1 + 2
rnorm(100)
3 - 4
5 * 6
7 / 8
1 + 2 * 3
(1 + 2) * 3
17 %/% 4
17 %% 4
2 ^ 4
2 ^ 4.3
log(4)       # natural log
log10(4)     # log in base 10
log(4, 10)   # same as above
sqrt(9)      # square root
abs(3-4)     # absolute value
exp(1)       # exponential

us.population <- 3.24e8 # From Wolfram|Alpha
us.area <- 3719000      # From Wolfram|Alpha
us.pop.density <- us.population / us.area
us.pop.density
( us.pop.density <- us.population / us.area )
rm(us.pop.density) # gets rid of the us.pop.density variable
rm(list = ls(all = TRUE))

help(sqrt)
?sqrt
?"+"
example(sqrt)

has.diabetes    <- TRUE         # logical  (note case!)
patient.name    <- "Jane Doe"   # character
moms.age        <- NA           # used to represent an unknown ("missing") value
NY.socialite.iq <- NULL         # used to represent something that does not exist
is.enrolled  <- FALSE
is.candidate <- has.diabetes & ! is.enrolled
TRUE & FALSE
T | F
T & NA
F & NA
TRUE | NA
FALSE | NA
TRUE & ! FALSE & NA
as.numeric(has.diabetes)
as.numeric(is.enrolled)
as.character(us.population)
as.character(moms.age) # still NA - we still don't know!

x <- rnorm(100)
sum(x)
max(x)
summary(x)
plot(x)
hist(x)
colors <- c("red", "orange", "yellow", "green", "blue", "indigo", "violet")
colors <- c("infrared", colors, "ultraviolet")
colors[7]
colors[7] <- "purple"
indices <- 41:50
indices[1]
indices[2]
length(indices)
state.name[indices]
summary(state.area)
cutoff <- 37317
state.area < cutoff
small.states <- state.name[state.area < cutoff]
"New York" %in% state.name[state.area < cutoff]
"Rhode Island" %in% state.name[state.area < cutoff]
which(state.area < cutoff)
state.name[which(state.area < cutoff)]
state.area[state.name == "Wyoming"]
names(state.area) <- state.name
state.area["Wyoming"]
state.area[c("Wyoming", "Alaska")]
state.area[small.states]
min(state.area)
state.area[which.min(state.area)]

seq(1, 10)     # same as 1:10
seq(1, 4, 0.5) # shows all numbers from 1 to 4, incrementing by 0.5 each time
?seq
seq(0, 1, length.out = 10)
seq(from = 1, to = 4, by = 0.5)
seq(from = 0, to = 1, length.out = 10)
seq(to = 99)
rep(colors, 2)
rep(colors, times = 2) # same as above
rep(colors, each = 2)
rep(colors, each = 2, times = 2)
rep(colors, length.out = 10)

x <- 0:9
y <- seq(from = 0, to = 90, by = 10)
x + y
(1:5) + y
(1:4) + y
y * 2
10 ^ (0:5)

sort(state.area)               # sorts the areas of the states from smallest to largest
order(state.area)              # returns a vector of the positions of the sorted elements
state.name[order(state.area)]  # sort the state names by state size
state.name[order(state.area, decreasing = TRUE)]
                               # sort the state names by state size
sample(state.name, 4)              # randomly picks four states
sample(state.name)                 # randomly permute the entire vector of state names
sample(state.name, replace = TRUE) # selection with replacement

rev(x)    # reverses the vector
sum(x)    # sums all the elements in a numeric or logical vector
cumsum(x) # returns a vector of cumulative sums (or a running total)
diff(x)   # returns a vector of differences between adjacent elements
max(x)    # returns the largest element
min(x)    # returns the smallest element
range(x)  # returns a vector of the smallest and largest elements
mean(x)   # returns the arithmetic mean

state.division
levels(state.division)
str(state.division)
class(state.division)
pony.colors <- sample(colors, size = 500, replace = TRUE)
str(pony.colors)
pony.colors.f <- factor(pony.colors)
str(pony.colors.f)
plot(pony.colors.f)
pony.colors.f <- factor(pony.colors, levels=colors)
str(pony.colors.f)
plot(pony.colors.f)
plot(state.division)
state.division <- factor(state.division, levels = sort(levels(state.division)))
plot(state.division)
levels(state.division)
levels(state.division) <- c("ENC", "ESC", "MA", "MT", "NE", "PAC", "SA", "WNC", "WSC")
plot(state.division)
state.name[state.division == "NE"]
mean(state.area[state.division == "NE"]) / mean(state.area[state.division == "WSC"])
t.test(state.area[state.division == "SA"], state.area[state.division == "MT"])