I've heard all good things about ggplot2 and I sincerely believe they're true. However, my only experience has been trying to plot 2 overlapping timeseries that didn't have the same length to the same plot area. I found that to be nearly impossible in ggplot2 and almost trivial with the builtin plot and lines commands in R.

Like a lot of good tools, it seems that ggplot2 makes the common case extremely easy, but it can be a struggle to make it work in uncommon situations.

> s1 <- data.frame(x=c(1,2,3), y=c(7,8,7),series=rep("s1", 3))

> s2 <- data.frame(x=c(1,2,3,4), y=c(6,7,6,7), series=rep("s2", 4))

> s <- rbind(s1, s2)

> qplot(x, y, data=s, geom=c('point', 'line'), color=series)

Thanks. I guess this speaks more to my lack of lack of experience.

To be clear this applies to data frames, not the language as a whole. If you are creating a data frame by reading in data then `read.table(...whatever..., as.is = TRUE)` will read in character columns as character strings and not as factors. `data.frame()` has a `stringsAsFactors` argument which can be set to `FALSE` and one can change the global default with: `options(stringsAsFactors = FALSE)` .

Agreed, this was a huge pain point for me early on; I started setting the necessary option to force stringsAsCharacters rather than specifying that arg in read.csv everytime.

Edit: I had mentioned data.table as being better than dplyr for performance reasons, but it has a unique learning curve and isn't really good for beginners

> If you're learning R, learn to use dplyr for data manipulation

I had been learning data.table, but I really like dplyr's % operator and the compositional functions better. I think I'm going to make the move to dplyr.

We're in the process of adding more detailed vignettes illustrating more clearly the philosophy behind data.table's `i, j, by`. Should make things lot easier for beginners - https://github.com/Rdatatable/data.table/issues/944

data.table is not just fast, but is also more memory efficient - we want to highlight in the vignettes as well.

And timings are quite relevant even on 10 million rows: https://gist.github.com/arunsrinivasan/db6e1ce05227f120a2c9

For much larger data, check the project page: https://github.com/Rdatatable/data.table/wiki/Benchmarks-%3A...

FWIW The performance difference is insignificant unless your working with >>10 of millions of rows.

I thought dplyr (note, not plyr, dplyr is the next iteration of plyr implemented mostly in native code) was pretty much as fast as data.table.