CS计算机代考程序代写 ## —-echo=FALSE———————————————————-

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set.seed(2317)

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62 + 5 – 7 * 9 + 15/3 – 2^2 # ^ means “to the power of”

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2 + 3 / 4^3
2 + (3 / 4)^3

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log(25) # Logarithm of 25 using natural base e
sqrt(4) # Square root of 4
pi * 4^2 # Area of a circle of radius 4
a <- pi * 4^2 # Create the variable "a" a # Print "a" ## ----echo=TRUE----------------------------------------------------------- rm(list = ls()) ## ----echo=TRUE----------------------------------------------------------- x <- c(4.1, -1.3, 2.5, -0.6, -21.7) x ## ----echo=TRUE----------------------------------------------------------- x <- c(1, 3, 9, 10) x y <- c(30, 35, 4) y z <- c(x, y) z ## ----echo=TRUE----------------------------------------------------------- n <- 5 1:n - 1 1:(n - 1) ## ----echo=TRUE----------------------------------------------------------- z[2] z[1:4] z[c(1, 2, 6)] z[2] <- 2015 ## ----echo=TRUE----------------------------------------------------------- seq(1, 2, by = 0.1) seq(1, 2, length.out = 20) ## ----echo=TRUE----------------------------------------------------------- A <- matrix(c(2, -1, 4, 2, -1, 3), nrow = 2) # create a matrix A ## ----echo=TRUE----------------------------------------------------------- A[1, 2] # extract the element in first 1st row and 2nd column A[1, ] # extract the first row mean(A) # sample average for all the elements ## ----echo=TRUE----------------------------------------------------------- u1 <- c("male", "female") u2 <- c("apple", "pear", "kiwi", "orange") u1 u2 ## ----echo=TRUE----------------------------------------------------------- labels <- paste(c("X", "Y"), 1:10, sep = "") labels ## ----echo=TRUE----------------------------------------------------------- c(1, 2) + c(2, 5) ## ----echo=TRUE----------------------------------------------------------- c(1, 2, 3) + c(2, 5) ## ----echo=TRUE----------------------------------------------------------- 1:5 + 3 ## ----echo=TRUE----------------------------------------------------------- mean(z) ## ----echo=TRUE----------------------------------------------------------- z <- rnorm(10) # generate a vector with 10 observations from N(0,1) z ## ----echo=TRUE----------------------------------------------------------- x <- rnorm(10, mean = 10, sd = 2) x ## ----echo=TRUE, fig.height=4, fig.width=4-------------------------------- x <- rnorm(100, mean = 10, sd = 2) # sample of size 100 from N(10, 4) y <- rnorm(100, mean = 0, sd = 4) # sample of size 100 from N(0, 16) plot(x, y, xlab = "Name of variable x", ylab = "Name of variable y") ## ----echo=TRUE, fig.height=4, fig.width=4-------------------------------- boxplot(x, y, names = c("x", "y")) # creates paired boxplots ## ----echo=TRUE, fig.height=4, fig.width=4-------------------------------- hist(x, freq = FALSE, nclass = 10) ## ----echo=TRUE----------------------------------------------------------- qqnorm(x) qqline(x) ## ----echo=TRUE----------------------------------------------------------- x <- rnorm(100) # generate 3 vectors of length 100 y <- 2 * x + rnorm(100, 0, 0.8) z <- 0.5 * x + rnorm(100, 0, 0.5) t <- data.frame(x, y, z) # create a data frame summary(t$x) # summary statistics for x plot(t) # scatter plot matrix ## ----echo=TRUE----------------------------------------------------------- L <- list(one = 1, two = c(1, 2), five = seq(0, 1, length.out = 5)) L L$five + 10 ## ----echo=TRUE----------------------------------------------------------- # Construct and store a simple data frame. t <- data.frame(x = c(1, 2, 3), y = c(30, 20, 10)) t write.table(t, file = "mydata.txt", row.names = FALSE) # save file t2 <- read.table(file = "mydata.txt", header = TRUE) # load file t2 ## ----echo=TRUE----------------------------------------------------------- x <- c(rnorm(10), NA, rnorm(2)) ## ----echo=TRUE----------------------------------------------------------- min(x) ## ----echo=TRUE----------------------------------------------------------- min(x, na.rm = TRUE) mean(x, na.rm = TRUE) ## ----echo=TRUE----------------------------------------------------------- x <- rnorm(10) ## ----echo=TRUE----------------------------------------------------------- if (mean(x) > median(x)) {
“The mean is greater than the median”
} else {
“The mean is smaller than the median”
}

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B <- 1000 # number of runs n <- 5 # sample size xbar.seq <- 1:B # a vector of size to be filled with means for (i in 1:B) { sample <- rnorm(5) xbar.seq[i] <- mean(sample) } hist(xbar.seq) # plot the results ## ----echo=TRUE, fig.width=4, fig.height=4-------------------------------- myfun <- function(x) { # Specifies function name and argument y <- x^2 # Specifies what the function should do return(y) # Returned value } myfun(1.5) # Computes 1.5^2 x <- seq(-2, 2, length.out = 100) # Plots the new function plot(x, myfun(x), type = "l" ) ## ----echo=TRUE----------------------------------------------------------- mymedian <- function(x) { n <- length(x) m <- sort(x)[(n + 1) / 2] return(m) } ## ----echo=TRUE----------------------------------------------------------- mymedian <- function(x) { n <- length(x) if (n %% 2 == 1) { # odd med <- sort(x)[(n + 1) / 2] } else { # even middletwo <- sort(x)[(n / 2) + 0:1] med <- mean(middletwo) } return(med) } x <- rnorm(10) mymedian(x) median(x) exp1pdf <- function(x){ }