I am trying to create a matrix that will demonstrate a industry production projected in the next x years.

First I have created a for loop to create the columns names with: (10 years for example)

column.name <- vector()
for (i in 1:10) {
    object <- paste('ano.',i)
    column.name[i] <- object
}

then I saved my products in a vector

products <- c('a','b','c',)

In this case I have a machine that will be used for all three products. So, I have a participacion vector, indicating how much of each product will be produced considering the machine capacity (4800).

participation<- c(0.60,0.20,0.20)
amount <- 4800

finaly i tried to multiplicate 'participation' by amout with a for loop

prod <- matrix(length(products):length(column.name))
for (j in 1:length(column.name)) {
  for (i in 1:length(product)) {
    dado <- participation[i]*amount
    prod[i,j] <- dado
  }
  return(prod)
}

theoretically I would get a matrix with production values for each year by product, which would be used to calculate the income. However, all I got was a 1x1 matrix with null value.

Could you help me?

CodePudding user response：

You were close, here is a working version of your example:

column.name <- vector()
for (i in 1:10) {
  object <- paste('ano.',i)
  column.name[i] <- object
}

products <- c('a','b','c')

participation<- c(0.60,0.20,0.20)
amount <- 4800

prod <- matrix(data = NA, nrow = length(products), ncol = length(column.name))

for (j in 1:length(column.name)) {
  for (i in 1:length(products)) {
    dado <- participation[i]*amount
    prod[i,j] <- dado
  }
}

A few things to consider:

• matrix() creates a new matrix with parameters: data, nrow, ncol (in that order). You should either specify each parameter in order or explicitly name parameters (ie. nrow=) otherwise your first parameter gets interpreted as the data parameter.
• Careful around typos! You had length(product) rather than length(products), you had an extra comma in products, etc.
• A deeper question, outside the scope of your specific ask, is whether you should be using matrices versus data.frames for your analysis.

CodePudding user response：

Are you looking for something like this?

column.name <- paste0('ano.', 1:10)
products <- c('a','b','c')
participation<- c(0.60,0.20,0.20)
amount <- 4800

mat <- matrix(NA, 
              nrow = 3, 
              ncol = 10,
              dimnames = list(product = products,
                              ano = column.name))


mat[] <- participation * amount

mat
#>        ano
#> product ano.1 ano.2 ano.3 ano.4 ano.5 ano.6 ano.7 ano.8 ano.9 ano.10
#>       a  2880  2880  2880  2880  2880  2880  2880  2880  2880   2880
#>       b   960   960   960   960   960   960   960   960   960    960
#>       c   960   960   960   960   960   960   960   960   960    960

^{Created on 2021-10-22 by the reprex package (v2.0.0)}

Another option is to use the outer function, and name the rows and columns of the matrix after it is produced

participation<- c(0.60,0.20,0.20)
amount <- rep(4800, 10)

mat <- outer(participation, amount)

colnames(mat) <- paste0('ano.', 1:10)
row.names(mat) <- c('a','b','c')

mat
#>   ano.1 ano.2 ano.3 ano.4 ano.5 ano.6 ano.7 ano.8 ano.9 ano.10
#> a  2880  2880  2880  2880  2880  2880  2880  2880  2880   2880
#> b   960   960   960   960   960   960   960   960   960    960
#> c   960   960   960   960   960   960   960   960   960    960

^{Created on 2021-10-22 by the reprex package (v2.0.0)}