R/replacer.R

In trinker/qdap2: Bridging the gap between qualitative data and quantitative analysis

#' Replace Cells in a Matrix or Data Frame
#' 
#' Replace elements of a dataframe, matrix or vector with least restrictive 
#' class.
#' 
#' @param dat Data; either a dataframe, matrix or vector.
#' @param replace Element to replace.
#' @param with Replacement element.
#' @return Returns a dataframe, matrix or vector with the element replaced. 
#' @keywords replace
#' @export
#' @examples
#' replacer(mtcars[1:10, ], 0, "REP")
#' replacer(mtcars[1:10, ], 4, NA)
#' replacer(c("a", "b"), "a", "foo")
#' #replace missing values (NA)
#' dat <- data.frame(matrix(sample(c(1:3, NA), 25, TRUE), ncol=5))
#' replacer(dat, NA, "FOO")
replacer <- 
function(dat, replace=0, with="-"){ 
  h <- is.vector(dat)
  i <- is.matrix(dat)
  j <- is.data.frame(dat)
  if (is.numeric(replace)){
    NAMES <- colnames(dat)
    if (h) {
      dat <- data.frame(dat)
    }
    not.num <- Negate(is.numeric)
    dat1 <- dat[, sapply(dat, not.num), drop = FALSE]
    inds <- which(sapply(dat, is.numeric))
    subdf <- function(df, ii) {
      do.call("data.frame", c(as.list(df)[ii, drop=FALSE], check.names=FALSE))
    }
    dat2 <- subdf(dat, inds)
  } else {
    dat2 <- dat
  }
  y <- as.matrix(dat2)
  if (is.na(replace)) {
    y[is.na(y)] <- with
  } else { 
    y[y==replace] <- with
  }
  if (is.numeric(replace)){    
    y <- data.frame(dat1, y, check.names = FALSE)
    m <- NAMES[!NAMES%in%names(y)]
    if(length(m) == 1) names(y)[1] <- m
    y <- subdf(y, NAMES)
  }
  if(h) y <- as.vector(y)
  if(i) y <- as.matrix(y)
  if(j) y <- as.data.frame(y, check.names = FALSE)
  return(y)
}