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

Tue Oct 25 05:58:27 CEST 2011

Thanks all, for your great help!
--Peter

Op 24-10-2011 18:23, R. Michael Weylandt schreef:
> 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<jholtman at 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<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
>>
>> What is the problem that you are trying to solve?
>>
>> ______________________________________________
>> 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.
>>