Data cleaning of dollar values and percentage in R

Question

I\'ve been searching for a number of packages in R to help me in converting dollar values to nice numerical values. I don\'t seem to be able to find one (in plyr package for

Answer 1

A solution that uses parse and eval:

ToNumber <- function(X)
{
  A <- gsub("%","*1e-2",gsub("K","*1e+3",gsub("M","*1e+6",gsub("\\$|,","",as.character(X)),fixed=TRUE),fixed=TRUE),fixed=TRUE)
  B <- try(sapply(A,function(a){eval(parse(text=a))}),silent=TRUE)
  if (is.numeric(B)) return (as.numeric(B)) else return(X)
}

#----------------------------------------------------------------------
# Example:
X <-
  read.table( header=TRUE,
              text = 
   'Category LaunchedProjects TotalDollars SuccessfulDollars UnsuccessfulDollars LiveDollars  LiveProjects SuccessRate
        Food            3,069    "$16.79 M"         "$13.18 M"            "$2.78 M"  "$822.64 K" 189      39.27%
     Theater            4,155    "$13.45 M"         "$12.01 M"            "$1.22 M"  "$217.86 K" 111      64.09%
      Comics            2,242    "$12.88 M"         "$11.07 M"          "$941.31 K"  "$862.18 K" 134      46.11%
     Fashion            2,799     "$9.62 M"          "$7.59 M"            "$1.44 M"  "$585.98 K" 204      27.24%
 Photography            2,794     "$6.76 M"          "$5.48 M"            "$1.06 M"  "$220.75 K"  83      36.81%
       Dance            1,185     "$3.43 M"          "$3.13 M"          "$225.82 K"    "$71,322"  40      70.22%' )

numX <- as.data.frame(lapply(as.list(X),ToNumber))

options(width=1000)
print(numX,row.names=FALSE)

#    Category LaunchedProjects TotalDollars SuccessfulDollars UnsuccessfulDollars LiveDollars LiveProjects SuccessRate
#        Food             3069     16790000          13180000             2780000      822640          189      0.3927
#     Theater             4155     13450000          12010000             1220000      217860          111      0.6409
#      Comics             2242     12880000          11070000              941310      862180          134      0.4611
#     Fashion             2799      9620000           7590000             1440000      585980          204      0.2724
# Photography             2794      6760000           5480000             1060000      220750           83      0.3681
#       Dance             1185      3430000           3130000              225820       71322           40      0.7022