Most important criteria using a multicriteria method in R

[AntPedro]

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

[technocrat]

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