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)
log10(4)
log(4, 10)
sqrt(9)
abs(3-4)
exp(1)

us_population <- 3.31e8 # From Wolfram|Alpha, 2020 estimate
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) 

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) 

x <- rnorm(100)
sum(x)
max(x)
summary(x)
plot(x)
hist(x)


my_colors <- c("red", "orange", "yellow", "green", "blue", "indigo", "violet")
colors <- c("infrared", colors, "ultraviolet")
my_colors <- c("infrared", my_colors, "ultraviolet")
length(my_colors)
my_colors[7]
my_colors[7] <- "purple"

a_numeric_vector <- vector(mode="numeric", length=1000)
a_numeric_vector[50] <- 5
a_numeric_vector[750] <- 10
plot(a_numeric_vector)

data()
state.name
state.area

indices <- 41:50
indices[1]
indices[2]
length(indices)
state.name[indices]

# state.name[c(1:10, (length(state.name)-9):length(state.name))]

summary(state.area)
cutoff <- 37317
# cutoff <- summary(state.area)[2]
state.area < cutoff
small_states <- state.name[state.area < cutoff]
"New York" %in% small_states
"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(state.area == min(state.area))]
state.area[which.min(state.area)]

seq(1, 10)
seq(1, 4, 0.5)
?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(my_colors, 2)
rep(my_colors, times = 2) # same as above
rep(my_colors, each = 2)
rep(my_colors, each = 2, times = 2)
rep(my_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

sort(state.area)
order(state.area)
state.name[order(state.area)]
state.name[order(state.area, decreasing = TRUE)]

sample(state.name, 4)
sample(state.name)
sample(state.name, replace = TRUE)

rev(x)
sum(x)
cumsum(x)
diff(x)
max(x)
min(x)
range(x)
mean(x)

state.division
levels(state.division)
str(state.division)
class(state.division)

pony_colors <- sample(my_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 = my_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"])

rm(state.division)