##Load Packages and Data
library(Deducer)
library(psych)
library(catspec)
library(gdata)
library(Hmisc)


library(xtable)
library(psych)
x <- read.csv(file = "/Users/erinschneider/Desktop/IDFDataFiles/China/CHINA_MEDS_DATA.csv", sep = ",")


splice3 <- function (x, y, w,
                    colnames.splice = NULL,
                    pc = TRUE) {
  z <- data.frame(matrix(rbind(x, y, w), nrow(x)))
  rownames(z) <- rownames(x)
  if (ncol(z) / 2 == length(colnames.splice)) colnames.splice <- rep(colnames.splice, 2)
  colnames(z) <- colnames.splice
  z
}

splice <- function (x, y,
                    colnames.splice=NULL,
                    pc=TRUE) {
  z<-data.frame(matrix(rbind(x,y),nrow(x)))
  rownames(z)<-rownames(x)
  if (pc) colnames.splice <- matrix(rbind(colnames(x), rep("%", ncol(x))), nrow=1)
  if (is.null(colnames.splice)) {
    colnames.splice <- matrix(rbind(paste(deparse(substitute(x)), ".", colnames(x), sep=""),
                                    paste(deparse(substitute(y)), ".", colnames(y), sep="")), nrow=1)
  }
  colnames(z) <- colnames.splice
  z
}




ci <- function(x){
    mean <- mean(x, na.rm=TRUE)
    s <- sd(x, na.rm=TRUE)
    n <- length(na.omit(x))
    error <- qnorm(0.975)*s/sqrt(n)
    left <- mean-error
    right <- mean+error
    conf <- c(left, right)
    conf
}
yesno <- function (x) {
    if (is.na(x)) {
        y <- NA
    } else {
        if (x == "Yes") {
            y <- 1
        } else {
            y <- 0
        }
        y
    }
}


y <- x[x$case == "Diabetes" | x$case == "No Diabetes",]
y$case <- factor(y$case, levels = c("Diabetes", "No Diabetes"))
dm <- y[y$case == "Diabetes",]
nd <- y[y$case == "No Diabetes",]


##Mean Meds Costs by Med Type
##Met
c <- y[y$cost.yr.use & y$case == "Diabetes" & y$public == "Yes",]
met1 <- c$cost.yr.use
aggregate.table(met1, c$met, c$location, FUN=mean, na.rm=TRUE)

d <- y[y$cost.yr.use & y$case == "Diabetes" & y$private == "Yes",]
met1 <- d$cost.yr.use
aggregate.table(met1, d$met, d$location, FUN=mean, na.rm=TRUE)

##SU
c <- y[y$cost.yr.use & y$case == "Diabetes" & y$public == "Yes",]
price <- c$cost.yr.use
aggregate.table(price, c$su, c$location, FUN=mean, na.rm=TRUE)

d <- y[y$cost.yr.use & y$case == "Diabetes" & y$private == "Yes",]
price <- d$cost.yr.use
aggregate.table(price, d$su, d$location, FUN=mean, na.rm=TRUE)

##Acar
c <- y[y$cost.yr.use & y$case == "Diabetes" & y$public == "Yes",]
price <- c$cost.yr.use
aggregate.table(price, c$acar, c$location, FUN=mean, na.rm=TRUE)

d <- y[y$cost.yr.use & y$case == "Diabetes" & y$private == "Yes",]
price <- d$cost.yr.use
aggregate.table(price, d$acar, d$location, FUN=mean, na.rm=TRUE)

##Insulin
c <- y[y$cost.yr.use & y$case == "Diabetes" & y$public == "Yes",]
price <- c$cost.yr.use
aggregate.table(price, c$ins, c$location, FUN=mean, na.rm=TRUE)

d <- y[y$cost.yr.use & y$case == "Diabetes" & y$private == "Yes",]
price <- d$cost.yr.use
aggregate.table(price, d$ins, d$location, FUN=mean, na.rm=TRUE)

