Sorry David,
In my attempt to simplify example and just include the code I felt was
necessary I left out the loading of ggplot2, which then imports reshape2,
and which was actually used in the code I provided. Sorry to the mistake
and my misunderstanding of where the reshape function was coming from.
Should have checked that more carefully.
Thanks,
Nate
On Thu, Dec 13, 2012 at 9:48 AM, David Winsemius <dwinsem...@comcast.net>wrote:
>
> On Dec 13, 2012, at 9:16 AM, Nathan Miller wrote:
>
> Hi all,
>>
>> I have played a bit with the "reshape" package and function along with
>> "melt" and "cast", but I feel I still don't have a good handle on how to
>> use them efficiently. Below I have included a application of "reshape"
>> that
>> is rather clunky and I'm hoping someone can offer advice on how to use
>> reshape (or melt/cast) more efficiently.
>>
>>
> You do realize that the 'reshape' function is _not_ in the reshape
> package, right? And also that the reshape package has been superseded by
> the reshape2 package?
>
> --
> David.
>
>
>> #For this example I am using climate change data available on-line
>>
>> file <- ("
>> http://processtrends.com/**Files/RClimate_consol_temp_**anom_latest.csv<http://processtrends.com/Files/RClimate_consol_temp_anom_latest.csv>
>> ")
>> clim.data <- read.csv(file, header=TRUE)
>>
>> library(lubridate)
>> library(reshape)
>>
>> #I've been playing with the lubridate package a bit to work with dates,
>> but
>> as the climate dataset only uses year and month I have
>> #added a "day" to each entry in the "yr_mn" column and then used "dym"
>> from
>> lubridate to generate the POSIXlt formatted dates in
>> #a new column clim.data$date
>>
>> clim.data$yr_mn<-paste("01", clim.data$yr_mn, sep="")
>> clim.data$date<-dym(clim.data$**yr_mn)
>>
>> #Now to the reshape. The dataframe is in a wide format. The columns GISS,
>> HAD, NOAA, RSS, and UAH are all different sources
>> #from which the global temperature anomaly has been calculated since 1880
>> (actually only 1978 for RSS and UAH). What I would like to
>> #do is plot the temperature anomaly vs date and use ggplot to facet by the
>> different data source (GISS, HAD, etc.). Thus I need the
>> #data in long format with a date column, a temperature anomaly column, and
>> a data source column. The code below works, but its
>> #really very clunky and I'm sure I am not using these tools as efficiently
>> as I can.
>>
>> #The varying=list(3:7) specifies the columns in the dataframe that
>> corresponded to the sources (GISS, etc.), though then in the resulting
>> #reshaped dataframe the sources are numbered 1-5, so I have to reassigned
>> their names. In addition, the original dataframe has
>> #additional data columns I do not want and so after reshaping I create
>> another! dataframe with just the columns I need, and
>> #then I have to rename them so that I can keep track of what everything
>> is.
>> Whew! Not the most elegant of code.
>>
>> d<-reshape(clim.data, varying=list(3:7),idvar="date"**,
>> v.names="anomaly",direction="**long")
>>
>> d$time<-ifelse(d$time==1,"**GISS",d$time)
>> d$time<-ifelse(d$time==2,"HAD"**,d$time)
>> d$time<-ifelse(d$time==3,"**NOAA",d$time)
>> d$time<-ifelse(d$time==4,"RSS"**,d$time)
>> d$time<-ifelse(d$time==5,"UAH"**,d$time)
>>
>> new.data<-data.frame(d$date,d$**time,d$anomaly)
>> names(new.data)<-c("date","**source","anomaly")
>>
>> I realize this is a mess, though it works. I think with just some help on
>> how better to work this example I'll probably get over the learning hump
>> and actually figure out how to use these data manipulation functions more
>> cleanly.
>>
>> Any advice or assistance would be appreciated.
>> Thanks,
>> Nate
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________**________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/**listinfo/r-help<https://stat.ethz.ch/mailman/listinfo/r-help>
>> PLEASE do read the posting guide http://www.R-project.org/**
>> posting-guide.html <http://www.R-project.org/posting-guide.html>
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
> David Winsemius, MD
> Alameda, CA, USA
>
>
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