Question

我在数据表中有一个纵向数据集，类似于下面的简化示例：

> head(data)
   Country     ID        Date         Value
1:   AT        AT6306    2012-11-01   16.2
2:   AT        AT6306    2012-11-02   12.2
3:   AT        AT6306    2012-11-03   11.3
4:   AT        AT6306    2012-11-04   14.2
5:   AT        AT6306    2012-11-05   17.3
6:   AT        AT6306    2012-11-06   12.5

> tail(data)
   Country     ID        Date         Value
1:   SE        SE0935    2014-06-25   16.2
2:   SE        SE0935    2014-06-26   12.2
3:   SE        SE0935    2014-06-27   11.3
4:   SE        SE0935    2014-06-28   14.2
5:   SE        SE0935    2014-06-29   17.3
6:   SE        SE0935    2014-06-30   12.5

ID是面板变量，它完全独特，各国之间没有重叠。日期范围仅查看唯一值，范围从2012-10-23到2014-09-30。显然，Date的范围对于每个ID并不相同。此外，可能存在缺失值。为了拥有一个平衡的面板，我想填补我的数据集的空白。

根据@akron的建议调整答案here，我会执行以下操作：

data2 <- data[, CJ(ID=unique(ID), Date=unique(Date))]
setkey(data2, ID, Date)

data.new <- merge(data, data2, by=c("ID", "Date"), all.y = TRUE)
setkey(data.new, ID, Date)

使用选项all.y = TRUE，R因此为data中的每个缺失日期添加行。但是，现在ID和Date以外的所有字段都是空白的，如果该行在data之前不存在。也就是说，我的数据看起来像这样

> head(data.new)
   Country     ID        Date         Value
1:   NA        AT6306    2012-10-23   NA
2:   NA        AT6306    2012-10-24   NA
3:   NA        AT6306    2012-10-25   NA
4:   NA        AT6306    2012-10-26   NA
5:   NA        AT6306    2012-10-27   NA
6:   NA        AT6306    2012-10-28   NA

我确实希望Value为NA，因为它已丢失。但是，由于Country对于给定的ID没有变化，我希望填写该字段。

Answer 1

library(data.table)
DT <- data.table(dat)
setkey(DT, Date, Country, ID)
res <- DT[CJ(seq(min(Date), max(Date), by='1 day'), 
                        unique(Country), unique(ID))]

 head(res)
#    Country   ID       Date Value
#1:      AT  935 2012-11-01    NA
#2:      AT 6306 2012-11-01  16.2
#3:      SE  935 2012-11-01    NA
#4:      SE 6306 2012-11-01    NA
#5:      AT  935 2012-11-02    NA
#6:      AT 6306 2012-11-02  12.2

更新

你可以做的一个选择是

DT <- data.table(dat)
DT[,CountryID:= paste(Country,ID)]
setkey(DT, Date, CountryID)
DT1 <- DT[CJ(unique(Date), unique(CountryID))][,
      c('Country', 'ID'):=  list(gsub("[ 0-9]", "", CountryID),
               gsub("[^ 0-9]", "", CountryID)),][,-5, with=FALSE]


head(DT1,3)
#     Country    ID       Date Value
#1:      AT  6306 2012-11-01  16.2
#2:      SE   935 2012-11-01    NA
#3:      AT  6306 2012-11-02  12.2

nrow(DT1)
#[1] 24

数据

dat <- structure(list(Country = c("AT", "AT", "AT", "AT", "AT", "AT", 
"SE", "SE", "SE", "SE", "SE", "SE"), ID = c(6306L, 6306L, 6306L, 
6306L, 6306L, 6306L, 935L, 935L, 935L, 935L, 935L, 935L), Date = structure(c(15645, 
15646, 15647, 15648, 15649, 15650, 15669, 15670, 15671, 15672, 
15673, 15674), class = "Date"), Value = c(16.2, 12.2, 11.3, 14.2, 
17.3, 12.5, 16.2, 12.2, 11.3, 14.2, 17.3, 12.5)), .Names = c("Country", 
"ID", "Date", "Value"), row.names = c("1:", "2:", "3:", "4:", 
"5:", "6:", "1:1", "2:1", "3:1", "4:1", "5:1", "6:1"), class = "data.frame")

R：在纵向数据中插入缺少日期而不会丢失信息

1 个答案:

更新

数据