Data Analysis Process and Constants in R

Data Analysis Process

• Data Collection: Gathering raw data from various sources
• Data Cleaning: Identifying and handling missing values, outliers, and inconsistencies
• Data Transformation: Converting data into a suitable format for analysis (e.g., scaling, normalization)
• Exploratory Data Analysis (EDA): Summarizing and visualizing data to gain insights
• Model Building: Developing and selecting appropriate statistical or machine learning models
• Model Evaluation: Assessing the model's performance using appropriate metrics
• Interpretation and Communication: Drawing meaningful conclusions and communicating findings effectively

Predefined Constants in R

• pi: Represents the mathematical constant π (approximately 3.14159)
• LETTERS: A character vector containing all uppercase letters of the alphabet
• letters: A character vector containing all lowercase letters of the alphabet
• Inf: Represents positive infinity
• NA: Represents missing values
• NaN: Represents "Not a Number" (e.g., result of 0/0)

Machine-Generated Unstructured Data

• Social Media Posts (e.g., tweets, Facebook posts, Instagram comments)
• Sensor Data: Data from IoT devices, weather stations, medical equipment
• Log Files: Records of system events, user activity, and application errors
• Audio/Video Recordings (e.g., speech, music, videos)

Basic Arithmetic Operations in R

• +: Addition
• -: Subtraction
• *: Multiplication
• /: Division
• ^: Exponentiation
• %%: Modulus (remainder after division)
• %/%: Integer division

Continuous Variable

• A continuous variable can take on any value within a given range
• Examples: Height, weight, temperature, time

Array in R

• An array is a multi-dimensional data structure in R
• Example: my_array <- array(1:24, dim = c(2, 3, 4)) creates a 3-dimensional array with dimensions 2x3x4

Hypothesis Testing

• Null Hypothesis (H0): A statement of no effect or no relationship between variables
• Alternative Hypothesis (H1): A statement that contradicts the null hypothesis

Proportion Tests

• Z-test for proportions: Compares the proportion of successes in two independent samples
• Chi-squared test for proportions: Compares the proportions of successes in more than two groups
• Fisher's exact test: Used for small sample sizes in chi-squared tests
• Binomial test: Tests whether the observed number of successes in a sample differs significantly from the expected number under a given probability