R Programming MCQs with answers Page - 110

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Q. What is the purpose of the stringdist package in R?

  • (A) Tools for working with big data using distributed computing
  • (B) Text manipulation and string distance calculations
  • (C) Data manipulation and transformation
  • (D) Natural language processing (NLP)

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Q. In R, which package is commonly used for parallel computing, especially for tasks involving loops?

  • (A) parallelR
  • (B) doParallel
  • (C) foreach
  • (D) multicore

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Q. What does the rstan package in R primarily facilitate?

  • (A) Bayesian statistical modeling with Stan
  • (B) Time series analysis and forecasting
  • (C) Handling large datasets with parallel computing
  • (D) Interactive data visualization and exploration

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Q. In R, what is the role of the DT package?

  • (A) Creating dynamic documents and reports
  • (B) Interactive data tables and data exploration
  • (C) Time series analysis and forecasting
  • (D) Geospatial data analysis

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Q. What is the primary use of the feather package in R?

  • (A) Data manipulation and transformation
  • (B) Efficiently reading and writing data frames
  • (C) Time series analysis and forecasting
  • (D) Handling large datasets with parallel computing

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Q. In R, which package is commonly used for working with data from the World Bank?

  • (A) worldbankR
  • (B) wbData
  • (C) globalstats
  • (D) wbtools

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Q. What is the role of the glmnet package in R?

  • (A) Regularized linear regression and classification
  • (B) Data manipulation and transformation
  • (C) Handling large datasets with parallel computing
  • (D) Interactive data visualization and exploration

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Q. In R, which package provides functions for conducting survival analysis with support for time-varying covariates?

  • (A) survival
  • (B) survminer
  • (C) coxph
  • (D) timeSurv

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Q. In R, which package is commonly used for creating and customizing interactive maps?

  • (A) maps
  • (B) leaflet
  • (C) geospatialR
  • (D) plotly

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Q. What is the purpose of the reticulate package in R?

  • (A) Integration of R with Python
  • (B) Data manipulation and transformation
  • (C) Statistical analysis and modeling
  • (D) Natural language processing (NLP)

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