我的数据是组级别的。 data外观如下所示。
我的实际数据是“价值”＆amp;所需数据为“Expected_Value”。
我尝试了以下代码：
setDT(file_to_share)[,Expected_Value := na.locf(na.locf(Value, na.rm=FALSE), fromLast=TRUE),by = c("Group_A", "Group_B")]
但是在这段代码中，插补是在整个缺失值上完成的。如果缺失值介于值之间，我想计算缺失值。缺失值将是先前可用值的复制。
如果有人可以指导我如何做，那将是一个很大的帮助。
注意：我尝试使用data.table和zoo进行计算。但任何其他方法也会这样做。
答案 0 :(得分：2)
即使您使用的是data.table解决方案，也可以使用tidyverse方法。 （如果时间允许，我可以尝试转换为data.table。
我们的想法是创建一个分组变量以捕获您的周数，并在分组GroupA，groupB和周（此处称为fill）下的grp值。我们还创建了Value到fill的副本（tidyr术语为.direction = 'up'）。然后我们创建另一个分组变量，其累积总和为NA值，并在假设新组大小（Value，{NA的情况下将Group_A列中的值替换为GROUP_B {1}}，grp和grp1）为1，其value1为NA。这给出了预期的结果。
library(tidyverse)
df2 <- df1 %>%
mutate(Date = as.POSIXct(Date, format = '%m/%d/%Y')) %>%
mutate(value1 = Value) %>%
group_by(Group_A, GROUP_B, grp = cumsum(format(Date, '%d')=='01'))%>%
fill(Value) %>%
fill(value1, .direction = 'up') %>%
mutate(grp1 = cumsum(is.na(Value))) %>%
group_by(Group_A, GROUP_B, grp, grp1) %>%
mutate(new = n(), Value = replace(Value, new == 1 | is.na(value1), NA)) %>%
ungroup() %>%
select(-c(value1, grp, grp1, new))
给出，
# A tibble: 42 × 5 Group_A GROUP_B Date Value Expected_Value <chr> <chr> <dttm> <int> <int> 1 GROUP_1 Group_1_1 2017-01-01 NA NA 2 GROUP_1 Group_1_1 2017-01-02 NA NA 3 GROUP_1 Group_1_1 2017-01-03 34 34 4 GROUP_1 Group_1_1 2017-01-04 20 20 5 GROUP_1 Group_1_1 2017-01-05 20 20 6 GROUP_1 Group_1_1 2017-01-06 20 20 7 GROUP_1 Group_1_1 2017-01-07 38 38 8 GROUP_1 Group_1_2 2017-01-01 35 35 9 GROUP_1 Group_1_2 2017-01-02 28 28 10 GROUP_1 Group_1_2 2017-01-03 28 28 # ... with 32 more rows
#Where,
identical(df2$Value, df2$Expected_Value)
#[1] TRUE
答案 1 :(得分：2)
OP要求仅填写每组中其他值之间的NA值。这意味着在应用NA时，在每个组的开头或结尾跳过任何zoo::na.locf()值序列。
使用data.table，可以通过识别要跳过的行的索引和一种反连接来完成：
library(data.table)
setDT(DT)[!DT[, {
na_grp <- rleid(is.na(Value))
.I[na_grp %in% c(1L, max(na_grp))]
}, by = .(Group_A, GROUP_B)]$V1, Value := zoo::na.locf(Value)][]
Group_A GROUP_B Date Value Expected_Value 1: GROUP_1 Group_1_1 1/1/2017 NA NA 2: GROUP_1 Group_1_1 1/2/2017 NA NA 3: GROUP_1 Group_1_1 1/3/2017 34 34 4: GROUP_1 Group_1_1 1/4/2017 20 20 5: GROUP_1 Group_1_1 1/5/2017 20 20 6: GROUP_1 Group_1_1 1/6/2017 20 20 7: GROUP_1 Group_1_1 1/7/2017 38 38 8: GROUP_1 Group_1_2 1/1/2017 35 35 9: GROUP_1 Group_1_2 1/2/2017 28 28 10: GROUP_1 Group_1_2 1/3/2017 20 28 11: GROUP_1 Group_1_2 1/4/2017 32 32 12: GROUP_1 Group_1_2 1/5/2017 39 39 13: GROUP_1 Group_1_2 1/6/2017 28 28 14: GROUP_1 Group_1_2 1/7/2017 NA NA 15: GROUP_2 Group_1_11 1/1/2017 NA NA 16: GROUP_2 Group_1_11 1/2/2017 NA NA 17: GROUP_2 Group_1_11 1/3/2017 40 40 18: GROUP_2 Group_1_11 1/4/2017 32 32 19: GROUP_2 Group_1_11 1/5/2017 20 20 20: GROUP_2 Group_1_11 1/6/2017 NA NA 21: GROUP_2 Group_1_11 1/7/2017 NA NA 22: GROUP_2 Group_1_21 1/1/2017 NA NA 23: GROUP_2 Group_1_21 1/2/2017 32 32 24: GROUP_2 Group_1_21 1/3/2017 36 36 25: GROUP_2 Group_1_21 1/4/2017 36 36 26: GROUP_2 Group_1_21 1/5/2017 28 28 27: GROUP_2 Group_1_21 1/6/2017 33 33 28: GROUP_2 