#811 in Computers & technology books
Reddit mentions of R in Action
Sentiment score: 3
Reddit mentions: 5
We found 5 Reddit mentions of R in Action. Here are the top ones.
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Height | 9.25 Inches |
Length | 7.38 Inches |
Number of items | 1 |
Release date | August 2011 |
Weight | 1.78353969958 Pounds |
Width | 1 Inches |
Here's what I'd recommend.
GETTING STARTED WITH DATA SCIENCE
If you're interested in learning data science I'd suggest the following:
 
Tools
Learning these has several benefits: they streamline your workflow. They speed up your learning process, since they are very easy to use. And perhaps most importantly, they really teach you how to think about analyzing data. GGplot2 has a deep underlying structure to the syntax, based on the Grammar of Graphics theoretical framework. I won’t go into that too much, but suffice it to say, when you learn the ggplot2 syntax, you’re actually learning how to think about data visualization in a very deep way. You’ll eventually understand how to create complex visualizations without much effort.
 
Skill Areas
My recommendations are:
(But, again I recommend learning these in R’s ggplot2.) The reason I recommend these is
As with any discipline, you need to learn the foundations first; this will dramatically speed your progress in the intermediate to advanced stages.
I usually suggest learning these with dummy data (for simplicity) but if you have a simple .csv file, that should work to.
After you learn data visualization, I suggest that you “back into” data management. For this, you should find a dataset and learn to reshape it.
The core data management skills:
You can start learning these here. Again, I recommend learning these in R’s dplyr because dplyr makes these tasks very straight forward. It also teaches you how to think about data wrangling in terms of workflow: the “chaining operator” in dplyr helps you wire these commands together in a way that really matches the analytics workflow. dplyr makes it seamless.
ML is sort of like the “data science 301” course vs. the 102 and 103 levels of the data-vis and data manipulation stuff I outlined above.
Here, I’ll just give book recos:
This is a highly regarded introduction
After you get these foundations, then you can move on to specialize in a particular area.
 
OTHER RESOURCES:
Data Visualization
 
TL;DR
I'd recommend learning R for data science before Python. Learn data visualization first (with R's ggplot2), using simple data or dummy data. Then find a more complicated dataset. Learn data manipulation second (with R's dplyr), and practice data manipulation on your more complex data. Learn machine learning last.
From one R newbie to another, I have found the website Quick-R to be the most helpful. I was roped into using R on my thesis and have had to learn everything as I go, so I don't have the background that a lot of other R users do. That said, it's nice to have a condensed, plain-english reference without all the alphabet soup of theory.
I also use A Beginner's Guide to R to figure out the basic stuff and R in Action for more advanced topics. I don't know what your statistical background is, but you should also try to find a solid stats book related to your field.
As a last bit, and this may not be a popular sentiment on this sub, but I have found very little help through the r-help files, mailing lists, or stackoverflow. Most times the posts are written by and for people with much greater ability than mine so I have a really hard time gleaning any useful information. Also, I have found that there are a lot of condescending attitudes throughout and it's soured me to those sources. In my experience, the R community at large is not very friendly towards beginners.
Good luck on your adventuRe.
R in action
Also the coursera data science specialization.
I'm more of a book person than a youtube tutorial person, so I've been using R in Action (I like it because he gets to graphics fairly early).
If you've used a statistical package before (like Stata, SAS, or SPSS), there's a fairly decent amount of crossover; a lot of the adjustment is just syntax changes.
You can definitely go straight command line, but I'm quite partial to RStudio, which has a nice little wrapper around the R prompt.
Are you doing andrew ng's course on Machine learning? :)
I like this book quite a bit. http://www.amazon.com/R-Action-Robert-Kabacoff/dp/1935182390
These labs for learning stats using R. http://www.openintro.org/stat/labs.php?stat_book=os