Question

My program takes a data.frame and crunches the numbers. At one point, values from j-th column are multiplied by a predefined values that depends on the column name (species name, actually – it’s en ecological index). So far, I’ve been providing these values via a second data.frame by matching column names. What would be an efficient way of integrating fixed variable values within a function? I would like my program to be as portable as possible, without the need for a second data.frame file.

EDIT

This is the function. I’m trying to improve the second line (index <- read.table…) so that it would not depend on the outside source.

macroIndex <- function(obj, index) {
    index <- read.table("conv.csv", header=T, dec=",")
    a <- c()
    b <- names(obj)
    for (i in 2:length(obj)) {
        obj[i] <- obj[i] * index[which(index==b[i]), 2]
    }
    obj
}

Another solution I tried, while it may not seem pretty, it gets the job done. I use dput(index) and create a permanent object which I then insert into my function.

Answer 1

Well, you need to map your column names to another value, so you have to store it somehow. I would say that a named list would be a more appropriate data structure, although at the end of the day it doesn’t make a big difference.

Here’s some sample data:

df <- data.frame(a=1:5, b=2:6)
mapping <- list(a=3, b=4)

Here’s a simple example of using the list:

for(i in 1:ncol(df)) df[,i] <- df[,i] * mapping[[colnames(df)[i]]]

Regarding Tal’s recommendation for using a matrix: that is true so long as every value in your data frame is of the same type. If you have mixed types, then you need to stick with a data frame.