Reddit reviews Discovering Statistics Using R
We found 21 Reddit comments about Discovering Statistics Using R. Here are the top ones, ranked by their Reddit score.
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Reddit reviews Discovering Statistics Using RWe found 21 Reddit comments about Discovering Statistics Using R. Here are the top ones, ranked by their Reddit score.
On mobile but this is copied and pasted from a comment I made awhile back.
Most of my personal R code for spatial analysis is largely uncommented but I will share some resources I base a lot it off of.
Books:
Websites:
Rpubs:
Python:
Edit: I used to work in public health academia so the links are geared towards that field.
For a stats program, I recommend learning R. It's free and powerful. No need to worry or wonder whether your university will have a license, as you would for SPSS, SAS, STATA, etc.
If you really have no stats background, you might find Andy Field's popular book series Discovering Statistics to be a good place to start. He's re-written this for several stats programs, but you'd want the one called Discovering Statistics Using R. That should be plenty to keep you busy. If not, then go a bit more into factor analysis, structural equation modeling, and Bayesian statistics.
I've really enjoyed Discovering Statistics using R by Andy Field. The book is written more like prose than a textbook, and is rarely dry. It requires you to learn how to use the R programming language as well, but I think it is very worth it. Everything he teaches, he teaches it at the conceptual level first and then shows you how to perform the tests using R. A great bonus is that R is great for data visualization, and being able to visualize large data sets quickly really helps get a better understanding of the data you are working with, which helps learn the theory.
You must learn an application heavy book in 2018. Preferably in R unless you can program, in that case maybe Python.
I will link you two perfect books with very little math that people from any discipline can understand and are very well written. Both heavy on application in R with accompanying websites with all the code. (dont worry, R code is easy and the vast majority of R users are not programmers in the traditional sense). The first book I link does go into some more advanced topics, but everything is explained in a very common language. Its accompanying website also has lecture videos from the prof who wrote it.
https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469
^^ I emailed andy some time ago and he wants to release edition 2 next year probably
https://www.amazon.com/Understanding-Applying-Basic-Statistical-Methods/dp/1119061393
Trust me, these two books are what you want to look into.
NOTE some idiot is going to try to suggest to you a book called "Introduction to statistical learning" (mainly a supervised machine-learning book which is stats-focused) by the standford stats team. Do not start with this book if you want to learn traditional stats (like you point out in your post). No one who recommends you this book has considered your needs. I see this recommended every single day for all the wrong reasons. It actually makes me frustrated. It's a great book but has confused many people because of its name. Is it a stats book? Yeah. Is it an ML book, yeah? Is it a traditional stats book? Nope. Anything that says "_____ learning" is probably a machine learning book. Sorry for the rant.
An EXCELLENT response. I'm a research psychologist and I think that is an absolutely fair summary of what to expect if you go the grad school route.
As far as "what you're expected to know", this will vary by which of the four options you choose; the best RAs i've mentored are always interested in two things: stats and current literature. Google Scholar your favorite topic in psychology and the backwards/forwards links will connect you to a wealth of ideas. As for stats, they get a bad rap, but i can suggest a few books that might turn around anyone with stats-loathing:
-Andy Field's SPSS/R how-to books. Honestly, the man has probably done more for beginning psychologists than Starbucks. Very readable, even good for more developed psychologists to get ideas for new analyses.
http://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469/ref=sr_1_2?s=books&ie=UTF8&qid=1419965487&sr=1-2.
-Mac & Creel: Bible for signal detection theory, a cool way of thinking about perception as a process of separating signal from noise.
http://www.amazon.com/Detection-Theory-A-Users-Guide/dp/0805842314
This book is a amazing:
Discovering Statistics Using R
by Andy Field
If you are doing self-study, it is easy to lose momentum. This book is hilarious, personal, and transcends the textbook genre.
Amazon Link
I like Discovering Statistics using R . Great book for learning the basics of hypothesis testing, a little bit of math, and you learn how to do it in R; not to mention there are a few bits you’ll chuckle at. There are also other books for other programs in this series (SPSS, SAS).
I have an excellent statistics text book that I am using to learn stats: Discovering Statistics Using R by Andy Field. My approach is to do the exercise in R first, then try to reproduce the same result in Python. It's slow going, but it's a real learning experience.
Now this is an interesting and difficult question, which depends on many things. For starters, if you find this process frustrating that is unfortunate, because learning how to code is usually a trail and error + revise your work process. In other words, its supposed to be both frustrating and rewarding, like a hard (text-based) video game. For me its half of the fun.
What you want to learn really depends on the context. If you are really diving into econ, Stata is still very common. More stats-heavy, new, or interdisciplinary fields tend to use R. If you work with big, live datasets, or work with computer scientists, learning Python is always a plus. But obviously start with one.
I am proficient in stata simply because I had classes in it, it is difficult for me to advice how to self study. I learned most of the basics through An Introduction to Modern Econometrics Using Stata, and later on most of my R through R for Stata Users (Statistics and Computing) . I also learned some R through Discovering Statistics Using R, but I find Field obnoxiously failing to be funny, so I wouldn't reccomend it.
