[R] Conditional Weighted Average (ddply or any other function)
Punit Anand
anandpunit at gmail.com
Fri Mar 1 20:13:37 CET 2013
Hi John,
The sample size is huge involving 10,000 + firms. I have put a
representative sample using dput ( Name, ticker and country have been
changed so that firms cannot be identified due to proprietary data
set, also EPS is not required and removed from the dataset)
structure(list(NAME = structure(c(8L, 8L, 8L, 8L, 8L, 8L, 7L,
7L, 7L, 7L, 7L, 7L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L,
6L, 6L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("CCC", "CTAX",
"INN", "NOB", "SH", "SZ", "WASH", "WILLSON"), class = "factor"),
Ticker = structure(c(7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L,
8L, 8L, 8L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L,
6L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("CC13",
"CT56", "INN12", "NB12", "SH12", "SZ12", "W12", "W15"), class = "factor"),
Industry = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 5L, 5L,
5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Commercial &
Professional Serv",
"Energy", "Media", "Retail", "Transportation"), class = "factor"),
Sector = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Consumer
Discretionary",
"Energy", "Industrials"), class = "factor"), Country = structure(c(4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L), .Label = c("Brazil", "China", "India", "UK"), class =
"factor"),
FISCALYEAR = structure(c(3L, 2L, 1L, 4L, 5L, 6L, 3L, 2L,
1L, 4L, 5L, 6L, 3L, 2L, 1L, 4L, 5L, 6L, 3L, 3L, 2L, 1L, 4L,
5L, 6L, 3L, 2L, 1L, 4L, 5L, 6L, 3L, 2L, 1L, 4L, 5L, 6L, 3L,
2L, 1L, 4L, 5L, 6L, 3L, 2L, 1L, 4L, 5L, 6L), .Label = c("FY-1",
"FY-2", "FY-3", "FY0", "FY1", "FY2"), class = "factor"),
ROE = c(0.026, 0.0656, 0.1621, 0.1885, 0.1968, 0.2126, 0.0207,
0.0319, 0.0963, 0.0431, 0.066, 0.066, 0.0707, 0.0797, 0.0781,
0.078, 0.098, 0.126, 0.0352, 0.2625, 0.3714, 0.2929, 0.3133,
0.2509, 0.2398, 0.2779, 0.1109, 0.0509, 0.069, 0.1017, 0.1298,
0.5842, 0.3953, 0.4429, 0.3616, 0.26, 0.2, 0.4472, 0.2912,
0.21, 0.2849, 0.3553, 0.4347, 0.3289, 0.3846, 0.2643, 0.0458,
0.1265, 0.28), MKT = c(2919236084, 836858582, 2015182617,
3399344971, 4324821777, 4324821777, 7619453125, 3579844727,
4132238281, 3712239990, 2879757813, 2879757813, 1525237793,
700357605, 1814942993, 1858225342, 1242890503, 1242890503,
1879700000, 557093400, 224900300, 1634700000, 1443200000,
3582664735, 3582664735, 5830366211, 10660833984, 9024061523,
7628660645, 9154108398, 9154108398, 7064532227, 1804380005,
6331067871, 10445639648, 9153587891, 9153587891, 6231200000,
4.078e+09, 10107500000, 12460300000, 17800051556, 17800051556,
513478700, 260993500, 882575400, 1.151e+09, 855938413, 855938413
)), .Names = c("NAME", "Ticker", "Industry", "Sector", "Country",
"FISCALYEAR", "ROE", "MKT"), class = "data.frame", row.names = c(NA,
-49L))
Thanks,
Punit
> On Fri, Mar 1, 2013 at 12:51 PM, John Kane <jrkrideau at inbox.com> wrote:
> See below
>
>
>> -----Original Message-----
>> From: anandpunit at gmail.com
>> Sent: Fri, 1 Mar 2013 12:36:53 -0500
>> To: jrkrideau at inbox.com
>> Subject: Re: [R] Conditional Weighted Average (ddply or any other
>> function)
>>
>> Hi John,
>>
>> I was using symbols, Column ROE, EPS, MKTCAP are numeric, Name,
>> Ticker, Sector, Country, FISCALYEAR or Year are character strings.
>>
>> and column "Year" is referring to "FISCALYEAR"
>>
> Definitely a no-no in R-help. :) We really need some representative sample data to play with. See https://github.com/hadley/devtools/wiki/Reproducibility for some general pointers on how to compose a good question. The fact that you included the code you are using was excellent but without some data it is rather useless.
>
> The easiest way to supply data is to use the dput() function. Example with your file named "testfile":
> dput(testfile)
> Then copy the output and paste into your email. This is what I did with your data that I pasted into my email . I added the dat1 <- to it.
>
> For large data sets, you can just supply a representative sample. Usually, dput(head(testfile, 100)) will be sufficient.
>
> I hope this is of some help.
>
>
>>
>> On Fri, Mar 1, 2013 at 12:31 PM, John Kane <jrkrideau at inbox.com> wrote:
>>> 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
>>>
>>>> ddply (dataread , .(Sector, FISCALYEAR), summarise, > WROE=wavg(ROE,
>>>> MKTCAP)))
>>>
>>> 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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>>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>>> PLEASE do read the posting guide
>>>> http://www.R-project.org/posting-guide.html
>>>> and provide commented, minimal, self-contained, reproducible code.
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