Topics for today!

Today we will review the basic elements of data analysis. To start we will examine:

Lists + Matrices
Dataframes
Writing basic functions

1. Topic: Lists + Matrices

## Lists
# We can make lists the same way we make vectors
test_list = list(1, "one", T)
test_list[4:6] <- c(0, "zero", F) 

# And access elements the same way
test_list[c(2,5)] # returns the second and fifth elt.

## [[1]]
## [1] "one"
## 
## [[2]]
## [1] "zero"

# Use lists when you're putting elts of many different types together.

## Matricies
# We can make arrays intro matrices using the matrix function
# syntax: matrix(data_vector, num_rows, num_columns)
# note the matrix is filled by the array column by column
# Ex. 
random_sample = rnorm(0,1,20)
first_matrix = matrix(random_sample, 4, 5)

# We can access elements of the matrix using brackets 
# Syntax for accessing matrix elts is matrix[row_indices, col_indices]
first_matrix[1,1] # gets the element in first row and first column

## [1] NA

first_matrix[1,] # gets the elements in the first row from ALL columns

## [1] NA NA NA NA NA

first_matrix[,1:2] # gets the elements in ALL rows and the first two columns

##      [,1] [,2]
## [1,]   NA   NA
## [2,]   NA   NA
## [3,]   NA   NA
## [4,]   NA   NA

2. Topic: Dataframes

# Dataframes are the workhorse of R, they are the most important way for storing data.
?data.frame # putting '?' before a function will tell you about it!

# Setting up the data
names = c("Mark", "Mary", "Maddy", "Margret")
grades = c(100, 97, 99, 98)
cheater = c(T, F, F, F)

# The data.frame(vectors) command builds a dataframe out of vectors
gradebook = data.frame(names, grades, cheater) # Here we build the dataframe gradebook. Note the columns are named and inherit the names of the input vectors.
summary(gradebook) # summary looks at each column and tells us about it

##     names               grades        cheater       
##  Length:4           Min.   : 97.00   Mode :logical  
##  Class :character   1st Qu.: 97.75   FALSE:3        
##  Mode  :character   Median : 98.50   TRUE :1        
##                     Mean   : 98.50                  
##                     3rd Qu.: 99.25                  
##                     Max.   :100.00

gradebook[2,] # We can query dataframes like matricies

# In dataframes columns have names corresponding to the vectors they were built from
names(gradebook) # the names(dataframe) command returns all the column names

## [1] "names"   "grades"  "cheater"

# We can query dataframes by these names
gradebook$names

## [1] "Mark"    "Mary"    "Maddy"   "Margret"

gradebook[gradebook$names == "Mary",] # More querying examples

trunc_gradebook = gradebook[c("names", "grades")] # When we request a two dimensional object from a dataframe, whats returned is itself a dataframe.

# Many native functions in R are designed to be run on dataframes. We will spend a lot of time with dataframes when we start working with real data. For now it's enough to think of them as general matrices with named columns

3. Topic: Functions

General info: A defined function consists of a a name, inputs and outputs.

To write a function we need to use the following syntax

Syntax:

function_name = function(input_1, input_2){ run code here return(variable) }

# The shell of function looks like this

func_name = function(input){
  # code
  # code
  # return(result)
}

Example 1: Square Function

# Lets write a function that takes in a number and returns it's square
func_square = function(N){
  ret = N^2
  return(ret)
}

# Lets test our function
func_square(7)

## [1] 49

7^2 # of course we could just use the carrot, this is a simple example

## [1] 49

Example 2: Compute the hypotenuse of a triangle given the other two

# Lets write a function that takes in two sides of a right triangle and computes the hypotenuse.
func_pythag = function(side_1, side_2){
  hypot = side_1^2 + side_2^2
  return(sqrt(hypot))
}

# Lets test our function
a = 3
b = 7
func_pythag(3,7)

## [1] 7.615773

func_pythag(7,3)

## [1] 7.615773

# func_pythag("3", "7") # What will happen when we give it characters?

Example 3: Number of Columns of a DataFrame

# The nrows() counts the number of rows in a dataframe. Lets write a function that counts the number of columns
count_columns = function(df){
  first_row = df[1,]
  num_cols = length(first_row)
  return(num_cols)
}

# Lets test our function
data = mtcars # This is a dataframe native to R, type ?mtcars to learn more!
count_columns(data)

## [1] 11

Lecture 3 - Introduction to R II