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Shifting non-NA cells to the left



2019 Community Moderator ElectionHow to move cells with a value row-wise to the left in a dataframeUsing R to shift values to the left of data.frameshift right cell contents in a row by condition (or taking the previous value) in dplyrMove dataframe columns values to the left (based on na)data.table shift right all cell values by number of na within each row [R]Shift cells to left in R data.frameHow to join (merge) data frames (inner, outer, left, right)?Convert data.frame columns from factors to charactersWhy is `[` better than `subset`?How to remove records in a dataframeShift column in pandas dataframe up by one?data.table vs dplyr: can one do something well the other can't or does poorly?How to move cells with a value row-wise to the left in a dataframeAdding Proportionate Na's in a columnDividing across multiple columns in r using mutate_at callCopying Column values in pandas to non zero cells and aggreagating columns after it










2















There are many NA's in my dataset and I need to shift all those cells (at row level) to the left.



Example- my dataframe:



 df=data.frame(x=c("l","m",NA,NA,"p"),y=c(NA,"b","c",NA,NA),z=c("u",NA,"w","x","y"))
df
x y z
1 l <NA> u
2 m b <NA>
3 <NA> c w
4 <NA> <NA> x
5 p <NA> y


I want the above dataframe converted into this:



 x y z
1 l u NA
2 m b NA
3 c w NA
4 x <NA> NA
5 p y NA


Please help.



Thanks.










share|improve this question




























    2















    There are many NA's in my dataset and I need to shift all those cells (at row level) to the left.



    Example- my dataframe:



     df=data.frame(x=c("l","m",NA,NA,"p"),y=c(NA,"b","c",NA,NA),z=c("u",NA,"w","x","y"))
    df
    x y z
    1 l <NA> u
    2 m b <NA>
    3 <NA> c w
    4 <NA> <NA> x
    5 p <NA> y


    I want the above dataframe converted into this:



     x y z
    1 l u NA
    2 m b NA
    3 c w NA
    4 x <NA> NA
    5 p y NA


    Please help.



    Thanks.










    share|improve this question


























      2












      2








      2


      2






      There are many NA's in my dataset and I need to shift all those cells (at row level) to the left.



      Example- my dataframe:



       df=data.frame(x=c("l","m",NA,NA,"p"),y=c(NA,"b","c",NA,NA),z=c("u",NA,"w","x","y"))
      df
      x y z
      1 l <NA> u
      2 m b <NA>
      3 <NA> c w
      4 <NA> <NA> x
      5 p <NA> y


      I want the above dataframe converted into this:



       x y z
      1 l u NA
      2 m b NA
      3 c w NA
      4 x <NA> NA
      5 p y NA


      Please help.



      Thanks.










      share|improve this question
















      There are many NA's in my dataset and I need to shift all those cells (at row level) to the left.



      Example- my dataframe:



       df=data.frame(x=c("l","m",NA,NA,"p"),y=c(NA,"b","c",NA,NA),z=c("u",NA,"w","x","y"))
      df
      x y z
      1 l <NA> u
      2 m b <NA>
      3 <NA> c w
      4 <NA> <NA> x
      5 p <NA> y


      I want the above dataframe converted into this:



       x y z
      1 l u NA
      2 m b NA
      3 c w NA
      4 x <NA> NA
      5 p y NA


      Please help.



      Thanks.







      r dataframe data-manipulation






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Mar 3 '18 at 10:08









      Henrik

      42.1k994110




      42.1k994110










      asked Apr 25 '14 at 5:57









      sidpatsidpat

      361220




      361220






















          4 Answers
          4






          active

          oldest

          votes


















          12














          You can use the standard apply function:



          df=data.frame(x=c("l","m",NA,NA,"p"),y=c(NA,"b","c",NA,NA),z=c("u",NA,"w","x","y"))
          df2 = as.data.frame(t(apply(df,1, function(x) return(c(x[!is.na(x)],x[is.na(x)]) ) )))
          colnames(df2) = colnames(df)

