Actually, I think your point rather showcases that Julia could be useful for the statistics community! Due to performance reasons you occasionally have to abandon R to use Numpy, but for everything else you use R. Thus, in a sense you still have the problem that Julia tries to solve: You constantly have to jump between two different languages depending on the problem size.
Also, just like you chose Numpy, someone else might choose Julia. So this turns into a Numpy versus Julia comparison. And here I feel like (and this is not really an argument, just my gut feeling) Julia might be better at attracting people with a non-CS background, who want to implement some statistical methods or analyze their biological datasets.
In regards to graphics, there is really nice work being done by Daniel Jones. His Gadfly.jl (https://github.com/dcjones/Gadfly.jl) package implements the grammar of graphics and it can already produce high quality plots similar to ggplot2.
I will also disagree with your statement about Julia not catering to statisticians. Especially as datasets grow, performance keeps becoming more and more important. And this essentially means that statisticians implementing new methods in R packages have to drop down to C++ via Rcpp anyway. And this is exactly the problem Julia tackles. Of course, you could argue that practitioners do not care about the trouble the method developer went through in order to optimize the code via Rcpp. Yet, the practitioner will just use the language which offers the statistical tools he needs, thus Julia will draw him in, if it can first attract method developers.
There's also the people working on systems biology. People there usually use Matlab to do all of their simulations, modelling, fitting the data, etc. Yet, for some of the data, such as gene expression data, which is fed into the models, it is a lot easier to analyze/normalize it with packages available in R/BioConductor. Here Julia could also provide a unifying interface, so you won't need to be constantly switching languages.
Oh wow, I am so sorry. I don't really want to imagine what would have happened if I had been bullied for that long..
Indeed, I was made fun of for maybe 10 minutes; and it completely changed me. Back in 2005, last day of school (8th grade), many of us had gathered in a park in order to hang out and just spend time together before the start of the summer holidays. And then, the girl I had a crush on for the past two years, approached me with 3-4 other friends of her. She told me that she really liked me. It felt so thrilling and exciting and I was so happy and I told her that I liked her as well...
And then they left, and met up with a group of another ~30 people or so, maybe a 100 meters away. They all started laughing.. I felt so empty, I wanted to disappear, when one of them came to tell me "Oh by the way it was a joke that she likes you".
That summer I started playing MMORPGs, did not go to the beach once, just stayed home. Indeed, I spent the rest of high school pretty much isolated (although luckily eventually I replaced MMORPGs with actually studying). Whenever a girl showed interest in me, I immediately avoided her as much as possible, since I thought she just wanted to make fun of me. And it took 8 years, maybe a bit longer, until I started trusting people again.
So, why did I write all of this? Partially, because it came into memory after reading this thread. But more importantly, I am really scared by the fact, that the bullying I experienced was so so minor compared to the other stories here; yet it affected me so fundamentally.
Also, just like you chose Numpy, someone else might choose Julia. So this turns into a Numpy versus Julia comparison. And here I feel like (and this is not really an argument, just my gut feeling) Julia might be better at attracting people with a non-CS background, who want to implement some statistical methods or analyze their biological datasets.