This came up just in an answer to another question here. When you rbind
two data frames, it matches columns by name rather than index, which can lead to unexpected behavior:
> df<-data.frame(x=1:2,y=3:4) > df x y 1 1 3 2 2 4 > rbind(df,df[,2:1]) x y 1 1 3 2 2 4 3 1 3 4 2 4
Of course, there are workarounds. For example:
rbind(df,rename(df[,2:1],names(df))) data.frame(rbind(as.matrix(df),as.matrix(df[,2:1])))
On edit: rename
from the plyr
package doesn't actually work this way (although I thought I had it working when I originally wrote this...). The way to do this by renaming is to use SimonO101's solution:
rbind(df,setNames(df[,2:1],names(df)))
Also, maybe surprisingly,
data.frame(rbindlist(list(df,df[,2:1])))
works by index (and if we don't mind a data table, then it's pretty concise), so this is a difference between do.call(rbind)
.
The question is, what is the most concise way to rbind
two data frames where the names don't match? I know this seems trivial, but this kind of thing can end up cluttering code. And I don't want to have to write a new function called rbindByIndex
. Ideally it would be something like rbind(df,df[,2:1],byIndex=T)
.
Example: Ignore Column Names when Using rbind() Function For this, we have to use the setNames and names functions to temporarily rename the column names of our second data frame. After running the previous R code the combined data frame shown in Table 3 has been created.
Method 1 : Using plyr package rbind. fill() method in R is an enhancement of the rbind() method in base R, is used to combine data frames with different columns. The column names are number may be different in the input data frames. Missing columns of the corresponding data frames are filled with NA.
cbind() and rbind() both create matrices by combining several vectors of the same length. cbind() combines vectors as columns, while rbind() combines them as rows. Let's use these functions to create a matrix with the numbers 1 through 30.
0), rbind has the capacity to to join two data sets with the same name columns even if they are in different order.
You might find setNames
handy here...
rbind(df, setNames(rev(df), names(df))) # x y #1 1 3 #2 2 4 #3 3 1 #4 4 2
I suspect your real use-case is somewhat more complex. You can of course reorder columns in the first argument of setNames
as you wish, just use names(df)
in the second argument, so that the names of the reordered columns match the original.
This seems pretty easy:
mapply(c,df,df[,2:1]) x y [1,] 1 3 [2,] 2 4 [3,] 3 1 [4,] 4 2
For this simple case, though, you have to turn it back into a dataframe (because mapply
simplifies it to a matrix):
as.data.frame(mapply(c,df,df[,2:1])) x y 1 1 3 2 2 4 3 3 1 4 4 2
Important note 1: There appears to be a downside of type coercion when your dataframe contains vectors of different types:
df<-data.frame(x=1:2,y=3:4,z=c('a','b')) mapply(c,df,df[,c(2:1,3)]) x y z [1,] 1 3 2 [2,] 2 4 1 [3,] 3 1 2 [4,] 4 2 1
Important note 2: It also is terrible if you have factors.
df<-data.frame(x=factor(1:2),y=factor(3:4)) mapply(c,df[,1:2],df[,2:1]) x y [1,] 1 1 [2,] 2 2 [3,] 1 1 [4,] 2 2
So, as long as you have all numeric data, it's okay.
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