Module_V_Pointers_R_Programming_VTU.pptx

Module_V_Pointers_R_Programming_VTU.pptx

• 1.
  Module V –Pointers in R Programming Packages, Frames, Debugging, Code Manipulation, Compilation
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  1. Packages inR • • A package is a collection of functions, data, and documentation. • • Structure: R/, data/, man/, DESCRIPTION, NAMESPACE. • • Commands: • install.packages('ggplot2') • library(ggplot2) • • Creating a package: use devtools. • • Popular packages: dplyr, ggplot2, caret,
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  2. Frames inR • • Data frame: two-dimensional table-like structure. • • Each column can have different data types. • • Example: • df <- data.frame(Name=c('Ram','Sita'), Age=c(25,28)) • • Access elements: df$Name, df[1,], df[,2] • • Function environments create local frames.
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  3. Debugging inR • • Common errors: syntax, logical, runtime. • • Debugging tools: • - traceback(): shows call stack • - browser(): pauses execution • - debug() / undebug(): step into/exit • - recover(): inspect environment after error • • Use print() or cat() for tracing.
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  4. Code Manipulation •• Execute or modify R code dynamically. • • Functions: eval(), parse(), quote(), substitute(). • • Example: • expr <- quote(x + y) • eval(expr, list(x=10, y=5)) • • Profiling: Rprof(), summaryRprof().
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  5. Compilation inR • • R supports byte-code compilation for speed. • • Use compiler package: • cmpfun(myfun) • • JIT (Just-In-Time) compilation: • enableJIT(3) • • Interface with C/C++ using Rcpp.
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  Summary • • Packagesextend R functionalities. • • Frames hold data or variable environments. • • Debugging helps fix syntax and logical errors. • • Code manipulation allows dynamic execution. • • Compilation improves performance.