R help - Feb 2005 - subset data.frame with value != in all columns

I am trying to extract rows from a data.frame based on the
presence/absence of a single value in any column.  I've figured out how
to do get the positive matches, but the remainder (rows without this
value) eludes me.  Mining the help pages and archives brought me,
frustratingly,  very close, as you'll see below. 

My goal: two data frames, one with -99 in at least one column in each
row, one with no occurrences of -99. I want to preserve rownames in
each.

My questions: 
Is there a cleaner way to extract all rows containing a specified
value?
How can I extract all rows that don't have this value in any col?

#create dummy dataset
x <- data.frame(
c1=c(-99,-99,-99,4:10),
c2=1:10,
c3=c(1:3,-99,5:10),
c4=c(10:1),
c5=c(1:9,-99))

#extract data.frame of rows with -99 in them
for(i in 1:ncol(x))
{
y<-subset(x, x[,i]==-99, drop=FALSE);
ifelse(i==1, z<-y, z <- rbind(z,y));
}

#various attempts to get rows not containing "-99":

# this attempt was to create, in "list", the exclusion formula for
each
column.
# Here, I couldn't get subset to recognize "list" as the correct
type.
# e.g. it works if I paste the value of list in the subset command
{
for(i in 1:ncol(x)){
if(i==1)
list<-paste("x[",i,"]!=-99", sep="")
else
list<-paste(list," ", " & x[",i,"]!=-99",
sep="")
}
y<-subset(x, list, drop=FALSE);
}

# this will do it for one col, but if I index more
# it returns all rows
y <- x[!(x[,3] %in% -99),]

# this also works for one col
y<-x[x[,1]!=-99,]

# but if I index more, I get extra rows of NAs
y<-x[x[,1:5]!=-99,]

Thanks in advance.
Tim Howard

platform i386-pc-mingw32
arch     i386           
os       mingw32        
system   i386, mingw32  
status                  
major    2              
minor    0.1            
year     2004           
month    11             
day      15             
language R

Tim Howard wrote:
> I am trying to extract rows from a data.frame based on the
> presence/absence of a single value in any column.  I've figured out how
> to do get the positive matches, but the remainder (rows without this
> value) eludes me.  Mining the help pages and archives brought me,
> frustratingly,  very close, as you'll see below. 
> 
> My goal: two data frames, one with -99 in at least one column in each
> row, one with no occurrences of -99. I want to preserve rownames in
> each.
> 
> My questions: 
> Is there a cleaner way to extract all rows containing a specified
> value?
> How can I extract all rows that don't have this value in any col?
> 
> #create dummy dataset
> x <- data.frame(
> c1=c(-99,-99,-99,4:10),
> c2=1:10,
> c3=c(1:3,-99,5:10),
> c4=c(10:1),
> c5=c(1:9,-99))
> 
<snip>

Tim,

How about using ?apply. Provided all your columns are numeric, you can do:

drop <- apply(x, 1, function(row) all(row != -99))
x[drop, ]

--sundar

How about this?

#extract data.frame of rows with -99 in them

subset(x, apply(x, 1, function(x){any(x == -99)}))

#extract data.frame of rows not containing -99 in them

subset(x, apply(x, 1, function(x){all(x != -99)}))

