inst/datafixes.R

In dtkaplan/DCF: Distributing materials for the DCF course

names(MedicareSpending) <- c("drgDefinition", "idProvider", 
                             "nameProvider", "addressProvider", 
                             "cityProvider", "stateProvider", 
                             "zipProvider", "referralRegion", 
                             "totalDischarges", "aveCharges", 
                             "avePayments", "drg")

MedicareProviders <- unique( MedicareSpending[,c(2,3,4,5,6,7,8)])
MedicareCharges <- MedicareSpending[,c(12,2,9,10,11)]

getAllCIAdata <- function() {
    Meta <- CIAdata()
    Codes <- Meta$Code
    CountryData <- CIAdata(Codes[1]) %>% select(country) # just the country names
    for (k in 1:nrow(Meta) ) {
        thisVar <- CIAdata(Codes[k])
        names(thisVar) <- c("country", Meta$Name[k]) 
        CountryData <- merge(CountryData, thisVar, all=TRUE)
    }
    return(CountryData)
}

## Country Groups

### GGG: Global Governance Group

#Source: http://www.mfa.gov.sg/content/mfa/overseasmission/newyork/nyemb_statements/global_governance_group/2012/201209/press_20122809.html

GGG <- data.frame( country=c("Bahamas, The", "Bahrain", "Barbados", "Botswana", "Brunei", "Chile", "Costa Rica", "Finland", 
         "Guatemala", "Jamaica", "Kuwait", "Liechtenstein", "Luxembourg", "Malaysia", "Monaco", "Montenegro", 
         "New Zealand", "Panama", "Peru", "Philippines", "Qatar", "Rwanda", "San Marino", "Senegal", 
         "Singapore", "Slovenia", "Switzerland", "United Arab Emirates", "Uruguay","Vietnam"),GGG="yes" )

### G20

#Source: https://www.g20.org/about_g20/g20_members

G20 <- data.frame(country=c("Argentina", "Australia", "Brazil", "Canada", "China", "France", "Germany", "India", "Indonesia", 
"Italy", "Japan", "Korea, South", "Mexico", "Russia", "Saudi Arabia", "South Africa", 
"Turkey", "United Kingdom", "United States", "European Union"),G20="yes")

### G8

#Source: http://en.wikipedia.org/wiki/G8

G8 <- data.frame(country=c("Canada", "France", "Germany", "Italy", "Japan", "Russia", "United Kingdom", "United States"),G8="yes")

## put them in one data frame with a variable for each Group.

#a <- merge( select(CountryData,country),G8, all=TRUE)
a <- merge( G8, G20, all=TRUE )
a <- merge( a, GGG, all=TRUE)
a <- mutate(a, 
            G20=ifelse(is.na(G20), FALSE, TRUE),
            G8=ifelse(is.na(G8), FALSE, TRUE),
            GGG=ifelse(is.na(GGG), FALSE, TRUE))
CountryGroups <- a # Then save in CountryGroups.rda

## NCI 60

newNames <- c("cellLine",        "tissue",          "age",             "sex",             "prior.treatment",
"epithelial" ,     "histology",       "source"  ,        "ploidy",          "p53",            
"mdr",             "doublingtime")
names(nci60cellLine) <- newNames

## Put the names to have a lower case first level

lowerFirst <- function( S ) {
  substr(S, 0,1) <- tolower(substr(S,0,1))
  return(S)
}
}


# for converting factors to character strings in an entire data frame

convert_factor_to_character <- function(data) {
  kinds <- lapply(data, class)
  for (k in 1:length(kinds)) {
    if(kinds[k] == "factor") data[k] <- as.character(data[[k]])
  }
  return(data)
}