Reddit reviews Data Manipulation with R (Use R!)

Okay, I'll take these one at a time:
-Excel, high volume: I want to caveat this by saying "I don't know the full details of your company", but you probably need a database (my reading of the above is that your company doesn't have one). You're at a scale where excel is really error prone, likely to crash, etc. The trouble, is that I don't recommend building the database yourself; database design and construction is a skill in and of itself. Without knowing the specific capabilities and needs of your company, my instinct is that your company may need to consider upgrading it's technology and building out a small team of people in support of data analytics.

-R (dplyr, reshape, data wrangling): These packages and tools are wonderful. That said, how to use them properly is a huge topic, and it would be nearly impossible in a forum setting to give you actionable advice on using them, specific to your needs. That said, I'd highly recommend the following book: Data Manipulation With R. Additionally, I'll be covering plyr, data shaping, etc at my blog. It might be a while, but I plan to explain data shaping in more detail (how to think about it, workflow, etc).

-Python: It depends on your needs, but you may want to hold off on Python. To be clear, I love python. It's easy to learn, easy to read/write, and functional. But, for most of the things you'll need to do, other tools are good, or better. For visualization, Tableau and R are better (at least as of Fall 2014. There's a Python library being developed called Bokeh that looks very promising.) For data cleaning/shaping/wrangling, a mix of SQL and R (plyr) is as good or better than something you'd do in Python. If you have a strong need for web-scraping, Python is probably a good solution, but in my experience, web-scraping is more of an advanced topic. Most people learn web-scraping after they've already mastered data shaping, visualization, and other fundamentals. So, if you have a very strong need for web-scraping, then consider it, but otherwise I'd recommend learning it after R.