本文介绍了在执行计算时将单个数据帧行拆分为多行的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!

问题描述

我有一个类似于df1的df,我想在其中拆分行,以使HOURS列的间隔为4,如df2所示。我将如何解决此问题以及建议使用哪些软件包?

I have a df akin to df1 where I want to break out the rows so that the HOURS column is in intervals of 4, shown in df2. How would I approach this problem and what packages are recommended?

ID在给定的一天中可以有多个序列。例如,一个ID可以在给定的一天中列出2-3次,并被分配一个以上的单位和一个以上的CODE。

IDs can have more than one sequence on a given day. For example, an ID can be listed 2-3 times on a given day, being assigned more than one unit and and more than one CODE.

需要以下内容:


  • 所有分类数据必须保持一致子行(例如,每个子行上的CODE保持不变)

  • 如果余数小于4,则应在最后一行列出余数(例如df2 ;; B行)

  • 如果子行在下一个日期开始或结束,则应相应地更新日期列(例如df2; E行)

df1(当前)

EMPLID TIME_RPTG_CD START_DATE_TIME     END_DATE_TIME       Hrs_Time_Worked
   <chr>  <chr>        <dttm>              <dttm>                        <dbl>
 1 X00007 REG          2014-07-03 16:00:00 2014-07-03 02:00:00            10.0

df2(所需)

 EMPLID TIME_RPTG_CD START_DATE_TIME     END_DATE_TIME       Hrs_Time_Worked
   <chr>  <chr>        <dttm>              <dttm>                        <dbl>
1 X00007 REG          2014-07-03 16:00:00 2014-07-03 20:00:00            4.0
1 X00007 REG          2014-07-03 20:00:00 2014-07-04 24:00:00            4.0
1 X00007 REG          2014-07-04 24:00:00 2014-07-04 02:00:00            2.0


推荐答案

library(tidyverse)
library(lubridate)
df1%>%
 group_by(Row)%>%
 mutate(S=paste(START_DATE,START_TIME),
        HOURS=list((n<-c(rep(4,HOURS%/%4),HOURS%%4))[n!=0]))%>%
 unnest()%>%
 mutate(E=dmy_hm(S)+hours(cumsum(HOURS)),
        S=E-hours(unlist(HOURS)),
        START_DATE=format(S,"%d-%b-%y"),
        END_DATE=format(E,"%d-%b-%y"),
        START_TIME=format(S,"%H:%M"),
        END_TIME=format(E,"%H:%M"),S=NULL,E=NULL)
# A tibble: 6 x 9
# Groups:   Row [3]
  Row      ID UNIT  CODE  START_DATE END_DATE  START_TIME END_TIME HOURS
  <chr> <int> <chr> <chr> <chr>      <chr>     <chr>      <chr>    <dbl>
1 A         1 3ESD  REG   06-Aug-14  06-Aug-14 01:00      05:00       4.
2 A         1 3ESD  REG   06-Aug-14  06-Aug-14 05:00      07:00       2.
3 B         2 3E14E OE2   12-Aug-14  13-Aug-14 21:00      01:00       4.
4 C         3 3E5E  REG   19-Aug-14  20-Aug-14 21:00      01:00       4.
5 C         3 3E5E  REG   20-Aug-14  20-Aug-14 01:00      05:00       4.
6 C         3 3E5E  REG   20-Aug-14  20-Aug-14 05:00      07:00       2.

这篇关于在执行计算时将单个数据帧行拆分为多行的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!

10-24 16:14