Introduction to R

install.packages("RMySQL")

install.packages("DMwR2")

library(DMwR2)

installed.packages()

library()

old.packages()

update.packages()

RSiteSearch('neural networks')

4 + 3 / 5^2

rnorm(4, mean = 10, sd = 2)
mean(sample(1:10, 5))

plot(x=sample(1:10,5),y=sample(1:10,5),
     main="Five random points",xlab="X values",ylab="Y values")

vat <- 0.2

vat

y <- 39
y
y <- 43
y

z <- 5
w <- z^2
w
i <- (z * 2 + 45)/2
i

ls()
rm(vat,y,z,w,i)

max(4, 5, 6, 12, -4)

max(sample(1:100, 30))

exists("se")

se <- function(x) {
   v <- var(x)
   n <- length(x)
   return(sqrt(v/n))
}

mySample <- rnorm(100, mean=20, sd=4)
se(mySample)

convMeters <- function(val, to="inch") {
    mult <- switch(to,inch=39.3701,foot=3.28084,yard=1.09361,mile=0.000621371,NA)
    if (is.na(mult)) stop("Unknown target unit of length.")
    else return(val*mult)
}
convMeters(23,"foot")
convMeters(40,"inch")
convMeters(40)
convMeters(2.4,"km")

convMeters(56.2,"yard")

convMeters(to="yard",val=56.2)

convMeters(56.2,to="yard")

f(10,43.2,tol=0.25)

v <- c(4, 7, 23.5, 76.2, 80)
v
length(v)
mode(v)

v <- c(4, 7, 23.5, 76.2, 80, "rrt")
v
mode(v)

u <- c(4, 6, NA, 2)
u
k <- c(TRUE, FALSE, FALSE, NA, TRUE)
k

u[2]

k[4] <- TRUE
k

x <- vector()

x[3] <- 45
x

length(x)
x[10]
x[5] <- 4
x

v <- c(45, 243, 78, 343, 445, 44, 56, 77)
v
v <- c(v[5], v[7])
v

v <- c(4, 7, 23.5, 76.2, 80)
sqrt(v)

v1 <- c(4, 6, 87)
v2 <- c(34, 32.4, 12)
v1 + v2

v1 <- c(4, 6, 8, 24)
v2 <- c(10, 2)
v1 + v2

v1 <- c(4, 6, 8, 24)
v2 <- c(10, 2, 4)
v1 + v2

v1 <- c(4, 6, 8, 24)
2 * v1

g <- c("f", "m", "m", "m", "f", "m", "f", "m", "f", "f")
g

g <- factor(g)
g

g[3]
g[3] == "m"

other.g <- factor(c("m", "m", "m", "m", "m"), levels = c("f","m"))
other.g

table(g)
table(other.g)

a <- factor(c('adult','adult','juvenile','juvenile','adult',
              'adult','adult','juvenile','adult','juvenile'))
table(a, g)

t <- table(a, g)
margin.table(t, 1)
margin.table(t, 2)

prop.table(t, 1)
prop.table(t, 2)
prop.table(t)

x <- 1:100

10:15 - 1
10:(15 - 1)

5:0

seq(-4, 1, 0.5)

seq(from = 1, to = 5, length = 4)
seq(from = 1, to = 5, length = 2)
seq(length = 10, from = -2, by = 0.2)

rep(5, 10)
rep("hi", 3)
rep(1:2, 3)
rep(1:2, each = 3)

gl(3, 5)
gl(2, 5, labels = c("female", "male"))

rnorm(10)

rnorm(4, mean = 10, sd = 3)

rt(5, df = 10)

x <- c(0, -3, 4, -1, 45, 90, -5)
x > 0

x[x > 0]

x[x <= -2 | x > 5]
x[x > 40 & x < 100]

x[c(4, 6)]
x[1:3]
y <- c(1, 4)
x[y]

x[-1]
x[-c(4, 6)]
x[-(1:3)]

pH <- c(4.5, 7, 7.3, 8.2, 6.3)
names(pH) <- c("area1", "area2", "mud", "dam", "middle")
pH

pH <- c(area1 = 4.5, area2 = 7, mud = 7.3, dam = 8.2, middle = 6.3)

pH["mud"]
pH[c("area1", "dam")]

