Duplicate names in the pivot column

I have a problem involving inefficient coding. My code works, but in my
actual application it takes a very long time to execute. I have included
a reprex here that uses the same code, but with a much smaller-scale
application.

The data frame I am working with (df in my reprex) is in long form and I
want to change it to wide form. My problem is that the pivot column,
column 2 in my reprex, has some duplicate strings, so the pivot doesn't
work well (df1 in my reprex). I want to find all the duplicates and tag
them so they are no longer duplicates. My code succeeds (df3 in my
reprex). But in the real application there can be over 100 "cases" and
the for loops grind on far too long.

I encounter this problem frequently in the datasets I use, so I am
looking for a general solution that is as efficient as possible. Any
help will be much appreciated.

Philip

``` r
library(tidyverse)
df <- data.frame(time=c(1,1,1,1,1,1,2,2,2,2,2,2),
                  y=c("A","B","C","B","D","C","A","B","C","B","D","C"),
                  z=sample(1:100,12,replace=TRUE),stringsAsFactors=FALSE)
df1 <- pivot_wider(df,id_cols=1,names_from=y,values_from=z)
#> Warning: Values in `z` are not uniquely identified; output will
contain list-cols.
#> * Use `values_fn = list(z = list)` to suppress this warning.
#> * Use `values_fn = list(z = length)` to identify where the duplicates
arise
#> * Use `values_fn = list(z = summary_fun)` to summarise duplicates
fixcol <- function(dfm,cases,per,s,tag) {
   # dfm is the data frame
   # s is the target column number, containing character names
   # tag is a string to be added to a duplicate name
   # cases is the number of rows for a single time period
   # per is the number of time periods
   # all time periods must have the same number of rows
   for (k in 1:per) {
     for (i in (1+(k-1)*cases):(k*cases-1)) {
       for (j in (i+1):(k*cases)) {
         if (dfm[j,s]==dfm[i,s]) { # found a duplicate
           dfm[j,s] <- paste0(dfm[i,s],tag) # fix the duplicate
           dfm[j,s]
         }
       }
     }
   }
   return(dfm)
}
df2 <- fixcol(df,6,2,2,"_dup")
df3 <- pivot_wider(df2,id_cols=1,names_from=y,values_from=z)
```

<sup>Created on 2020-03-28 by the [reprex
package](https://reprex.tidyverse.org) (v0.3.0)</sup>______________________________________________

I have a problem involving inefficient coding. My code works, but in my

actual application it takes a very long time to execute. I have
included 
a reprex here that uses the same code, but with a much smaller-scale 
application.

The data frame I am working with (df in my reprex) is in long form and
I 
want to change it to wide form. My problem is that the pivot column, 
column 2 in my reprex, has some duplicate strings, so the pivot doesn't

work well (df1 in my reprex). I want to find all the duplicates and tag

them so they are no longer duplicates. My code succeeds (df3 in my 
reprex). But in the real application there can be over 100 "cases" and 
the for loops grind on far too long.

I encounter this problem frequently in the datasets I use, so I am 
looking for a general solution that is as efficient as possible. Any 
help will be much appreciated.

Philip

``` r
library(tidyverse)
df <- data.frame(time=c(1,1,1,1,1,1,2,2,2,2,2,2),
                 y=c("A","B","C","B","D","C","A","B","C","B","D","C"),
               z=sample(1:100,12,replace=TRUE),stringsAsFactors=FALSE)
df1 <- pivot_wider(df,id_cols=1,names_from=y,values_from=z)
#> Warning: Values in `z` are not uniquely identified; output will 
contain list-cols.
#> * Use `values_fn = list(z = list)` to suppress this warning.
#> * Use `values_fn = list(z = length)` to identify where the
duplicates 
arise
#> * Use `values_fn = list(z = summary_fun)` to summarise duplicates
fixcol <- function(dfm,cases,per,s,tag) {
  # dfm is the data frame
  # s is the target column number, containing character names
  # tag is a string to be added to a duplicate name
  # cases is the number of rows for a single time period
  # per is the number of time periods
  # all time periods must have the same number of rows
  for (k in 1:per) {
    for (i in (1+(k-1)*cases):(k*cases-1)) {
      for (j in (i+1):(k*cases)) {
        if (dfm[j,s]==dfm[i,s]) { # found a duplicate
          dfm[j,s] <- paste0(dfm[i,s],tag) # fix the duplicate
          dfm[j,s]
        }
      }
    }
  }
  return(dfm)
}
df2 <- fixcol(df,6,2,2,"_dup")
df3 <- pivot_wider(df2,id_cols=1,names_from=y,values_from=z)
```

<sup>Created on 2020-03-28 by the [reprex 
package](https://reprex.tidyverse.org) (v0.3.0)</sup>

Hi Phil,
Sorry it's not in the environment you are using, but perhaps this will 
help:

taby<-table(df$y)
ynames<-names(taby)
for(yval in 1:length(taby)) {
 if(taby[yval] > 1) {
  cat(paste(ynames[yval],1:taby[yval],sep=""),"\n")
  df$y[which(df$y == 
ynames[yval])]<-paste(ynames[yval],1:taby[yval],sep="")
 }
}

Jim

On Sun, Mar 29, 2020 at 12:19 PM <phil at philipsmith.ca> wrote:

I have a problem involving inefficient coding. My code works, but in 
my
actual application it takes a very long time to execute. I have 
included
a reprex here that uses the same code, but with a much smaller-scale
application.

The data frame I am working with (df in my reprex) is in long form and 
I
want to change it to wide form. My problem is that the pivot column,
column 2 in my reprex, has some duplicate strings, so the pivot 
doesn't
work well (df1 in my reprex). I want to find all the duplicates and 
tag
them so they are no longer duplicates. My code succeeds (df3 in my
reprex). But in the real application there can be over 100 "cases" and
the for loops grind on far too long.

I encounter this problem frequently in the datasets I use, so I am
looking for a general solution that is as efficient as possible. Any
help will be much appreciated.

Philip

``` r
library(tidyverse)
df <- data.frame(time=c(1,1,1,1,1,1,2,2,2,2,2,2),
                  
y=c("A","B","C","B","D","C","A","B","C","B","D","C"),
                  
z=sample(1:100,12,replace=TRUE),stringsAsFactors=FALSE)
df1 <- pivot_wider(df,id_cols=1,names_from=y,values_from=z)
#> Warning: Values in `z` are not uniquely identified; output will
contain list-cols.
#> * Use `values_fn = list(z = list)` to suppress this warning.
#> * Use `values_fn = list(z = length)` to identify where the 
duplicates
arise
#> * Use `values_fn = list(z = summary_fun)` to summarise duplicates
fixcol <- function(dfm,cases,per,s,tag) {
   # dfm is the data frame
   # s is the target column number, containing character names
   # tag is a string to be added to a duplicate name
   # cases is the number of rows for a single time period
   # per is the number of time periods
   # all time periods must have the same number of rows
   for (k in 1:per) {
     for (i in (1+(k-1)*cases):(k*cases-1)) {
       for (j in (i+1):(k*cases)) {
         if (dfm[j,s]==dfm[i,s]) { # found a duplicate
           dfm[j,s] <- paste0(dfm[i,s],tag) # fix the duplicate
           dfm[j,s]
         }
       }
     }
   }
   return(dfm)
}
df2 <- fixcol(df,6,2,2,"_dup")
df3 <- pivot_wider(df2,id_cols=1,names_from=y,values_from=z)
```

<sup>Created on 2020-03-28 by the [reprex
package](https://reprex.tidyverse.org) 
(v0.3.0)</sup>______________________________________________