# R macros for Math 325, originally written spring 2005.

source("http://www.math.jmu.edu/~garrenst/math300.dir/Rmacros")

# Math 324 and 325, modified 6/14/05
scan2 <- function(file.name, matrix.format=FALSE, character.rm=FALSE,
                  nrow=NULL, ncol=NULL, disk.drive=NULL, course.num=325)  {
  # Scans in a matrix or vector.
  # `file.name' is the name of the data set to be scanned.
  #   If `file.name' is the complete path, then `disk.drive' is ignored.
  #   Path is assumed to be complete if `file.name' does not begin with 
  #   `example', `exercise', `problem' or `table'.
  # If `matrix.format' is TRUE, then data are attempted to be saved as a rectangular matrix.
  # If `matrix.format' is FALSE, then data are saved as a vector.
  # If `character.rm' is TRUE, then remove all character (i.e., non-numeric) data.
  # `nrow' is the desired number of rows for the matrix in the output. 
  #   If `nrow' is NULL, then `nrow' is determined by the function.
  # `ncol' is the desired number of columns for the matrix in the output. 
  #   If `ncol' is NULL, then `ncol' is determined by the function.
  # `disk.drive' is the disk drive where the data are located, is NULL for web-based data,
  #    and is "linux" for a disk drive on a linux-based computer.
  # Some choices for `disk.drive' are NULL, "linux", "a", "b", "c", "d", "e", ....
  # `course.num' is the course number and may be numeric or character.
  # Missing values, denoted by `.', are assumed to be numeric.
  # If the first row of data contains a missing value, then `nrow' or `ncol' should be nonNULL.
  course.num <- as.numeric(course.num)
  if (is.null(disk.drive))  {
    full.name <- paste("http://www.math.jmu.edu/~garrenst/math", course.num,
                       ".dir/datasets/", file.name, sep="")  }
  else {
    if (disk.drive=="linux" & course.num==325) {
      full.name <- paste("/mnt/cdrom/SAS/DATASETS.SAS/", file.name, sep="")  }
    if (disk.drive!="linux" & course.num==325) {
      full.name <- paste(disk.drive, ":/SAS/DATASETS.SAS/", file.name, sep="") }   }
  data <- scan(full.name, what=character(), quiet=TRUE, comment.char="#")
  a.character <- FALSE ;    options(warn = -1)
  if (course.num==324) {
    for (i in 1:length(data))  {
      if ( is.na(as.numeric(data[i])) ) {a.character <- TRUE}   }    }
  if (course.num==325) {
    for (i in 1:length(data))  {
      if ( is.na(as.numeric(data[i])) & data[i]!="." ) {a.character <- TRUE}
      if ( data[i]=="." )   data[i] <- NA   }   }   # Missing data.
  options(warn = 0)
  if (a.character & character.rm) {
    options(warn = -1) ;    data1 <- NULL
    for (i in 1:length(data))  {
      if ( !is.na(as.numeric(data[i])) || is.na(data[i]) )   data1 <- c(data1, data[i])   }
    options(warn = 0) ;     data <- data1        }
  if (!a.character || character.rm)    data <- as.numeric(data)
  if (matrix.format)    {
    if (!is.null(nrow))   data <- matrix( data, nrow=nrow, byrow=TRUE )
    if (!is.null(ncol))   data <- matrix( data, ncol=ncol, byrow=TRUE )
    if (is.null(nrow) & is.null(ncol)) {
      ncol <- 0 ;   line.num <- 0
      while (ncol==0 & line.num<=1000)  {
        line.num <- line.num+1
        first.line <- scan(full.name, what=character(), nlines=line.num,
                           quiet=TRUE, comment.char="#")
        if (!a.character || !character.rm) {ncol <- length(first.line)}
        else {
          options(warn = -1)
          for (i in 1:length(first.line))  {
            if ( !is.na(as.numeric(first.line[i])) || is.na(first.line[i]) ) {ncol <- ncol+1}   }
          options(warn = 0)        }         }
      if (ncol!=0)  data <- matrix( data, ncol=ncol, byrow=TRUE )    }  }   
  return(data)
} # Garren, 2005


# Math 324 and 325
read.table2 <- function(file.name, course.num=325, na.strings=".", ...)  {
  # Reads in a table.
  # `file.name' is the name of the data set to be scanned.
  # `course.num' is the course number and may be numeric or character.
  # `na.strings' is a vector of strings which are to be interpreted as `NA'
  #       values.  Blank fields are also considered to be missing values.
  # `...' are the optional arguments used in `read.table'.
  full.name <- paste("http://www.math.jmu.edu/~garrenst/math", as.numeric(course.num),
                     ".dir/datasets/", file.name, sep="")
  return(read.table(full.name, na.strings=na.strings, ...))
} # Garren, 2005

# stratified.survey <- NULL;   
stratified.survey <- NULL;   cluster.survey <- NULL;   ratio.survey <- NULL