Book on data/error analysis using R language?

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SUMMARY

The discussion centers on recommendations for books focused on data and error analysis using the R programming language. Two notable titles are highlighted: "R For Data Science," which emphasizes data manipulation and visualization using Tidyverse syntax, and "An Introduction to Statistical Learning," which provides a mathematically rigorous approach to modeling and error analysis using base R. The conversation also touches on the relevance of Fortran in scientific computing, despite its age, and suggests that Python may be a more versatile language for data science.

PREREQUISITES
  • Familiarity with R programming language
  • Understanding of data manipulation and visualization techniques
  • Basic knowledge of statistical modeling concepts
  • Awareness of Tidyverse and base R syntax differences
NEXT STEPS
  • Research "R For Data Science" for practical data manipulation techniques
  • Explore "An Introduction to Statistical Learning" for advanced statistical modeling
  • Learn about Tidyverse syntax and its applications in R
  • Investigate Python's role in data science for comparative analysis
USEFUL FOR

This discussion is beneficial for data scientists, statisticians, and R programmers seeking to enhance their skills in data and error analysis, as well as those considering the transition to Python for data science applications.

LCSphysicist
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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.
 
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Maybe have a look at some web articles before buying a book, e.g.
https://data-flair.training/blogs/debugging-in-r-programming/
 
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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.
 
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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.
 
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