mirror of
https://github.com/sharkdp/bat
synced 2024-12-25 19:53:06 +00:00
170 lines
4.2 KiB
R
170 lines
4.2 KiB
R
# take input from the user
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num = as.integer(readline(prompt="Enter a number: "))
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factorial = 1
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# check is the number is negative, positive or zero
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if(num < 0) {
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print("Sorry, factorial does not exist for negative numbers")
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} else if(num == 0) {
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print("The factorial of 0 is 1")
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} else {
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for(i in 1:num) {
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factorial = factorial * i
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}
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print(paste("The factorial of", num ,"is",factorial))
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}
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x <- 0
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if (x < 0) {
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print("Negative number")
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} else if (x > 0) {
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print("Positive number")
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} else
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print("Zero")
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x <- 1:5
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for (val in x) {
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if (val == 3){
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next
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}
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print(val)
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}
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x <- 1
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repeat {
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print(x)
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x = x+1
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if (x == 6){
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break
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}
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}
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`%divisible%` <- function(x,y)
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{
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if (x%%y ==0) return (TRUE)
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else return (FALSE)
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}
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switch("length", "color" = "red", "shape" = "square", "length" = 5)
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[1] 5
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recursive.factorial <- function(x) {
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if (x == 0) return (1)
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else return (x * recursive.factorial(x-1))
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}
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pow <- function(x, y) {
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# function to print x raised to the power y
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result <- x^y
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print(paste(x,"raised to the power", y, "is", result))
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}
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A <- read.table("x.data", sep=",",
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col.names=c("year", "my1", "my2"))
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nrow(A) # Count the rows in A
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summary(A$year)
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A$newcol <- A$my1 + A$my2 # Makes a new
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newvar <- A$my1 - A$my2 # Makes a
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A$my1 <- NULL # Removes
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str(A)
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summary(A)
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library(Hmisc)
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contents(A)
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describe(A)
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set.seed(102) # This yields a good illustration.
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x <- sample(1:3, 15, replace=TRUE)
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education <- factor(x, labels=c("None", "School", "College"))
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x <- sample(1:2, 15, replace=TRUE)
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gender <- factor(x, labels=c("Male", "Female"))
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age <- runif(15, min=20,max=60)
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D <- data.frame(age, gender, education)
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rm(x,age,gender,education)
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print(D)
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# Table about education
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table(D$education)
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# Table about education and gender --
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table(D$gender, D$education)
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# Joint distribution of education and gender --
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table(D$gender, D$education)/nrow(D)
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# Add in the marginal distributions also
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addmargins(table(D$gender, D$education))
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addmargins(table(D$gender, D$education))/nrow(D)
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# Generate a good LaTeX table out of it --
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library(xtable)
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xtable(addmargins(table(D$gender, D$education))/nrow(D),
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digits=c(0,2,2,2,2))
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by(D$age, D$gender, mean)
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by(D$age, D$gender, sd)
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by(D$age, D$gender, summary)
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a <- matrix(by(D$age, list(D$gender, D$education), mean), nrow=2)
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rownames(a) <- levels(D$gender)
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colnames(a) <- levels(D$education)
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print(a)
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print(xtable(a))
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dat <- read.csv(file = "files/dataset-2013-01.csv", header = TRUE)
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interim_object <- data.frame(rep(1:100, 10),
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rep(101:200, 10),
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rep(201:300, 10))
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object.size(interim_object)
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rm("interim_object")
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ls()
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rm(list = ls())
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vector1 <- c(5,9,3)
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vector2 <- c(10,11,12,13,14,15)
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array1 <- array(c(vector1,vector2),dim = c(3,3,2))
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vector3 <- c(9,1,0)
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vector4 <- c(6,0,11,3,14,1,2,6,9)
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array2 <- array(c(vector1,vector2),dim = c(3,3,2))
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matrix1 <- array1[,,2]
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matrix2 <- array2[,,2]
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result <- matrix1+matrix2
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print(result)
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column.names <- c("COL1","COL2","COL3")
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row.names <- c("ROW1","ROW2","ROW3")
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matrix.names <- c("Matrix1","Matrix2")
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result <- array(c(vector1,vector2),dim = c(3,3,2),dimnames = list(row.names,
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column.names, matrix.names))
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print(result[3,,2])
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print(result[1,3,1])
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print(result[,,2])
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# Load the package required to read JSON files.
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library("rjson")
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result <- fromJSON(file = "input.json")
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print(result)
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x <- c(151, 174, 138, 186, 128, 136, 179, 163, 152, 131)
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y <- c(63, 81, 56, 91, 47, 57, 76, 72, 62, 48)
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relation <- lm(y~x)
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print(relation)
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relation <- lm(y~x)
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png(file = "linearregression.png")
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plot(y,x,col = "blue",main = "Height & Weight Regression",
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abline(lm(x~y)),cex = 1.3,pch = 16,xlab = "Weight in Kg",ylab = "Height in cm")
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dev.off()
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data <- c("East","West","East","North","North","East","West","West","West","East","North")
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print(data)
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print(is.factor(data))
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factor_data <- factor(data)
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print(factor_data)
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print(is.factor(factor_data))
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v <- c(7,12,28,3,41)
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# Give the chart file a name.
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png(file = "line_chart_label_colored.jpg")
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plot(v,type = "o", col = "red", xlab = "Month", ylab = "Rain fall", main = "Rain fall chart")
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