remove

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

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