[R] remove

[Jeff Newmiller]

Your question mystifies me, since it looks to me like you already know the answer. 
-- 
Sent from my phone. Please excuse my brevity.

On February 12, 2017 3:30:49 PM PST, Val <valkremk at gmail.com> wrote:
>Hi Jeff and all,
> How do I get the  number of unique first names   in the two data sets?
>
>for the first one,
>result2 <- DF[ 1 == err2, ]
>length(unique(result2$first))
>
>
>
>
>On Sun, Feb 12, 2017 at 12:42 AM, Jeff Newmiller
><jdnewmil at dcn.davis.ca.us> wrote:
>> The "by" function aggregates and returns a result with generally
>fewer rows
>> than the original data. Since you are looking to index the rows in
>the
>> original data set, the "ave" function is better suited because it
>always
>> returns a vector that is just as long as the input vector:
>>
>> # I usually work with character data rather than factors if I plan
>> # to modify the data (e.g. removing rows)
>> DF <- read.table( text=
>> 'first  week last
>> Alex    1  West
>> Bob     1  John
>> Cory    1  Jack
>> Cory    2  Jack
>> Bob     2  John
>> Bob     3  John
>> Alex    2  Joseph
>> Alex    3  West
>> Alex    4  West
>> ', header = TRUE, as.is = TRUE )
>>
>> err <- ave( DF$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.
>>>
>>
>>
>---------------------------------------------------------------------------
>> Jeff Newmiller                        The     .....       .....  Go
>Live...
>> DCN:<jdnewmil at dcn.davis.ca.us>        Basics: ##.#.       ##.#.  Live
>Go...
>>                                       Live:   OO#.. Dead: OO#.. 
>Playing
>> Research Engineer (Solar/Batteries            O.O#.       #.O#.  with
>> /Software/Embedded Controllers)               .OO#.       .OO#. 
>rocks...1k
>>
>---------------------------------------------------------------------------