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

Joshua Wiley jwiley.psych at gmail.com
Sun Oct 23 03:27:42 CEST 2011


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/



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