minitest

x <- sqrt(10)
x^3.14

## [1] 37.15352

set.seed(10)
rnorm(n = 1,mean = 1,sd =1)

## [1] 1.018746

set.seed(21)
rnorm(n = 1,mean = 1.1,sd =1)

## [1] 1.893013

lm

## function (formula, data, subset, weights, na.action, method = "qr", 
##     model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE, 
##     contrasts = NULL, offset, ...) 
## {
##     ret.x <- x
##     ret.y <- y
##     cl <- match.call()
##     mf <- match.call(expand.dots = FALSE)
##     m <- match(c("formula", "data", "subset", "weights", "na.action", 
##         "offset"), names(mf), 0L)
##     mf <- mf[c(1L, m)]
##     mf$drop.unused.levels <- TRUE
##     mf[[1L]] <- quote(stats::model.frame)
##     mf <- eval(mf, parent.frame())
##     if (method == "model.frame") 
##         return(mf)
##     else if (method != "qr") 
##         warning(gettextf("method = '%s' is not supported. Using 'qr'", 
##             method), domain = NA)
##     mt <- attr(mf, "terms")
##     y <- model.response(mf, "numeric")
##     w <- as.vector(model.weights(mf))
##     if (!is.null(w) && !is.numeric(w)) 
##         stop("'weights' must be a numeric vector")
##     offset <- model.offset(mf)
##     mlm <- is.matrix(y)
##     ny <- if (mlm) 
##         nrow(y)
##     else length(y)
##     if (!is.null(offset)) {
##         if (!mlm) 
##             offset <- as.vector(offset)
##         if (NROW(offset) != ny) 
##             stop(gettextf("number of offsets is %d, should equal %d (number of observations)", 
##                 NROW(offset), ny), domain = NA)
##     }
##     if (is.empty.model(mt)) {
##         x <- NULL
##         z <- list(coefficients = if (mlm) matrix(NA_real_, 0, 
##             ncol(y)) else numeric(), residuals = y, fitted.values = 0 * 
##             y, weights = w, rank = 0L, df.residual = if (!is.null(w)) sum(w != 
##             0) else ny)
##         if (!is.null(offset)) {
##             z$fitted.values <- offset
##             z$residuals <- y - offset
##         }
##     }
##     else {
##         x <- model.matrix(mt, mf, contrasts)
##         z <- if (is.null(w)) 
##             lm.fit(x, y, offset = offset, singular.ok = singular.ok, 
##                 ...)
##         else lm.wfit(x, y, w, offset = offset, singular.ok = singular.ok, 
##             ...)
##     }
##     class(z) <- c(if (mlm) "mlm", "lm")
##     z$na.action <- attr(mf, "na.action")
##     z$offset <- offset
##     z$contrasts <- attr(x, "contrasts")
##     z$xlevels <- .getXlevels(mt, mf)
##     z$call <- cl
##     z$terms <- mt
##     if (model) 
##         z$model <- mf
##     if (ret.x) 
##         z$x <- x
##     if (ret.y) 
##         z$y <- y
##     if (!qr) 
##         z$qr <- NULL
##     z
## }
## <bytecode: 0x7fc1c6be28f8>
## <environment: namespace:stats>

set.seed(3)
x <- runif(n = 1,min = 10000,max = 10000000)
y <- rep("NA", times =x)
length(y)

## [1] 1688734

set.seed(202)
rnorm(n = 1, mean = 12.1,sd = 1)

## [1] 10.96822

set.seed(103)
sample(x = LETTERS,size = 1,replace = TRUE)

## [1] "H"

set.seed(1.011)
x1 <- rnorm(n = 10,mean = 10.1,sd = 2.1)
x2 <- rnorm(n = 10,mean = 11,sd = 2.2)
t.test(x1,x2)

## 
##  Welch Two Sample t-test
## 
## data:  x1 and x2
## t = -1.29, df = 16.071, p-value = 0.2153
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -3.0915348  0.7518686
## sample estimates:
## mean of x mean of y 
##  10.37763  11.54746

set.seed(1.10111)
x <- matrix(data = NA,nrow = runif(1,1000,10000), ncol= runif(1,1000,10000))
dim(x)

## [1] 3389 4349

set.seed(112.304)
x <- rnorm(654, 12, 10)
x[654]

## [1] 8.683049