R中间隔的忙百分比

我在一家医院工作。我们的医生在夜间和夜间随叫随到。有时可能没有病人来，所以他们可以休息。在其他时间，许多患者会立即到场。 他们写下何时开始和何时停止治疗患者。使用lubridate包，我可以将这些数据转换为具有特定日期的时间间隔。这些时间间隔的长度会有很大的不同，因为治疗可能或多或少复杂。此外，当很多事情发生时，医生可能会在患者之间来回走动。因此，典型的条目如下所示：“2016-06-11 21:45:00 UTC”“2016-06-11 22:35:00 UTC”

为了了解一天中哪些时间通常很忙，哪些时间比较慢，我想使用这些数据。一周中的不同日子也可以这样做。 整个事情应该看起来像一个条形图，显示一天中任何时候的平均职业（例如，晚上8点到9点之间100%的职业，凌晨1点到2点之间40%的职业）。 我的问题是我不知道怎么做。ggplot不会处理时间间隔，我还没有找到任何包可以处理时间间隔的平均值或百分比

我希望我能弄清楚我需要什么，我的问题是什么。我不是一个有经验的程序员，但很乐意学习

非常感谢

瓦伦丁

编辑：

对不起，我应该想到的。这就是我所做的：

>Daten<-read.csv2("Dienstdatum.csv")
>Beginn<-parse_date_time(Daten$Beginn,"dmy HM“,tz="CET“)
>Ende<-parse_date_time(Daten$Ende,"dmy HM",tz="CET“)
##Interval with date information
>Daten$Intervalle<-interval(Beginn,Ende) 
##Intervals stripped of date
>Daten$Beg<-as.POSIXct(strftime(Beginn, format="%H:%M:%S"), format="%H:%M:%S")
> Daten$dur<-as.duration(Daten$Intervall)
> Daten$Interv<-as.interval(Daten$dur , Daten$Beg)
## add weekdays
>Daten$Wochentage<-weekdays(Beginn)

---

>DatenBeginnEnde因此，在四处挖掘和尝试不同的东西之后，以下是我的想法和对我有效的方法。这个称呼有点复杂，冗长的总线显然给出了准确的结果。在试图找到答案的过程中，我了解了矢量化的爱好者（请原谅我的德语口音），因为生成矢量化代码将计算结果所需的时间缩短到了3分钟左右，而在此之前，我在96小时左右没有完成计算后就停止了计算

请注意，记录日期列表（并非每个医生都会完成轮班记录）是一张excel表格，包含简单日期。记录时间工作间隔列表是某人开始在一列中看到患者，而在另一列中不再看到该患者的日期和时间。下一行将是类似的开始和停止时间和日期

文本中的所有变量都是德语或德语单词的缩写，但我希望我的评论足以理解发生了什么。另外，很多代码都是针对特定于我的情况的问题编写的

特别感谢用户PhiSeu和用户3507085，他们在解决方案的各个方面为我提供了帮助

#read dates
package(lubridate)
Daten<-read.csv2(„file.csv")
#convert start dates to POSIX
Daten$Beginn<-parse_date_time(Daten$Beginn,"dmy HM",tz="CET")
#prevent overlap by adding one second
Daten$Beginn<-Daten$Beginn+1
#convert end dates to POSIX
Daten$Ende<-parse_date_time(Daten$Ende,"dmy HM",tz="CET")
#remove empty rows
Daten<-na.omit(Daten)
#create intervals in which people worked
Daten$Intervall<-interval(Daten$Beginn,Daten$Ende)
#read dates on which people worked
doku<-read.csv2(„dates.csv“,header=FALSE)
doku<-parse_date_time(doku$V1,"%d.%m.%Y",tz="cet")

#create a start time of 09 A.M. for shifts
doku<-data.frame(cbind(doku,doku+32400))
#add column names
names(doku)<-c("Datum","Beginn")
#convert to POSIX
doku$Datum<-as.POSIXct(doku$Datum,origin="1970-01-01",tz="cet")
doku$Beginn<-as.POSIXct(doku$Beginn,origin="1970-01-01",tz="cet")

#Loop to create 15 min intervals for each documented shift spanning 24 hour against which actual working hours will be checked

begin <- as.POSIXct(doku$Beginn)

