Un-nest a nested list in R

LinearizeNestedList.R

LinearizeNestedList <- function(NList, LinearizeDataFrames=FALSE,
                                NameSep="/", ForceNames=FALSE) {
    # LinearizeNestedList:
    #
    # https://sites.google.com/site/akhilsbehl/geekspace/
    #         articles/r/linearize_nested_lists_in_r
    #
    # Akhil S Bhel
    # 
    # Implements a recursive algorithm to linearize nested lists upto any
    # arbitrary level of nesting (limited by R's allowance for recursion-depth).
    # By linearization, it is meant to bring all list branches emanating from
    # any nth-nested trunk upto the top-level trunk s.t. the return value is a
    # simple non-nested list having all branches emanating from this top-level
    # branch.
    #
    # Since dataframes are essentially lists a boolean option is provided to
    # switch on/off the linearization of dataframes. This has been found
    # desirable in the author's experience.
    #
    # Also, one'd typically want to preserve names in the lists in a way as to
    # clearly denote the association of any list element to it's nth-level
    # history. As such we provide a clean and simple method of preserving names
    # information of list elements. The names at any level of nesting are
    # appended to the names of all preceding trunks using the `NameSep` option
    # string as the seperator. The default `/` has been chosen to mimic the unix
    # tradition of filesystem hierarchies. The default behavior works with
    # existing names at any n-th level trunk, if found; otherwise, coerces simple
    # numeric names corresponding to the position of a list element on the
    # nth-trunk. Note, however, that this naming pattern does not ensure unique
    # names for all elements in the resulting list. If the nested lists had
    # non-unique names in a trunk the same would be reflected in the final list.
    # Also, note that the function does not at all handle cases where `some`
    # names are missing and some are not.
    #
    # Clearly, preserving the n-level hierarchy of branches in the element names
    # may lead to names that are too long. Often, only the depth of a list
    # element may only be important. To deal with this possibility a boolean
    # option called `ForceNames` has been provided. ForceNames shall drop all
    # original names in the lists and coerce simple numeric names which simply
    # indicate the position of an element at the nth-level trunk as well as all
    # preceding trunk numbers.
    #
    # Returns:
    # LinearList: Named list.
    #
    # Sanity checks:
    #
    stopifnot(is.character(NameSep), length(NameSep) == 1)
    stopifnot(is.logical(LinearizeDataFrames), length(LinearizeDataFrames) == 1)
    stopifnot(is.logical(ForceNames), length(ForceNames) == 1)
    if (! is.list(NList)) return(NList)
    #
    # If no names on the top-level list coerce names. Recursion shall handle
    # naming at all levels.
    #
    if (is.null(names(NList)) | ForceNames == TRUE)
        names(NList) <- as.character(1:length(NList))
    #
    # If simply a dataframe deal promptly.
    #
    if (is.data.frame(NList) & LinearizeDataFrames == FALSE)
        return(NList)
    if (is.data.frame(NList) & LinearizeDataFrames == TRUE)
        return(as.list(NList))
    #
    # Book-keeping code to employ a while loop.
    #
    A <- 1
    B <- length(NList)
    #
    # We use a while loop to deal with the fact that the length of the nested
    # list grows dynamically in the process of linearization.
    #
    while (A <= B) {
        Element <- NList[[A]]
        EName <- names(NList)[A]
        if (is.list(Element)) {
            #
            # Before and After to keep track of the status of the top-level trunk
            # below and above the current element.
            #
            if (A == 1) {
                Before <- NULL
            } else {
                Before <- NList[1:(A - 1)]
            }
            if (A == B) {
                After <- NULL
            } else {
                After <- NList[(A + 1):B]
            }
            #
            # Treat dataframes specially.
            #
            if (is.data.frame(Element)) {
                if (LinearizeDataFrames == TRUE) {
                    #
                    # `Jump` takes care of how much the list shall grow in this step.
                    #
                    Jump <- length(Element)
                    NList[[A]] <- NULL
                    #
                    # Generate or coerce names as need be.
                    #
                    if (is.null(names(Element)) | ForceNames == TRUE)
                        names(Element) <- as.character(1:length(Element))
                    #
                    # Just throw back as list since dataframes have no nesting.
                    #
                    Element <- as.list(Element)
                    #
                    # Update names
                    #
                    names(Element) <- paste(EName, names(Element), sep=NameSep)
                    #
                    # Plug the branch back into the top-level trunk.
                    #
                    NList <- c(Before, Element, After)
                }
                Jump <- 1
            } else {
                NList[[A]] <- NULL
                #
                # Go recursive! :)
                #
                if (is.null(names(Element)) | ForceNames == TRUE)
                    names(Element) <- as.character(1:length(Element))
                Element <- LinearizeNestedList(Element, LinearizeDataFrames,
                                               NameSep, ForceNames)
                names(Element) <- paste(EName, names(Element), sep=NameSep)
                Jump <- length(Element)
                NList <- c(Before, Element, After)
            }
        } else {
            Jump <- 1
        }
        #
        # Update book-keeping variables.
        #
        A <- A + Jump
        B <- length(NList)
    }
    return(NList)
}

Can you please explain how to use this function to convert json file into cvs