过滤方法

Question

Ciao

这是我的复制示例。

HAVE <- data.frame(ID=c(1,1,1,2,2,2,3,3,3,4,4,4,5,5,5,6,6,6),
                   ABSENCE=c(NA,NA,NA,0,0,0,0,0,1,NA,0,NA,0,1,2,0,0,0),
                   TIME=c(1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3))


WANT <- data.frame(ID=c(1,2,3,4,5,6),
                   ABSENCE=c(NA,0,1,0,1,0),
                   TIME=c(NA,3,3,2,2,3))

高数据文件HAVE是我需要转换为WANT的文件。因此，基本上，对于每个ID，我需要标识第一个非零值，并且该值将进入数据文件WANT。如果所有缺勤值为NA，则TIME为NA。如果ABSENCE的所有值均为0，则我报告WANT中最后一个可能的行（反映在TIME变量中）

这是我的尝试：

WANT <- group_by(HAVE,ID) %>% slice(seq_len(min(which(ABSENCE > 0), n())))

但是如果只有0，我不知道如何取0行中的最后一行。

Answer 1

library(data.table)
setDT(HAVE)

res = unique(HAVE[, .(ID)])

# look up first ABSENCE > 0
res[, c("ABSENCE", "TIME") := unique(HAVE[ABSENCE > 0], by="ID")[.SD, on=.(ID), .(ABSENCE, TIME)]]

# if nothing was found, look up last ABSENCE == 0
res[is.na(ABSENCE), c("ABSENCE", "TIME") := unique(HAVE[ABSENCE == 0], by="ID", fromLast=TRUE)[.SD, on=.(ID), .(ABSENCE, TIME)]]

# check
all.equal(as.data.frame(res), WANT)
# [1] TRUE

   ID ABSENCE TIME
1:  1      NA   NA
2:  2       0    3
3:  3       1    3
4:  4       0    2
5:  5       1    2
6:  6       0    3

我使用的是data.table，因为tidyverse不and never will不支持子分配/仅修改条件选择的行（如此处的is.na(ABSENCE)）。

如果可以使两个规则彼此更一致，则应该在左连接或OP尝试的单个group_by + slice中实现。好的，这是一种方法，尽管看起来无法调试：

HAVE %>% 
  arrange(ID, -(ABSENCE > 0), TIME*(ABSENCE > 0), -TIME) %>% 
  distinct(ID, .keep_all = TRUE)

  ID ABSENCE TIME
1  1      NA    3
2  2       0    3
3  3       1    3
4  4       0    2
5  5       1    2
6  6       0    3

Answer 2

基于子集.RLock()/Unlock()行计数器，也使用data.table：

.I

Answer 3

这里有两种使用dplyr和自定义函数的方法。两者都依赖于按TIME排序的数据。

过滤方法

# We'll use this function inside filter() to keep only the desired rows
flag_wanted <- function(absence){

  flags <- rep(FALSE, length(absence))

  if (any(absence > 0, na.rm = TRUE)) {
  # There's a nonzero value somewhere in x; we want the first one.

    flags[which.max(absence > 0)] <- TRUE

  } else if (any(absence == 0, na.rm = TRUE)) {
  # There's a zero value somewhere in x; we want the last one.

    flags[max(which(absence == 0))] <- TRUE

  } else {
  # All values are NA; we want the last row

    flags[length(absence)] <- TRUE

  }
  return(flags) 
}

# After filtering, we have to flip TIME to NA if ABSENCE is NA
HAVE %>%
  arrange(ID, TIME) %>%
  group_by(ID) %>%
  filter(flag_wanted(ABSENCE)) %>%
  mutate(TIME = ifelse(is.na(ABSENCE), NA, TIME)) %>%
  ungroup()

# A tibble: 6 x 3
     ID ABSENCE  TIME
  <dbl>   <dbl> <dbl>
1    1.     NA    NA 
2    2.      0.    3.
3    3.      1.    3.
4    4.      0.    2.
5    5.      1.    2.
6    6.      0.    3.

filter()步骤将数据框缩小为所需的行。由于它不会修改TIME值，因此我们也需要mutate()。

总结方法

# This function captures the general logic of getting the value of one variable
# based on the value of another
get_wanted <- function(of_this, by_this){

  # If there are any positive values of `by_this`, use the first
  if (any(by_this > 0, na.rm = TRUE)) {

    return( of_this[ which.max(by_this > 0) ] )

  }

  # If there are any zero values of `by_this`, use the last
  if (any(by_this == 0, na.rm = TRUE)) {

    return( of_this[ max(which(by_this == 0)) ] )

  }  
  # Otherwise, use NA
  return(NA)     
}

HAVE %>%
  arrange(ID, TIME) %>%
  group_by(ID) %>%
  summarize(TIME = get_first_nz(of_this = TIME, by_this = ABSENCE),
            ABSENCE = get_first_nz(of_this = ABSENCE, by_this = ABSENCE))

# A tibble: 6 x 3
     ID  TIME ABSENCE
  <dbl> <dbl>   <dbl>
1    1.   NA      NA 
2    2.    3.      0.
3    3.    3.      1.
4    4.    2.      0.
5    5.    2.      1.
6    6.    3.      0.

汇总的顺序很重要，因为我们要覆盖变量，因此这种方法存在风险。如果先总结WANT，然后总结TIME，它只会产生输出ABSENCE。

如果有的话，取第一个非0值或最后一个0值

3 个答案:

过滤方法

总结方法