dplyr::complete/fill 一个时间序列，但只适用于有限的时间段

Question

我正在尝试使用 dplyr::complete 和 fill 来填补动物体重时间序列中的空白（大部分时间大约每周称重），但我只想在一定范围内做到这一点。

在以下示例数据集中，缺少几个日期：2020 年 1 月 29 日的一次称重和 March/April 中的一系列 4 周缺失。我们可以接受缺少 1 周的称重（例如在 1/29）并且可以 "filling" 降低两周的原始体重，但不想再进一步了。第二组缺失数据应该只需要再补13天，然后剩下的缺口应该是wt_g.

的NA

library(tidyverse)
library(lubridate)

animalwts <- tibble::tribble(
      ~Animal,     ~WtDate, ~Wt_g,
      "A",  "1/1/2020",   20L,
      "A",  "1/8/2020",   21L,
      "A", "1/15/2020",   21L,
      "A", "1/22/2020",   23L,
      "A",  "2/5/2020",   25L,
      "A", "2/12/2020",   23L,
      "A", "2/19/2020",   24L,
      "A", "2/26/2020",   23L,
      "A",  "3/4/2020",   22L,
      "A",  "4/8/2020",   24L
    ) %>%
        mutate(WtDate = mdy(WtDate))

以下代码用于完成日期系列并填写所有缺失数据

animalwts %>%
  group_by(Animal) %>%
  complete(WtDate = seq.Date(min(WtDate), max(WtDate), by = "day")) %>%
  fill(Wt_g)

但我正在尝试弄清楚如何 complete 所有日期，但仅 fill 从任何给定日期起最多两周的权重，并为任何进一步缺失的数据添加 NA .

如果可能，我想留下 "in the pipe"。

Answer 1

像这样？

library(tidyverse)
library(lubridate)

animalwts %>%
  group_by(Animal) %>%
  mutate(NA_lag = WtDate - lag(WtDate),
         last_measurement_date = WtDate) %>% 
  complete(WtDate = seq.Date(min(WtDate), max(WtDate), by = "day")) %>%
  fill(Wt_g) %>% 
  fill(last_measurement_date) %>% 
  group_by(last_measurement_date, NA_lag) %>% 
  mutate(days_missing = row_number()) %>% 
  mutate(Wt_g = if_else(days_missing > 14, NA_integer_, Wt_g))

数据

animalwts <- tibble::tribble(
  ~Animal,     ~WtDate, ~Wt_g,
  "A",  "1/1/2020",   20L,
  "A",  "1/8/2020",   21L,
  "A", "1/15/2020",   21L,
  "A", "1/22/2020",   23L,
  "A",  "2/5/2020",   25L,
  "A", "2/12/2020",   23L,
  "A", "2/19/2020",   24L,
  "A", "2/26/2020",   23L,
  "A",  "3/4/2020",   22L,
  "A",  "4/8/2020",   24L
) %>%
  mutate(WtDate = mdy(WtDate))

dplyr::complete/fill 一个时间序列，但只适用于有限的时间段

dplyr::complete/fill a time sequence, but only for limited stretches of time

r

time-series

fill

dplyr