# clean data in R (not programmatically)
setwd('S:/Courses/stat-renaes/Stat404')
nonsales=read.csv("nonsales.csv",header=T)

fix(nonsales)

rm(nonsales) # because we fixed it and I want to "fix" it again

# clean data programmatically
nonsales=read.csv("nonsales.csv",header=T)
attach(nonsales)

# Need country variable to be US or AU (not us or au)
nonsales$Country=toupper(Country)



# Use package DataCombine (and yes there are other merging methods in this we will # look at)
install.packages('DataCombine')
library('DataCombine')

# FindReplace

# this command will replace values (categorical)
# the easiest way to use this is to create vectors of new names
# use from and to in the new names

ABData <- data.frame(a = c("London, UK", "Oxford, UK", "Berlin, DE", "Hamburg, DE", "Oslo, NO"), b = c(8, 0.1, 3, 2, 1))

Replaces <- data.frame(from = c("UK", "DE"), to = c("England", "Germany"))

install.package("DataCombine")
library(DataCombine)

ABNewDF <- FindReplace(data = ABData, Var = "a", replaceData = Replaces, from = "from", to = "to", exact = FALSE)

# conditional

# if( )
x=5
if(x<0){
	print('Positive number')
}

# if else

x=-5
if(x > 0){
   print("Non-negative number")
} else {
   print("Negative number")
}

# nested if else

x <- 0
if (x < 0) {
   print("Negative number")
} else if (x > 0) {
   print("Positive number")
} else
   print("Zero")

# ifelse( )

x=2
ifelse(x>0,print("Positive number"),print("Negative number"))