# Understanding data types and strucutres and how to operate on those.

### R has 6 atomic data types [a set of values having pre-defined characteristics]
# use typeof() or class() to see what kind of object is it.

typeof("a") # character
class('2') # character
class(2.5) # numeric
class(2L) # integer ; the L tells R to store this as an integer
class(TRUE) # logical 
# TRUE and FALSE can be represented as T and F, but it is recommended to use the full names. 
F <- 5
class(F)
typeof(1+2i) # complex 

##### R has many data structures
### Atomic vectors [a sequence of values]
# The most basic data structures in r. 
vector() # default vector is logical 
character(5) # Create an empty character vector
numeric(10)

# Create by specificying their content. R will guess the types. 
x <- c(1, 2, 3, 4, 5)
x
class(x) # integer

y <- c(TRUE, FALSE, TRUE, FALSE)
class(y) # logical

z <- c("Learn", "R")
class(z) # character

# Examing vectors. 
length(z)
str(z) # show content of an object. Can also be used to examine complicated objects.
large <- 1:10000
large
head(large)
tail(large)

# Adding elements.
z <- c(z, "Today")
z

# Sequence of numbers
x <- c(1:5) # / 1:5
seq(5)
?seq
seq(1, 10, by = 2) # seq(10,2,-3)

# Missing data and special values.
x <- c(1, NA, 4, 5, NA)
class(x)
is.na(x) # is.na() indicates if the elements of the vectors represent missing data
anyNA(x) # if the vector contains any missing data

-1/0 # Inf: infinity
0/0 # NaN: Not a Number 

## Conversion between modes of storage: coertion
x
class(as.character(x))

y <- c("5", "6", "7")
as.numeric(y)

mix <- c(x, z)
mix
class(mix) # When different data types are combined into the same vector, they are forced to the same type. 
mix
# The source of many surprises!! Always be aware of data types. 

## Vectors can have names. 
names(y) 
names(y) <- c("a", "b", "c","d")
y

### Factors. 
# Factors are important in R. Factors look like characters but are used to represent categorical information. 
colors <- c("red", "blue", "blue", "purple", "red")
class(colors)
colors <- as.factor(colors)
class(colors)

# Factors have "levels"
colors
levels(colors)
str(colors)

# Levels of factors can be represented by numbers. Be very careful when converting factors and  numberic values. 
some_numbers <- c(1, 2, 6, 9, 10, 2, 6)
class(some_numbers)
as.numeric(as.character(as.factor(some_numbers)))



### Matrix
# An atomic vectors with dimensions: only one data type is allowed. 
m <- matrix(nrow = 2, ncol = 3)
m
dim(m) # dimension of the matrix

m <- matrix(1:6, nrow = 2, ncol = 3)
m # By default, matrices are filled column-wise.

m <- matrix(1:6, nrow = 2, ncol = 3, byrow = TRUE)
m

# Combine vectors to create a matrix or combine matrices by columns or rows. 
x <- 1:3
y <- 6:8
cbind(x, y)
m2 <- rbind(x, y)
m2
rbind(m, m2)

colnames(m)
colnames(m) <- c("a", "b", "c")
colnames(m2) <- z
rbind(m, m2)

rownames(m) <- c("a", "b")

### List
# A list is a special type of vector. Each element can be a different type. 
x <- list(1, "a", TRUE, 1+4i)
x


# lists can also have names
names(x)
names(x) <- c("a", "b", "c", "d")
x

myList <- list(numbers = y, char = z)
myList

# Lists are useful!!

### Data Frames! 
# Very important data structure in R. 
# A data frame is a special type of list where every element of the list has the SAME length. 

# Create a data frame manually: 
dat <- data.frame(names = c("a", "b", "c"),
                  height = c(100, 200, 300),
                  weight = c(34, 56, 90))
dat
class(dat) # data.frame
typeof(dat) # It is acutally a special list!