          > df
          x y z
          1 l <NA> u
          2 m b <NA>
          3 <NA> c w
          4 <NA> <NA> x
          5 p <NA> y
          > df2
          x y z
          1 l u <NA>
          2 m b <NA>
          3 c w <NA>
          4 x <NA> <NA>
          5 p y <NA>





          share|improve this answer






























            3














            Thanks to @Richard Scriven for good observation



            A) with is.na and order, lapply and rbind for aggregation



            nosort.df<-do.call(rbind,lapply(1:nrow(df),function(x) z=df[x,][order(is.na(df[x,]))];colnames(z)<-c("x","y","z");return(z) ))

            > nosort.df
            x y z
            1 l u <NA>
            2 m b <NA>
            3 c w <NA>
            4 x <NA> <NA>
            5 p y <NA>


            B) if sorted rows are required:



            with sort, lapply and rbind



            sort.df<-do.call(rbind,lapply(1:nrow(df),function(x) z=sort(df[x,],na.last=TRUE);colnames(z)<-c("x","y","z");return(z) ))

            > sort.df
            x y z
            1 l u <NA>
            2 b m <NA>
            3 c w <NA>
            4 x <NA> <NA>
            5 p y <NA>





            share|improve this answer




















            • 1





              Wait, you're sorting the row? This will change the position of values that don't need to be changed.

              – Rich Scriven
              Apr 25 '14 at 6:42



















            1














            If you won't get shorter answer, this should help:



            df=data.frame(x=c("l","m",NA,NA,"p"),y=c(NA,"b","c",NA,NA),z=c("u",NA,"w","x","y"))
            sapply(df,as.character)


            for(i in 1:nrow(df))
            sub <- df[i,c(which(!is.na(df[i,])),which(is.na(df[i,])))]
            colnames(sub) <- colnames(df)
            df[i,] <- sub






            share|improve this answer

























            • The three as.character statements could be combined with sapply(df,as.character)

              – Silence Dogood
              Apr 25 '14 at 7:06






            • 1





              thx... I need to start to use those pply functions more.

              – Pigeon
              Apr 25 '14 at 7:23


















            0














            If you don't want to use VBA, you can try the following steps.



            1. Select your dataset
            2. Replace NA will empty cells
            3. press F5 and select blanks ok
            4. right click on any of the selection and delete (left)


            I hope this helps.






            share|improve this answer
























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              4 Answers
              4






              active

              oldest

              votes








              4 Answers
              4






              active

              oldest

              votes









              active

              oldest

              votes






              active

              oldest

              votes









              12














              You can use the standard apply function:



              df=data.frame(x=c("l","m",NA,NA,"p"),y=c(NA,"b","c",NA,NA),z=c("u",NA,"w","x","y"))
              df2 = as.data.frame(t(apply(df,1, function(x) return(c(x[!is.na(x)],x[is.na(x)]) ) )))
              colnames(df2) = colnames(df)

              > df
              x y z
              1 l <NA> u
              2 m b <NA>
              3 <NA> c w
              4 <NA> <NA> x
              5 p <NA> y
              > df2
              x y z
              1 l u <NA>
              2 m b <NA>
              3 c w <NA>
              4 x <NA> <NA>
              5 p y <NA>





              share|improve this answer



























                12














                You can use the standard apply function:



                df=data.frame(x=c("l","m",NA,NA,"p"),y=c(NA,"b","c",NA,NA),z=c("u",NA,"w","x","y"))
                df2 = as.data.frame(t(apply(df,1, function(x) return(c(x[!is.na(x)],x[is.na(x)]) ) )))
                colnames(df2) = colnames(df)

