栏中的多个日期：R

如何解决栏中的多个日期：R

好的，我经历了几个问题，我认为我的数据中有一个可以解决的模式（也就是说，我可以区分dmy和mdy）。

让我为数据集提供一些背景知识。数据集包含有关当前隔离期间出行人员的信息。因此，当我向下滚动数据集时，我看到大多数日期是以M-D-Y格式输入的，而有些则是以dmy格式输入的。

因此对于此数据集，（数据集包含截至八月的信息）。歧义可以通过以下方式解决：

1. 日期不能在将来。因此，这解决了08-12-2020和12-08-2020之间的差异。而且，我只有直到六月的数据。
2. 日子比几个月快。如果我看到一个序列（无论是dmy还是mdy），如果我看到每隔几行就有一个数字变化（比如说20），那么我知道变化的数字是一天而不是一个月。 示例：

在这种情况下如何正确分配日期？

解决方法

因此，如果我理解正确，则您的数据按时间顺序排列，并且您对测量数据的时间范围有所了解。

有鉴于此，这是我创建加扰数据然后重新解析它的目的：

library(dplyr)
library(lubridate)
library(zoo) # for na.locf function

# create some scrambled data to work with
df <- tibble(
    date_ground_truth = rep(seq(from = ymd('20190801'),to = ymd('20200730'),by = 1),each = 5)
  ) %>%
  mutate(
    date_inconsistent_chr = format(date_ground_truth,ifelse(runif(nrow(.),1) > 0.3,'%Y/%m/%d','%Y/%d/%m'))
  )

# providing here the date range in which your observations lie. I only know that it maxes end of July 2020,so my end result has some remaining unknowns at the start
daterange_known_min <- NA_Date_
daterange_known_max <- ymd('20200730')

# initiate a cleaned df - for any date where we have a day > 12,we know that it can only be one format of YMD/YDM
df_recleaned <- df %>%
  mutate(
    date_parsed_ymd = as.Date(date_inconsistent_chr,'%Y/%m/%d'),# try YMD
    date_parsed_ydm = as.Date(date_inconsistent_chr,'%Y/%d/%m'),# try YDM
    
    date_parsed_deducted = case_when( # write out the clear cut cases
      day(date_parsed_ymd) > 12 ~ date_parsed_ymd,day(date_parsed_ydm) > 12 ~ date_parsed_ydm,date_parsed_ymd == date_parsed_ydm ~ date_parsed_ymd,T ~ NA_Date_
    )
  )

# we will run over the data until we can not deduct any more new dates from what we've learnt so far:

new_guesses_possible <- T
while(new_guesses_possible) {
  # how many dates did we already deduct?
  num_deducted_dates_before <- df_recleaned %>% filter(!is.na(date_parsed_deducted)) %>% nrow()
  
  # deduct more,based on our knowledge that the dates are chronological and within a certain time-frame
  df_recleaned <- df_recleaned %>%
    mutate(
      earliest_possible_date = coalesce(na.locf(date_parsed_deducted,na.rm = F),daterange_known_min),last_possible_date = coalesce(na.locf(date_parsed_deducted,fromLast = T,daterange_known_max),ymd_guess_in_range = coalesce(date_parsed_ymd >= earliest_possible_date & date_parsed_ymd <= last_possible_date,F),ydm_guess_in_range = coalesce(date_parsed_ydm >= earliest_possible_date & date_parsed_ydm <= last_possible_date,date_parsed_deducted = case_when(
        # keep clear cases
        !is.na(date_parsed_deducted) ~ date_parsed_deducted,# if the ymd-guess falls between the last clear case and next clear case,take ymd
        ymd_guess_in_range & !ydm_guess_in_range ~ date_parsed_ymd,# same approach for ydm
        ydm_guess_in_range & !ymd_guess_in_range ~ date_parsed_ydm,# cover the cases where we don't know either the last or next clear parsed date
        # if one of the parsed dates falls before the "first possible date",take the other one.
        #   (if no daterange_known_min is given,these rows will result in NA and not do anything...)
        date_parsed_ymd < daterange_known_min ~ date_parsed_ydm,date_parsed_ydm < daterange_known_min ~ date_parsed_ymd,# inversely,if one parsed option falls after the "last possible date",ignore it.
        date_parsed_ymd > daterange_known_max ~ date_parsed_ydm,date_parsed_ydm > daterange_known_max ~ date_parsed_ymd
      )
    )
  
  # how many dates did we now deduct?
  num_deducted_dates_after <- df_recleaned %>% filter(!is.na(date_parsed_deducted)) %>% nrow()
  
  # do we need to go on?
  new_guesses_possible <- num_deducted_dates_after > 0 & num_deducted_dates_before != num_deducted_dates_after
}

# kick out all those extra columns :)
df_recleaned_final <- df_recleaned %>%
  select(
    -date_parsed_ymd,-date_parsed_ydm,-earliest_possible_date,-last_possible_date,-ymd_guess_in_range,-ydm_guess_in_range
  )

在我的示例中，这修复了2019年8月第一周之后的所有日期。
如果数据之间的间隔更长，它可能会给您带来不同的结果。