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>______________________________________________
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