You might also need the assign() function which is sort of the opposite of get()
Michael
On Mon, Oct 24, 2011 at 12:15 PM, jim holtman <jholt...@gmail.com> wrote:
> Write a function that encapsulates the following three lines:
>
>
> city1997<- dataCleaning(read.csv2("C:\\city\\year1997.txt"))
> city1997<- wasteCalculations(city1997, year = 1997)
> if (city1997[1,1] == "Time") {city1997<- timeCalculations(city1997)}
>
> and then pass in the appropriate parameters.
>
> On Mon, Oct 24, 2011 at 12:09 PM, Wet Bell Diver <wetbelldi...@gmail.com>
> wrote:
>>
>> Thanks so much, this is very very helpful.
>>
>> I do have one remaining question here. I definitely see the value of making
>> a list of the datasets, an advise I will definitely follow. However, for
>> educational purposes, I would still like to know how to automate the
>> following without using a list:
>>
>> city1997<- dataCleaning(read.csv2("C:\\city\\year1997.txt"))
>> city1997.waste<- wasteCalculations(city1997, year = 1997)
>> if (city1997.waste[1,1] == "Time") {city1997.time<-
>> timeCalculations(city1997)}
>> city1998<- dataCleaning(read.csv2("C:\\city\\year1998.txt"))
>> city1998.waste<- wasteCalculations(city1998, year = 1998)
>> if (city1998.waste[1,1] == "Time") {city1998.time<-
>> timeCalculations(city1998)}
>> city1999<- dataCleaning(read.csv2("C:\\city\\year1999.txt"))
>> city1999.waste<- wasteCalculations(city1999, year = 1999)
>> if (city1999.waste[1,1] == "Time") {city1999.time<-
>> timeCalculations(city1999)}
>> save(city1997, city1998, city1999, city1997.waste, city1998.waste,
>> city1999.waste, city1997.time, city1998.time, city1999.time, file =
>> "cities.Rdata")
>>
>> so, how do I create objects with appropriate names and then have functions
>> applied to them. (this is only an example of the kinds of manipulations I
>> need to do, but if I can get the above to work, then I can figure out the
>> rest for myself).
>> Thanks for your help, can you solve this final piece of the puzzle as well?
>>
>> --Peter
>>
>>
>>
>> Op 23-10-2011 3:51, R. Michael Weylandt schreef:
>>>
>>> I had no idea mget() existed. How helpful!
>>>
>>> Thanks,
>>>
>>> MW
>>>
>>> On Sat, Oct 22, 2011 at 9:27 PM, Joshua Wiley<jwiley.ps...@gmail.com>
>>> wrote:
>>>>
>>>> Or simplify things down:
>>>>
>>>> cityList<- mget(paste("city", 1997:2011, sep = ''), envir = .GlobalEnv)
>>>>
>>>> mget returns a list, all in one step.
>>>>
>>>> Cheers,
>>>>
>>>> Josh
>>>>
>>>> On Sat, Oct 22, 2011 at 6:19 PM, R. Michael Weylandt
>>>> <michael.weyla...@gmail.com> wrote:
>>>>>
>>>>> A small clarification: the correct syntax would have been
>>>>>
>>>>> vector("list", length(n))
>>>>>
>>>>> Michael
>>>>>
>>>>> On Sat, Oct 22, 2011 at 4:29 PM, R. Michael Weylandt
>>>>> <michael.weyla...@gmail.com> <michael.weyla...@gmail.com> wrote:
>>>>>>
>>>>>> The more R way to do something like this is to put all your dataframes
>>>>>> into a list and then run
>>>>>>
>>>>>> lappy(cityList, dataCleaning) # for example
>>>>>>
>>>>>> To get them into a list in the first place try this
>>>>>>
>>>>>> n = 1997:2011
>>>>>> cityList<- vector(length(n), 'list')
>>>>>> for (i in n){
>>>>>> cityList[[i]]<- get(paste("city", i, sep="")
>>>>>> }
>>>>>>
>>>>>> Hope this helps,
>>>>>>
>>>>>> Michael
>>>>>>
>>>>>>
>>>>>> On Oct 22, 2011, at 3:13 PM, Wet Bell Diver<wetbelldi...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> R2.13.2, W7x64
>>>>>>>
>>>>>>> Dear list,
>>>>>>>
>>>>>>> Excuse my ignorance, but I have gone through the R help (?parse,
>>>>>>> ?eval, etc.) and still really don't know how to do the following.