hope this helps,

Chuck Cleland

Tim Howard wrote:
> I am trying to extract rows from a data.frame based on the
> presence/absence of a single value in any column.  I've figured out how
> to do get the positive matches, but the remainder (rows without this
> value) eludes me.  Mining the help pages and archives brought me,
> frustratingly,  very close, as you'll see below. 
> 
> My goal: two data frames, one with -99 in at least one column in each
> row, one with no occurrences of -99. I want to preserve rownames in
> each.
> 
> My questions: 
> Is there a cleaner way to extract all rows containing a specified
> value?
> How can I extract all rows that don't have this value in any col?
> 
> #create dummy dataset
> x <- data.frame(
> c1=c(-99,-99,-99,4:10),
> c2=1:10,
> c3=c(1:3,-99,5:10),
> c4=c(10:1),
> c5=c(1:9,-99))
> 
> #extract data.frame of rows with -99 in them
> for(i in 1:ncol(x))
> {
> y<-subset(x, x[,i]==-99, drop=FALSE);
> ifelse(i==1, z<-y, z <- rbind(z,y));
> }
> 
> #various attempts to get rows not containing "-99":
> 
> # this attempt was to create, in "list", the exclusion formula
for each
> column.
> # Here, I couldn't get subset to recognize "list" as the
correct type.
> # e.g. it works if I paste the value of list in the subset command
> {
> for(i in 1:ncol(x)){
> if(i==1)
> list<-paste("x[",i,"]!=-99", sep="")
> else
> list<-paste(list," ", " &
x[",i,"]!=-99", sep="")
> }
> y<-subset(x, list, drop=FALSE);
> }
> 
> # this will do it for one col, but if I index more
> # it returns all rows
> y <- x[!(x[,3] %in% -99),]
> 
> # this also works for one col
> y<-x[x[,1]!=-99,]
> 
> # but if I index more, I get extra rows of NAs
> y<-x[x[,1:5]!=-99,]
> 
> Thanks in advance.
> Tim Howard
> 
> platform i386-pc-mingw32
> arch     i386           
> os       mingw32        
> system   i386, mingw32  
> status                  
> major    2              
> minor    0.1            
> year     2004           
> month    11             
> day      15             
> language R
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html
> 
-- 
Chuck Cleland, Ph.D.
NDRI, Inc.
71 West 23rd Street, 8th floor
New York, NY 10010
tel: (212) 845-4495 (Tu, Th)
tel: (732) 452-1424 (M, W, F)
fax: (917) 438-0894

apply, of course, does the trick exceptionally well. Thank you,
everyone, for the help.

tim
>>> Chuck Cleland <ccleland at optonline.net> 02/03/05 03:10PM
>>>
How about this?

#extract data.frame of rows with -99 in them

subset(x, apply(x, 1, function(x){any(x == -99)}))

#extract data.frame of rows not containing -99 in them

subset(x, apply(x, 1, function(x){all(x != -99)}))

hope this helps,

Chuck Cleland

Tim Howard wrote:
> I am trying to extract rows from a data.frame based on the
> presence/absence of a single value in any column.  I've figured out
how
> to do get the positive matches, but the remainder (rows without this
> value) eludes me.  Mining the help pages and archives brought me,
> frustratingly,  very close, as you'll see below. 
> 
> My goal: two data frames, one with -99 in at least one column in
each
> row, one with no occurrences of -99. I want to preserve rownames in
> each.
> 
> My questions: 
> Is there a cleaner way to extract all rows containing a specified
> value?
> How can I extract all rows that don't have this value in any col?
> 
> #create dummy dataset
> x <- data.frame(
> c1=c(-99,-99,-99,4:10),
> c2=1:10,
> c3=c(1:3,-99,5:10),
> c4=c(10:1),
> c5=c(1:9,-99))
> 
> #extract data.frame of rows with -99 in them
> for(i in 1:ncol(x))
> {
> y<-subset(x, x[,i]==-99, drop=FALSE);
> ifelse(i==1, z<-y, z <- rbind(z,y));
> }
> 
> #various attempts to get rows not containing "-99":
> 
> # this attempt was to create, in "list", the exclusion formula
for
each
> column.
> # Here, I couldn't get subset to recognize "list" as the
correct
type.
> # e.g. it works if I paste the value of list in the subset command
> {
> for(i in 1:ncol(x)){
> if(i==1)
> list<-paste("x[",i,"]!=-99", sep="")
> else
> list<-paste(list," ", " &
x[",i,"]!=-99", sep="")
> }
> y<-subset(x, list, drop=FALSE);
> }
> 
> # this will do it for one col, but if I index more
> # it returns all rows
> y <- x[!(x[,3] %in% -99),]
> 
> # this also works for one col
> y<-x[x[,1]!=-99,]
> 
> # but if I index more, I get extra rows of NAs
> y<-x[x[,1:5]!=-99,]
> 
> Thanks in advance.
> Tim Howard
> 
> platform i386-pc-mingw32
> arch     i386           
> os       mingw32        
> system   i386, mingw32  
> status                  
> major    2              
> minor    0.1            
> year     2004           
> month    11             
> day      15             
> language R
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help 
> PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html 
> 
-- 
Chuck Cleland, Ph.D.
NDRI, Inc.
71 West 23rd Street, 8th floor
New York, NY 10010
tel: (212) 845-4495 (Tu, Th)
tel: (732) 452-1424 (M, W, F)
fax: (917) 438-0894

