[R] cycling through a long list of files and names

[jim holtman]

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 <wetbelldiver at 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.psych at 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.weylandt at 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.weylandt at gmail.com>  <michael.weylandt at 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<wetbelldiver at 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 at 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 at 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 at 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

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