how to match values in columns by group or within category (groupwise) with other column(having multiple values) in same dataframe in R










3















So, I have data table in R which looks like follows:



Claim failure Part code matchcode
23 F1 P1 A B,A,C
23 F1 P1 D B,A,C
23 F2 P2 D B,A,C
23 F2 P2 E B,A,C
45 F1 P4 X Y,Z,X
45 F1 P4 Y Y,Z,X
45 F1 P4 A Y,Z,X
45 F1 P1 F Y,Z,X
45 F1 P1 H Y,Z,X


So the idea here is for every group (claim, failure and part) if 'any' of the code in 'code' matches with list of the codes in 'matchcode' column then than group should have Match column as True.



So expected output should be as following:



Claim failure Part Match 
23 F1 P1 TRUE
23 F2 P2 FALSE
45 F1 P4 TRUE
45 F1 P1 FALSE


The size of this data.table is huge so will be needing a optimized solution.
please help :)










share|improve this question






















  • If you want to vectorize/optimize regex operation, always use the stringi package. That will be by far faster than any alternative (at Ieast I know of). For your purposes I would just do library(stringi) ; DT[, .(Match = any(stri_detect_fixed(matchcode, code))), by = .(Claim, failure, Part)]. And I would also do some searching as vectorizing grepl is something that was discussed many times already

    – David Arenburg
    Nov 11 '18 at 11:20















3















So, I have data table in R which looks like follows:



Claim failure Part code matchcode
23 F1 P1 A B,A,C
23 F1 P1 D B,A,C
23 F2 P2 D B,A,C
23 F2 P2 E B,A,C
45 F1 P4 X Y,Z,X
45 F1 P4 Y Y,Z,X
45 F1 P4 A Y,Z,X
45 F1 P1 F Y,Z,X
45 F1 P1 H Y,Z,X


So the idea here is for every group (claim, failure and part) if 'any' of the code in 'code' matches with list of the codes in 'matchcode' column then than group should have Match column as True.



So expected output should be as following:



Claim failure Part Match 
23 F1 P1 TRUE
23 F2 P2 FALSE
45 F1 P4 TRUE
45 F1 P1 FALSE


The size of this data.table is huge so will be needing a optimized solution.
please help :)










share|improve this question






















  • If you want to vectorize/optimize regex operation, always use the stringi package. That will be by far faster than any alternative (at Ieast I know of). For your purposes I would just do library(stringi) ; DT[, .(Match = any(stri_detect_fixed(matchcode, code))), by = .(Claim, failure, Part)]. And I would also do some searching as vectorizing grepl is something that was discussed many times already

    – David Arenburg
    Nov 11 '18 at 11:20













3












3








3


1






So, I have data table in R which looks like follows:



Claim failure Part code matchcode
23 F1 P1 A B,A,C
23 F1 P1 D B,A,C
23 F2 P2 D B,A,C
23 F2 P2 E B,A,C
45 F1 P4 X Y,Z,X
45 F1 P4 Y Y,Z,X
45 F1 P4 A Y,Z,X
45 F1 P1 F Y,Z,X
45 F1 P1 H Y,Z,X


So the idea here is for every group (claim, failure and part) if 'any' of the code in 'code' matches with list of the codes in 'matchcode' column then than group should have Match column as True.



So expected output should be as following:



Claim failure Part Match 
23 F1 P1 TRUE
23 F2 P2 FALSE
45 F1 P4 TRUE
45 F1 P1 FALSE


The size of this data.table is huge so will be needing a optimized solution.
please help :)










share|improve this question














So, I have data table in R which looks like follows:



Claim failure Part code matchcode
23 F1 P1 A B,A,C
23 F1 P1 D B,A,C
23 F2 P2 D B,A,C
23 F2 P2 E B,A,C
45 F1 P4 X Y,Z,X
45 F1 P4 Y Y,Z,X
45 F1 P4 A Y,Z,X
45 F1 P1 F Y,Z,X
45 F1 P1 H Y,Z,X


So the idea here is for every group (claim, failure and part) if 'any' of the code in 'code' matches with list of the codes in 'matchcode' column then than group should have Match column as True.



