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 outhow> 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 ineach> 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 foreach> column. > # Here, I couldn't get subset to recognize "list" as the correcttype.> # 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 HowardHow 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.htmlPetr 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 ona> 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() andthe> simple replace. Was it apply's fault or something else? > > Note how much faster unique(which()) is; wow! Thanks to MarcSchwartz> 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 inany> > 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 matchthe> 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 > > ______________________________________________ > 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.htmlPetr Pikal petr.pikal at precheza.cz
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