R
Learn how to navigate R programming in data science by understanding its limitations with big data, differences between Inf and NaN, and key functions such as with, by, next, and setdiff. Discover how to create custom functions, combine datasets, and plot multiple graphs, enhancing your coding skills for effective data analysis.
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What are the limitations of R?
R has many limitations, but some directly affect data analysis. These limitations are as follows:
It needs to load all data into memory (RAM), so it is not appropriate for big data analysis.
Processing in R is slower than in other programming tools, and if the package's maintainer no longer sustains its package, then some R scripts do not work with the newer version of R.
What is the difference between Inf and NaN?
The Inf keyword represents the infinity value. For example, if we divide 1 by 0, we get infinity. If we add infinity to infinity, we get infinity. But if we subtract infinity from infinity, we don't get infinity. There is no value defined for this subtraction. That means if a number can't represent a value, it is referred to as NaN (not a number) in R.
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