Getting Started with R

Part 1

Programming in R

  • Programming: Writing instructions for a computer to perform specific tasks.

  • R Language: A language and environment designed for statistical computing and graphics.

Why R?

  • Comprehensive Statistical Analysis: R provides a wide array of statistical tests, models, and analyses.
  • Rich Visualization Libraries: Libraries like ggplot2 allow for sophisticated data visualizations.
  • Open Source: Free to use, and benefits from a large community that contributes packages and updates.
  • Extensible: Over 15,000 packages in the CRAN repository for various applications.
  • Data Handling Capabilities: R can process both structured and unstructured data.
  • Platform Independent: Runs on various operating systems.

Notable Companies Using R: Google, Facebook, Airbnb, Uber, and many more use R for data analysis.

Variables

  • Definition: A storage area in programming to hold and manipulate data.

  • Importance: Allows for data storage, retrieval, and manipulation.

  • Analogy: Think of variables as labeled storage boxes.

Naming Variables

  • Begin with a letter
  • Avoid spaces (use underscores)
  • Case-sensitive.
age <- 25
student_name <- "John"
pi_value = 3.14

Assignment

Storing a value inside a variable.

x <- 5          # Preferred in R
total = 100     # Also works
7 -> z          # Rare

Displaying Variable Value

  • Type the variable name, or
x
[1] 5
  • Use the print() function
print(total)
[1] 100

Data Types

Classifications of data based on its nature.

number = 5                        # Numeric (Scalar)
number
[1] 5
messsage = "Hello"                # Character (String)
messsage
[1] "Hello"
flag = TRUE                       # Logical
flag
[1] TRUE
grades_vector = c(90, 85, 88)     # Vector
grades_vector
[1] 90 85 88
matrix_data = matrix(1:6, nrow=2) # Matrix
matrix_data
     [,1] [,2] [,3]
[1,]    1    3    5
[2,]    2    4    6
students_df = data.frame(Name=c("Anna", "Bob"), 
                         Age=c(23, 25))  # Dataframe
students_df
  Name Age
1 Anna  23
2  Bob  25
info = list(Name="John", Scores=c(90, 85, 88))  # List
info
$Name
[1] "John"

$Scores
[1] 90 85 88

Accessing Data Elements

Methods to extract specific data or subsets from data structures.

  • Using Square Brackets:
  1. For vectors: Extract specific elements
third_grade = grades_vector[3]
third_grade
[1] 88
  1. For matrices: Extract rows, columns, or individual elements.
first_row = matrix_data[1,]
first_row
[1] 1 3 5
second_column = matrix_data[,2]
second_column
[1] 3 4
element_1_2 = matrix_data[1,2]
element_1_2
[1] 3
  1. For data frames: Extract rows, columns, or specific data.
Anna_data = students_df[1,]
Anna_data
  Name Age
1 Anna  23
Age_column = students_df[, "Age"]
Age_column
[1] 23 25
  1. Exclude specific elements using negative indices:
all_but_third = grades_vector[-3]
all_but_third
[1] 90 85
  1. Access rows using boolean logic:
students_above_23 = students_df[students_df$Age > 23,]
students_above_23
  Name Age
2  Bob  25
  • Using $ for Data Frames: To access specific columns by name.
ages = students_df$Age
ages
[1] 23 25

Checking Object Type

  • Purpose: To identify the data type or structure of an object.
  • Function: class()
class(number)
[1] "numeric"
class(grades_vector)
[1] "numeric"
class(students_df)
[1] "data.frame"

Changing Object Type

  • Purpose: To convert data from one type to another.
  • Functions: as.numeric(), as.character(), as.logical(), etc.
number
[1] 5
class(number)
[1] "numeric"
converted_number = as.character(number)
converted_number
[1] "5"
class(converted_number)
[1] "character"
  • Q: What will happen when running the following line?
converted_number + 5
  • A: Error in converted_number + 5: ! non-numeric argument to binary operator

Special Values

  • NA: Missing data
  • Inf: Infinity e.g., 10/0
  • NaN: Result of invalid operations e.g., 0/0
  • NULL: Absence of a value

Factors

Data type for categorical data

gender = factor(c("male", "female", "male"))
gender
[1] male   female male  
Levels: female male
levels(gender)
[1] "female" "male"

  • Question: what will be the output of the following? as.numeric(gender)

  • Answer: 2, 1, 2

Size of Objects

  • Purpose: Determine dimensions or length.
length(grades_vector)
[1] 3
nrow(students_df)
[1] 2
ncol(students_df)
[1] 2
dim(students_df)
[1] 2 2

Mathematical Operations

  • +: Addition
  • -: Subtraction
  • *: Multiplication
  • /: Division
  • ^: Exponentiation (raising to a power)
  • %%: Modulus (remainder after division)
result_add = 3 + 5       # Addition
result_sub = 8 - 3       # Subtraction
result_mul = 4 * 7       # Multiplication
result_div = 8 / 2       # Division
result_exp = 2^3         # Exponentiation
result_mod = 8 %% 3      # Modulus

Order of Operations

PEDMAS Rule:

  • P: Parentheses - Always start with operations inside parentheses or brackets.
  • E: Exponents - Next, handle powers and square root operations.
  • MD: Multiplication and Division - Process them as they appear from left to right.
  • AS: Addition and Subtraction - Handle them last, moving from left to right.

  • Q: 3 + 5 * 2

  • A: 13

  • Q: (3 + 5) * 2

  • A: 16

  • Q: 2 ^ 2 * 3

  • A: 12

  • Tip: Always use parentheses for clarity, even if not strictly needed.

Logical Operations

Operations that return TRUE or FALSE based on certain conditions:

  • ==: Equal to
  • !=: Not equal to
  • <: Less than
  • >: Greater than
  • <=: Less than or equal to
  • >=: Greater than or equal to
  • &: Logical AND
  • |: Logical OR
  • !: Logical NOT
(3 < 4) & (7 > 6)
(3 > 4) | (7 > 6)
!(5 == 5)

Vector and Matrix Operations

grades_vector
[1] 90 85 88
grades_vector + c(5, 5, 5)
[1] 95 90 93
grades_vector * 1.1
[1] 99.0 93.5 96.8
matrix_data
     [,1] [,2] [,3]
[1,]    1    3    5
[2,]    2    4    6
matrix_data * 2
     [,1] [,2] [,3]
[1,]    2    6   10
[2,]    4    8   12
matrix_data %*% t(matrix_data)
     [,1] [,2]
[1,]   35   44
[2,]   44   56

Useful Functions

paste("Hello", "World!", sep=" ")
[1] "Hello World!"
c("apple"=5, "banana"=10)
 apple banana 
     5     10 
colnames(students_df)
[1] "Name" "Age" 
min(grades_vector)
[1] 85
highest_grade = max(grades_vector)
highest_grade
[1] 90
which(grades_vector == highest_grade)
[1] 1

Sorting and Ranking

  1. sort(): Organize elements in ascending or descending order.
numbers = c(5, 2, 9, 3)
sort(numbers)
[1] 2 3 5 9
  1. order(): Returns the indices that would arrange the data into ascending or descending order.
names = c("Vicky", "Cristina", "Barcelona")
order(names)
[1] 3 2 1
  1. rank(): Provides the rank of each element when the data is sorted. In case of ties, it assigns the average rank.
scores = c(85, 95, 85, 90)
rank(scores)
[1] 1.5 4.0 1.5 3.0

Briefly,

  • sort() directly arranges the data.
  • order() provides indices for the arranged data.
  • rank() gives the position of each data point in the sorted order.