使用 R 基于多个条件过滤记录的优雅方式

Question

我有一个如下所示的数据框

test_df <- data.frame("subject_id" = c(1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3), 
                      "date_1" = c("01/01/2003", "12/31/2007", "12/30/2008", "12/31/2005",
                                   "01/01/2007", "01/01/2013", "12/31/2008", "03/04/2006", 
                                   "12/31/2009", "01/01/2015", "01/01/2009"))

我想做的是

按升序排列每个主题的日期（组内升序排列）
根据以下标准删除每个主题的日期记录（年份无关紧要）：

2a。如果主题的第一条记录是 1 月 1 日，则仅删除 12 月 31 日的记录例如：subject_id = 1

2b。如果主题的第一条记录是 12 月 31 日，则仅删除 1 月 1 日的记录例如：subject_id = 2

2c。如果受试者的非第一条记录中同时包含 12 月 31 日和 1 月 1 日（即从第二条记录到其记录末尾），则仅删除 12 月 31 日的记录 ex:subject_id = 3

我正在尝试以下

sorted <- test_df %>% arrange(date_1,group_by = subject_id) #Am I right in sorts the dates within group?
test_df$month = month(test_df$date_1)  #get the month
test_df$day = day(test_df$date_1)  #get the year
filter(test_df, month==12 and day == 31)  # doesn't work here

你能帮我看看如何根据我的条件筛选出记录吗？

我希望我的输出如下所示

Answer 1

这不是我写过的最漂亮的代码，但它确实有效。我假设过滤器是按顺序执行的；否则，第二个和第三个过滤器会剔除所有主题 2。

    test_df %>%
      mutate(date_1 = as.Date(as.character(date_1), format = "%m/%d/%Y"),
      month = as.numeric(format(date_1, "%m")),
      day = as.numeric(format(date_1, "%d"))) %>%
      group_by(subject_id) %>%
      arrange(date_1) %>%
      filter(!(rep(month[1] == 1 & day[1] == 1, n()) & month == 12 & day == 31)) %>%
      filter(!(rep(month[1] == 12 & day[1] == 31, n()) & month == 1 & day == 1)) %>%
      filter(!(rep(sum(month[-1] == 1 & day[-1] == 1) > 0 & sum(month[-1] == 12 & day[-1] == 31) > 0, n()) & month == 12 & day == 31)) %>%
      ungroup() %>%
      arrange(subject_id, date_1)

      subject_id date_1     month   day
           <dbl> <date>     <dbl> <dbl>
    1          1 2003-01-01     1     1
    2          1 2008-12-30    12    30
    3          2 2005-12-31    12    31
    4          2 2008-12-31    12    31
    5          3 2006-03-04     3     4
    6          3 2009-01-01     1     1
    7          3 2015-01-01     1     1

Answer 2

starting_names <- names(test_df)

test_df %>% 
  mutate(date_1 = lubridate::mdy(date_1)) %>% 
  group_by(subject_id) %>% 
  arrange() %>%
  mutate(
    without_year = format(date_1, "%m-%d"),
    first_date = first(without_year),
    has_both = all(c("01-01", "12-31") %in% tail(without_year, -1))
  ) %>%
  filter(!(first_date == "01-01" & without_year == "12-31")) %>%
  filter(!(first_date == "12-31" & without_year == "01-01")) %>%
  filter(!(first_date != "01-01" & first_date != "12-31" & has_both == TRUE & without_year == "12-31")) %>%
  select(all_of(starting_names)) %>%
  ungroup()

给出：

# A tibble: 7 x 2
  subject_id date_1    
       <dbl> <date>    
1          1 2003-01-01
2          1 2008-12-30
3          2 2005-12-31
4          2 2008-12-31
5          3 2006-03-04
6          3 2015-01-01
7          3 2009-01-01

Answer 3

您也许还可以尝试使用一些润滑剂的基本解决方案：

library(lubridate)
# put date_1 as date
test_df$date_1 <- lubridate::mdy(test_df$date_1)
# create the field that's going to be the filter
test_df$cntrl <- paste0(month(test_df$date_1),day(test_df$date_1))

现在的想法是按组 subject_id 在列表中拆分你的 df，然后 lapply 一个使用你的条件进行过滤的函数：

# split as list
listed <- split(test_df, test_df$subject_id)

# order each df: requested and fundamental for the function
listed <- lapply(listed, function(df){df[order(df$date_1),]})

# here the function: it's a nested if else statement on the field
filtering <- function(x){if
                        (head(x,1)$cntrl == "11") { x[x$cntrl != '1231', ] }
                        else if
                        (head(x,1)$cntrl == "1231") { x[x$cntrl != '11', ] }
                        else if
                        ( "11" %in% tail(x,nrow(x)-1)$cntrl & "1231" %in% tail(x,nrow(x)-1)$cntrl) { x[x$cntrl != '1231', ] }
                        else(x)}

# lapply it!
listed  <- lapply(listed, function(x)filtering(x))

# now as a dataframe, removing the useless column:
res <- do.call(rbind, listed)[,-3]

# lastly you can rename the rownames
rownames(res) <- 1:nrow(res)

res
 subject_id     date_1
1          1 2003-01-01
2          1 2008-12-30
3          2 2005-12-31
4          2 2008-12-31
5          3 2006-03-04
6          3 2009-01-01
7          3 2015-01-01

使用 R 基于多个条件过滤记录的优雅方式

Elegant way to filter records based on multiple criteria using R

datetime

r

filter

lubridate

dplyr