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

[R. Michael Weylandt]

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/
>