##Gllow
c <- y[y$cost.yr.use & y$case == "Diabetes" & y$public == "Yes",]
price <- c$cost.yr.use
aggregate.table(price, c$other.gllow, c$location, FUN=mean, na.rm=TRUE)

d <- y[y$cost.yr.use & y$case == "Diabetes" & y$private == "Yes",]
price <- d$cost.yr.use
aggregate.table(price, d$other.gllow, d$location, FUN=mean, na.rm=TRUE)

##Lipid
a <- y[y$cost.yr.use & y$case == "No Diabetes" & y$public == "Yes",]
price <- a$cost.yr.use
aggregate.table(price, a$lipid, a$location, FUN=mean, na.rm=TRUE)

b <- y[y$cost.yr.use & y$case == "No Diabetes" & y$private == "Yes",]
price <- b$cost.yr.use
aggregate.table(price, b$lipid, b$location, FUN=mean, na.rm=TRUE)

c <- y[y$cost.yr.use & y$case == "Diabetes" & y$public == "Yes",]
price <- c$cost.yr.use
aggregate.table(price, c$lipid, c$location, FUN=mean, na.rm=TRUE)

d <- y[y$cost.yr.use & y$case == "Diabetes" & y$private == "Yes",]
price <- d$cost.yr.use
aggregate.table(price, d$lipid, d$location, FUN=mean, na.rm=TRUE)

##BP
a <- y[y$cost.yr.use & y$case == "No Diabetes" & y$public == "Yes",]
price <- a$cost.yr.use
aggregate.table(price, a$bp, a$location, FUN=mean, na.rm=TRUE)

b <- y[y$cost.yr.use & y$case == "No Diabetes" & y$private == "Yes",]
price <- b$cost.yr.use
aggregate.table(price, b$bp, b$location, FUN=mean, na.rm=TRUE)

c <- y[y$cost.yr.use & y$case == "Diabetes" & y$public == "Yes",]
price <- c$cost.yr.use
aggregate.table(price, c$bp, c$location, FUN=mean, na.rm=TRUE)

d <- y[y$cost.yr.use & y$case == "Diabetes" & y$private == "Yes",]
price <- d$cost.yr.use
aggregate.table(price, d$bp, d$location, FUN=mean, na.rm=TRUE)

##Anticoags
a <- y[y$cost.yr.use & y$case == "No Diabetes" & y$public == "Yes",]
price <- a$cost.yr.use
aggregate.table(price, a$anticoag, a$location, FUN=mean, na.rm=TRUE)

b <- y[y$cost.yr.use & y$case == "No Diabetes" & y$private == "Yes",]
price <- b$cost.yr.use
aggregate.table(price, b$anticoag, b$location, FUN=mean, na.rm=TRUE)

c <- y[y$cost.yr.use & y$case == "Diabetes" & y$public == "Yes",]
price <- c$cost.yr.use
aggregate.table(price, c$anticoag, c$location, FUN=mean, na.rm=TRUE)

d <- y[y$cost.yr.use & y$case == "Diabetes" & y$private == "Yes",]
price <- d$cost.yr.use
aggregate.table(price, d$anticoag, d$location, FUN=mean, na.rm=TRUE)

##NonDM
a <- y[y$cost.yr.use & y$case == "No Diabetes" & y$public == "Yes",]
price <- a$cost.yr.use
aggregate.table(price, a$nondm, a$location, FUN=mean, na.rm=TRUE)

b <- y[y$cost.yr.use & y$case == "No Diabetes" & y$private == "Yes",]
price <- b$cost.yr.use
aggregate.table(price, b$nondm, b$location, FUN=mean, na.rm=TRUE)

c <- y[y$cost.yr.use & y$case == "Diabetes" & y$public == "Yes",]
price <- c$cost.yr.use
aggregate.table(price, c$nondm, c$location, FUN=mean, na.rm=TRUE)

d <- y[y$cost.yr.use & y$case == "Diabetes" & y$private == "Yes",]
price <- d$cost.yr.use
aggregate.table(price, d$nondm, d$location, FUN=mean, na.rm=TRUE)