>>>>>>> I have the general following structure that I would like to automate
>>>>>>> [edited to make it shorter]:
>>>>>>>
>>>>>>> city1997<- dataCleaning(read.csv2("C:\\city\\year1997.txt"))
>>>>>>> city1997<- wasteCalculations(city1997, year = 1997)
>>>>>>> if (city1997[1,1] == "Time") {city1997<- timeCalculations(city1997)}
>>>>>>> city1998<- dataCleaning(read.csv2("C:\\city\\year1998.txt"))
>>>>>>> city1998<- wasteCalculations(city1998, year = 1998)
>>>>>>> if (city1998[1,1] == "Time") {city1998<- timeCalculations(city1998)}
>>>>>>> city1999<- dataCleaning(read.csv2("C:\\city\\year1999.txt"))
>>>>>>> city1999<- wasteCalculations(city1999, year = 1999)
>>>>>>> if (city1999[1,1] == "Time") {city1999<- timeCalculations(city1999)}
>>>>>>>
>>>>>>> [....etc., all the way through....]
>>>>>>>
>>>>>>> city2011<- dataCleaning(read.csv2("C:\\city\\year2011.txt"))
>>>>>>> city2011<- wasteCalculations(city2011, year = 2011)
>>>>>>> if (city2011[1,1] == "Time") {city2011<- timeCalculations(city2011)}
>>>>>>>
>>>>>>> city.df<- data.frame(city1997$waste, city1998$waste, city1999$waste,
>>>>>>> ...,city2011$waste)
>>>>>>> save(city1997, city1998, city1999, ...., city2011, city.df, file =
>>>>>>> "city.Rdata")
>>>>>>>
>>>>>>> and then the same thing with: municipality1981 through
>>>>>>> municipality2011
>>>>>>> and then the same thing with: county1985 through county2011
>>>>>>> So, for both city, municipality, and county, across a (varying) range
>>>>>>> of years the functions "dataCleaning", "wasteCalculations", and
>>>>>>> "timeCalculations" are called and the final objects are pulled together
>>>>>>> in a
>>>>>>> dataframe and are then all saved together.
>>>>>>> I can get all of this done manually (generating LONG repetitive code),
>>>>>>> but I have A LOT of data that needs to be processed like this and that
>>>>>>> becomes tedious and very repetitious. Besides, it feels silly to do
>>>>>>> such a
>>>>>>> task manually when using the powerful R language. Unfortunately, I have
>>>>>>> no
>>>>>>> clue how to do this. I have been wrestling with "parse", "eval",
>>>>>>> "substitute" but I have to admit that I just don't seem to really
>>>>>>> understand
>>>>>>> how they work. Anyway, I can't get this to work, but have the feeling
>>>>>>> it can
>>>>>>> be done in a few lines. Who can help me with the code and the
>>>>>>> explanation of
>>>>>>> why that code works?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Peter Verbeet
>>>>>>>
>>>>>>> ______________________________________________
>>>>>>> R-help@r-project.org mailing list
>>>>>>> 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.
>>>>>
>>>>> ______________________________________________
>>>>> R-help@r-project.org mailing list
>>>>> 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.
>>>>>
>>>>
>>>>
>>>> --
>>>> Joshua Wiley
>>>> Ph.D. Student, Health Psychology
>>>> Programmer Analyst II, ATS Statistical Consulting Group
>>>> University of California, Los Angeles
>>>> https://joshuawiley.com/
>>>>
>>
>> ______________________________________________
>> R-help@r-project.org mailing list
>> 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.
>>
>
>
>
> --
> Jim Holtman
> Data Munger Guru
>
> What is the problem that you are trying to solve?
>
> ______________________________________________
> R-help@r-project.org mailing list
> 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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