Most important criteria using a multicriteria method in R

Is there someone here who has experience working with the multicriteria method in R?

Sorry if the question is incoherent, but I would like to know how I can find out which of the criteria used is the most important using some multi-criteria method in R. Can you help me?

#database
df1<- matrix(c(9000,2000,8000,1200,800,1000,7000,1200,9000),
             nrow=3,
             ncol=3,
             byrow=TRUE)
colnames(df1) <- c("Consumption","Waste total","logistic costs")
row.names(df1) <- c("Propertie1","Propertie2","Propertie3")

           Consumption Waste total logistic costs
Propertie1        9000        2000           8000
Propertie2        1200         800           1000
Propertie3        7000        1200           9000

If important is considered to be the proportion of costs for some combination of property and expense types

#database
df1<- matrix(c(9000,2000,8000,1200,800,1000,7000,1200,9000),
             nrow=3,
             ncol=3,
             byrow=TRUE)
colnames(df1) <- c("Consumption","Waste total","logistic costs")
row.names(df1) <- c("Propertie1","Propertie2","Propertie3")

# proportion of total cost across all properties, types
prop.table(df1)
#>            Consumption Waste total logistic costs
#> Propertie1  0.22959184  0.05102041      0.2040816
#> Propertie2  0.03061224  0.02040816      0.0255102
#> Propertie3  0.17857143  0.03061224      0.2295918

rowSums(df1)
#> Propertie1 Propertie2 Propertie3 
#>      19000       3000      17200
rowSums(df1)/sum(df1)
#> Propertie1 Propertie2 Propertie3 
#> 0.48469388 0.07653061 0.43877551

# proportion of total cost of all properties by type
colSums(df1)
#>    Consumption    Waste total logistic costs 
#>          17200           4000          18000
colSums(df1) / sum(df1)
#>    Consumption    Waste total logistic costs 
#>      0.4387755      0.1020408      0.4591837

# rowwise (by property)
df1[1,1:3]
#>    Consumption    Waste total logistic costs 
#>           9000           2000           8000
df1[1,1:3]/ sum(df1[1,1:3])
#>    Consumption    Waste total logistic costs 
#>      0.4736842      0.1052632      0.4210526
df1[2,1:3]
#>    Consumption    Waste total logistic costs 
#>           1200            800           1000
df1[2,1:3]/ sum(df1[2,1:3])
#>    Consumption    Waste total logistic costs 
#>      0.4000000      0.2666667      0.3333333
df1[3,1:3]
#>    Consumption    Waste total logistic costs 
#>           7000           1200           9000
df1[3,1:3]/ sum(df1[3,1:3])
#>    Consumption    Waste total logistic costs 
#>     0.40697674     0.06976744     0.52325581

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