Comparing two data frames with different number of rows

January 24, 2013
By

(This article was first published on Jason.Bryer.org Blog - R, and kindly contributed to R-bloggers)

I posted a question over on StackOverflow on an efficient way of comparing two data frames with the same column structure, but with different rows. What I would like to end up with is an n x m logical matrix where n and m are the number of rows in the first and second data frames, respectively; and the value at the ith row and jth column indicates whether all the values from row i from data frame one is equal to row j from data frame two. To provide some context, this will be used in a propensity score matching algorithm to identify candidate matches that match exactly on any number of covariates. In addition to the approaches I had, joran provided an approach using the Vectorize function (thanks again as I learned another nice function). I decided to put three approaches to a race…

To understand what I need, I’ll start with a small example with two data frames, one with 4 rows, the other with 3, and each has two variables, one logical and the other numeric. As an aside, I only need this to work for integers, factors, characters, and logical types therefore avoiding issues of comparing numerics.

> df1 <- data.frame(row.names=1:4, var1=c(TRUE, TRUE, FALSE, FALSE), var2=c(1,2,3,4))
> df2 <- data.frame(row.names=5:7, var1=c(FALSE, TRUE, FALSE), var2=c(5,2,3))
> df1
   var1 var2
1  TRUE    1
2  TRUE    2
3 FALSE    3
4 FALSE    4
> df2
   var1 var2
5 FALSE    5
6  TRUE    2
7 FALSE    3

First, let’s consider the case when there is only one variable:

> system.time({
+ 	df3 <- sapply(df2$var1, FUN=function(x) { x == df1$var1 })
+ 	dimnames(df3) <- list(row.names(df1), row.names(df2))
+ })
   user  system elapsed 
      0       0       0 
> df3
      5     6     7
1 FALSE  TRUE FALSE
2 FALSE  TRUE FALSE
3  TRUE FALSE  TRUE
4  TRUE FALSE  TRUE

This is pretty straight forward. Now I want the same type of result, but to compare more than one column (in the final implementation I need to handle any number of columns so not necessarily limited to one or two).

The first approach uses nested apply functions.

> system.time({
+ 	m1 <- t(as.matrix(df1))
+ 	m2 <- as.matrix(df2)
+ 	df4 <- apply(m2, 1, FUN=function(x) { apply(m1, 2, FUN=function(y) { all(x == y) } ) })
+ })
   user  system elapsed 
  0.001   0.000   0.001 
> df4
      5     6     7
1 FALSE FALSE FALSE
2 FALSE  TRUE FALSE
3 FALSE FALSE  TRUE
4 FALSE FALSE FALSE

Secondly, using the Vectorize and outer functions.

> system.time({
+ 	foo <- Vectorize(function(x,y) { all(df1[x,] == df2[y,]) })
+ 	df5 <- outer(1:nrow(df1), 1:nrow(df2), FUN=foo)
+ })
   user  system elapsed 
  0.005   0.000   0.006 
> df5
      [,1]  [,2]  [,3]
[1,] FALSE FALSE FALSE
[2,] FALSE  TRUE FALSE
[3,] FALSE FALSE  TRUE
[4,] FALSE FALSE FALSE

Lastly, we’ll create a new character vector by pasting the other variables together.

> system.time({
+ 	df1$var3 <- apply(df1, 1, paste, collapse='.')
+ 	df2$var3 <- apply(df2, 1, paste, collapse='.')
+ 	df6 <- sapply(df2$var3, FUN=function(x) { x == df1$var3 })
+ 	dimnames(df6) <- list(row.names(df1), row.names(df2))
+ })
   user  system elapsed 
  0.000   0.000   0.001 
> df6
      5     6     7
1 FALSE FALSE FALSE
2 FALSE  TRUE FALSE
3 FALSE FALSE  TRUE
4 FALSE FALSE FALSE

We can already see with this small example that the Vectorize approach is the slowest. However, let’s try a larger example. First we’ll create two data frames, one with 1,000 rows and the second with 1,500. The resulting matrix will be 1,000 x 1,500.

set.seed(2112)
df1 <- data.frame(row.names=1:1000, 
				  var1=sample(c(TRUE,FALSE), 1000, replace=TRUE), 
				  var2=sample(1:10, 1000, replace=TRUE) )
df2 <- data.frame(row.names=1001:2500, 
				  var1=sample(c(TRUE,FALSE), 1500, replace=TRUE),
				  var2=sample(1:10, 1500, replace=TRUE))

Nested apply functions approach:

> system.time({
+ 	m1 <- t(as.matrix(df1))
+ 	m2 <- as.matrix(df2)
+ 	df4 <- apply(m2, 1, FUN=function(x) { apply(m1, 2, FUN=function(y) { all(x == y) } ) })
+ })
   user  system elapsed 
 10.807   0.043  11.096 

Vectorize approach:

> system.time({
+ 	foo <- Vectorize(function(x,y) { all(df1[x,] == df2[y,]) })
+ 	df5 <- outer(1:nrow(df1), 1:nrow(df2), FUN=foo)
+ })
   user  system elapsed 
390.904   0.808 392.134 

Combined columns approach:

> system.time({
+ 	df1$var3 <- apply(df1, 1, paste, collapse='.')
+ 	df2$var3 <- apply(df2, 1, paste, collapse='.')
+ 	df6 <- sapply(df2$var3, FUN=function(x) { x == df1$var3 })
+ 	dimnames(df6) <- list(row.names(df1), row.names(df2))
+ })
   user  system elapsed 
  0.421   0.000   0.422 

The combined column approach is by far the fasted way, and it makes good since. It is a bit surprising (at least to me), how much worse the Vectorize and outer functions are. Moreover, I am a bit concerned about potential issues with the paste method and doing comparisons on those results. Please feel free to leave comments below if there are other approaches.

To leave a comment for the author, please follow the link and comment on his blog: Jason.Bryer.org Blog - R.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.