So expected output should be as following:



Claim failure Part Match 
23 F1 P1 TRUE
23 F2 P2 FALSE
45 F1 P4 TRUE
45 F1 P1 FALSE


The size of this data.table is huge so will be needing a optimized solution.
please help :)







r data.table






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked Nov 10 '18 at 22:08









Rahul RajaramRahul Rajaram

225




225












  • If you want to vectorize/optimize regex operation, always use the stringi package. That will be by far faster than any alternative (at Ieast I know of). For your purposes I would just do library(stringi) ; DT[, .(Match = any(stri_detect_fixed(matchcode, code))), by = .(Claim, failure, Part)]. And I would also do some searching as vectorizing grepl is something that was discussed many times already

    – David Arenburg
    Nov 11 '18 at 11:20

















  • If you want to vectorize/optimize regex operation, always use the stringi package. That will be by far faster than any alternative (at Ieast I know of). For your purposes I would just do library(stringi) ; DT[, .(Match = any(stri_detect_fixed(matchcode, code))), by = .(Claim, failure, Part)]. And I would also do some searching as vectorizing grepl is something that was discussed many times already

    – David Arenburg
    Nov 11 '18 at 11:20
















If you want to vectorize/optimize regex operation, always use the stringi package. That will be by far faster than any alternative (at Ieast I know of). For your purposes I would just do library(stringi) ; DT[, .(Match = any(stri_detect_fixed(matchcode, code))), by = .(Claim, failure, Part)]. And I would also do some searching as vectorizing grepl is something that was discussed many times already

– David Arenburg
Nov 11 '18 at 11:20





If you want to vectorize/optimize regex operation, always use the stringi package. That will be by far faster than any alternative (at Ieast I know of). For your purposes I would just do library(stringi) ; DT[, .(Match = any(stri_detect_fixed(matchcode, code))), by = .(Claim, failure, Part)]. And I would also do some searching as vectorizing grepl is something that was discussed many times already

– David Arenburg
Nov 11 '18 at 11:20












3 Answers
3






active

oldest

votes


















3














... and a solution using data.table.



library(data.table)
grepl_v <- Vectorize(grepl)
DT[, .(Match = any(grepl_v(code, matchcode))), by = .(Claim, failure, Part)]
# Claim failure Part Match
#1: 23 F1 P1 TRUE
#2: 23 F2 P2 FALSE
#3: 45 F1 P4 TRUE
#4: 45 F1 P1 FALSE


data



DT <- fread("Claim failure Part code matchcode
23 F1 P1 A B,A,C
23 F1 P1 D B,A,C
23 F2 P2 D B,A,C
23 F2 P2 E B,A,C
45 F1 P4 X Y,Z,X
45 F1 P4 Y Y,Z,X
45 F1 P4 A Y,Z,X
45 F1 P1 F Y,Z,X
45 F1 P1 H Y,Z,X")





share|improve this answer
































    1














    Here is a dplyr solution.



    library(dplyr)

    dat %>%
    rowwise() %>%
    mutate(Match = grepl(code, matchcode)) %>%
    group_by(Claim, failure, Part) %>%
    mutate(Match = any(Match)) %>%
    select(-code, -matchcode) %>%
    unique
    ## A tibble: 4 x 4
    ## Groups: Claim, failure, Part [4]
    # Claim failure Part Match
    # <int> <fct> <fct> <lgl>
    #1 23 F1 P1 TRUE
    #2 23 F2 P2 FALSE
    #3 45 F1 P4 TRUE
    #4 45 F1 P1 FALSE
    #Warning message:
    #Grouping rowwise data frame strips rowwise nature


    Don't worry about the warning, it simply tells you that after grep'ing rowwise, the pipe groups by certain variables and therefore the processing is no longer row by row.



    Data.



    dat <- read.table(text = "
    Claim failure Part code matchcode
    23 F1 P1 A B,A,C
    23 F1 P1 D B,A,C
    23 F2 P2 D B,A,C
    23 F2 P2 E B,A,C
    45 F1 P4 X Y,Z,X
    45 F1 P4 Y Y,Z,X
    45 F1 P4 A Y,Z,X
    45 F1 P1 F Y,Z,X
    45 F1 P1 H Y,Z,X
    ", header = TRUE)





    share|improve this answer






























      1














      base solution:



      d$match <- apply(d, 1, function(x) return(grepl(x[['code']], x['matchcode'])))

      # Claim failure Part code matchcode match
      # 1 23 F1 P1 A B,A,C 1
      # 2 23 F1 P1 D B,A,C 0
      # 3 23 F2 P2 D B,A,C 0
      # 4 23 F2 P2 E B,A,C 0
      # 5 45 F1 P4 X Y,Z,X 1
      # 6 45 F1 P4 Y Y,Z,X 1
      # 7 45 F1 P4 A Y,Z,X 0
      # 8 45 F1 P1 F Y,Z,X 0
      # 9 45 F1 P1 H Y,Z,X 0


