R/tidy_cashflows.R

In qmj: Quality Scores for the Russell 3000

#' Makes raw cash flow data usable and readable.
#'
#' Processes raw cash flow data from quantmod to return 
#' a tidied data frame. Raw cash flow data must be formatted 
#' in a list such that every element is a data frame or 
#' matrix containing quantmod data.
#' 
#' \code{tidy_cashflows} produces a data frame that is 'tidy' 
#' or more readily readable by a user and usable by other 
#' functions within this package.
#' 
#' @param x A list of raw cash flow data produced from quantmod
#' 
#' @return Returns a data set that's been 'tidied' up for use
#' by other functions in this package.
#' 
#' @seealso \code{\link{get_info}}
#' @seealso \code{\link{tidyinfo}}
#' @seealso \code{\link{tidy_balancesheets}}
#' @seealso \code{\link{tidy_incomestatements}}

tidy_cashflows <- function(x) {
  
  ## Calls tidy_helper to construct a list of data.frames and merges the list elements into one large data.frame
  # cashflows <- do.call(rbind, lapply(x, tidy_helper))
  cashflows <- dplyr::bind_rows(lapply(x, tidy_helper))
  
  ## Remove all rows that are solely NAs.
  cashflows <- cashflows[rowSums(!is.na(cashflows)) >= 1, ]
  rownames(cashflows) <- NULL
  
  ## These are the categories we expect from the raw data, with abbreviations for each of the variables found in the cash flows
  ## The wanted categories were based on previous outputs from Google Finance API which is discontinued now
  ## So we have to map the current output (from Yahoo Finance) to previous categories
  cashflows <- cashflows[, c('ticker', 'year', 'order', 'Depreciation.Amortization.Depletion', 'Depreciation.And.Amortization', 'Change.In.Working.Capital', 'Changes.In.Cash', 'Cash.Dividends.Paid')]
  names(cashflows) <- c("ticker", "year", "order", "DP.DPL", "AM", "CWC", "CX", "DIVC")
  cashflows
}