It is not at all clear what you are doing. You state that the data set you are using is what I have called dat1 : see dput form below.
As far as I can see there is no numerical value in there.
##===========data set in dput form================#
dat1 <- structure(list(Name = c("N1", "N1", "N1", "N1", "N1", "N1", "N1",
"N2", "N2", "N2", "N2", "N2", "N2", "N2"), Ticker = c("T1", "T1",
"T1", "T1", "T1", "T1", "T1", "T2", "T2", "T2", "T2", "T2", "T2",
"T2"), Sector = c("S1", "S1", "S1", "S1", "S1", "S1", "S1", "S2",
"S2", "S2", "S2", "S2", "S2", "S2"), Industry = c("I1", "I1", "I1", "I1", "I1", "I1", "I1", "I2", "I2", "I2", "I2", "I2", "I2",
"I2"), Country = c("C1", "C1", "C1", "C1", "C1", "C1", "C1",
"C2", "C2", "C2", "C2", "C2", "C2", "C2"), Year = c("FY-4", "FY-3",
"FY-2", "FY-1", "FY0", "FY1", "FY2", "FY-4", "FY-3", "FY-2",
"FY-2", "FY0", "FY2", "FY2"), ROE = c("ROE11", "ROE12", "ROE13",
"ROE14", "ROE15", "ROE16", "ROE17", "ROE21", "ROE22", "ROE23",
"ROE24", "ROE25", "ROE26", "ROE27"), EPS = c("EPS11", "EPS12",
"EPS13", "EPS14", "EPS15", "EPS16", "EPS17", "EPS21", "EPS22",
"EPS23", "EPS24", "EPS25", "EPS26", "EPS27"), MKTCAP = c("MKT11",
"MKT12", "MKT13", "MKT14", "MKT15", "MKT16", "MKT17", "MKT21",
"MKT22", "MKT23", "MKT24", "MKT25", "MKT26", "MKT27")), .Names = c("Name",
"Ticker", "Sector", "Industry", "Country", "Year", "ROE", "EPS",
"MKTCAP"), class = "data.frame", row.names = c(NA, -14L))
## =================end of dataset==================#
There is no FISCALYEAR variable that you specifed below
I think we need a bit more information.
John Kane
Kingston ON Canada
-----Original Message-----
From: anandpunit at gmail.com
Sent: Fri, 1 Mar 2013 11:01:42 -0500
To: r-help at r-project.org
Subject: [R] Conditional Weighted Average (ddply or any other function)
Hello R community,
I am computing weighted average statistic by using ddply function:
My data set is:
N1 T1 S1 I1 C1 FY-4 ROE11 EPS11 MKT11
N1 T1 S1 I1 C1 FY-3 ROE12 EPS12 MKT12
N1 T1 S1 I1 C1 FY-2 ROE13 EPS13 MKT13
N1 T1 S1 I1 C1 FY-1 ROE14 EPS14 MKT14
N1 T1 S1 I1 C1 FY0 ROE15 EPS15 MKT15
N1 T1 S1 I1 C1 FY1 ROE16 EPS16 MKT16
N1 T1 S1 I1 C1 FY2 ROE17 EPS17 MKT17
N2 T2 S2 I2 C2 FY-4 ROE21 EPS21 MKT21
N2 T2 S2 I2 C2 FY-3 ROE22 EPS22 MKT22
N2 T2 S2 I2 C2 FY-2 ROE23 EPS23 MKT23
N2 T2 S2 I2 C2 FY-2 ROE24 EPS24 MKT24
N2 T2 S2 I2 C2 FY0 ROE25 EPS25 MKT25
N2 T2 S2 I2 C2 FY2 ROE26 EPS26 MKT26
N2 T2 S2 I2 C2 FY2 ROE27 EPS27 MKT27
with colnames:
(Name,Ticker,Sector,Industry,Country,Year,ROE,EPS,MKTCAP)
I want to compute
1) Weighted ROE based on Sector and Fiscal Year.
For firm N1 of Sector S1 and Fiscalyear FY-3 weight is
MKT1 / SUM(MKT, where Sector = S1, Fiscalyear FY-3)
2) Weighted ROE based on Country and Fiscal Year.
For firm N1 of Country C1 and Fiscalyear FY-3 weight is
MKT1 / SUM(MKT, where Country = C1, Fiscalyear FY-3)
3) Weighted ROE based on Country, Sector and Fiscal Year.
For firm N1 of Country C1, Sector S1 and Fiscalyear FY-3
weight is MKT1 / SUM(MKT, where Country = C1, Sector = S1, Fiscalyear
FY-3)
4) Weighted ROE based on Country, Industry and Fiscal Year.
For firm N1 of Country C1, Industry I1 and Fiscalyear FY-3
weight is MKT1 / SUM(MKT, where Country = C1, Industry = I1, Fiscalyear
FY-3)
I tried using ddply function:
ddply (dataread , .(Sector, FISCALYEAR), summarise, WROE=wavg(ROE,
MKTCAP)))
where wavg <- function(x, wt) x %*% wt/sum(wt)
but this doesn't give me the right answer.
I could try subseting the data into different sectors and compute the
weighted average which doesn't look like an elegant solution and would
defeat the purpose of ddply
I coudn't think of properly using melt and cast functions to solve
this issue. Any help will be highly appreciated.
Thanks and Regards,
Punit
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