ch1_a.qmd

---
title-block-banner: true
bibliography: references.bib
---

## Operators and data types

### Basic operators

In this section, we will learn about some basic R operators that are used to perform operations on variables. Some most commonly used operators are shown in the table below.

<center>![](images/Basic_operators.png){width="70%"}</center>

> R follows the conventional order (sequence) to solve mathematical operations, abbreviated as BODMAS: Brackets, Orders (exponents), Division, Multiplication, Addition, and Subtraction

```{r Chapter 1, message = FALSE, warning = FALSE}
2+4+7 # Sum
4-5   # Subtraction
2*3   # Multiplication
1/2   # Division

# Order of operation
1/2*3+4-5
1/2*(3+4-5)
1/(2*(3+4-5))
1/(2*3+4-5) 
# Notice how output changes with the placement of operators

# Other operators:
2^3
log(10)
sqrt(4)
pi

# Clear the Environment
rm(list=ls()) # rm is for remove,ls is short for list. The empty parenthesis i.e. () signifies all content. 
```

### Basic data operations

In this section, we will create some vector data and apply built-in operations to examine the properties of a dataset.

```{r Basic data operation, message = FALSE, warning = FALSE}
# The "is equal to" or "assignment operator in R is "<-" or "=" 

# Generate sample data. Remember "c" comes from for "concatenate". 
data<-c(1,4,2,3,9)    # Try data = c(1,4,2,3,9). Is there any difference in data in both cases?

# rbind combines data by rows, and hence "r"bind
# cbind combines data by columns, and hence "c"bind

# Checking the properties of a dataset. Note: the na.rm argument ignores NA values in the dataset.
data=rbind(1,4,2,3,9) 
dim(data)           # [5,1]: 5 rows, 1 column
data[2,1]           # Show the value in row 2, column 1
data[c(2:5),1]      # Show a range of values in column 1
mean(data, na.rm=T) # Mean
max(data)           # Maximum
min(data)           # Minimum
sd(data)            # Standard deviation
var(data)           # Variance

summary(data) 
str(data)        # Prints structure of data
head(data)       # Returns the 1st 6 items in the object
head(data, 2)    # Print first 2
tail(data, 2)    # Print last 2

# Do the same, but with "c()" instead of "rbind"
data=c(1,4,2,3,9) 
dim(data)        # Note: dim is NULL
length(data)     # Length of a dataset is the number of variables (columns)

data[2]          # This should give you 4 

# Other operators work in the same way
mean(data)       # Mean
max(data)        # Maximum
min(data)        # Minimum
sd(data)         # Standard deviation
var(data)        # Variance

# Text data
data=c("LSU","SPESS","AgCenter","Tigers") 
data             # View
data[1]

# Mixed data
data=c(1,"LSU",10,"AgCenter") # All data is treated as text if one value is text
data[3]                       # Note how output is in quotes i.e. "10"

```

> *For help with a function in R, just type ? followed by the function to display information in the help menu. Try pasting `?sd` in the console.*

### Data types

In R, data is stored as an "array", which can be 1-dimensional or 2-dimensional. A 1-D array is called a "vector" and a 2-D array is a "matrix". A table in R is called a "data frame" and a "list" is a container to hold a variety of data types. In this section, we will learn how to create matrices, lists and data frames in R.

<center>![](images/list_visual.png){width="80%"}</center>

```{r Data types, message = FALSE, warning = FALSE}
# Lets make a random matrix
test_mat = matrix( c(2, 4, 3, 1, 5, 7), # The data elements 
  nrow=2,         # Number of rows 
  ncol=3,         # Number of columns 
  byrow = TRUE)   # Fill matrix by rows 

test_mat = matrix( c(2, 4, 3, 1, 5, 7),nrow=2,ncol=3,byrow = TRUE) # Same result 
test_mat


test_mat[,2]      # Display all rows, and second column
test_mat[2,]      # Display second row, all columns

# Types of datasets
out = as.matrix(test_mat)
out               # This is a matrix
out = as.array(test_mat)
out               # This is also a matrix
out = as.vector(test_mat)
out               # This is just a vector

# Data frame and list
data1=runif(50,20,30) # Create 50 random numbers between 20 and 30  
data2=runif(50,0,10)  # Create 50 random numbers between 0 and 10  

# Lists
out = list()        # Create and empty list
out[[1]] = data1    # Notice the brackets "[[ ]]" instead of "[ ]"
out[[2]] = data2
out[[1]]          # Contains data1 at this location

# Data frame
out=data.frame(x=data1, y=data2)

# Let's see how it looks!
plot(out$x, out$y)
plot(out[,1])
```

> For a data frame, the dollar "\$" sign invokes the variable selection. Imagine how one would receive merchandise in a store if you give \$ to the cashier. Data frame will list out the variable names for you of you when you show it some \$.