      Original version of this answer used grep(); thanks to markus for suggesting grepl()






      share|improve this answer
























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        3 Answers
        3






        active

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        3 Answers
        3






        active

        oldest

        votes









        active

        oldest

        votes






        active

        oldest

        votes









        3














        ... and a solution using data.table.



        library(data.table)
        grepl_v <- Vectorize(grepl)
        DT[, .(Match = any(grepl_v(code, matchcode))), by = .(Claim, failure, Part)]
        # Claim failure Part Match
        #1: 23 F1 P1 TRUE
        #2: 23 F2 P2 FALSE
        #3: 45 F1 P4 TRUE
        #4: 45 F1 P1 FALSE


        data



        DT <- fread("Claim failure Part code matchcode
        23 F1 P1 A B,A,C
        23 F1 P1 D B,A,C
        23 F2 P2 D B,A,C
        23 F2 P2 E B,A,C
        45 F1 P4 X Y,Z,X
        45 F1 P4 Y Y,Z,X
        45 F1 P4 A Y,Z,X
        45 F1 P1 F Y,Z,X
        45 F1 P1 H Y,Z,X")





        share|improve this answer





























          3














          ... and a solution using data.table.



          library(data.table)
          grepl_v <- Vectorize(grepl)
          DT[, .(Match = any(grepl_v(code, matchcode))), by = .(Claim, failure, Part)]
          # Claim failure Part Match
          #1: 23 F1 P1 TRUE
          #2: 23 F2 P2 FALSE
          #3: 45 F1 P4 TRUE
          #4: 45 F1 P1 FALSE


          data



          DT <- fread("Claim failure Part code matchcode
          23 F1 P1 A B,A,C
          23 F1 P1 D B,A,C
          23 F2 P2 D B,A,C
          23 F2 P2 E B,A,C
          45 F1 P4 X Y,Z,X
          45 F1 P4 Y Y,Z,X
          45 F1 P4 A Y,Z,X
          45 F1 P1 F Y,Z,X
          45 F1 P1 H Y,Z,X")





          share|improve this answer



























            3












            3








            3







            ... and a solution using data.table.



            library(data.table)
            grepl_v <- Vectorize(grepl)
            DT[, .(Match = any(grepl_v(code, matchcode))), by = .(Claim, failure, Part)]
            # Claim failure Part Match
            #1: 23 F1 P1 TRUE
            #2: 23 F2 P2 FALSE
            #3: 45 F1 P4 TRUE
            #4: 45 F1 P1 FALSE


            data



            DT <- fread("Claim failure Part code matchcode
            23 F1 P1 A B,A,C
            23 F1 P1 D B,A,C
            23 F2 P2 D B,A,C
            23 F2 P2 E B,A,C
            45 F1 P4 X Y,Z,X
            45 F1 P4 Y Y,Z,X
            45 F1 P4 A Y,Z,X
            45 F1 P1 F Y,Z,X
            45 F1 P1 H Y,Z,X")





            share|improve this answer















            ... and a solution using data.table.



            library(data.table)
            grepl_v <- Vectorize(grepl)
            DT[, .(Match = any(grepl_v(code, matchcode))), by = .(Claim, failure, Part)]
            # Claim failure Part Match
            #1: 23 F1 P1 TRUE
            #2: 23 F2 P2 FALSE
            #3: 45 F1 P4 TRUE
            #4: 45 F1 P1 FALSE


            data



            DT <- fread("Claim failure Part code matchcode
            23 F1 P1 A B,A,C
            23 F1 P1 D B,A,C
            23 F2 P2 D B,A,C
            23 F2 P2 E B,A,C
            45 F1 P4 X Y,Z,X
            45 F1 P4 Y Y,Z,X
            45 F1 P4 A Y,Z,X
            45 F1 P1 F Y,Z,X
            45 F1 P1 H Y,Z,X")






            share|improve this answer














            share|improve this answer



            share|improve this answer








            edited Nov 10 '18 at 22:43

























            answered Nov 10 '18 at 22:30









            markusmarkus

            10.8k1030




            10.8k1030























                1














                Here is a dplyr solution.



                library(dplyr)

                dat %>%
                rowwise() %>%
                mutate(Match = grepl(code, matchcode)) %>%
                group_by(Claim, failure, Part) %>%
                mutate(Match = any(Match)) %>%
                select(-code, -matchcode) %>%
                unique
                ## A tibble: 4 x 4
                ## Groups: Claim, failure, Part [4]
                # Claim failure Part Match
                # <int> <fct> <fct> <lgl>
                #1 23 F1 P1 TRUE
                #2 23 F2 P2 FALSE
                #3 45 F1 P4 TRUE
                #4 45 F1 P1 FALSE
                #Warning message:
                #Grouping rowwise data frame strips rowwise nature


                Don't worry about the warning, it simply tells you that after grep'ing rowwise, the pipe groups by certain variables and therefore the processing is no longer row by row.



