Computational Book on data/error analysis using R language?

AI Thread Summary
Recommendations for books focusing on data and error analysis using R, Python, or C++ were sought, with a preference against Fortran due to its perceived obsolescence. Key suggestions included "R For Data Science," which covers data handling and visualization but uses the Tidyverse syntax, potentially complicating debugging for those accustomed to base R. Another recommended title is "An Introduction to Statistical Learning," noted for its mathematical rigor and practical lab sections that utilize base R. The discussion also acknowledged the ongoing relevance of Fortran in scientific and engineering contexts, despite its age. Additionally, Python was mentioned as a versatile language for data science, appealing to those open to language options.
LCSphysicist
Messages
644
Reaction score
162
Hello there.
Could you please recommend me a book that focus on data/error analysis and that, at the same time, provides examples of how to use the R programming language to such things?
It could be using the python or c++ languages instead.
The only books i have came across use fortran, but since i think it is becoming outdate to learn this language, i have decided to not use it.
 
Physics news on Phys.org
Maybe have a look at some web articles before buying a book, e.g.
https://data-flair.training/blogs/debugging-in-r-programming/
 
  • Informative
Likes LCSphysicist
LCSphysicist said:
Hello there.
Could you please recommend me a book that focus on data/error analysis and that, at the same time, provides examples of how to use the R programming language to such things?
It could be using the python or c++ languages instead.
The only books i have came across use fortran, but since i think it is becoming outdate to learn this language, i have decided to not use it.
In my opinion (and I'm in good company regarding this), Fortran, now about 65 years old, is by no means outdated ##-## it's an understatement to say that it has a very rich set of libraries ##-## many scientists and engineers find it indispensable for their work.
 
  • Like
Likes LCSphysicist
My two favorites that I use regularly (the first of which I actually learned R from) are:

R For Data Science, this goes through everything from importing, cleaning, visualizing and modeling (although this is the weakest section of the book). A slight word of warning with this, though; they use the 'Tidyverse' syntax, which is pretty different from base R syntax. This can be jarring and frustrating when you're trying to debug errors!

An Introduction to Statistical Learning, this is a much more mathematically advanced book (although very well written) that goes into modelling and error analysis. I do some statistical modelling for my job and reference this constantly, particularly the lab sections which work through a full project. This does use base R language.
 
Python is probably more generally useful to know if you are indifferent between languages for doing data science.
 
This thread only works as a summary from the original source: List of STEM Masterworks in Physics, Mechanics, Electrodynamics... The original thread got very long and somewhat hard to read so I have compiled the recommendations from that thread in an online (Google Drive) spreadsheet. SUMMARY Permits are granted so you can make comments on the spreadsheet but I'll initially be the only one capable of edition. This is to avoid the possibility of someone deleting everything either by mistake...
By looking around, it seems like Dr. Hassani's books are great for studying "mathematical methods for the physicist/engineer." One is for the beginner physicist [Mathematical Methods: For Students of Physics and Related Fields] and the other is [Mathematical Physics: A Modern Introduction to Its Foundations] for the advanced undergraduate / grad student. I'm a sophomore undergrad and I have taken up the standard calculus sequence (~3sems) and ODEs. I want to self study ahead in mathematics...

Similar threads

Back
Top