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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