我有一个这样的数据表，只是更大了：
customer_id <- c("1","1","1","2","2","2","2","3","3","3")
account_id <- as.character(c(11,11,11,55,55,55,55,38,38,38))
time <- c(as.Date("2017-01-01","%Y-%m-%d"), as.Date("2017-05-01","%Y-%m-
%d"), as.Date("2017-06-01","%Y-%m-%d"),
as.Date("2017-02-01","%Y-%m-%d"), as.Date("2017-04-01","%Y-%m-
%d"), as.Date("2017-05-01","%Y-%m-%d"),
as.Date("2017-06-01","%Y-%m-%d"), as.Date("2017-01-01","%Y-%m-
%d"), as.Date("2017-04-01","%Y-%m-%d"),
as.Date("2017-05-01","%Y-%m-%d"))
tenor <- c(1,2,3,1,2,3,4,1,2,3)
variable_x <- c(87,90,100,120,130,150,12,13,15,14)
my_data <- data.table(customer_id,account_id,time,tenor,variable_x)
customer_id account_id time tenor variable_x
1 11 2017-01-01 1 87
1 11 2017-05-01 2 90
1 11 2017-06-01 3 100
2 55 2017-02-01 1 120
2 55 2017-04-01 2 130
2 55 2017-05-01 3 150
2 55 2017-06-01 4 12
3 38 2017-01-01 1 13
3 38 2017-04-01 2 15
3 38 2017-05-01 3 14
我应该观察每个对的customer_id，account_id从2017-01-01到2017-06-01的月度观察，但是对于某些customer_id，account_id对，则缺少这6个月序列中的某些日期。我想填写那些缺失的日期，以便每个对customer_id，account_id对都有6个月的观察值，只是缺少变量tenor和variable_x。也就是说，它应该看起来像这样：
customer_id account_id time tenor variable_x
1 11 2017-01-01 1 87
1 11 2017-02-01 NA NA
1 11 2017-03-01 NA NA
1 11 2017-04-01 NA NA
1 11 2017-05-01 2 90
1 11 2017-06-01 3 100
2 55 2017-01-01 NA NA
2 55 2017-02-01 1 120
2 55 2017-03-01 NA NA
2 55 2017-04-01 2 130
2 55 2017-05-01 3 150
2 55 2017-06-01 4 12
3 38 2017-01-01 1 13
3 38 2017-02-01 NA NA
3 38 2017-03-01 NA NA
3 38 2017-04-01 2 15
3 38 2017-05-01 3 14
3 38 2017-06-01 NA NA
我尝试使用创建一个从2017-01-01到2017-06-01的日期序列
ts = seq(as.Date("2017/01/01"), as.Date("2017/06/01"), by = "month")
然后使用
将其合并为原始数据ts = data.table(ts)
colnames(ts) = "time"
merged <- merge(ts, my_data, by="time", all.x=TRUE)
，但是它不起作用。拜托，您知道如何为每个customer_id，account_id对添加带有日期的此类行吗？
答案 0 :(得分：3)
我们可以加入。创建从min到max by'1 month'的时间序列，展开按'customer_id'，'account_id'分组的数据集，并将on与列和“时间”
ts1 <- seq(min(my_data$time), max(my_data$time), by = "1 month")
my_data[my_data[, .(time =ts1 ), .(customer_id, account_id)],
on = .(customer_id, account_id, time)]
# customer_id account_id time tenor variable_x
# 1: 1 11 2017-01-01 1 87
# 2: 1 11 2017-02-01 NA NA
# 3: 1 11 2017-03-01 NA NA
# 4: 1 11 2017-04-01 NA NA
# 5: 1 11 2017-05-01 2 90
# 6: 1 11 2017-06-01 3 100
# 7: 2 55 2017-01-01 NA NA
# 8: 2 55 2017-02-01 1 120
# 9: 2 55 2017-03-01 NA NA
#10: 2 55 2017-04-01 2 130
#11: 2 55 2017-05-01 3 150
#12: 2 55 2017-06-01 4 12
#13: 3 38 2017-01-01 1 13
#14: 3 38 2017-02-01 NA NA
#15: 3 38 2017-03-01 NA NA
#16: 3 38 2017-04-01 2 15
#17: 3 38 2017-05-01 3 14
#18: 3 38 2017-06-01 NA NA
或使用tidyverse
library(tidyverse)
distinct(my_data, customer_id, account_id) %>%
mutate(time = list(ts1)) %>%
unnest %>%
left_join(my_data)
或者使用complete中的tidyr
my_data %>%
complete(nesting(customer_id, account_id), time = ts1)
答案 1 :(得分：1)
另一种data.table方法：
my_data2 <- my_data[, .(time = seq(as.Date("2017/01/01"), as.Date("2017/06/01"),
by = "month")), by = list(customer_id, account_id)]
merge(my_data2, my_data, all.x = TRUE)
customer_id account_id time tenor variable_x
1: 1 11 2017-01-01 1 87
2: 1 11 2017-02-01 NA NA
3: 1 11 2017-03-01 NA NA
4: 1 11 2017-04-01 NA NA
5: 1 11 2017-05-01 2 90
6: 1 11 2017-06-01 3 100
7: 2 55 2017-01-01 NA NA
8: 2 55 2017-02-01 1 120
9: 2 55 2017-03-01 NA NA
10: 2 55 2017-04-01 2 130
11: 2 55 2017-05-01 3 150
12: 2 55 2017-06-01 4 12
13: 3 38 2017-01-01 1 13
14: 3 38 2017-02-01 NA NA
15: 3 38 2017-03-01 NA NA
16: 3 38 2017-04-01 2 15
17: 3 38 2017-05-01 3 14
18: 3 38 2017-06-01 NA NA