Class2_CodingBasicsI

Class 2 Summary

This week we started with coding basics. I will outline what we learned as a reference sheet.
R-Objects
- We started by looking at a few R-Objects. An R-Object defines the kind of variable you are working with. Here are the ones we looked at this class.
  - Scalars
    - A scalar is a single number, or “element”, as shown below.
```
MyScalar = 16
```
  - Vectors
    - A vector is made up of many numbers, or “elements”.
```
MyVector = c(1, 2, 3, 4, 5, 6, 7, 8, 9)
```
    - Quick Note: The c() function is the vector function. It combines all the inputs into a vector. Note that all inputs are separated by commas.
    - Indexing
      - Indexing is an important concept in all programming languages. Indexing is how you isolate different parts of a larger vector using the location of the element of interest, or the index. Luckily, this is easy in R. To index into a vector, you use square brackets []
        
        MyVector[3]
        
        ## [1] 3
      - You can also put a vector inside the square brackets [] using the vector function c( )
        
        MyVector[c(2, 6, 1, 7)]
        
        ## [1] 2 6 1 7
    - Data Types
      - Vectors can have one of three different data types, numeric, logical, and binary.
        
        Numeric
        
        Just the numbers we all know and love.
        
        NumVec = c(1, 45, 367, 43, 234)
        
        Logical
        
        Logic statements are a vital part of all programming. They are simple TRUE/FALSE or Binary statements, and were traditionally represented as 0 or 1. While most conditional statements accept 0 or 1 (for example, ByRow = 1 and ByRow = TRUE will work the same), to create a logic vector, you have to use TRUE/FALSE statements.
        
        LogVec = c(FALSE, FALSE, TRUE, FALSE, TRUE)
        
        Character
        
        Characters, often called strings, and essentialy words.They are defined in most programming langauges using quotation marks " ", and R is no exception.
        
        CharVec = c("Schepens", "Eye", "Research", "Institute")
      - As you can see, R automatically formats data types differently to make it easier to code. Each data type above appears as a different color in R.
      - ** A note of caution**
        
        Data types cannot be combined within a vector in R. Instead, R will transform the entire vector into the common denominator. For example, a number can not be represented as a binary statement, but we know that binary statements CAN be represented as numbers. If you try to combine a numeric and logical vector, R will convert it to a numeric vector.
  - Lists
    - Lists are the last R-Object we looked at. Lists are nice because they can organize different data types into a single variable.
```
MyList = list(Name = "John Smith", Age = 34, Married = TRUE)
```
    - You can isolate list elements using the dollar sign $.
```
MyList$Age
```
```
## [1] 34
```
```
MyList$Name
```
```
## [1] "John Smith"
```
```
MyList$Married
```
```
## [1] TRUE
```
    - Vectors can also be list elements.
```
PeopleList = list(Name = c("John", "Joe", "Bob"), Age = c(45, 78, 14))
```
      - You can index into vectors inside lists by combinging the dollar sign $ with square brackets [ ]
        
        PeopleList$Name[3]
        
        ## [1] "Bob"
        
        PeopleList$Age[1]
        
        ## [1] 45
Functions
- We wrapped up class talking about functions. Functions are a vital part of programming, they make our lives a lot easier and do basic presets a lot faster than if we did them by hand.
- Basic form
```
FunctioName(input1, input2, option1 = TRUE...)
```
- Useful functions
  - length() returns the length of a vector
```
length(MyVector)
```
```
## [1] 9
```
  - table() is useful when coutning repeated variables in a vector
```
Fruit = c("Apple", "Apple", "Pear", "Orange", "Pear", "Apple")
table(Fruit)
```
```
## Fruit
##  Apple Orange   Pear 
##      3      1      2
```

Class2_CodingBasicsI

Abigail Payne

1/29/2021

Class 2 Summary