                > df
                x y z
                1 l <NA> u
                2 m b <NA>
                3 <NA> c w
                4 <NA> <NA> x
                5 p <NA> y
                > df2
                x y z
                1 l u <NA>
                2 m b <NA>
                3 c w <NA>
                4 x <NA> <NA>
                5 p y <NA>





                share|improve this answer

























                  12












                  12








                  12







                  You can use the standard apply function:



                  df=data.frame(x=c("l","m",NA,NA,"p"),y=c(NA,"b","c",NA,NA),z=c("u",NA,"w","x","y"))
                  df2 = as.data.frame(t(apply(df,1, function(x) return(c(x[!is.na(x)],x[is.na(x)]) ) )))
                  colnames(df2) = colnames(df)

                  > df
                  x y z
                  1 l <NA> u
                  2 m b <NA>
                  3 <NA> c w
                  4 <NA> <NA> x
                  5 p <NA> y
                  > df2
                  x y z
                  1 l u <NA>
                  2 m b <NA>
                  3 c w <NA>
                  4 x <NA> <NA>
                  5 p y <NA>





                  share|improve this answer













                  You can use the standard apply function:



                  df=data.frame(x=c("l","m",NA,NA,"p"),y=c(NA,"b","c",NA,NA),z=c("u",NA,"w","x","y"))
                  df2 = as.data.frame(t(apply(df,1, function(x) return(c(x[!is.na(x)],x[is.na(x)]) ) )))
                  colnames(df2) = colnames(df)

                  > df
                  x y z
                  1 l <NA> u
                  2 m b <NA>
                  3 <NA> c w
                  4 <NA> <NA> x
                  5 p <NA> y
                  > df2
                  x y z
                  1 l u <NA>
                  2 m b <NA>
                  3 c w <NA>
                  4 x <NA> <NA>
                  5 p y <NA>






                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered Apr 25 '14 at 6:24









                  Hans RoggemanHans Roggeman

                  1,9461128




                  1,9461128























                      3














                      Thanks to @Richard Scriven for good observation



                      A) with is.na and order, lapply and rbind for aggregation



                      nosort.df<-do.call(rbind,lapply(1:nrow(df),function(x) z=df[x,][order(is.na(df[x,]))];colnames(z)<-c("x","y","z");return(z) ))

                      > nosort.df
                      x y z
                      1 l u <NA>
                      2 m b <NA>
                      3 c w <NA>
                      4 x <NA> <NA>
                      5 p y <NA>


                      B) if sorted rows are required:



                      with sort, lapply and rbind



                      sort.df<-do.call(rbind,lapply(1:nrow(df),function(x) z=sort(df[x,],na.last=TRUE);colnames(z)<-c("x","y","z");return(z) ))

                      > sort.df
                      x y z
                      1 l u <NA>
                      2 b m <NA>
                      3 c w <NA>
                      4 x <NA> <NA>
                      5 p y <NA>





                      share|improve this answer




















                      • 1





                        Wait, you're sorting the row? This will change the position of values that don't need to be changed.

                        – Rich Scriven
                        Apr 25 '14 at 6:42
















                      3














                      Thanks to @Richard Scriven for good observation



                      A) with is.na and order, lapply and rbind for aggregation



                      nosort.df<-do.call(rbind,lapply(1:nrow(df),function(x) z=df[x,][order(is.na(df[x,]))];colnames(z)<-c("x","y","z");return(z) ))

                      > nosort.df
                      x y z
                      1 l u <NA>
                      2 m b <NA>
                      3 c w <NA>
                      4 x <NA> <NA>
                      5 p y <NA>


                      B) if sorted rows are required:



                      with sort, lapply and rbind



                      sort.df<-do.call(rbind,lapply(1:nrow(df),function(x) z=sort(df[x,],na.last=TRUE);colnames(z)<-c("x","y","z");return(z) ))

                      > sort.df
                      x y z
                      1 l u <NA>
                      2 b m <NA>
                      3 c w <NA>
                      4 x <NA> <NA>
                      5 p y <NA>





                      share|improve this answer




















                      • 1





                        Wait, you're sorting the row? This will change the position of values that don't need to be changed.

