End goal is to take a dataframe and create a new column based on multiplication and addition of prior rows, i.e. if my multipliers are 0.1, 0.2, and 0.3, my addition is z [lag(z) * 0.1] ,then I want to take column Z and transform it 3 times as such (skipping the first row):

z <- 1:4*10
df <- data.frame(z)

I have been able to get the correct values by manually feeding in the rate and overwriting the existing column:

for (i in 1:nrow(df)) {
    if (i ==1)
      df[i,1] <- df[i,1]
    else
      df[i,1] <- df[i,1]   (df[i-1,1] * 0.1) 
  }

Separately, I can also create column placeholders for the new values:

for (i in seq(0.1, 0.3, by = 0.1)) {
  cola <- paste('col', i, sep = "_")
  df[[cola]] <- 0
}

However, I cannot seem to combine these loops and get the outcome in the above sample table. I have tried this:

for (i in 1:nrow(df2)) {
  for (j in seq(0.1, 0.3, by = 0.1)) {
    cola <- paste('col', j, sep = "_")
    df[[cola]] <- 0
    if (i ==1)
      df[[cola]] <- df[i,1]
    else
      df[[cola]] <- df[i,1]   (df[i-1,1] * j) 
  }
}

But it fills all the new columns with the same values for the whole column

Appreciate any suggestions. I'm not married to for loops if anyone has an alternative suggestion.

CodePudding user response：

Like this maybe?

Z <- 1:4*10
y <- seq(0.1, 0.3, by = 0.1)
df <- data.frame(Z)

for (i in 1:(length(Z)-1) 1){
  for (j in seq_along(y)){
    df[1,paste0('Z_', y[j])] = Z[1]
    df[i, paste0('Z_', y[j])] = Z[i] (df[i-1, paste0('Z_', y[j])]*y[j])
  }
}
df
#>    Z Z_0.1 Z_0.2 Z_0.3
#> 1 10 10.00 10.00 10.00
#> 2 20 21.00 22.00 23.00
#> 3 30 32.10 34.40 36.90
#> 4 40 43.21 46.88 51.07

^{Created on 2022-09-09 by the reprex package (v2.0.1)}