m <- c(45, 23, 66, 77, 33, 44, 56, 12, 78, 23)
m
dim(m) <- c(2, 5)
m

m <- matrix(c(45, 23, 66, 77, 33, 44, 56, 12, 78, 23), 2, 5)

m <- matrix(c(45, 23, 66, 77, 33, 44, 56, 12, 78, 23), 2, 5, byrow = TRUE)

m[2, 3]

m[-2, 1]
m[1, -c(3, 5)]

m[1, ]
m[, 4]

m[1, , drop = FALSE]
m[, 4, drop = FALSE]

m1 <- matrix(c(45, 23, 66, 77, 33, 44, 56, 12, 78, 23), 2, 5)
cbind(c(4, 76), m1[, 4])
m2 <- matrix(rep(10, 20), 4, 5)
m2
m3 <- rbind(m1[1, ], m2[3, ])
m3

results <- matrix(c(10, 30, 40, 50, 43, 56, 21, 30), 2, 4, byrow = TRUE)
colnames(results) <- c("1qrt", "2qrt", "3qrt", "4qrt")
rownames(results) <- c("store1", "store2")
results
results["store1", ]
results["store2", c("1qrt", "4qrt")]

a <- array(1:24, dim = c(4, 3, 2))
a

a[1, 3, 2]
a[1, , 2]
a[4, 3, ]
a[c(2, 3), , -2]

m <- matrix(c(45, 23, 66, 77, 33, 44, 56, 12, 78, 23), 2, 5)
m
m * 3
m1 <- matrix(c(45, 23, 66, 77, 33, 44), 2, 3)
m1
m2 <- matrix(c(12, 65, 32, 7, 4, 78), 2, 3)
m2
m1 + m2

my.lst <- list(stud.id=34453, 
               stud.name="John", 
               stud.marks=c(14.3,12,15,19))

my.lst

my.lst[[1]]
my.lst[[3]]

my.lst[1]

mode(my.lst[1])
mode(my.lst[[1]])

my.lst$stud.id

names(my.lst)
names(my.lst) <- c("id", "name", "marks")
my.lst

my.lst$parents.names <- c("Ana", "Mike")
my.lst

length(my.lst)

my.lst <- my.lst[-5]

other <- list(age = 19, sex = "male")
lst <- c(my.lst, other)
lst

unlist(my.lst)

my.dataset <- data.frame(site=c('A','B','A','A','B'),
                         season=c('Winter','Summer','Summer','Spring','Fall'),
                         pH = c(7.4,6.3,8.6,7.2,8.9))
my.dataset

my.dataset[3, 2]

my.dataset$pH
my.dataset[["site"]]

my.dataset[my.dataset$pH > 7, ]
my.dataset[my.dataset$site == "A", "pH"]
my.dataset[my.dataset$season == "Summer", c("site", "pH")]

my.dataset[pH > 7, ]

attach(my.dataset)
my.dataset[site=='B', ]
season

detach(my.dataset)
season

subset(my.dataset, pH > 8)
subset(my.dataset, season == "Summer", season:pH)

my.dataset[my.dataset$season == 'Summer','pH'] <- 
      my.dataset[my.dataset$season == 'Summer','pH'] + 1

my.dataset$NO3 <- c(234.5, 256.6, 654.1, 356.7, 776.4)
my.dataset

nrow(my.dataset)
ncol(my.dataset)
dim(my.dataset)

my.dataset <- edit(my.dataset)

new.data <- edit(data.frame())

names(my.dataset)
names(my.dataset) <- c("area", "season", "pH", "NO3")
my.dataset

names(my.dataset)[4] <- "PO4"
my.dataset

dat <- tibble(TempCels = sample(-10:40, size=100, replace=TRUE), 
              TempFahr = TempCels*9/5 + 32,
              Location = rep(letters[1:2], each=50))
dat

data(iris)
dim(iris)
class(iris)

library(tibble)
ir <- as_tibble(iris)
class(ir)
ir

print(ir, n=Inf, width=Inf)

iris[1:15, "Petal.Length"]
class(iris[1:15, "Petal.Length"])

ir[1:15, "Petal.Length"]

ir$Petal.Length[1:15]
ir[["Petal.Length"]][1:15]

select(filter(ir,Species=="setosa"),Petal.Width,Petal.Length)

filter(ir,Species == "setosa") %>% select(Petal.Width,Petal.Length)

source('mycode.R')

save(f,my.dataset,file='mysession.RData')

load('mysession.RData')

save.image()