                        – Rich Scriven
                        Apr 25 '14 at 6:42














                      3












                      3








                      3







                      Thanks to @Richard Scriven for good observation



                      A) with is.na and order, lapply and rbind for aggregation



                      nosort.df<-do.call(rbind,lapply(1:nrow(df),function(x) z=df[x,][order(is.na(df[x,]))];colnames(z)<-c("x","y","z");return(z) ))

                      > nosort.df
                      x y z
                      1 l u <NA>
                      2 m b <NA>
                      3 c w <NA>
                      4 x <NA> <NA>
                      5 p y <NA>


                      B) if sorted rows are required:



                      with sort, lapply and rbind



                      sort.df<-do.call(rbind,lapply(1:nrow(df),function(x) z=sort(df[x,],na.last=TRUE);colnames(z)<-c("x","y","z");return(z) ))

                      > sort.df
                      x y z
                      1 l u <NA>
                      2 b m <NA>
                      3 c w <NA>
                      4 x <NA> <NA>
                      5 p y <NA>





                      share|improve this answer















                      Thanks to @Richard Scriven for good observation



                      A) with is.na and order, lapply and rbind for aggregation



                      nosort.df<-do.call(rbind,lapply(1:nrow(df),function(x) z=df[x,][order(is.na(df[x,]))];colnames(z)<-c("x","y","z");return(z) ))

                      > nosort.df
                      x y z
                      1 l u <NA>
                      2 m b <NA>
                      3 c w <NA>
                      4 x <NA> <NA>
                      5 p y <NA>


                      B) if sorted rows are required:



                      with sort, lapply and rbind



                      sort.df<-do.call(rbind,lapply(1:nrow(df),function(x) z=sort(df[x,],na.last=TRUE);colnames(z)<-c("x","y","z");return(z) ))

                      > sort.df
                      x y z
                      1 l u <NA>
                      2 b m <NA>
                      3 c w <NA>
                      4 x <NA> <NA>
                      5 p y <NA>






                      share|improve this answer














                      share|improve this answer



                      share|improve this answer








                      edited Apr 25 '14 at 7:04

























                      answered Apr 25 '14 at 6:33









                      Silence DogoodSilence Dogood

                      3,1561815




                      3,1561815







                      • 1





                        Wait, you're sorting the row? This will change the position of values that don't need to be changed.

                        – Rich Scriven
                        Apr 25 '14 at 6:42













                      • 1





                        Wait, you're sorting the row? This will change the position of values that don't need to be changed.

                        – Rich Scriven
                        Apr 25 '14 at 6:42








                      1




                      1





                      Wait, you're sorting the row? This will change the position of values that don't need to be changed.

                      – Rich Scriven
                      Apr 25 '14 at 6:42






                      Wait, you're sorting the row? This will change the position of values that don't need to be changed.

                      – Rich Scriven
                      Apr 25 '14 at 6:42












                      1














                      If you won't get shorter answer, this should help:



                      df=data.frame(x=c("l","m",NA,NA,"p"),y=c(NA,"b","c",NA,NA),z=c("u",NA,"w","x","y"))
                      sapply(df,as.character)


                      for(i in 1:nrow(df))
                      sub <- df[i,c(which(!is.na(df[i,])),which(is.na(df[i,])))]
                      colnames(sub) <- colnames(df)
                      df[i,] <- sub






                      share|improve this answer

























                      • The three as.character statements could be combined with sapply(df,as.character)

                        – Silence Dogood
                        Apr 25 '14 at 7:06






                      • 1





                        thx... I need to start to use those pply functions more.

                        – Pigeon
                        Apr 25 '14 at 7:23















                      1














                      If you won't get shorter answer, this should help:



                      df=data.frame(x=c("l","m",NA,NA,"p"),y=c(NA,"b","c",NA,NA),z=c("u",NA,"w","x","y"))
                      sapply(df,as.character)


                      for(i in 1:nrow(df))
                      sub <- df[i,c(which(!is.na(df[i,])),which(is.na(df[i,])))]
                      colnames(sub) <- colnames(df)
                      df[i,] <- sub






                      share|improve this answer

























                      • The three as.character statements could be combined with sapply(df,as.character)

                        – Silence Dogood
                        Apr 25 '14 at 7:06






                      • 1





                        thx... I need to start to use those pply functions more.

