[R] remove
Jeff Newmiller
jdnewmil at dcn.davis.ca.us
Sun Feb 12 16:18:26 CET 2017
Exactly. Sort of like the optimisation of using which.max instead of max followed by which, though ideally the only intermediate vector would be the logical vector that says keep or don't keep.
--
Sent from my phone. Please excuse my brevity.
On February 11, 2017 11:19:11 PM PST, P Tennant <philipt900 at iinet.net.au> wrote:
>Hi Jeff,
>
>Why do you say ave() is better suited *because* it always returns a
>vector that is just as long as the input vector? Is it because that
>feature (of equal length), allows match() to be avoided, and as a
>result, the subsequent subsetting is faster with very large datasets?
>
>Thanks, Philip
>
>
>On 12/02/2017 5:42 PM, Jeff Newmiller wrote:
>> The "by" function aggregates and returns a result with generally
>fewer
>> rows than the original data. Since you are looking to index the rows
>> in the original data set, the "ave" function is better suited because
>
>> it always returns a vector that is just as long as the input vector:
>>
>> # I usually work with character data rather than factors if I plan
>> # to modify the data (e.g. removing rows)
>> DF <- read.table( text=
>> 'first week last
>> Alex 1 West
>> Bob 1 John
>> Cory 1 Jack
>> Cory 2 Jack
>> Bob 2 John
>> Bob 3 John
>> Alex 2 Joseph
>> Alex 3 West
>> Alex 4 West
>> ', header = TRUE, as.is = TRUE )
>>
>> err <- ave( DF$last
>> , DF[ , "first", drop = FALSE]
>> , FUN = function( lst ) {
>> length( unique( lst ) )
>> }
>> )
>> result <- DF[ "1" == err, ]
>> result
>>
>> Notice that the ave function returns a vector of the same type as was
>
>> given to it, so even though the function returns a numeric the err
>> vector is character.
>>
>> If you wanted to be able to examine more than one other column in
>> determining the keep/reject decision, you could do:
>>
>> err2 <- ave( seq_along( DF$first )
>> , DF[ , "first", drop = FALSE]
>> , FUN = function( n ) {
>> length( unique( DF[ n, "last" ] ) )
>> }
>> )
>> result2 <- DF[ 1 == err2, ]
>> result2
>>
>> and then you would have the option to re-use the "n" index to look at
>
>> other columns as well.
>>
>> Finally, here is a dplyr solution:
>>
>> library(dplyr)
>> result3 <- ( DF
>> %>% group_by( first ) # like a prep for ave or by
>> %>% mutate( err = length( unique( last ) ) ) # similar to
>ave
>> %>% filter( 1 == err ) # drop the rows with too many last
>> names
>> %>% select( -err ) # drop the temporary column
>> %>% as.data.frame # convert back to a plain-jane data
>frame
>> )
>> result3
>>
>> which uses a small set of verbs in a pipeline of functions to go from
>
>> input to result in one pass.
>>
>> If your data set is really big (running out of memory big) then you
>> might want to investigate the data.table or sqlite packages, either
>of
>> which can be combined with dplyr to get a standardized syntax for
>> managing larger amounts of data. However, most people actually aren't
>
>> running out of memory so in most cases the extra horsepower isn't
>> actually needed.
>>
>> On Sun, 12 Feb 2017, P Tennant wrote:
>>
>>> Hi Val,
>>>
>>> The by() function could be used here. With the dataframe dfr:
>>>
>>> # split the data by first name and check for more than one last name
>
>>> for each first name
>>> res <- by(dfr, dfr['first'], function(x) length(unique(x$last)) > 1)
>>> # make the result more easily manipulated
>>> res <- as.table(res)
>>> res
>>> # first
>>> # Alex Bob Cory
>>> # TRUE FALSE FALSE
>>>
>>> # then use this result to subset the data
>>> nw.dfr <- dfr[!dfr$first %in% names(res[res]) , ]
>>> # sort if needed
>>> nw.dfr[order(nw.dfr$first) , ]
>>>
>>> first week last
>>> 2 Bob 1 John
>>> 5 Bob 2 John
>>> 6 Bob 3 John
>>> 3 Cory 1 Jack
>>> 4 Cory 2 Jack
>>>
>>>
>>> Philip
>>>
>>> On 12/02/2017 4:02 PM, Val wrote:
>>>> Hi all,
>>>> I have a big data set and want to remove rows conditionally.
>>>> In my data file each person were recorded for several weeks.
>Somehow
>>>> during the recording periods, their last name was misreported.
>For
>>>> each person, the last name should be the same. Otherwise remove
>from
>>>> the data. Example, in the following data set, Alex was found to
>have
>>>> two last names .
>>>>
>>>> Alex West
>>>> Alex Joseph
>>>>
>>>> Alex should be removed from the data. if this happens then I want
>>>> remove all rows with Alex. Here is my data set
>>>>
>>>> df<- read.table(header=TRUE, text='first week last
>>>> Alex 1 West
>>>> Bob 1 John
>>>> Cory 1 Jack
>>>> Cory 2 Jack
>>>> Bob 2 John
>>>> Bob 3 John
>>>> Alex 2 Joseph
>>>> Alex 3 West
>>>> Alex 4 West ')
>>>>
>>>> Desired output
>>>>
>>>> first week last
>>>> 1 Bob 1 John
>>>> 2 Bob 2 John
>>>> 3 Bob 3 John
>>>> 4 Cory 1 Jack
>>>> 5 Cory 2 Jack
>>>>
>>>> Thank you in advance
>>>>
>>>> ______________________________________________
>>>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>>> 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 -- To UNSUBSCRIBE and more, see
>>> 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.
>>>
>>
>>
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>
>>
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