R 在具有多个观察期的数据框中添加缺少的日期值
提前谢谢 我试图为三个不同的个体添加未包含在观察期内的缺失日期值 我的数据如下所示:R 在具有多个观察期的数据框中添加缺少的日期值,r,date,for-loop,merge,sequence,R,Date,For Loop,Merge,Sequence,提前谢谢 我试图为三个不同的个体添加未包含在观察期内的缺失日期值 我的数据如下所示: IndID Date Event Number Percent 1 P01 2011-03-04 1 2 0.390 2 P01 2011-03-11 1 2 0.975 3 P01 2011-03-13 0 9 0.795 4 P01 2011-03-14 0 10 0.516 5 P01
IndID Date Event Number Percent
1 P01 2011-03-04 1 2 0.390
2 P01 2011-03-11 1 2 0.975
3 P01 2011-03-13 0 9 0.795
4 P01 2011-03-14 0 10 0.516
5 P01 2011-03-15 0 1 0.117
6 P01 2011-03-17 0 7 0.093
IndID
是个人ID(P01
,P03
,P06
)<代码>日期显然就是日期<代码>事件是一个二进制变量,指示事件是否发生(0
=否和1
=是)。列
Number
和Percent
不直接相关,但需要保留,因此包含在此处
我的示例数据帧(PostData
)包含在下面,使用的是dput
对于每个IndID
而言,第一个和最后一个日期分别是观察期的开始和结束,在观察期内有缺失的日期。在这里,我的目标是为每个人添加缺失的日期,并在事件
列中添加一个0
。其他列(Number
和Percent
)可以保留为空
已经很有用了,但是缺少关于我主要问题的信息——多个人
每个个体的观察期从min(PostData$Date)
到max(PostData$Date)
。我一直在尝试为每个人创建一个完整的日期序列,然后将其与for
循环中的现有数据帧合并。肯定有更好的办法
如有任何建议,我们将不胜感激
PostData <-structure(list(IndID = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L), .Label = c("P01", "P02", "P03", "P05", "P06", "P07",
"P08", "P09", "P10", "P11", "P12", "P13"), class = "factor"),
Date = structure(c(1299196800, 1299801600, 1299974400, 1300060800,
1300147200, 1300320000, 1300406400, 1310083200, 1310169600,
1310515200, 1310774400, 1310947200, 1311033600, 1311292800,
1311552000, 1323129600, 1323388800, 1323648000, 1323993600,
1324080000, 1324166400, 1324339200, 1327622400, 1327795200,
1327881600), class = c("POSIXct", "POSIXt"), tzone = "GMT"),
Event = c(1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L), Number = c(2L,
2L, 9L, 10L, 1L, 7L, 5L, 9L, 1L, 4L, 5L, 2L, 0L, 1L, 10L,
5L, 0L, 6L, 5L, 10L, 9L, 4L, 4L, 8L, 1L), Percent = c(0.39,
0.975, 0.795, 0.516, 0.117, 0.093, 0.528, 0.659, 0.308, 0.055,
0.185, 0.761, 0.132, 0.676, 0.368, 0.383, 0.272, 0.113, 0.974,
0.696, 0.941, 0.751, 0.758, 0.29, 0.15)), .Names = c("IndID",
"Date", "Event", "Number", "Percent"), row.names = c(NA, 25L),
class = "data.frame")
PostData试试这个。。这将添加具有正确ID的缺失日期,剩余字段为0
library(data.table)
library(plyr)
dtPostData = data.table(PostData)
minmaxTab = dtPostData[,list(minDate=min(Date),maxDate=max(Date)),by=IndID]
df = lapply(1:nrow(minmaxTab),function(x) {
temp = seq(minmaxTab$minDate[x],minmaxTab$maxDate[x],by=24*60*60)
temp = temp[!(temp %in% dtPostData[IndID == minmaxTab$IndID[x],]$Date)]
data.table(IndID = minmaxTab$IndID[x], Date = temp, Event = 0, Number = 0, Percent = 0)
})
df <- ldply(x, data.frame)
df
#Results
IndID Date Event Number Percent
1 P01 2011-03-05 0 0 0
2 P01 2011-03-06 0 0 0
3 P01 2011-03-07 0 0 0
4 P01 2011-03-08 0 0 0
5 P01 2011-03-09 0 0 0
6 P01 2011-03-10 0 0 0
7 P01 2011-03-12 0 0 0
8 P01 2011-03-16 0 0 0
9 P03 2011-07-10 0 0 0
库(data.table)
图书馆(plyr)
dtPostData=data.table(PostData)
minmaxTab=dtPostData[,list(minDate=min(Date),maxDate=max(Date)),by=IndID]
