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)
}
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