R MCQ

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R is a programming language and environment widely used for statistical computing, data analysis, and graphics.

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The "<-" operator is the conventional assignment operator in R, e.g. "x <- 5", though "=" also works in most contexts.

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print() outputs the value of an object to the console; typing just "x" at the top level also auto-prints it.

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length() returns the number of elements contained in a vector or other R object.

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c() is the combine function used to create vectors by combining individual values.

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A list in R can hold elements of different types and structures, unlike a vector which requires all elements to be the same type.

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class() returns the class attribute of an R object, such as "numeric", "character", or "data.frame".

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The "#" symbol starts a single-line comment in R; everything after it on that line is ignored by the interpreter.

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read.csv() is a base R function that reads comma-separated values files into a data frame.

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NA is a special constant representing a missing value in R data structures.

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The "$" operator accesses a named element of a list or column of a data frame, e.g. "df$column_name".

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Both "1:10" and "seq(1, 10)" generate the integer sequence from 1 to 10 in R.

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install.packages("packagename") downloads and installs a package from a repository like CRAN.

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library(packagename) attaches an already-installed package so its functions become available.

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Logical values TRUE and FALSE are coerced to 1 and 0 respectively in arithmetic, so TRUE + TRUE equals 2.

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summary() produces a quick statistical summary including min, max, median, mean, and quartiles for numeric data.

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Functions in R are created using "function(args) { body }" and assigned to a name with "<-".

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str() compactly displays the structure of an R object, showing types and a preview of values, useful for exploring data frames.

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is.na() tests each element of an object and returns TRUE for elements that are NA.

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matrix() accepts a vector of data along with nrow and/or ncol to define the matrix dimensions.

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The "%%" operator computes the modulo (remainder) of division; 5 divided by 2 leaves a remainder of 1.

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rm() removes one or more named objects from the current environment, e.g. "rm(x)".

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R uses 1-based indexing, so the first element of a vector is accessed with v[1], not v[0].

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as.numeric() coerces its argument to a numeric type, returning NA with a warning if conversion is not possible.

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R uses C-like syntax for conditionals: "if (condition) { ... } else { ... }".

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"for (variable in sequence) { body }" iterates the variable over each element of the sequence.

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Any comparison involving NA returns NA, since the missing value's comparison result is itself unknown.

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rbind() stacks data frames or matrices vertically (row-wise), while cbind() combines them column-wise.

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Both "?mean" and "help(mean)" open the documentation page for the mean() function.

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Inf represents positive infinity in R, e.g. as the result of "1/0".

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factor() converts a vector into a factor, which represents categorical data with a fixed set of levels.

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nrow() returns the number of rows in a data frame or matrix; ncol() returns the number of columns.

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"&&" performs a short-circuit logical AND on single logical values, commonly used in if() conditions, whereas "&" is vectorized.

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rnorm(n, mean, sd) generates n random numbers drawn from a normal distribution.

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getwd() returns the current working directory path; setwd() changes it.

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paste() concatenates strings with a space separator by default; use paste0() for no separator.

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The "::" operator accesses an exported function from a specific package namespace without attaching the whole package via library().

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head() returns the first n rows (default 6) of a data frame or vector, useful for quickly previewing data.

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The "<<-" superassignment operator assigns a value in an enclosing (often global) environment rather than the local function scope.

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"rm(list = ls())" removes all objects returned by ls() (the current environment's object names), effectively clearing the workspace.

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Single bracket "[" subsetting returns a list containing the selected elements, while double bracket "[[" extracts the actual element itself.

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sapply() is a user-friendly version of lapply() that attempts to simplify the output to a vector, matrix, or array when the results allow.

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The pipe operator passes the left-hand side as the first argument of the right-hand function, enabling readable left-to-right chains of data transformations.

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lapply() always returns a list of the same length as x, with each element being f applied to the corresponding element of x.

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mutate() creates new variables or transforms existing variables within a data frame while preserving existing columns.

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"%in%" tests membership: "x %in% y" returns a logical vector indicating whether each element of x is found in y.

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Environments are mutable and passed by reference, so modifying them inside a function affects the original; lists follow R's usual copy-on-modify value semantics.

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Setting ordered = TRUE with specified levels creates an ordered factor, enabling meaningful "<" and ">" comparisons between levels based on their order.

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R's vectorized functions operate element-wise on whole vectors using efficient compiled code, avoiding the overhead of interpreted loop iteration.

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tapply() splits a vector into groups according to a factor and applies a function to each group, returning an array of results.

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identical() performs a strict, whole-object comparison returning a single logical, while "==" is vectorized and can yield NA for missing values or vectors of comparisons.

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The "..." argument captures any number of extra arguments, which can be forwarded to other functions called within the function body.

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Without na.rm = TRUE, the presence of any NA in the input causes functions like sum() or mean() to return NA; setting it to TRUE ignores NAs during computation.

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set.seed() fixes the state of R's pseudo-random number generator so that code involving randomness produces the same results each run.

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do.call() constructs and executes a function call from a function and a list of arguments, useful for dynamic function calls.

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Tibbles extend data frames with more predictable behaviors: they print more concisely, never use row names, and subsetting with "[" always returns a tibble.

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Reduce() successively applies a binary function to combine elements of a vector into a single value; here it sums 1+2+3+4 = 10.

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R uses lazy evaluation: argument expressions are only evaluated when first accessed within the function, which can affect default arguments referencing other arguments.

