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How to detect and mark changes in a column in another column

I am trying to note when the process begins and ends. The code should detect when the change begins and when it ends, marking it in another column.

Sample data:

date process 2007 0 2008 1 2009 1 2010 1 2011 1 2012 1 2013 0

Goal:

 date process Status 2007 0 NA 2008 1 Process_START 2009 1 NA 2010 1 NA 2011 1 NA 2012 1 Process_END 2013 0 NA
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2 answers

Perhaps by computing diff and lagging in both directions:

 dif <- diff(df1$process) df1$Status <- factor(c(NA, dif) - 2 * c(dif, NA), levels = -3:3) levels(df1$Status) <- c(rep(NA, 4), "Start", "End", "Start&End") # date process Status # 1 2007 0 <NA> # 2 2008 1 Start # 3 2009 1 <NA> # 4 2010 1 <NA> # 5 2011 1 <NA> # 6 2012 1 End # 7 2013 0 <NA>

Update

Version without factors:

 dif <- diff(df1$process) df1$Status <- c(NA, dif) - 2 * c(dif, NA) df1$Status <- c(rep(NA,4), "Start", "End", "Start&End")[df1$Status + 4]

Please note that in the case of a one-year process, you have a "Start and End" situation.

Update 2

If a series starts (or ends) using process = 1, the expected result may not be NA, but Start (or End):

 dif <- diff(df1$process) df1$Status <- c(df1$process[1], dif) - 2 * c(dif, -tail(df1$process,1)) df1$Status <- c(rep(NA,4), "Start", "End", "Start&End")[df1$Status + 4]

More complex example:

 set.seed(4) df1 <- data.frame(date = 2007:(2007+24), process = sample(c(0,1, 1), 25, TRUE))

The latest version produces:

 # date process Status # 1 2007 1 Start&End # 2 2008 0 <NA> # 3 2009 0 <NA> # 4 2010 0 <NA> # 5 2011 1 Start&End # 6 2012 0 <NA> # 7 2013 1 Start # 8 2014 1 <NA> # 9 2015 1 End # 10 2016 0 <NA> # 11 2017 1 Start&End # 12 2018 0 <NA> # 13 2019 0 <NA> # 14 2020 1 Start # 15 2021 1 <NA> # 16 2022 1 <NA> # 17 2023 1 <NA> # 18 2024 1 <NA> # 19 2025 1 <NA> # 20 2026 1 <NA> # 21 2027 1 <NA> # 22 2028 1 <NA> # 23 2029 1 <NA> # 24 2030 1 <NA> # 25 2031 1 End
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One option with data.table

 library(data.table)#v1.9.5+ setDT(df1)[, gr:= rleid(process)][,Status:=NA_character_][process==1, Status:=replace(Status, 1:.N %in% c(1, .N), c('Process_START', 'Process_END')) , gr][,gr:= NULL] # date process Status # 1: 2007 0 NA # 2: 2008 1 Process_START # 3: 2009 1 NA # 4: 2010 1 NA # 5: 2011 1 NA # 6: 2012 1 Process_END # 7: 2013 0 NA

Update

Or the modification will be

  setDT(df1)[, gr:= rleid(process)][process==1L, Status:=c(NA, 'Process_START', 'Process_END', 'Process_START_END')[(1:.N==1L) + 2*(1:.N==.N)+1] , gr][,gr:=NULL] # date process Status #1: 2007 0 NA #2: 2008 1 Process_START #3: 2009 1 NA #4: 2010 1 NA #5: 2011 1 NA #6: 2012 1 Process_END #7: 2013 0 NA

Using an example from a comment by @David Arenburg

  setDT(df1)[, gr:= rleid(process)][process==1L, Status:=c(NA, 'Process_START', 'Process_END', 'Process_START_END')[(1:.N==1L) + 2*(1:.N==.N)+1] , gr][,gr:=NULL] # date process Status #1: 2007 0 NA #2: 2008 1 Process_START #3: 2009 1 NA #4: 2010 1 NA #5: 2011 1 NA #6: 2012 1 Process_END #7: 2013 0 NA #8: 2013 0 NA #9: 2013 1 Process_START #10:2013 1 Process_END #11:2013 0 NA #12:2013 1 Process_START #13:2013 1 Process_END

And for a complicated example in @bergant post

  setDT(df1)[, gr:= rleid(process)][process==1L, Status:=c(NA, 'Process_START', 'Process_END', 'Process_START_END')[(1:.N==1L) + 2*(1:.N==.N)+1] , gr][,gr:=NULL] # date process gr Status # 1: 2007 1 1 Process_START_END # 2: 2008 0 2 NA # 3: 2009 0 2 NA # 4: 2010 0 2 NA # 5: 2011 1 3 Process_START_END # 6: 2012 0 4 NA # 7: 2013 1 5 Process_START # 8: 2014 1 5 NA # 9: 2015 1 5 Process_END #10: 2016 0 6 NA #11: 2017 1 7 Process_START_END #12: 2018 0 8 NA #13: 2019 0 8 NA #14: 2020 1 9 Process_START #15: 2021 1 9 NA #16: 2022 1 9 NA #17: 2023 1 9 NA #18: 2024 1 9 NA #19: 2025 1 9 NA #20: 2026 1 9 NA #21: 2027 1 9 NA #22: 2028 1 9 NA #23: 2029 1 9 NA #24: 2030 1 9 NA #25: 2031 1 9 Process_END
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Source: https://habr.com/ru/post/988268/

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