013/03/02 00:00:00.000   8.200000
11860 2013/05/25 00:00:00.000  29.500000
11860 2013/05/25 00:00:00.000   7.500000

实际上，我有超过40000个user_id。我想计算每个用户价格的前4周（不计算当前周）的总和。但是，日期是固定的，从2012年12月12日到2015年9月22日。为了避免每个用户的循环，我想到了像

这样的东西

df <- df %>% group_by(user_id) %>%
    mutate(price.lag1 = lag(prod_price, n = 1)) %>%
    mutate(amount4weeks = rollsum(x=price, 4, align = "right", fill = NA))

但是，它给了我一个错误，它只会将数据中的行作为“日期”。

我如何给出特定日期和/或我如何在单行中做我想要的日期？我的结果应该是这样的：

df$price4weeks = c(NA, 0.000000, 0.000000, 0.000000, 47.900000, 0.000000,  0.000000, 0.000000,  0.000000,  0.000000, NA, 7.174977, 27.500000, 22.901482, 27.500000,  8.200000,  8.200000,  8.200000,  6.900000,  6.900000,  0.000000, 7.500000,  7.500000,  7.500000,  7.500000,  0.000000,  0.000000,  0.000000, 27.500000,  0.000000,  7.500000,  0.000000,  0.000000,  0.000000,  0.000000, 0.000000,  7.500000, 27.500000,  6.900000, 33.400000,  0.000000,  0.000000, 26.500000,  0.000000,  0.000000, 26.500000, 34.400000, 27.500000,  7.500000,15.000000, 56.900000, NA, NA, 0.000000, 0.000000, 0.000000, 0.000000)

如果我在解释中遗漏了某些内容，请告诉我。

谢谢！

Answer 1

rollsum计算滚动k个数据点的总和。要在周内使用dplyr，您可以在数据中添加week_number列，然后使用sapply超过week_number来计算滚动金额。代码可能如下所示：

df <- mutate(df, week_number=cut.POSIXt(df$date, breaks="week", labels=FALSE))
df_new <- df %>% group_by(user_id) %>%
      do(mutate(.,total_4wk=sapply(week_number, function(n) sum(.$price[between(.$week_number, n -4, n-1)],na.rm=TRUE))))

具有固定日期的卷轴

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