R Basics and Environment

R Basics

• Data types: R has various data types, including numeric, character, logical, and complex.
• Data structures: Common structures include vectors, matrices, lists, and data frames.
• Vectors: Ordered sequences of elements of the same data type.
• Matrices: Two-dimensional arrays with rows and columns, all elements must be of the same data type.
• Lists: Heterogeneous collections of elements, can contain different data types.
• Data frames: Tabular data structures similar to spreadsheets, columns represent variables and rows represent observations.

R Environment

• Installing Packages: Use install.packages() to install R packages from repositories like CRAN.
• Managing Libraries: Use library() or require() to load packages into your current R session.

Data Input/Output

• Reading CSV: Use read.csv() to import data from Comma Separated Value files.
• Writing CSV: Use write.csv() to export data to CSV files.
• Excel: Use readxl package for reading Excel files and writexl for writing to Excel.

Manipulating Data

• Factors: Represent categorical data, simplifying analysis and visualization.
• Logical Operators: Employ AND (&), OR (|), NOT (!) for data filtering based on conditions.
• Loops: Use for, while, and repeat to execute code repeatedly.

Working With Data

• Apply Family Functions: apply for applying functions to arrays, lapply for lists, sapply for simplified output, tapply for applying functions to subsets, and vapply for type-checked results.
• Functions: Create user-defined functions with function().
• String Manipulation: Use the stringr package for functions like str_trim(), str_replace(), str_detect(), and str_split().

Data Visualization

• Base R Plotting: Use functions like plot(), hist(), boxplot() for basic visualizations.
• ggplot2: A powerful package for creating aesthetically pleasing and customizable graphs.
• dplyr: Simplifies data manipulation with functions such as filter(), select(), mutate(), arrange(), group_by(), and summarize().
• tidyr: Focuses on data tidying with functions like pivot_longer(), pivot_wider(), and separate().
• reshape2: Provides melt() and cast() functions for reshaping data.

Statistics and Modeling

• Basic Statistics: Calculate mean, median, mode, variance, and standard deviation using built-in functions.
• Probability Distributions: Generate and visualize probability distributions like normal, binomial, Poisson using functions like rnorm(), rbinom(), and rpois().
• Regression Analysis: Fit and interpret linear models with lm() function.
• Logistic Regression: Model binary outcomes with glm() function.
• Time Series Analysis: Analyze data over time using forecast package.
• Clustering Techniques: Group observations based on similarity using K-means or hierarchical clustering.
• Principal Component Analysis: Reduce dimensions and visualize data with prcomp().

Miscellaneous

• Working with Databases: Use DBI package for connecting to various databases.
• Handling Missing Data: Identify missing values with is.na() and utilize imputation methods.
• Data Aggregation: Combine and summarize data with functions like aggregate().

Advanced Techniques

• Decision Trees: Create and evaluate tree models with rpart package.
• Random Forests: Build and evaluate random forest models with randomForest package.
• Data Resampling Techniques: Use bootstrap and cross-validation for model evaluation and selection.
• Model Evaluation Metrics: Assess model performance with AUC, ROC, confusion matrix, and accuracy.
• RMarkdown: Create reproducible reports and presentations combining code, text, and visualizations.
• Shiny: Develop interactive web apps using frameworks like Shiny.
• APIs in R: Use httr package to interact with APIs and retrieve data.
• Text Mining: Use tm and tidytext packages for text analysis and sentiment analysis.
• Regular Expressions: Apply pattern matching and text manipulation with regular expressions.
• Parallel Computing: Utilize parallel and foreach packages for faster computation.
• Version Control with Git: Manage and track code changes with Git and GitHub.
• Object-Oriented Programming: Learn and utilize S3, S4, and R6 classes.
• Package Development: Build and share your own R packages.
• Spatial Data Analysis: Visualize and analyze geographical data using sf and sp packages.
• Integrating R with Python: Use reticulate package for seamless interaction between R and Python.

R Basics

• Data Types: R offers various data types, including numeric, character, logical, and complex.
• Data Structures: R handles data in vectors, matrices, arrays, lists, and data frames, each with unique properties and usage.

R Environment

• Package Management: R's package system allows for the installation and use of external libraries, expanding its functionality.
• Libraries: Libraries are collections of functions and datasets, enhancing the core R capabilities.

Data Input/Output

• Import Data: R can read data from various file formats including CSV, Excel, and text files.
• Export Data: Data can be written to these formats using functions like write.csv and write.table.

Vectors

• Vector Creation: Vectors are one-dimensional arrays created with c().
• Manipulation: Operations like subsetting, sorting, and applying functions are readily performed on vectors.

Matrices

• Matrix Creation: Matrices are two-dimensional arrays created with matrix().
• Indexing: Elements are accessed using square brackets [] with row and column indices.

Lists

• List Creation: Lists are flexible data structures capable of holding different data types in each element.
• Nested Lists: Lists can contain other lists, enabling hierarchical structures.