I'm now in the process of learning more Python, to do some more programming work on the side. As a start I used Learn Python 3 the Hard Way recommended to me by a very proficient friend of mine. This however does not give you much of an intro to stats in python, only the very very simple basics you can use as a vantage point for further work.
If you have learned the basics, tbe hest way to learn more is just to fool around. What is your field of interest? I like a lot of macro, so I used to just go to Quandl, pick some free databases, import them, and run some fun stuff. This is the best way to learn, especially if you for example try to merge free World bank databases with a different database from Quandl, as it will give you a lot of errors whilst merging and conversion problems later on.
If you are a bit more proficient you can start using websites like upwork to get some assignments. Usually it doesn't earn you much at the start, but the experience of actual assignments is the best way to self-teach. A different manner I like to do (if you are still studying) is offering your services (for free) to a professor. Ask him/her if there are still projects they are working on for which they need some to look at. Usually you will be treated solely as someone for the code, but it generally gives you a lot of experience and the right contacts.
These are just some of my thoughts. If you could provide some more context of where exactly you want to go, I could go into more detail.
Edit: What I forgot to say is that if it is not possible to study a course, I would recommend doing at least one MOOC to get you at a basic level.
I would recommend Discovering Statistics Using R. It goes through the math of the stats in a pretty solid way with example experiments and available data files if you want to work along with it. I have used the SPSS version with my intro and intermediate stats classes, so using it with R would be great. Plus, Fields is funny as hell.
Andy Field also has a book for learning statistics using R: https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469
I also recommend his book, it is quite a fun read.
field's "introduction to statistics using R" is the best book for my money.
EDIT: sorry I got the title wrong:
https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469
I found Andy Field's Discovering Statistics Using R to be quite helpful.
Discovering Statistics Using R by Andy Field is probably your best bet. It's pretty comprehensive in terms of what it covers and is easy (and enjoyable) to follow along with and understand.
> Maybe pick up a book and try to learn a little bit of R this summer?
Coursera has online courses if you want learn R and how to do statistics using R.
If you prefer a book, try "Discovering Statistics Using R" by Andy Field. I'm using it to self-study right now and I highly recommend it. It really explains statistical concepts well and it's very easy to read. Although it does sacrifice a lot of mathematics, you could compensate for it when you take up your statistics subject.
I do have to say that I've already taken up Econ Statistics and Econometrics (I had a horrible time however). Also, I've taken up the R course in Coursera so I'm not sure if my experience with the book is the same as yours. Read a couple of chapters and see if it is to your liking.
Second Tabachnik & Fiddle along with Hunter & Schmidt
Rogelberg's IO Handbook
Brannick & Levine's JA text
HLM - Raudennbush & Bryk
Ployhart et al's Staffing Organizations
I have lots of other O-oriented things, but those will depend on your specific area.
Edit: This!
Lots of great suggestions here. My grad program used SPSS but it annoyed me that someone had to pay for it, so I learned R. Like others mention, if you learn R it can be easier to go back to SPSS. Also, others who use SPSS might think you have some kind of superpower.
Like u/bobbyfiend says, the best is to do use it on some projects. This forces you to learn something that is important and you have interest in solving. The internet is amazing, and most answers in some form or another can be found on Stack Overflow (make sure to ask the questions in the proper format and search first), /r/rstats (a bit more friendly than stack overflow), or on some of the email lists.
In general, I would say there are a couple of resources that most people could benefit from as they start to learn:
-Andy Field's Discovering Statistics with R - It does have some irreverent humor, but is a good read
-Hadley Wickham's R for Data Science - this resource is free online but can also be bought through Amazon. Hadley is a R celebrity responsible for creating the 'tidyverse' series of packages - packages which make R more beginner friendly imo.
You will definitely want to look at your subspecialty and see if there are any people working in R there. They may have some other resources. Again, you can read books and watch courses all you want, but it is critical to practice (and practice using something you are interested in can help exceptionally). Ultimately, I used my dissertation as an excuse to dive into R - there was pain, and I probably could have done it quicker if I stayed in SPSS - but I learned a lot and now use R and Rmarkdown - and really do not think I plan on going back. Another user mentions looking at others' code, and this has also helped me to make my code more efficient and reproducible - a big strength of R (love that you can use Git).
I would second The Elements of Statistical Learning is a great text but it is not an entry level stats book. Maybe try Discovering Statistics Using R?
Seconding the Andy Field comment. http://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469
https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469
My program requires a number of stats classes and my advisor requires a number more than that. My program also offers a few data science-related specializations, which are, of course, optional, but great.
For some independent learning, Andy Field's Discovering Statistics Using R -- https://www.amazon.com/Discovering-Statistics-Using-Andy-Field/dp/1446200469/ref=sr_1_1?ie=UTF8&qid=1538060236&sr=8-1&keywords=discovering+statistics+using+r -- and datacamp.com are both handy resources.