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

[Wet Bell Diver]

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/
>>