Group_1_21 1/7/2017 40 40 29: GROUP_3 Group_1_13 1/1/2017 NA NA 30: GROUP_3 Group_1_13 1/2/2017 NA NA 31: GROUP_3 Group_1_13 1/3/2017 NA NA 32: GROUP_3 Group_1_13 1/4/2017 29 29 33: GROUP_3 Group_1_13 1/5/2017 31 31 34: GROUP_3 Group_1_13 1/6/2017 31 31 35: GROUP_3 Group_1_13 1/7/2017 34 34 36: GROUP_3 Group_1_23 1/1/2017 26 26 37: GROUP_3 Group_1_23 1/2/2017 33 33 38: GROUP_3 Group_1_23 1/3/2017 27 27 39: GROUP_3 Group_1_23 1/4/2017 23 23 40: GROUP_3 Group_1_23 1/5/2017 25 25 41: GROUP_3 Group_1_23 1/6/2017 41 41 42: GROUP_3 Group_1_23 1/7/2017 25 25 Group_A GROUP_B Date Value Expected_Value
NA /非NA值的条纹编号为.I中检索索引。 （由于Value将更新到位无论第一个或最后一个条纹是否包含NA都无关紧要;无论如何它们都不会更新。）DT[, {na_grp <- rleid(is.na(Value)); .I[na_grp %in% c(1L, max(na_grp))]}, by = .(Group_A, GROUP_B)]$V1 zoo::na.locf(Value)仅适用于每个组的“内部”条纹。DT <- structure(list(Group_A = c("GROUP_1", "GROUP_1", "GROUP_1", "GROUP_1",
"GROUP_1", "GROUP_1", "GROUP_1", "GROUP_1", "GROUP_1", "GROUP_1",
"GROUP_1", "GROUP_1", "GROUP_1", "GROUP_1", "GROUP_2", "GROUP_2",
"GROUP_2", "GROUP_2", "GROUP_2", "GROUP_2", "GROUP_2", "GROUP_2",
"GROUP_2", "GROUP_2", "GROUP_2", "GROUP_2", "GROUP_2", "GROUP_2",
"GROUP_3", "GROUP_3", "GROUP_3", "GROUP_3", "GROUP_3", "GROUP_3",
"GROUP_3", "GROUP_3", "GROUP_3", "GROUP_3", "GROUP_3", "GROUP_3",
"GROUP_3", "GROUP_3"), GROUP_B = c("Group_1_1", "Group_1_1",
"Group_1_1", "Group_1_1", "Group_1_1", "Group_1_1", "Group_1_1",
"Group_1_2", "Group_1_2", "Group_1_2", "Group_1_2", "Group_1_2",
"Group_1_2", "Group_1_2", "Group_1_11", "Group_1_11", "Group_1_11",
"Group_1_11", "Group_1_11", "Group_1_11", "Group_1_11", "Group_1_21",
"Group_1_21", "Group_1_21", "Group_1_21", "Group_1_21", "Group_1_21",
"Group_1_21", "Group_1_13", "Group_1_13", "Group_1_13", "Group_1_13",
"Group_1_13", "Group_1_13", "Group_1_13", "Group_1_23", "Group_1_23",
"Group_1_23", "Group_1_23", "Group_1_23", "Group_1_23", "Group_1_23"
), Date = c("1/1/2017", "1/2/2017", "1/3/2017", "1/4/2017", "1/5/2017",
"1/6/2017", "1/7/2017", "1/1/2017", "1/2/2017", "1/3/2017", "1/4/2017",
"1/5/2017", "1/6/2017", "1/7/2017", "1/1/2017", "1/2/2017", "1/3/2017",
"1/4/2017", "1/5/2017", "1/6/2017", "1/7/2017", "1/1/2017", "1/2/2017",
"1/3/2017", "1/4/2017", "1/5/2017", "1/6/2017", "1/7/2017", "1/1/2017",
"1/2/2017", "1/3/2017", "1/4/2017", "1/5/2017", "1/6/2017", "1/7/2017",
"1/1/2017", "1/2/2017", "1/3/2017", "1/4/2017", "1/5/2017", "1/6/2017",
"1/7/2017"), Value = c(NA, NA, 34L, 20L, NA, NA, 38L, 35L, 28L,
NA, 32L, 39L, 28L, NA, NA, NA, 40L, 32L, 20L, NA, NA, NA, 32L,
36L, NA, 28L, 33L, 40L, NA, NA, NA, 29L, 31L, NA, 34L, 26L, 33L,
27L, 23L, 25L, 41L, 25L), Expected_Value = c(NA, NA, 34L, 20L,
20L, 20L, 38L, 35L, 28L, 28L, 32L, 39L, 28L, NA, NA, NA, 40L,
32L, 20L, NA, NA, NA, 32L, 36L, 36L, 28L, 33L, 40L, NA, NA, NA,
29L, 31L, 31L, 34L, 26L, 33L, 27L, 23L, 25L, 41L, 25L)), .Names = c("Group_A",
"GROUP_B", "Date", "Value", "Expected_Value"), row.names = c(NA,
-42L), class = "data.frame")