On Thu, 2005-02-03 at 14:57 -0500, Tim Howard wrote: 
> I am trying to extract rows from a data.frame based on the
> presence/absence of a single value in any column.  I've figured out how
> to do get the positive matches, but the remainder (rows without this
> value) eludes me.  Mining the help pages and archives brought me,
> frustratingly,  very close, as you'll see below. 
> 
> My goal: two data frames, one with -99 in at least one column in each
> row, one with no occurrences of -99. I want to preserve rownames in
> each.
> 
> My questions: 
> Is there a cleaner way to extract all rows containing a specified
> value?
> How can I extract all rows that don't have this value in any col?
> 
> #create dummy dataset
> x <- data.frame(
> c1=c(-99,-99,-99,4:10),
> c2=1:10,
> c3=c(1:3,-99,5:10),
> c4=c(10:1),
> c5=c(1:9,-99))
> 
> #extract data.frame of rows with -99 in them
> for(i in 1:ncol(x))
> {
> y<-subset(x, x[,i]==-99, drop=FALSE);
> ifelse(i==1, z<-y, z <- rbind(z,y));
> }
> 
> #various attempts to get rows not containing "-99":
> 
> # this attempt was to create, in "list", the exclusion formula
for each
> column.
> # Here, I couldn't get subset to recognize "list" as the
correct type.
> # e.g. it works if I paste the value of list in the subset command
> {
> for(i in 1:ncol(x)){
> if(i==1)
> list<-paste("x[",i,"]!=-99", sep="")
> else
> list<-paste(list," ", " &
x[",i,"]!=-99", sep="")
> }
> y<-subset(x, list, drop=FALSE);
> }
> 
> # this will do it for one col, but if I index more
> # it returns all rows
> y <- x[!(x[,3] %in% -99),]
> 
> # this also works for one col
> y<-x[x[,1]!=-99,]
> 
> # but if I index more, I get extra rows of NAs
> y<-x[x[,1:5]!=-99,]
> 
> Thanks in advance.
> Tim Howard

How about this, presuming that your data frame is all numeric:

For rows containing -99:
> x[unique(which(x == -99, arr.ind = TRUE)[, 1]), ]
    c1 c2  c3 c4  c5
1  -99  1   1 10   1
2  -99  2   2  9   2
3  -99  3   3  8   3
4    4  4 -99  7   4
10  10 10  10  1 -99


For rows not containing -99:
> x[-unique(which(x == -99, arr.ind = TRUE)[, 1]), ]
  c1 c2 c3 c4 c5
5  5  5  5  6  5
6  6  6  6  5  6
7  7  7  7  4  7
8  8  8  8  3  8
9  9  9  9  2  9


What I have done here is to use which(), setting arr.ind = TRUE. This
returns the row, column indices for the matches to the boolean
statement. The first column returned by which() in this case are the row
numbers matching the statement, so I take the first column only.

Since it is possible that more than one element in a row can match the
boolean, I then use unique() to get the singular row values.

Thus, I can use the returned row indices above to subset the data frame.

HTH,

Marc Schwartz

On Thu, 03 Feb 2005 14:57:58 -0500 Tim Howard wrote:
> I am trying to extract rows from a data.frame based on the
> presence/absence of a single value in any column.  I've figured out
> how to do get the positive matches, but the remainder (rows without
> this value) eludes me.  Mining the help pages and archives brought me,
> frustratingly,  very close, as you'll see below. 
> 
> My goal: two data frames, one with -99 in at least one column in each
> row, one with no occurrences of -99. I want to preserve rownames in
> each.
> 
> My questions: 
> Is there a cleaner way to extract all rows containing a specified
> value?
> How can I extract all rows that don't have this value in any col?
> 
> #create dummy dataset
> x <- data.frame(
> c1=c(-99,-99,-99,4:10),
> c2=1:10,
> c3=c(1:3,-99,5:10),
> c4=c(10:1),
> c5=c(1:9,-99))
Assuming that there are no NAs (in fact, the -99 looks like it
might be coding NAs...), the following works:
R> is.na(x) <- x == -99
R> na.omit(x)
  c1 c2 c3 c4 c5
5  5  5  5  6  5
6  6  6  6  5  6
7  7  7  7  4  7
8  8  8  8  3  8
9  9  9  9  2  9