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merge() performs a database-style join of two data frames based on common columns or specified keys.

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"&" is vectorized over all elements, while "&&" (and "||") evaluate left to right and stop as soon as the result is determined, working on single values.

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table() builds a contingency table of counts for each unique value (or combination of values) in the input vector(s).

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regexpr() finds the position of the first regex match, and regmatches() extracts the actual matched substring(s) from x using those positions.

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S3 is R's informal OOP system: a generic function like print() dispatches to a class-specific method (e.g. print.myclass) based on the object's class attribute.

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str_detect() returns TRUE or FALSE for each element of x depending on whether the pattern is found, similar to grepl() in base R.

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group_by() splits a data frame into groups based on column values, and summarise() then computes one or more summary statistics for each group.

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match.arg() is commonly used with arguments like "type = c('a', 'b', 'c')" to validate that the supplied value is one of the allowed choices and supply a default.

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By default, selecting a single column from a data frame with "[" simplifies the result to a vector; drop = FALSE preserves the data frame structure.

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switch(expr, case1 = result1, case2 = result2, ...) evaluates expr and returns the result corresponding to the matching case.

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apply(X, MARGIN, FUN) applies FUN over rows when MARGIN = 1, and over columns when MARGIN = 2.

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Sys.time() returns the current system date and time as a POSIXct object.

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R's copy-on-modify semantics mean two variables can initially point to the same data, but as soon as one is modified, R makes a copy so the other remains unchanged.

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Vectorize() wraps a scalar function so it can be called with vector arguments, internally using mapply to apply it element-wise.

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complete.cases() returns TRUE for rows that contain no NA values across all (or specified) columns, useful for filtering complete observations.

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on.exit() schedules code (such as cleanup actions) to run when the enclosing function returns, whether normally or due to an error.

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is.function() returns TRUE if its argument is a function, which is useful since functions are first-class objects in R.

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Assigning to names(df) replaces the column names of the data frame with the provided vector, in positional order.

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The "%>%" pipe operator originates from the magrittr package; base R introduced its own native pipe "|>" starting in version 4.1.

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as.Date() parses a string into R's Date class, enabling operations like subtraction between dates and formatted output via format().

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pivot_longer() gathers columns into rows (wide-to-long), while pivot_wider() spreads rows into columns (long-to-wide), useful for reshaping data for analysis or plotting.

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ggplot2 builds plots in layers: aes() maps data columns to visual properties (axes), and geom_point() adds a layer of points, creating a scatter plot.

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S4 is a more rigorous OOP system with formally declared classes, slots with types, validity methods, and support for multiple dispatch, contrasting with S3's convention-based approach.

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A function's environment determines where it looks up variables not defined locally or passed as arguments; reassigning it changes this lexical scope.

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Functions using NSE (like subset() with base R, or dplyr verbs with tidy evaluation) capture argument expressions unevaluated, allowing data frame column names to be used directly as if they were variables.

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Memoization caches the results of expensive function calls keyed by their arguments, often implemented using a closure over an environment that stores past results.

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Rcpp simplifies calling C++ functions from R and passing data between the two languages, often used to speed up computationally intensive operations.

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force(x) simply evaluates x, which is used to "lock in" the current value of a variable used in a closure due to R's lazy evaluation, avoiding common loop-variable-capture bugs.

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quote() returns the unevaluated expression as a language object, which can later be passed to eval() to execute it in a specified environment.

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Function arguments in R are represented internally as "promises" — pairs of an expression and environment — that are evaluated on first access, implementing lazy evaluation.

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tryCatch() provides structured handling with separate handlers for different condition classes (error, warning, message, etc.), while try() is a simpler wrapper that just catches errors and continues.

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The ":=" operator in data.table performs in-place modification by reference, which is significantly more memory- and time-efficient for large datasets compared to copy-on-modify approaches.

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match.call() returns the function call as it was made, with arguments matched and named according to the function's formal arguments, often used for logging or constructing informative messages.

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R automatically performs garbage collection, reclaiming memory occupied by objects that are no longer reachable, though gc() can be called manually to request collection.

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mclapply() parallelizes apply-style operations via forking, beneficial for CPU-intensive tasks on multi-core Unix-like systems, but fork-based parallelism is not well supported on Windows and has process-spawning overhead.

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sys.call() gives the call expression (function name and arguments as written), whereas sys.function() gives the function definition itself, useful for introspection in advanced metaprogramming.

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Unlike most R objects which use copy-on-modify semantics, R6 and Reference Class (R5) objects are mutable and modified in place by their methods, similar to objects in languages like Python or Java.

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missing() detects whether an argument was omitted from the call (without evaluating it), whereas is.null() requires evaluating the argument and checks if its resulting value is NULL.

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Active bindings (via makeActiveBinding() or R6's "active" list) define accessor functions that run on every read/write, enabling computed or validated properties.

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bquote() is like quote() but supports "unquoting" via .(...) syntax, allowing selected sub-expressions to be evaluated and spliced into the otherwise-unevaluated expression.

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ALTREP lets certain vectors (like integer sequences from ":" or memory-mapped data) be stored in compact alternative representations rather than fully expanded arrays, saving memory and time.

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Because functions are first-class values in R, they can be manipulated like any other object — stored, passed around, and returned — which underlies higher-order functions such as Map(), Filter(), and Reduce().