Introduction
Boys like their toys, and this is not different with statistical packages. It's a perpetual and heated debate and when you've landed at some point and think your workflow is good, technology passes you and sets you back.
Here's an
old discussion that I first consulted, but below I make my own considerations. In short: I'd use Python for big data, Stata for analysis, and R if I have to (e.g. for some graphs). Everything else I would ditch.
Stata
Stata is my program of choice. It is quite expensive, but mind you that for a couple of hundred euros, depending on the flavour, you'll not only get an easy and robust statistical software package, but also fast support, a good community, useful user commands, and a great documentation source. Fun fact: all documentation is read by the wife of the founder, who's not a statistician but perhaps even smarter. If she doesn't understand what the statisticians are saying, it goes back to the drawing board.
The bad things: forget about ever copy-pasting anything. You'll also need to have a lot of memory on your computer, as Stata loads the whole file and just one at a time (although you can 'preserve' a file temporarily to work on something else in between).
Python
Python is the next language I will learn. I have used chunks without understanding what I was doing, but I like the sound of the language, and it's the logical step-up after Stata, it seems. Many people are using it and so will I.
R
I don't like R. There is a thorough discussion
here, circling around leaving Stata for R, but ending up in concluding what I conclude about: R is a mixture of a coding language like Python and a statistical language like Stata, but because it is open source the support is unsure, the community tends to be geeky and unfriendly, the documentation is poor, and the language consistency - even if the structure is good because it is a programming language - is bad. Some commands have their own inner programming language and that is plain bad.
The good things: it is free and R Studio is a great user interface. It has good graphic capabilities, and
Mplus
I don't know Mplus. Colleagues use it when there are issues with missing values, and the programmers are said to be the best statisticians in the world. So it must be good, but I don't use it.
SAS
This is old software. It is too complicated, and while it can do a lot through obscure options, it is not flexible enough to do what you want.
SPSS
This is bad software. It is a scandal that some universities still teach this.
Some R resources
Apparently the single best manual for R:
https://r4ds.had.co.nz.
