R for DS

R Interview Q&A for Data Science

Key R concepts and workflow topics for DS interviews.

1Why use R in Data Science?easy
Answer: R is strong in statistics, visualization, and reproducible analysis workflows.
2What are vectors in R?easy
Answer: Vectors are 1D homogeneous data structures and a core R building block.
3Data frame vs tibble?medium
Answer: Tibbles are modern data frames with cleaner printing and safer defaults.
4What is factor in R?medium
Answer: Factor stores categorical variables with predefined levels.
5What does dplyr provide?easy
Answer: Grammar for data manipulation: filter, select, mutate, summarize, arrange, joins.
6What is %>% pipe operator?easy
Answer: It passes output of one step as input to the next, improving readability.
7How does ggplot2 work?medium
Answer: It uses grammar of graphics: map aesthetics, add geoms, facets, and scales.
8What is NA handling in R?medium
Answer: Use functions like is.na(), na.omit(), and imputations via tidyverse/modeling.
9What are lists in R?easy
Answer: Lists are heterogeneous containers that can hold mixed object types.
10Base R vs tidyverse?medium
Answer: Base R is foundational; tidyverse offers consistent, expressive data workflows.
11What is caret package used for?medium
Answer: It streamlines model training, preprocessing, tuning, and validation.
12What is RMarkdown?easy
Answer: A reproducible document format combining code, output, and narrative.
13How to improve R performance?medium
Answer: Prefer vectorized operations, efficient packages like data.table, and profiling hotspots.
14When use R instead of Python?medium
Answer: For heavy statistical analysis, quick visualization, and academic/research workflows.
15One-line R summary for interviews?easy
Answer: R is a statistics-first ecosystem optimized for analysis and communication.
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