                Data.



                dat <- read.table(text = "
                Claim failure Part code matchcode
                23 F1 P1 A B,A,C
                23 F1 P1 D B,A,C
                23 F2 P2 D B,A,C
                23 F2 P2 E B,A,C
                45 F1 P4 X Y,Z,X
                45 F1 P4 Y Y,Z,X
                45 F1 P4 A Y,Z,X
                45 F1 P1 F Y,Z,X
                45 F1 P1 H Y,Z,X
                ", header = TRUE)





                share|improve this answer



























                  1














                  Here is a dplyr solution.



                  library(dplyr)

                  dat %>%
                  rowwise() %>%
                  mutate(Match = grepl(code, matchcode)) %>%
                  group_by(Claim, failure, Part) %>%
                  mutate(Match = any(Match)) %>%
                  select(-code, -matchcode) %>%
                  unique
                  ## A tibble: 4 x 4
                  ## Groups: Claim, failure, Part [4]
                  # Claim failure Part Match
                  # <int> <fct> <fct> <lgl>
                  #1 23 F1 P1 TRUE
                  #2 23 F2 P2 FALSE
                  #3 45 F1 P4 TRUE
                  #4 45 F1 P1 FALSE
                  #Warning message:
                  #Grouping rowwise data frame strips rowwise nature


                  Don't worry about the warning, it simply tells you that after grep'ing rowwise, the pipe groups by certain variables and therefore the processing is no longer row by row.



                  Data.



                  dat <- read.table(text = "
                  Claim failure Part code matchcode
                  23 F1 P1 A B,A,C
                  23 F1 P1 D B,A,C
                  23 F2 P2 D B,A,C
                  23 F2 P2 E B,A,C
                  45 F1 P4 X Y,Z,X
                  45 F1 P4 Y Y,Z,X
                  45 F1 P4 A Y,Z,X
                  45 F1 P1 F Y,Z,X
                  45 F1 P1 H Y,Z,X
                  ", header = TRUE)





                  share|improve this answer

























                    1












                    1








                    1







                    Here is a dplyr solution.



                    library(dplyr)

                    dat %>%
                    rowwise() %>%
                    mutate(Match = grepl(code, matchcode)) %>%
                    group_by(Claim, failure, Part) %>%
                    mutate(Match = any(Match)) %>%
                    select(-code, -matchcode) %>%
                    unique
                    ## A tibble: 4 x 4
                    ## Groups: Claim, failure, Part [4]
                    # Claim failure Part Match
                    # <int> <fct> <fct> <lgl>
                    #1 23 F1 P1 TRUE
                    #2 23 F2 P2 FALSE
                    #3 45 F1 P4 TRUE
                    #4 45 F1 P1 FALSE
                    #Warning message:
                    #Grouping rowwise data frame strips rowwise nature


                    Don't worry about the warning, it simply tells you that after grep'ing rowwise, the pipe groups by certain variables and therefore the processing is no longer row by row.



                    Data.



                    dat <- read.table(text = "
                    Claim failure Part code matchcode
                    23 F1 P1 A B,A,C
                    23 F1 P1 D B,A,C
                    23 F2 P2 D B,A,C
                    23 F2 P2 E B,A,C
                    45 F1 P4 X Y,Z,X
                    45 F1 P4 Y Y,Z,X
                    45 F1 P4 A Y,Z,X
                    45 F1 P1 F Y,Z,X
                    45 F1 P1 H Y,Z,X
                    ", header = TRUE)





                    share|improve this answer













                    Here is a dplyr solution.



                    library(dplyr)

                    dat %>%
                    rowwise() %>%
                    mutate(Match = grepl(code, matchcode)) %>%
                    group_by(Claim, failure, Part) %>%
                    mutate(Match = any(Match)) %>%
                    select(-code, -matchcode) %>%
                    unique
                    ## A tibble: 4 x 4
                    ## Groups: Claim, failure, Part [4]
                    # Claim failure Part Match
                    # <int> <fct> <fct> <lgl>
                    #1 23 F1 P1 TRUE
                    #2 23 F2 P2 FALSE
                    #3 45 F1 P4 TRUE
                    #4 45 F1 P1 FALSE
                    #Warning message:
                    #Grouping rowwise data frame strips rowwise nature


                    Don't worry about the warning, it simply tells you that after grep'ing rowwise, the pipe groups by certain variables and therefore the processing is no longer row by row.