hth,
Z
> #extract data.frame of rows with -99 in them
> for(i in 1:ncol(x))
> {
> y<-subset(x, x[,i]==-99, drop=FALSE);
> ifelse(i==1, z<-y, z <- rbind(z,y));
> }
> 
> #various attempts to get rows not containing "-99":
> 
> # this attempt was to create, in "list", the exclusion formula
for
> # each
> column.
> # Here, I couldn't get subset to recognize "list" as the
correct type.
> # e.g. it works if I paste the value of list in the subset command
> {
> for(i in 1:ncol(x)){
> if(i==1)
> list<-paste("x[",i,"]!=-99", sep="")
> else
> list<-paste(list," ", " &
x[",i,"]!=-99", sep="")
> }
> y<-subset(x, list, drop=FALSE);
> }
> 
> # this will do it for one col, but if I index more
> # it returns all rows
> y <- x[!(x[,3] %in% -99),]
> 
> # this also works for one col
> y<-x[x[,1]!=-99,]
> 
> # but if I index more, I get extra rows of NAs
> y<-x[x[,1:5]!=-99,]
> 
> Thanks in advance.
> Tim Howard
> 
> platform i386-pc-mingw32
> arch     i386           
> os       mingw32        
> system   i386, mingw32  
> status                  
> major    2              
> minor    0.1            
> year     2004           
> month    11             
> day      15             
> language R
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
> http://www.R-project.org/posting-guide.html
>

"Tim Howard" <tghoward at gw.dec.state.ny.us> writes:
> I am trying to extract rows from a data.frame based on the
> presence/absence of a single value in any column.  I've figured out how
> to do get the positive matches, but the remainder (rows without this
> value) eludes me.  Mining the help pages and archives brought me,
> frustratingly,  very close, as you'll see below. 
> 
> My goal: two data frames, one with -99 in at least one column in each
> row, one with no occurrences of -99. I want to preserve rownames in
> each.
> 
> My questions: 
> Is there a cleaner way to extract all rows containing a specified
> value?
> How can I extract all rows that don't have this value in any col?
> 
> #create dummy dataset
> x <- data.frame(
> c1=c(-99,-99,-99,4:10),
> c2=1:10,
> c3=c(1:3,-99,5:10),
> c4=c(10:1),
> c5=c(1:9,-99))
> subset(x,apply(x==-99,1,any))
    c1 c2  c3 c4  c5
1  -99  1   1 10   1
2  -99  2   2  9   2
3  -99  3   3  8   3
4    4  4 -99  7   4
10  10 10  10  1 -99
> subset(x,!apply(x==-99,1,any))
  c1 c2 c3 c4 c5
5  5  5  5  6  5
6  6  6  6  5  6
7  7  7  7  4  7
8  8  8  8  3  8
9  9  9  9  2  9


-- 
   O__  ---- Peter Dalgaard             Blegdamsvej 3  
  c/ /'_ --- Dept. of Biostatistics     2200 Cph. N   
 (*) \(*) -- University of Copenhagen   Denmark      Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk)             FAX: (+45) 35327907

Hi

maybe there are other options but

x[rowSums(x==-99)>=1,]

seems to extract all rows which have at least one -99 in them

Cheers
Petr


On 3 Feb 2005 at 14:57, Tim Howard wrote:
> I am trying to extract rows from a data.frame based on the
> presence/absence of a single value in any column.  I've figured out
> how to do get the positive matches, but the remainder (rows without
> this value) eludes me.  Mining the help pages and archives brought me,
> frustratingly,  very close, as you'll see below. 
> 
> My goal: two data frames, one with -99 in at least one column in each
> row, one with no occurrences of -99. I want to preserve rownames in
> each.
> 
> My questions: 
> Is there a cleaner way to extract all rows containing a specified
> value? How can I extract all rows that don't have this value in any
> col?
> 
> #create dummy dataset
> x <- data.frame(
> c1=c(-99,-99,-99,4:10),
> c2=1:10,
> c3=c(1:3,-99,5:10),
> c4=c(10:1),
> c5=c(1:9,-99))
> 
> #extract data.frame of rows with -99 in them
> for(i in 1:ncol(x))
> {
> y<-subset(x, x[,i]==-99, drop=FALSE);
> ifelse(i==1, z<-y, z <- rbind(z,y));
> }
> 
> #various attempts to get rows not containing "-99":
> 
> # this attempt was to create, in "list", the exclusion formula
for
> # each
> column.
> # Here, I couldn't get subset to recognize "list" as the
correct type.
> # e.g. it works if I paste the value of list in the subset command
> {
> for(i in 1:ncol(x)){
> if(i==1)
> list<-paste("x[",i,"]!=-99", sep="")
> else
> list<-paste(list," ", " &
x[",i,"]!=-99", sep="")
> }
> y<-subset(x, list, drop=FALSE);
> }
> 
> # this will do it for one col, but if I index more
> # it returns all rows
> y <- x[!(x[,3] %in% -99),]
> 
> # this also works for one col
> y<-x[x[,1]!=-99,]
> 
> # but if I index more, I get extra rows of NAs
> y<-x[x[,1:5]!=-99,]
> 
> Thanks in advance.
> Tim Howard
> 
> platform i386-pc-mingw32
> arch     i386           
> os       mingw32        
> system   i386, mingw32  
> status                  
> major    2              
> minor    0.1            
> year     2004           
> month    11             
> day      15             
> language R
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
> http://www.R-project.org/posting-guide.html
Petr Pikal
petr.pikal at precheza.cz

