我有一个data.frame，每周数据如下：
structure(list(date = structure(1:9, .Label = c("2017-01-01",
"2017-01-08", "2017-01-15", "2017-01-22", "2017-01-29", "2017-02-05",
"2017-02-12", "2017-02-19", "2017-02-26"), class = "factor"),
value = c("6", "5", "5", "5", "5", "5", "6", "8", "10")), row.names = c(NA,
-9L), class = "data.frame")
现在，我想通过平滑处理来计算每日数据，该平滑处理使值每天线性变化，变化量等于每周变化的七分之一。因此，预期输出为：
structure(list(date = structure(c(17167, 17168, 17169, 17170,
17171, 17172, 17173, 17174, 17175, 17176, 17177, 17178, 17179,
17180, 17181, 17182, 17183, 17184, 17185, 17186, 17187, 17188,
17189, 17190, 17191, 17192, 17193, 17194, 17195, 17196, 17197,
17198, 17199, 17200, 17201, 17202, 17203, 17204, 17205, 17206,
17207, 17208, 17209, 17210, 17211, 17212, 17213, 17214, 17215,
17216, 17217, 17218, 17219, 17220, 17221, 17222, 17223, 17224,
17225), class = "Date"), value = c(6, 5.857, 5.714, 5.571, 5.429,
5.286, 5.143, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5.143, 5.286, 5.429, 5.571,
5.714, 5.857, 6, 6.286, 6.571, 6.857, 7.143, 7.429, 7.714, 8,
8.286, 8.571, 8.857, 9.143, 9.429, 9.714, 10, 8.57, 7.14)), row.names = c(NA,
-59L), class = c("tbl_df", "tbl", "data.frame"))
请注意，最后两个日期不在从第一个数据帧开始的时间范围内，并且由于从最近观察到的值（10）到0计算得出的差异，它们减少了（10-0）/7。
如何获得预期的输出？
答案 0 :(得分：2)
这有帮助吗？
library(tidyverse)
library(lubridate)
#>
#> Attache Paket: 'lubridate'
#> The following object is masked from 'package:base':
#>
#> date
library(padr)
mydf<- data.frame(stringsAsFactors=FALSE,
index = c(1, 2, 3, 4, 5, 6, 7, 8, 9),
date = c("01.01.2017", "08.01.2017", "15.01.2017", "22.01.2017",
"29.01.2017", "05.02.2017", "12.02.2017", "19.02.2017",
"26.02.2017"),
value = c(6, 5, 5, 5, 5, 5, 6, 8, 10)
)
mydf %>%
mutate(date=lubridate::dmy(date)) %>%
pad(.,interval="day", end_val=lubridate::dmy("28.02.2017")) %>%
mutate(value2=approx(date,value,date)$y)
#> index date value value2
#> 1 1 2017-01-01 6 6.000000
#> 2 NA 2017-01-02 NA 5.857143
#> 3 NA 2017-01-03 NA 5.714286
#> 4 NA 2017-01-04 NA 5.571429
#> 5 NA 2017-01-05 NA 5.428571
#> 6 NA 2017-01-06 NA 5.285714
#> 7 NA 2017-01-07 NA 5.142857
#> 8 2 2017-01-08 5 5.000000
#> 9 NA 2017-01-09 NA 5.000000
#> 10 NA 2017-01-10 NA 5.000000
#> 11 NA 2017-01-11 NA 5.000000
#> 12 NA 2017-01-12 NA 5.000000
#> 13 NA 2017-01-13 NA 5.000000
#> 14 NA 2017-01-14 NA 5.000000
#> 15 3 2017-01-15 5 5.000000
#> 16 NA 2017-01-16 NA 5.000000
#> 17 NA 2017-01-17 NA 5.000000
#> 18 NA 2017-01-18 NA 5.000000
#> 19 NA 2017-01-19 NA 5.000000
#> 20 NA 2017-01-20 NA 5.000000
#> 21 NA 2017-01-21 NA 5.000000
#> 22 4 2017-01-22 5 5.000000
#> 23 NA 2017-01-23 NA 5.000000
#> 24 NA 2017-01-24 NA 5.000000
#> 25 NA 2017-01-25 NA 5.000000
#> 26 NA 2017-01-26 NA 5.000000
#> 27 NA 2017-01-27 NA 5.000000
#> 28 NA 2017-01-28 NA 5.000000
#> 29 5 2017-01-29 5 5.000000
#> 30 NA 2017-01-30 NA 5.000000
#> 31 NA 2017-01-31 NA 5.000000
#> 32 NA 2017-02-01 NA 5.000000
#> 33 NA 2017-02-02 NA 5.000000
#> 34 NA 2017-02-03 NA 5.000000
#> 35 NA 2017-02-04 NA 5.000000
#> 36 6 2017-02-05 5 5.000000
#> 37 NA 2017-02-06 NA 5.142857
#> 38 NA 2017-02-07 NA 5.285714
#> 39 NA 2017-02-08 NA 5.428571
#> 40 NA 2017-02-09 NA 5.571429
#> 41 NA 2017-02-10 NA 5.714286
#> 42 NA 2017-02-11 NA 5.857143
#> 43 7 2017-02-12 6 6.000000
#> 44 NA 2017-02-13 NA 6.285714
#> 45 NA 2017-02-14 NA 6.571429
#> 46 NA 2017-02-15 NA 6.857143
#> 47 NA 2017-02-16 NA 7.142857
#> 48 NA 2017-02-17 NA 7.428571
#> 49 NA 2017-02-18 NA 7.714286
#> 50 8 2017-02-19 8 8.000000
#> 51 NA 2017-02-20 NA 8.285714
#> 52 NA 2017-02-21 NA 8.571429
#> 53 NA 2017-02-22 NA 8.857143
#> 54 NA 2017-02-23 NA 9.142857
#> 55 NA 2017-02-24 NA 9.428571
#> 56 NA 2017-02-25 NA 9.714286
#> 57 9 2017-02-26 10 10.000000
编辑版本中包含了jakes评论：
mydf <- mydf %>%
mutate(date=lubridate::dmy(date))
end_row <- tibble(date=max(mydf$date)+7,value=0)
bind_rows(mydf, end_row) %>%
pad(.,interval="day") %>%
mutate(value2=approx(date,value,date)$y) %>%
filter(date<dmy("28.02.2017")+1)