IronistM

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Description: A set of R functions to implement the Independent RFM scoring and the RFM scoring with input breaks.
Author: Jack Han     http://www.DataApple.net   email: jackhan2008 # qq.com
Version: 1.3  
Date:   23 Dec 2013 	
Usage:  Read the article "RFM Customer Analysis with R Language" http://www.dataapple.net/?p=84
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IronistM

# weeknum
eval wnum=strftime(_time, "%V")
# DayOfWeek
eval DayOfWeekC=strftime(_time, "%a") 
# hour
eval hour=strftime(_time,"%H:00")
# date in format %y-%m-%d, eg. 13-12-17
eval  Date=strftime(_time, "%y-%m-%d")

IronistM

Function NumLeft(NumStrings As Variant, Optional Delim As String = " ") As Variant
Dim i As Long, numrows As Long, OutA() As Variant, j As Long, NumDig As Long, Numstring As String, StringLen As Long

    If TypeName(NumStrings) = "Range" Then NumStrings = NumStrings.Value2
    If TypeName(NumStrings) = "String" Then
        numrows = 1
    Else

        numrows = UBound(NumStrings)
    End If

IronistM

# Function to clean names
     standardize_name <- function(string) {
          capped <- grep("^[^A-Z]*$", string, perl = TRUE)
          substr(string[capped], 1, 1) <- toupper(substr(string[capped], 1, 1))
          # string <- gsub('_',' ',string, fixed=TRUE)
          return(string)
     }  

IronistM

# There are several domain name servers that allow 
# for bulk searching of domain names.
# http://www.godaddy.com/bulk-domain-search.aspx
# http://www.namestation.com/bulk-domain-search
# However, they do not provide any wildcard support 
# and instead expect users to supply their own list.
# Which is not a problem if you are an R coder.
 
# This single command will copy to the clipboard a list 
# of mysite1 mysite2 mysite3....mysite500. You can then 

IronistM

# Load bare essentials
library("ggplot2")
library("plyr")
library("reshape2")
library("timeSeries")
library("TTR")
library("forecast")

# Things you might want to change

IronistM

# Create explicit data frames and TS objects given a list
market.list<- c("GR","CY","RU","RO","PL","UA","ROW")
for (i in market.list) {
  tmp <- subset(data,Market==i)
  tmp<-ts(tmp) # It's a timeseries afterall...
  # Let's save the data somewhere so we don't get back all the time
  filename <- paste("data_", i, sep="")
  assign(filename, tmp)
  save(filename, file=paste(filename, ".rda",sep=""))
}

IronistM

dic <- data.frame(old = c("cat", "dog", "coffee", "tea", "tee"), new = c("kissa", "koira", "kahvi", "tee", "tii")) 
sapply( unique(as.character(dic$old)) , function(i) { test.words[as.character(test.words) == i] <<- as.character(dic$new[as.character(dic$old) == i]) }) 
test.words

IronistM

require(fpp)
# Please make sure at least v3.10 of the forecast is loaded
# before running these examples.

# Three examples
beer <- aggregate(ausbeer)
plot(beer)
plot(a10)
plot(taylor)

IronistM

!TCL=4, 
!TITLE=Qliktech Set Analysis
!SORT=N
!CHARSET=ANSI

!TEXT=Month To Date Last Year
=sum({$<CAL_YEAR={$(=max(CAL_YEAR)-1)},CAL_DATE = {"<=$(=addmonths(max(CAL_DATE),-12))"},CAL_MONTH={"=$(=max(CAL_ MONTH))"}>} SALES)) 


!