Vectorize to increase speed of Counter().

[jdblischak]

Thanks for the great package. My colleagues and I find UpSet plots very useful.

For one of our larger data sets, we observed that upset() takes ~5 seconds to complete. After profiling, we found that the following lines contribute the large majority of this compute time (~3 seconds).

UpSetR/R/MainBar.R

Lines 29 to 31 in df2151f

	for(i in 1:nrow(Freqs)){
	Freqs$degree[i] <- rowSums(Freqs[ i ,1:num_sets])
	}

In this PR, I converted the above to:

Freqs$degree <- rowSums(Freqs[, 1:num_sets])

This reduced the runtime of upset() to ~2 seconds.

The results should be identical, but to test I ran the following example code from ?upset:

movies <- read.csv(system.file("extdata", "movies.csv", package = "UpSetR"),
                   header=TRUE, sep=";" )
upset(movies, nsets = 7, nintersects = 30, mb.ratio = c(0.5, 0.5),
      order.by = c("freq", "degree"), decreasing = c(TRUE,FALSE))

Before:

After:

[jdblischak]

In one of our analyses with 18 sets, we identified another bottleneck in Counter():

UpSetR/R/MainBar.R

Lines 50 to 53 in b14854a

	for( i in 1:nrow(Freqs)){
	Freqs$x[i] <- i
	Freqs$color <- mbar_color
	}

Vectorizing the above to:

  Freqs$x <- 1:nrow(Freqs)
  Freqs$color <- mbar_color

decreased the compute time from over 5 minutes to ~5 seconds for our use case.

And for consistency, I made the same change to specific_intersections().