Data Frames

• Data Frame Creation: Data frames are tabular structures with columns of different data types, commonly used for storing datasets.
• Manipulation: Operations like subsetting, filtering, and transforming are essential for data frame manipulation.

Factors

• Categorical Data: Factors represent categorical data with levels, useful for analysis and visualization.

Logical Operators

• Comparisons: R uses logical operators like ==, !=, <, >, <=, >= to compare values.
• Conditional Statements: if, else, and else if structures enable conditional execution based on logical expressions.

Loops

• For Loops: Iterate over elements of a sequence or vector.
• While Loops: Repeat code as long as a condition is true.
• Repeat Loops: Execute code indefinitely until stopped with break.

Apply Family Functions

• Apply Functions: Improve code readability for applying functions to elements of vectors, lists, matrices, or data frames.
• lapply, sapply, tapply, vapply: Apply a function to each element of a list or matrix.

Functions

• User-Defined Functions: Create custom functions for specific tasks, enhancing code reusability.

String Manipulation

• Stringr Package: Provides a comprehensive set of functions for manipulating strings.
• Base R: Basic string functions are available in the core R distribution.

Dates and Times

• Lubridate Package: Makes working with dates and times easier, offering functionalities for calculations and conversions.

Basic Statistics

• Descriptive Statistics: Calculate mean, median, mode, variance, and standard deviation to summarize data distributions.

Probability Distributions

• Distributions: Generate and visualize common probability distributions like normal, binomial, and Poisson.

Data Visualization Basics

• Base R Plotting: Create basic plots using functions like plot, hist, and boxplot.

ggplot2 Basics

• ggplot2 Package: Provides a grammar of graphics for creating visually appealing and customizable plots.

ggplot2 Advanced

• Themes: Customize the appearance of plots with themes.
• Facets: Create multiple plots based on different categorical variables.
• Scales: Adjust the scales on axes for better visualization.

dplyr Basics

• Data Manipulation: dplyr package provides efficient tools for filtering, selecting, mutating, and arranging data.

dplyr Advanced

• Grouping: Group data based on specific variables.
• Summarizing: Calculate summary statistics for each group.
• Joins: Combine data from different data frames.

tidyr Basics

• Data Tidying: tidyr package provides tools for reshaping data into a tidy format.
• pivot_longer and pivot_wider: Reshape data between long and wide formats.
• separate: Split a single column into multiple columns based on a delimiter.

Data Transformation with reshape2

• Melting and Casting: Reshape data using melt and cast functions for analysis and visualization.

Working with Databases

• DBI and RMySQL: Connect R to databases like MySQL using packages like DBI and RMySQL.

Data Aggregation

• aggregate function: Calculate summary statistics for grouped data.
• Other Summarization Functions: Aggregate data based on specific criteria.

Handling Missing Data

• Detection: Identify missing data using is.na().
• Imputation: Replace missing data with reasonable values for analysis.

Regression Analysis

• Simple Linear Regression: Fit a line to data to model the relationship between two variables.
• Multiple Regression: Model the relationship between a dependent variable and multiple independent variables.

Logistic Regression

• Binary Classification: Predict the probability of an event occurring based on predictor variables.

Time Series Analysis

• Time Series Data: Analyze data collected over time.
• Forecast Package: Provides tools for time series forecasting.

ARIMA Models

• Autoregressive Integrated Moving Average (ARIMA): Forecast time series data by modeling the relationship between past values and future values.

Clustering Techniques

• K-Means Clustering: Partition data points into distinct clusters based on their similarity.
• Hierarchical Clustering: Group data points into a hierarchical tree based on their similarity.

Principal Component Analysis (PCA)

• Dimensionality Reduction: Reduce the number of variables in a dataset while preserving important information.
• Visualization: Visualize high-dimensional data in fewer dimensions.

### Decision Trees

• rpart Package: Build and evaluate decision tree models for classification and regression.

Random Forests

• randomForest Package: Implement Random Forest models, an ensemble method combining multiple decision trees.

Data Resampling Techniques

• Bootstrap: Create multiple datasets by resampling with replacement.
• Cross-validation: Split data into training and testing sets for model evaluation.

Model Evaluation Metrics

• AUC and ROC: Evaluate model performance in binary classification problems.
• Confusion Matrix: Summarize classification results.
• Accuracy: Measure the overall correctness of predictions.

RMarkdown

• Reproducible Reports: Create reports with code, results, and visualizations.
• Presentations: Craft interactive presentations with R code and output.

Shiny Basics

• Interactive Web Apps: Build web applications with interactive elements using Shiny.

Shiny Advanced

• Inputs and Outputs: Design user interfaces with interactive elements and dynamic outputs.

### APIs in R

• httr Package: Connect to and retrieve data from web APIs.

Text Mining Basics

• tm and tidytext Packages: Analyze text data for patterns and insights.