# copy begin time for loop
begin_new <- begin

# create duration object
aufl <- duration(15, "mins")

# count times for loop
times <- 24*60/15

# create dataframe with begin time
Intervall <- data.frame(begin,stringsAsFactors = FALSE)

for (i in 1:times){

  cat("test",i,"\n")

  # save old time for interval calculation
  begin_start <- begin_new
  # add 15 Minutes to original time
  begin_new <- begin_new + aufl

  cat(begin_new,"\n")

  # create an interval object between 
  new_dur <- interval(begin_start,begin_new)

  # bind to original dataframe
  Intervall <- cbind(Intervall,new_dur) 

}

# Add column names
vec_names <- paste0("v",c(1:(times+1)))
colnames(Intervall) <- vec_names


#create a matrix of the number of seconds worked in each of the above 15 intervals by checking the amount of intersection between 15 intervals and documented intervals of work

test<-vector()
Tabelle<-matrix(nrow=length(doku$Beginn),ncol=times)
Tabelle[is.na(Tabelle)]<-0
for (j in 1:length(doku$Beginn)){
for (k in 1:times){
test<-as.duration(intersect(Daten$Intervall,Intervall[j,k+1]))
test[is.na(test)]<-0
test<-sum(test)
Tabelle[j,k]<-test}}


#cadd start time to the above matrix
Ausw<-data.frame(cbind(Tabelle,begin))
#convert to POSIX
Ausw$begin<-as.POSIXct(Ausw$begin,origin="1970-01-01",tz="cet")

##analysis of data
#common to all days of the week
#create labels for 15 min intervals
Labels<-c("09","09:15","09:30","09:45","10","10:15","10:30","10:45","11","11:15","11:30","11:45","12","12:15","12:30","12:45","13","13:15","13:30","13:45","14","14:15","14:30","14:45","15","15:15","15:30","15:45","16","16:15","16:30","16:45","17","17:15","17:30","17:45","18","18:15","18:30","18:45","19","19:15","19:30","19:45","20","20:15","20:30","20:45","21","21:15","21:30","21:45","22","22:15","22:30","22:45","23","23:15","23:30","23:45","00","00:15","00:30","00:45","01","01:15","01:30","01:45","02","02:15","02:30","02:45","03","03:15","03:30","03:45","04","04:15","04:30","04:45","05","05:15","05:30","05:45","06","06:15","06:30","06:45","07","07:15","07:30","07:45","08","08:15","08:30","08:45")

##analysis for weekends
#how many percent people worked on average in any of the 15 min intervals on a saturday or sunday
Wochenende<-apply(Ausw[Ausw$wtag==c(1,7),1:times],MARGIN=2,FUN=sum)
Prozent<-Wochenende/length(Ausw$begin[Ausw$wtag==c(1,7)]) /as.numeric(aufl)*100

#add labels
names(Prozent)<-Labels
#plot as barplot and add axis labels
b=barplot(Prozent,axes = F,axisnames=F,main="Durchschnittliche Arbeitsbelastung am Wochenende",sub="über 100%: Übergabezeiten",xlab="Uhrzeit",ylab="Prozent")
axis(1,at=c(b[seq(1,length(Labels),4)],b[length(b)]+diff(b)[1]),labels = c(Labels[seq(1,length(Labels),4)],"09"))
axis(2,at=seq(0,160,25),las=2)


##analysos monday to friday
Woche<-apply(Ausw[Ausw$wtag==c(2,3,4,5,6),1:times],MARGIN=2,FUN=sum)
Prozent2<-Woche/length(Ausw$begin[Ausw$wtag==c(2,3,4,5,6)]) /as.numeric(aufl)*100
#add labels
names(Prozent2)<-Labels
#plot as barplot and add axis labels
b2=barplot(Prozent2,axes = F,axisnames=F,main="Durchschnittliche Arbeitsbelastung Montag - Freitag",,xlab="Uhrzeit",ylab="Prozent“,ylim=c(0,100))
axis(1,at=c(b2[seq(1,length(Labels),4)],b2[length(b2)]+diff(b2)[1]),labels = c(Labels[seq(1,length(Labels),4)],"09"))
axis(2,at=seq(0,160,25),las=2)

#读取日期
包装（润滑油）
日期请发布代码，以获得最佳尝试。谢谢。你能提供一些示例数据吗？
dput（head（Daten））
的输出将非常有用，因为它允许我们重现您的一些工作数据。