Because I'll be doing this on big datasets and time is important, I
thought I'd time all the different approaches that were suggested on a
small dataframe. The results were very instructive so I thought I'd pass
them on. I also discovered that my numeric columns (e.g. -9999.000)
weren't found by apply() but were found by which() and the simple
replace. Was it apply's fault or something else?

Note how much faster unique(which()) is; wow! Thanks to Marc Schwartz
for this blazing solution.
> nrow(in.df)
[1] 40000
#extract rows with no -9999
> system.time(x <- subset(in.df, apply(in.df, 1,
function(in.df){all(in.df != -9999)})))
[1] 3.25 0.00 3.25   NA   NA
> system.time(y<- in.df[-unique(which(in.df == -9999, arr.ind = TRUE)[,
1]), ])
[1] 0.17 0.00 0.17   NA   NA
> system.time({is.na(in.df) <-in.df == -9999; z <- na.omit(in.df)})
[1] 0.25 0.02 0.26   NA   NA
> nrow(x);nrow(y);nrow(z)
[1] 39990
[1] 39626
[1] 39626

#extract rows with -9999
> system.time(d<-subset(in.df, apply(in.df, 1,
function(in.df){any(in.df == -9999)})))
[1] 3.40 0.00 3.45   NA   NA
> system.time(e<-in.df[unique(which(in.df == -9999, arr.ind = TRUE)[,
1]), ])
[1] 0.11 0.00 0.11   NA   NA
> nrow(d); nrow(e)
[1] 10
[1] 374

Tim Howard

>>> Marc Schwartz <MSchwartz at MedAnalytics.com> 02/03/05
03:24PM >>>
On Thu, 2005-02-03 at 14:57 -0500, Tim Howard wrote: 
  ... snip...
> My questions: 
> Is there a cleaner way to extract all rows containing a specified
> value?
> How can I extract all rows that don't have this value in any col?
> 
> #create dummy dataset
> x <- data.frame(
> c1=c(-99,-99,-99,4:10),
> c2=1:10,
> c3=c(1:3,-99,5:10),
> c4=c(10:1),
> c5=c(1:9,-99))
> 
..snip...

How about this, presuming that your data frame is all numeric:

For rows containing -99:
> x[unique(which(x == -99, arr.ind = TRUE)[, 1]), ]
    c1 c2  c3 c4  c5
1  -99  1   1 10   1
2  -99  2   2  9   2
3  -99  3   3  8   3
4    4  4 -99  7   4
10  10 10  10  1 -99


For rows not containing -99:
> x[-unique(which(x == -99, arr.ind = TRUE)[, 1]), ]
  c1 c2 c3 c4 c5
5  5  5  5  6  5
6  6  6  6  5  6
7  7  7  7  4  7
8  8  8  8  3  8
9  9  9  9  2  9


What I have done here is to use which(), setting arr.ind = TRUE. This
returns the row, column indices for the matches to the boolean
statement. The first column returned by which() in this case are the
row
numbers matching the statement, so I take the first column only.

Since it is possible that more than one element in a row can match the
boolean, I then use unique() to get the singular row values.

Thus, I can use the returned row indices above to subset the data
frame.

HTH,

Marc Schwartz

Petr,
   Thank you!  Yes, rowSums appears to be even a little bit faster than
unique(which()), and it also maintains the original order. I do want
original order maintained, but I first apply a function to one of my
data.frames (that without any -9999s ... yes, these do represent nulls,
as someone asked earlier) and rbind these two dataframes back together,
so I need to sort (by rownames) after the rbind (there doesn't seem to
be a sortby option in rbind). 
   I apologize for not jumping on rowSums earlier, I hadn't caught on
that it was summing counts of occurrence of the search value, not
summing the search value itself.
   Thanks again, this is very instructive and *very* helpful.
humbly,
Tim
>>> "Petr Pikal" <petr.pikal at precheza.cz> 02/07/05
02:12AM >>>
Hi Tim

