library(reshape2)

read_mouse_liver_cancer_data = function(){

    dose = c(0.0, 0.3, 0.35, 0.45, 0.6, 0.75, 1.0, 1.5) ##* 1.e-4
    dose_header = sprintf( 'dose_%d', 1:length(dose) )

    amt_of_data = c(8, 9) ## even rows have 8 entries of valid data; odd lines have 9 entries of valid data
    even_rows = c()
    odd_rows = c()
    ## Read the data row by row (is there a better way?):
    ##
    fn = '../../Data/mouse-liver-cancer.dat'
    rl = readLines(fn, warn=FALSE)
    for( ln in 1:length(rl) ){
        aod = amt_of_data[(ln %% 2) + 1]
        parts = strsplit(trimws(rl[ln]), '\\s+')[[1]]
        if( ln %% 2 == 0 ){
            even_rows = rbind(even_rows, t(parts[1:aod]) )
        }else{
            odd_rows = rbind(odd_rows, t(parts[1:aod]) )
        }
    }

    NE = data.frame(even_rows) ## N(umber) E(xposed)
    colnames(NE) = dose_header

    ND = data.frame(odd_rows) ## N(umber) D(eveloped) cancer
    months_on_study = factor(ND[, 1], levels=ND[, 1], ordered=TRUE)
    ND = ND[, -1]
    colnames(ND) = dose_header

    ## Convert our date to numbers:
    NE = as.data.frame(lapply(NE, function(x){as.numeric(as.character(x))}))
    ND = as.data.frame(lapply(ND, function(x){as.numeric(as.character(x))}))

    NE$months_on_study = months_on_study
    ND$months_on_study = months_on_study

    ## Flatten our data to pass out:
    ##
    NE = melt(NE, id.vars=c('months_on_study'), variable.name='dose_amt', value.name='number_exposed')
    NE$dose_amt = rep(dose, each=length(months_on_study), times=1)

    ND = melt(ND, id.vars=c('months_on_study'), variable.name='dose_amt', value.name='number_developed')
    ND$dose_amt = rep(dose, each=length(months_on_study), times=1)

    ## Combine the two datasets:
    ##
    DF = NE
    DF$number_developed = ND$number_developed
    
    return(DF)

}