I'm trying to order this dataframe by population and date, so I'm using the order() and rank() functions:
> df <- data.frame(idgeoville = c(5, 8, 4, 3, 4, 5, 8, 8),
                   date       = c(rep(1950, 4), rep(2000, 4)),
                   population = c(500, 450, 350, 350, 650, 500, 500, 450))
> df
   idgeoville date    population
1  5          1950     500
2  8          1950     450
3  4          1950     350
4  3          1950     350
5  4          2000     650
6  5          2000     500
7  8          2000     500
8  8          2000     450
With ties.method = "first" I have no problem, finally I'm producing this dataframe:
   idgeoville date    population  rank
1  5          1950     500        1
2  8          1950     450        2
3  4          1950     350        3
4  3          1950     350        4
5  4          2000     650        1
6  5          2000     500        2
7  8          2000     500        3
8  8          2000     450        4
But in fact, I want a dataframe with equal ranking for equal population rank, like this:
   idgeoville date    population  rank
1  5          1950     500        1
2  8          1950     450        2
3  4          1950     350        3
4  3          1950     350        3
5  4          2000     650        1
6  5          2000     500        2
7  8          2000     500        2
8  8          2000     450        3
How can I resolve this problem with R? With a custom ties.method() or another R tricks?
More simple way:
pop.rank <- as.numeric(factor(population))
I believe there is no option to do it with rank; here is a custom function that will do what you want, but it may be too slow if your data is huge:
Rank<-function(d) {
    j<-unique(rev(sort(d)));
    return(sapply(d,function(dd) which(dd==j)));
}
This answers a slightly different question, namely how to sort a data.frame object based on multiple columns. To do this, you could use the function sort_df in package reshape:
> library(reshape)
> sort_df(df,vars=c('date','population'))
  idgeoville date population
3          4 1950        350
4          3 1950        350
2          8 1950        450
1          5 1950        500
8          8 2000        450
6          5 2000        500
7          8 2000        500
5          4 2000        650
 
         
                                         
                                         
                                         
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