R: removing rows and replacing values using conditions from multiple columns

I want to filter out all values of var3 < 5 while keeping at least one occurrence of each value of var1.

> foo <- data.frame(var1=c(1, 1, 8, 8, 5, 5, 5), var2=c(1,2,3,2,4,6,8), var3=c(7,1,1,1,1,1,6))
> foo
  var1 var2 var3
1    1    1 开发者_开发百科   7
2    1    2    1
3    8    3    1
4    8    2    1
5    5    4    1
6    5    6    1
7    5    8    6

subset(foo, (foo$var3>=5)) would remove row 2 to 6 and I would have lost var1==8.

• I want to remove the row if there is another value of var1 that fulfills the condition foo$var3 >= 5. See row 5.
• I want to keep the row, assiging NA to var2 and var3 if all occurrences of a value var1 do not fulfill the condition foo$var3 >= 5.

This is the result I expect:

  var1 var2 var3
1    1    1    7
3    8   NA   NA
7    5    8    6

This is the closest I got:

> foo$var3[ foo$var3 < 5 ] = NA
> foo$var2[ is.na(foo$var3) ] = NA
> foo
  var1 var2 var3
1    1    1    7
2    1   NA   NA
3    8   NA   NA
4    8   NA   NA
5    5   NA   NA
6    5   NA   NA
7    5    8    6

Now I just need to know how to conditionally remove the right rows (2, 3 or 4, 5, 6): Remove the row if var2 & var3 are NA and if the value of var1 has more than 1 occurrence.

But there is surely a much simpler/elegant way to approach this little problem.

edit: changed foo to resemble my use case more

---

The fastest way is to use merge:

> merge(foo[foo$var3>5,],unique(foo$var1),by.x=1,by.y=1,all.y=T)
  var1 var2 var3
1    1    1    7
2    5    8    6
3    8   NA   NA

unique(foo$var1) gives the unique values in var1. These ones are mapped against the dataframe where var3 is larger than five. You take the first column of every argument (all.x=1, all.y=1) and you say that all values in y should be represented (all.y=T). See also ?merge.

If you want to preserve the order, then :

> merge(foo[foo$var3>5,],unique(foo$var1),by.x=1,by.y=1,
+ all.y=T)[order(unique(foo$var1)),]
  var1 var2 var3
1    1    1    7
3    8   NA   NA
2    5    8    6

merge sorts the variable on which the mapping happens. order gives this sorting, so you can reverse it using that order as indices. See also ?order.

---

After you do:

foo$var3[ foo$var3 < 5 ] = NA
foo$var2[ is.na(foo$var3) ] = NA

You need to remove rows containing NA that are also duplicate values of var1:

foo[!(!complete.cases(foo) & duplicated(foo$var1)), ]

Think of this line as identifying lines that contain NA values AND duplicate var1 values, then selecting everything else.

Edit: If the first row in a dataframe for a given value of var1 has a value of var3 that you want to exclude, my solution doesn't work. You'll need to order the data.frame first to make sure that the complete cases come first:

foo <- foo[order(foo$var2),]   # ordering on var3 should be the same
foo[!(!complete.cases(foo) & duplicated(foo$var1)), ]

---

rbind(r <- subset(foo, (foo$var3>=5)), 
      unique(transform(subset(foo, !var1%in%r$var1), var2=NA, var3=NA)))

step-by-step:

r <- subset(foo, (foo$var3>=5))

r2 <- subset(foo, !var1%in%r$var1) # extract var1 != r$var1
r3 <- transform(r2, var2=NA, var3=NA) # replace var2 and var3 with NA
r4 <- unique(r3) # remove duplicates

rbind(r, r4) # bind them

---

Here's a way using the plyr package functions ddply and colwise, and the subset function. First define a helper function null2na:

null2na <- function(x) if ( length(x) == 0 ) NA else x

Next define the function filter that we want to apply to each sub-data-frame that has a specific value for var1:

filter <- function(df) cbind( data.frame( var1 = df[1,1]),
                              colwise(null2na) (subset(df, var3 >= 5)[,-1]))

Now do the ddply on foo by var1:

> ddply(foo, .(var1), filter)
  var1 var2 var3
1    1    1    7
2    5    8    6
3    8   NA   NA

---

Try this:

foo <- data.frame(var1= c(1, 1, 2, 3, 3, 4, 4, 5), 
     var2=c(9, 5, 13, 9, 12, 11, 13, 9), 
     var3=c(6, 8, 3, 6, 4, 7, 2, 9))
f2=foo[which(foo$var3>5),]

missing = which(!(foo$var1 %in% f2$var1))
f3 = rbind(f2, list(foo$var1[missing], rep(NA, length(missing)),rep(NA,length(missing))))
f3[order(f3$var1),]

The last row is only needed if you care about the order (assuming that the data is ordered on var1 in the first place=.