                    Data.



                    dat <- read.table(text = "
                    Claim failure Part code matchcode
                    23 F1 P1 A B,A,C
                    23 F1 P1 D B,A,C
                    23 F2 P2 D B,A,C
                    23 F2 P2 E B,A,C
                    45 F1 P4 X Y,Z,X
                    45 F1 P4 Y Y,Z,X
                    45 F1 P4 A Y,Z,X
                    45 F1 P1 F Y,Z,X
                    45 F1 P1 H Y,Z,X
                    ", header = TRUE)






                    share|improve this answer












                    share|improve this answer



                    share|improve this answer










                    answered Nov 10 '18 at 22:26









                    Rui BarradasRui Barradas

                    16.3k51730




                    16.3k51730





















                        1














                        base solution:



                        d$match <- apply(d, 1, function(x) return(grepl(x[['code']], x['matchcode'])))

                        # Claim failure Part code matchcode match
                        # 1 23 F1 P1 A B,A,C 1
                        # 2 23 F1 P1 D B,A,C 0
                        # 3 23 F2 P2 D B,A,C 0
                        # 4 23 F2 P2 E B,A,C 0
                        # 5 45 F1 P4 X Y,Z,X 1
                        # 6 45 F1 P4 Y Y,Z,X 1
                        # 7 45 F1 P4 A Y,Z,X 0
                        # 8 45 F1 P1 F Y,Z,X 0
                        # 9 45 F1 P1 H Y,Z,X 0


                        Original version of this answer used grep(); thanks to markus for suggesting grepl()






                        share|improve this answer





























                          1














                          base solution:



                          d$match <- apply(d, 1, function(x) return(grepl(x[['code']], x['matchcode'])))

                          # Claim failure Part code matchcode match
                          # 1 23 F1 P1 A B,A,C 1
                          # 2 23 F1 P1 D B,A,C 0
                          # 3 23 F2 P2 D B,A,C 0
                          # 4 23 F2 P2 E B,A,C 0
                          # 5 45 F1 P4 X Y,Z,X 1
                          # 6 45 F1 P4 Y Y,Z,X 1
                          # 7 45 F1 P4 A Y,Z,X 0
                          # 8 45 F1 P1 F Y,Z,X 0
                          # 9 45 F1 P1 H Y,Z,X 0


                          Original version of this answer used grep(); thanks to markus for suggesting grepl()






                          share|improve this answer



























                            1












                            1








                            1







                            base solution:



                            d$match <- apply(d, 1, function(x) return(grepl(x[['code']], x['matchcode'])))

                            # Claim failure Part code matchcode match
                            # 1 23 F1 P1 A B,A,C 1
                            # 2 23 F1 P1 D B,A,C 0
                            # 3 23 F2 P2 D B,A,C 0
                            # 4 23 F2 P2 E B,A,C 0
                            # 5 45 F1 P4 X Y,Z,X 1
                            # 6 45 F1 P4 Y Y,Z,X 1
                            # 7 45 F1 P4 A Y,Z,X 0
                            # 8 45 F1 P1 F Y,Z,X 0
                            # 9 45 F1 P1 H Y,Z,X 0


                            Original version of this answer used grep(); thanks to markus for suggesting grepl()






                            share|improve this answer















                            base solution:



                            d$match <- apply(d, 1, function(x) return(grepl(x[['code']], x['matchcode'])))

                            # Claim failure Part code matchcode match
                            # 1 23 F1 P1 A B,A,C 1
                            # 2 23 F1 P1 D B,A,C 0
                            # 3 23 F2 P2 D B,A,C 0
                            # 4 23 F2 P2 E B,A,C 0
                            # 5 45 F1 P4 X Y,Z,X 1
                            # 6 45 F1 P4 Y Y,Z,X 1
                            # 7 45 F1 P4 A Y,Z,X 0
                            # 8 45 F1 P1 F Y,Z,X 0
                            # 9 45 F1 P1 H Y,Z,X 0


                            Original version of this answer used grep(); thanks to markus for suggesting grepl()







                            share|improve this answer














                            share|improve this answer



                            share|improve this answer








                            edited Nov 10 '18 at 22:32

























                            answered Nov 10 '18 at 22:27









                            12b345b6b7812b345b6b78

                            782115




                            782115



























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