df=lappy(1:nrow(minmaxTab),函数(x){
temp=seq(minmaxTab$minDate[x],minmaxTab$maxDate[x],by=24*60*60)
temp=temp[!(temp%在%dtPostData[IndID==minmaxTab$IndID[x],]$Date中)
data.table(IndID=minmaxTab$IndID[x],日期=temp,事件=0,数字=0,百分比=0)
})
dfA基本R版本:
do.call(rbind,
by(
PostData,
PostData$IndID,
function(x) {
out <- merge(
data.frame(
IndID=x$IndID[1],
Date=seq.POSIXt(min(x$Date),max(x$Date),by="1 day")
),
x,
all.x=TRUE
)
out$Event[is.na(out$Event)] <- 0
out
}
)
)
do.call(rbind,
借(
PostData,
PostData$IndID,
功能(x){
out计算最小和最大时间(从历元起的秒数):
使用序列生成缺失日期的列表:
list_of_dates = seq(min_time,max_time, 86400) #since there are 86400 seconds in a day
list_of_dates = as.Date(as.POSIXct( list_of_dates ), origin = '1970-01-01 00:00.00 UTC')
#convert back to a date
构建缺少IndID和Date组合的列表
temp = merge(unique(PostData$IndID),list_of_dates)
names(temp) = c("IndID","Date")
data_missing_indID_date = temp[!which(temp$IndID %in% PostData$IndID & temp$Date %in% PostData$Date ),]
构建其余的列:
data_missing_indID_date$Event = 0
data_missing_indID_date$Number = NA
data_missing_indID_date$Percent = NA
rbind
将其绑定到原始数据帧:
final_data = rbind(PostData, data_missing_indID_date)
她的是一个dplyr
解决方案。基于示例数据,结果是一个包含89行的data.frame,我希望这就是您想要的结果
require(dplyr)
PostData %>%
mutate(Date = as.Date(as.character(Date))) %>%
group_by(IndID) %>%
do(left_join(data.frame(IndID = .$IndID[1], Date = seq(min(.$Date), max(.$Date), 1)), .,
by=c("IndID", "Date"))) %>%
mutate(Event = ifelse(is.na(Event), 0, Event))
# IndID Date Event Number Percent
#1 P01 2011-03-04 1 2 0.390
#2 P01 2011-03-05 0 NA NA
#3 P01 2011-03-06 0 NA NA
#4 P01 2011-03-07 0 NA NA
#5 P01 2011-03-08 0 NA NA
#6 P01 2011-03-09 0 NA NA
#7 P01 2011-03-10 0 NA NA
#8 P01 2011-03-11 1 2 0.975
#...
#84 P06 2012-01-25 0 NA NA
#85 P06 2012-01-26 0 NA NA
#86 P06 2012-01-27 1 4 0.758
#87 P06 2012-01-28 0 NA NA
#88 P06 2012-01-29 0 8 0.290
#89 P06 2012-01-30 0 1 0.150
感谢您提供了非常有用的解决方案!结果非常有用。中的做了什么(left_join(data.frame(IndID=.$IndID[1],Date=seq(min(.$Date),max(.$Date),1)),,by=c(“IndID”,“Date”))
做了什么?还有,为什么选择$IndID[1]
dplyr中使用了do
操作符对分组数据应用任意函数。您可以通过在控制台上键入?do
了解更多信息。我使用了$IndID[1]
在创建新的数据框时,在每组数据中都有ID变量,我只选择了每个ID的第一个匹配项,这样就可以按照日期序列的长度循环使用。
final_data = rbind(PostData, data_missing_indID_date)
require(dplyr)
PostData %>%
mutate(Date = as.Date(as.character(Date))) %>%
group_by(IndID) %>%
do(left_join(data.frame(IndID = .$IndID[1], Date = seq(min(.$Date), max(.$Date), 1)), .,
by=c("IndID", "Date"))) %>%
mutate(Event = ifelse(is.na(Event), 0, Event))
# IndID Date Event Number Percent
#1 P01 2011-03-04 1 2 0.390
#2 P01 2011-03-05 0 NA NA
#3 P01 2011-03-06 0 NA NA
#4 P01 2011-03-07 0 NA NA
#5 P01 2011-03-08 0 NA NA
#6 P01 2011-03-09 0 NA NA
#7 P01 2011-03-10 0 NA NA
#8 P01 2011-03-11 1 2 0.975
#...
#84 P06 2012-01-25 0 NA NA
#85 P06 2012-01-26 0 NA NA
#86 P06 2012-01-27 1 4 0.758
#87 P06 2012-01-28 0 NA NA
#88 P06 2012-01-29 0 8 0.290
#89 P06 2012-01-30 0 1 0.150