在比较 R 中的列和行后用值填充空矩阵

Question

Can I ask you how do I with the R language compare a table (attributes: starting machine name, target machine name and incremental id) and an empty matrix (where we have the target machine name in the columns and the starting machine name in the rows ). If there is a match between the start and finish then I want to compile with the incremental id value within the array.

enter image description here

enter image description here

I expect that inside the array is incremental id

Answer 1

It would be nice if you can provide a reproducible example ( https://reprex.tidyverse.org/ ). Based on your images, I assumed that your dataset look like this one:

machine_table <- data.frame(
  incremental_id = rep(1:2, times=5),
  start_machine = paste0("A", 1:5),
  target_machine = factor(c("A1", "A2"), levels = paste0("A", 1:5))
)

machine_table

To create your matrix you have multiple possibilities, here you will first filter the row with a matching start and target machine before pivoting and converting to a matrix.

First, convert the machine columns to factors as we already know them.

machine_table$start_machine <-  factor(machine_table$start_machine, levels = paste0("A", 1:5))
machine_table$target_machine <- factor(machine_table$target_machine, levels = paste0("A", 1:5))

Then select only the rows where the machines match and pivot the pivot and expand with unmatched machine names. You can use machine_table instead of match in pivot_wider to do the same operation on the whole dataset.

match <- machine_table[machine_table$start_machine == machine_table$target_machine, ]
match_table <- tidyr::pivot_wider(match, 
                   id_cols = "start_machine",  id_expand = T,
                   names_from = "target_machine", names_expand = T,
                   values_from =  "incremental_id")

After that you only need to convert this table to a matrix, and renames the rows

machine_matrix <- as.matrix(match_table[, -1])
row.names(machine_matrix) <- match_table$start_machine
machine_matrix

If you have multiple value for one pair of machine you can specify an aggregating function like min, max, sd, etc. as values_fn argument in pivot_wider .

EDIT: Based on you comment, here is an approach with a for loop, looping through all the rows and columns of the matrix and row of the dataframe.

for (i in seq(nrow(tabellaMatrice))) {
  for (j in seq(ncol(tabellaMatrice))) {
    for (r in seq(nrow(tab2))) {
      ifelse(tab2$Da[r]==rownames(tabellaMatrice)[i] & tab2$A[r]==colnames(tabellaMatrice)[j],
             tabellaMatrice[i,j] <- tab2$`Numero incrementale`[r],
             NA
      )
    }
  }
}

If there is multiple values for the same machine pair, the last value in the dataframe will erase the other ones.