                        – Pigeon
                        Apr 25 '14 at 7:23













                      1












                      1








                      1







                      If you won't get shorter answer, this should help:



                      df=data.frame(x=c("l","m",NA,NA,"p"),y=c(NA,"b","c",NA,NA),z=c("u",NA,"w","x","y"))
                      sapply(df,as.character)


                      for(i in 1:nrow(df))
                      sub <- df[i,c(which(!is.na(df[i,])),which(is.na(df[i,])))]
                      colnames(sub) <- colnames(df)
                      df[i,] <- sub






                      share|improve this answer















                      If you won't get shorter answer, this should help:



                      df=data.frame(x=c("l","m",NA,NA,"p"),y=c(NA,"b","c",NA,NA),z=c("u",NA,"w","x","y"))
                      sapply(df,as.character)


                      for(i in 1:nrow(df))
                      sub <- df[i,c(which(!is.na(df[i,])),which(is.na(df[i,])))]
                      colnames(sub) <- colnames(df)
                      df[i,] <- sub







                      share|improve this answer














                      share|improve this answer



                      share|improve this answer








                      edited Apr 25 '14 at 7:24

























                      answered Apr 25 '14 at 6:30









                      PigeonPigeon

                      41328




                      41328












                      • The three as.character statements could be combined with sapply(df,as.character)

                        – Silence Dogood
                        Apr 25 '14 at 7:06






                      • 1





                        thx... I need to start to use those pply functions more.

                        – Pigeon
                        Apr 25 '14 at 7:23

















                      • The three as.character statements could be combined with sapply(df,as.character)

                        – Silence Dogood
                        Apr 25 '14 at 7:06






                      • 1





                        thx... I need to start to use those pply functions more.

                        – Pigeon
                        Apr 25 '14 at 7:23
















                      The three as.character statements could be combined with sapply(df,as.character)

                      – Silence Dogood
                      Apr 25 '14 at 7:06





                      The three as.character statements could be combined with sapply(df,as.character)

                      – Silence Dogood
                      Apr 25 '14 at 7:06




                      1




                      1





                      thx... I need to start to use those pply functions more.

                      – Pigeon
                      Apr 25 '14 at 7:23





                      thx... I need to start to use those pply functions more.

                      – Pigeon
                      Apr 25 '14 at 7:23











                      0














                      If you don't want to use VBA, you can try the following steps.



                      1. Select your dataset
                      2. Replace NA will empty cells
                      3. press F5 and select blanks ok
                      4. right click on any of the selection and delete (left)


                      I hope this helps.






                      share|improve this answer





























                        0














                        If you don't want to use VBA, you can try the following steps.



                        1. Select your dataset
                        2. Replace NA will empty cells
                        3. press F5 and select blanks ok
                        4. right click on any of the selection and delete (left)


                        I hope this helps.






                        share|improve this answer



























                          0












                          0








                          0







                          If you don't want to use VBA, you can try the following steps.



                          1. Select your dataset
                          2. Replace NA will empty cells
                          3. press F5 and select blanks ok
                          4. right click on any of the selection and delete (left)


                          I hope this helps.






                          share|improve this answer















                          If you don't want to use VBA, you can try the following steps.



                          1. Select your dataset
                          2. Replace NA will empty cells
                          3. press F5 and select blanks ok
                          4. right click on any of the selection and delete (left)


                          I hope this helps.







                          share|improve this answer














                          share|improve this answer



                          share|improve this answer








                          edited Oct 30 '18 at 18:48









                          SeGa

                          4,66431036




                          4,66431036










                          answered Jun 21 '15 at 5:45









                          szakwaniszakwani

                          318313




                          318313



























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