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On Tue, Sep 23, 2014 at 2:37 AM, Ilya Kipnis <[email protected]> wrote: > Jason, > > This isn't a finance problem. This is the wrong list to post on. Does your > class have a discussion forum? > > -Ilya > > On Mon, Sep 22, 2014 at 8:36 PM, Jason Eyerly <[email protected]> > wrote: > >> Hello Folks, >> I’m doing a study for a coursera Data Science class, and I am >> trying to determine if American’s financial satisfaction has any >> correlation to the percentage return of the S&P500 in the year prior. I >> have to calculate the proportion of "Satisfied" and "More Or Less" compared >> to the total number of observations for year "X", starting at 1989. After >> computing that for each year, I need to place them in a column at the end >> of the dataset similar to what we see with "PercentChange”. However, the >> years only go from 1989 - 2012. Calculating it for each observation seems >> tedious and inefficient. The end result is a chart where the X-Axis is each >> different percent change, and the Y-Axis is the proportion that are >> satisfied. What's the most efficient way to do this? Sorry for posting all >> of my code, but I don’t know what’s important and what isn’t. I realize I >> probably didn’t code everything in the most efficient way possible. >> >> require(Quandl) >> require(lubridate) >> require(zoo) >> require(xts) >> >> myGSS <- load(url("http://bit.ly/dasi_gss_data";)) >> >> year <- gss$year >> finSat <- gss$satfin >> >> relativeTable <- data.frame(year, finSat) >> relativeTable <- subset(relativeTable, year > "1988" & !is.na(finSat)) >> >> >> spReturns <- Quandl("SANDP/ANNRETS", trim_start="1970-01-11", >> trim_end="2012-12-31", authcode="nwy3a_Gmd7TSS9fVirxT", >> collapse="annual") >> >> percentChange <- spReturns$"Total Return Change" >> >> spReturns$"Year Ending" <- format((spReturns$"Year Ending"), "%Y") >> spReturns$"Year Ending" <- as.numeric(spReturns$"Year Ending") >> spReturns$"Year Ending" <- spReturns[,1] + 1 #the following year >> >> combined <- merge(relativeTable, spReturns, by.x = "year", by.y = "Year >> Ending") >> names(combined)[6] <- "percentChange" >> >> finalResults <- data.frame(combined$year, combined$finSat, >> combined$percentChange) >> names(finalResults)[1] <- "Year" >> names(finalResults)[2] <- "FinancialSatisfaction" >> names(finalResults)[3] <- "PercentChange" >> >> finalResults$PercentChange <- finalResults$PercentChange * 100 >> >> Regards, >> Jason E. >> [[alternative HTML version deleted]] >> >> >> _______________________________________________ >> [email protected] mailing list >> https://stat.ethz.ch/mailman/listinfo/r-sig-finance >> -- Subscriber-posting only. If you want to post, subscribe first. >> -- Also note that this is not the r-help list where general R questions >> should go. >> > > [[alternative HTML version deleted]] > > _______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-sig-finance > -- Subscriber-posting only. If you want to post, subscribe first. > -- Also note that this is not the r-help list where general R questions > should go. -- google.com/+arnaudgabourygabx _______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance -- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
