Question

如何将这些数据汇总15分钟（时钟时间）累计秒以及每个 loc 的唯一ID数量？

> dput(df)
structure(list(id = c(131, 146, 160, 146, 160, 146, 160, 137, 
157, 144, 124, 144, 119, 119, 242, 242, 235, 235, 145, 262, 258, 
160, 145, 135, 148, 148, 143), loc = structure(c(1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("A", "B"), class = "factor"), 
    time = structure(c(1425197400, 1425197400, 1425197400, 1425197460, 
    1425197460, 1425197520, 1425197520, 1425197940, 1425198180, 
    1425198180, 1425198180, 1425198240, 1425198240, 1425198300, 
    1425198300, 1425198360, 1425198480, 1425198540, 1425198840, 
    1425198900, 1425346560, 1425346560, 1425347280, 1425347460, 
    1425347520, 1425347580, 1425347580), class = c("POSIXct", 
    "POSIXt")), secs = c(35, 60, 60, 60, 60, 19, 24, 0, 0, 60, 
    0, 46, 60, 28, 60, 48, 60, 18, 6, 0, 0, 43, 0, 37, 60, 27, 
    14)), .Names = c("id", "loc", "time", "secs"), row.names = c(NA, 
27L), class = "data.frame")

此示例的输出应如下所示：

> dput(df.out)
structure(list(unique.id = c(3, 7, 2, 2, 4), loc = c("A", "A", 
"A", "B", "B"), time = structure(c(1425172501, 1425173400, 1425174300, 
1425321900, 1425322800), class = c("POSIXct", "POSIXt"), tzone = "UTC"), 
    secs = c(318, 380, 6, 43, 138)), class = c("tbl_df", "tbl", 
"data.frame"), row.names = c(NA, -5L), .Names = c("unique.id", 
"loc", "time", "secs"))

我已成功使用包xts来计算秒数：

## disregarding the loc grouping:
df.test <- select(df, time, secs)
df.test <- na.omit(df.test) ##xts with period.sum does not like NA
df.test <- as.xts(df.test, order.by = df.test$time)
df.test <- period.sum(df.test$secs, endpoints(df.test , "mins", k=15))
df.test <- align.time(df.test , 15*60)

但是我无法做同样的事情来计算唯一ID 。顺便说一句，如果有人有一个更优雅的解决方案，我欢迎你提供意见（准备期间指标然后只需将所有内容提供给dplyr::group_by()::summarise()）

由于

Answer 1

这是使用dplyr的一种解决方案。将时间转换为15分钟，然后进行group_y / summary。

df$time<- as.POSIXct(ceiling(as.double(df$time) / (15*60)) * (15*60),
                         origin = '1970-01-01')
df %>%
  group_by(time, loc) %>%
  summarise(unique.id = n_distinct(id), secs = sum(secs)) %>%
  select(unique.id, loc, time, secs)

输出是：

Source: local data frame [5 x 4]
Groups: time [5]

  unique.id    loc                time  secs
      <int> <fctr>              <dttm> <dbl>
1         3      A 2015-03-01 03:15:00   318
2         7      A 2015-03-01 03:30:00   380
3         2      A 2015-03-01 03:45:00     6
4         2      B 2015-03-02 20:45:00    43
5         4      B 2015-03-02 21:00:00   138

随着时间的推移聚合，数量独特

1 个答案: