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### R BASICS WORKSHOP                                    ###
### PRESENTATION 3.1: CREATING OBJECTS AND TYPES OF DATA ###
###                                                      ###
### Center for Conservation and Sustainable Development  ###
### Missouri Botanical Garden                            ###
### Website: rbasicsworkshop.weebly.com                  ###
### Last modification: 2021-June-06                      ###
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### A. WHAT AN OBJECT IS AND HOW TO CREATE IT ##############

# Simply, an object in R is a piece of memory with a name. 
# Objects contains some information, which is often used for 
# figures or analyses.

# Objects can have multiple types of data and can be of 
# various classes


# The easiest way to create an object is using the arrow 
# operator *<-*

x <- 10 # This creates an object called *x* that contains 
        # the value 10

x # Typing the object name returns the information stored  
  # inside the object by printing it in the console


y <- 5.3 # This created another object called *y* that 
         # contains the value 5.3
y


# The arrow can also be pointing in the opposite direction
5.3 -> z
z



# It is often useful to store the output of a function in 
# an object:
seq(from = 4, to = 10, by = 2) # This just prints the 
                               # result in the screen

x <- seq(from=4, to=10, by=2) # This RE-WRITES the object 
                              # *x* with the sequence

x 
# Now the object *x* contains the sequence from 4 to 10 by 2


# Other examples:
b <- 1.4
x <- rnorm(100, 4, 2)
e <- rnorm (100, 0, 1)


y <- 4 + b*x + e
# Now the objects above are used to create one more object


plot(y ~ x)
abline(lm(y ~ x))
# Now the objects we created are used to make a scatterplot 
# with a regression line



### B. TYPES OF DATA WITHIN OBJECTS ########################

# There are 4 types of data commonly used in R:
# 1. Numerical - Numbers - e.g. 5, 0.4, 3.885
# 2. Characters - text - e.g. "a", "workshop", "R is awesome"
# 3. Logical - i.e. TRUE, FALSE
# 4. Special Values - e.g. NA, Inf, NULL, NaN


## 1-3. NUMERIC, CHARACTER AND LOGIC VALUES ##
# a. Let's suppose we have abundances of 5 species measured 
#    in a forest plot in two years (numeric values). 
abund.t1 <- c(0, 17, 34, 26, 82)
abund.t2 <- c(1, 17, 31, 27, 91)

# b. Let's suppose, we also have the names of the species 
#    (character data).
spp <- c("I.ynga", "I.edulis", 	"I.macrophylla", 
  "I.punctata", 	"I.alba")

# c. Also, let's suppose we need a variable that indicate  
#    if abundances have increased or not (logical values).
increm <- c(TRUE, FALSE, FALSE, TRUE, TRUE)


abund.t1
abund.t2
spp
increm


# The function *mode* can be used to return the type of data 
# inside an object.
mode(x=abund.t1)
mode(x=abund.t2)
mode(x=spp)
mode(x=increm)


# IMPORTANT: Note that character values NEED to be between 
# quotation marks. If not, R thinks you are using an object 
# with that name. Thus, this will generate an error:
spp.2 <- c(I.ynga, I.edulis, 	I.macrophylla, 
  I.punctata, 	I.alba)


# IMPORTANT: Note that logical values MUST NOT be between 
# quotation marks. If they are, R will interpret them as 
# characters:
increm.2 <- c("TRUE", "FALSE", "FALSE", "TRUE", "TRUE")

mode(increm.2)
identical(increm, increm.2)


## 4. SPECIAL VALUES ##

# There are multiple types of special values, but the most 
# common are NA and Inf

# 4.1. NA: missing value (not available)
x <- NA
x
length(x)
is.na(x)

# 4.2. Inf: infinite
100/0

-100/0

100-Inf