I can not say much about apply, but the code with unique(which()) 
gives you reordered rows in case of -9999 selection

try

set.seed(1)
in.df <- data.frame(
c1=rnorm(40000),
c2=rnorm(40000),
c3=rnorm(40000),
c4=rnorm(40000),
c5=rnorm(40000))
in.df[in.df>3] <- (-9999)

system.time(e <- in.df[unique(which(in.df == -9999, arr.ind = 
TRUE)[,1]), ])
system.time(e1 <- in.df[(rowSums(in.df == -9999)) != 0,])

all.equal(e,e1)

So if you mind you need to do reordering.

ooo<-order(as.numeric(rownames(e)))
all.equal(e[ooo,],e1)

Cheers
Petr

On 4 Feb 2005 at 11:17, Tim Howard wrote:
> Because I'll be doing this on big datasets and time is important, I
> thought I'd time all the different approaches that were suggested on
a
> small dataframe. The results were very instructive so I thought I'd
> pass them on. I also discovered that my numeric columns (e.g.
> -9999.000) weren't found by apply() but were found by which() and
the
> simple replace. Was it apply's fault or something else?
> 
> Note how much faster unique(which()) is; wow! Thanks to Marc
Schwartz
> for this blazing solution.
> 
> > nrow(in.df)
> [1] 40000
> #extract rows with no -9999
> > system.time(x <- subset(in.df, apply(in.df, 1,
> function(in.df){all(in.df != -9999)})))
> [1] 3.25 0.00 3.25   NA   NA
> > system.time(y<- in.df[-unique(which(in.df == -9999, arr.ind >
> TRUE)[,
> 1]), ])
> [1] 0.17 0.00 0.17   NA   NA
> > system.time({is.na(in.df) <-in.df == -9999; z <-
na.omit(in.df)})
> [1] 0.25 0.02 0.26   NA   NA
> 
> > nrow(x);nrow(y);nrow(z)
> [1] 39990
> [1] 39626
> [1] 39626
> 
> #extract rows with -9999
> > system.time(d<-subset(in.df, apply(in.df, 1,
> function(in.df){any(in.df == -9999)})))
> [1] 3.40 0.00 3.45   NA   NA
> > system.time(e<-in.df[unique(which(in.df == -9999, arr.ind TRUE)[,
> 1]), ])
> [1] 0.11 0.00 0.11   NA   NA
> 
> > nrow(d); nrow(e)
> [1] 10
> [1] 374
> 
> Tim Howard
> 
> 
> >>> Marc Schwartz <MSchwartz at MedAnalytics.com> 02/03/05
03:24PM >>>
> On Thu, 2005-02-03 at 14:57 -0500, Tim Howard wrote: 
>   ... snip...
> > My questions: 
> > Is there a cleaner way to extract all rows containing a specified
> > value? How can I extract all rows that don't have this value in
any
> > col?
> > 
> > #create dummy dataset
> > x <- data.frame(
> > c1=c(-99,-99,-99,4:10),
> > c2=1:10,
> > c3=c(1:3,-99,5:10),
> > c4=c(10:1),
> > c5=c(1:9,-99))
> > 
> ..snip...
> 
> How about this, presuming that your data frame is all numeric:
> 
> For rows containing -99:
> 
> > x[unique(which(x == -99, arr.ind = TRUE)[, 1]), ]
>     c1 c2  c3 c4  c5
> 1  -99  1   1 10   1
> 2  -99  2   2  9   2
> 3  -99  3   3  8   3
> 4    4  4 -99  7   4
> 10  10 10  10  1 -99
> 
> 
> For rows not containing -99:
> 
> > x[-unique(which(x == -99, arr.ind = TRUE)[, 1]), ]
>   c1 c2 c3 c4 c5
> 5  5  5  5  6  5
> 6  6  6  6  5  6
> 7  7  7  7  4  7
> 8  8  8  8  3  8
> 9  9  9  9  2  9
> 
> 
> What I have done here is to use which(), setting arr.ind = TRUE.
This
> returns the row, column indices for the matches to the boolean
> statement. The first column returned by which() in this case are the
> row numbers matching the statement, so I take the first column only.
> 
> Since it is possible that more than one element in a row can match
the
> boolean, I then use unique() to get the singular row values.
> 
> Thus, I can use the returned row indices above to subset the data
> frame.
> 
> HTH,
> 
> Marc Schwartz
> 
> ______________________________________________
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Petr Pikal
petr.pikal at precheza.cz