Sentiment Analysis

• Sentiment Analysis: Analyze text data to understand sentiment (positive, negative, neutral).

Regular Expressions

• Pattern Matching: Find and manipulate text using regular expressions.

Parallel Computing in R

• parallel and foreach Packages: Run code in parallel to enhance computational efficiency.

Version Control with Git

• Github: Integrate R projects with Git and GitHub for version control and collaboration.

Object-Oriented Programming in R

• S3, S4, and R6 Classes: Enhance code organization and reusability by implementing object-oriented programming concepts in R.

Package Development

• Create R Packages: Package your R code and data into reusable libraries.

Spatial Data Analysis

• sf and sp Packages: Analyze geographic data using packages designed for spatial analysis.

Integrating R with Python

• reticulate Package: Utilize Python libraries within R using the reticulate package.

R Basics

• Data types: R handles various data types, including numeric, character, logical, and complex.
• Data structures: Key data structures in R include vectors (one-dimensional arrays), matrices (two-dimensional arrays), arrays (multi-dimensional arrays), lists (ordered collections of elements), and data frames (tabular data with columns of different data types).

R Environment

• Installing packages: R packages extend its functionality by providing additional functions and datasets. Packages are installed using the install.packages() function.
• Managing libraries: Once installed, packages are loaded into the current R session using the library() function.

Data Input/Output

• Reading files: R can import data from various formats, including CSV (read.csv(), read.table()), Excel (readxl::read_excel()), and more.
• Writing files: The write.csv() and write.table() functions allow export of data frames to CSV or other delimited file formats.

Vectors

• Creation: Vectors are created using the c() function, which combines elements into a single vector.
• Manipulation: Vectors can be accessed using indexing, sliced, and modified by assignment.
• Operations: Arithmetic, logical, and comparison operations can be applied to vectors, resulting in element-wise calculations.

Matrices

• Creation: Matrices are created using the matrix() function, specifying the data, dimensions, and optional row/column names.
• Indexing: Elements in a matrix are accessed using row and column indices, e.g., matrix[row, col].
• Basic operations: Matrices support arithmetic, matrix multiplication, and transposition.

Lists

• Working with lists: Lists allow the storage of various data types and structures within a single object. Elements can be accessed by name or index.
• Nested lists: Lists can contain other lists, creating hierarchical structures.

Data Frames

• Creation: Data frames are constructed using the data.frame() function, combining vectors of equal length into columns.
• Manipulation: Data frames can be easily manipulated by adding, removing, or renaming columns, and rows can be accessed or filtered using indexing.
• Indexing: Elements are accessed using row and column names or indices.

Factors

• Categorical data handling: Factors are used to represent categorical variables in R. This provides a more efficient and informative way to work with categorical data compared to using character vectors.
• Manipulation: Factors can be reordered, levels can be modified, and levels can be combined.

Logical Operators

• AND, OR, NOT: Operators &, |, and ! are used to create logical expressions for conditional statements.
• Conditional statements: if and else statements execute different code blocks based on the outcome of a logical expression.

Loops

• For loops: Iterate over a sequence of values, executing a code block for each element.
• While loops: Execute a code block as long as a specific condition remains true.
• Repeat loops: Execute a code block an indefinite number of times until a specific condition is met.

Apply Family Functions

• Apply: Applies a function to the rows or columns of a matrix, returning the results as a vector.
• Lapply: Applies a function to each element of a list, returning a list of results.
• Sapply: Similar to lapply, but attempts to simplify the output.
• Tapply: Applies a function to subsets of data, grouped by a factor.
• Vapply: Similar to sapply, but requires a pre-defined type for the output.

Functions

• Creating functions: User-defined functions are defined using the function() keyword.
• Using functions: Once defined, functions can be called with specific arguments to achieve reusable calculations.

String Manipulation

• Using stringr: The stringr package provides a comprehensive set of tools for working with strings.
• Base R: Base R also offers functions like substr() and gsub() for basic string operations.

Dates and Times

• Using lubridate: The lubridate package simplifies date and time manipulation, providing functions for parsing, formatting, and performing calculations.

Basic Statistics

• Mean: The average of a dataset is calculated using the mean() function.
• Median: The central value in a sorted dataset is found using the median() function.
• Mode: The most frequent value in a dataset is identified using functions like table() and which.max().
• Variance: A measure of how spread out the data is from the mean, is calculated using the var() function.
• Standard deviation: The square root of the variance, indicating the typical deviation from the mean.

Probability Distributions

• Generating distributions: Various probability distributions can be generated using functions like rnorm() (normal), rbinom() (binomial), rexp() (exponential), etc.
• Visualizing distributions: Histograms, boxplots, and other graphical tools aid in visualizing distributions.

Data Visualization Basics

• Base R: Base R includes functions like plot(), hist(), and boxplot() for basic plotting.

ggplot2 Basics

• ggplot2: The ggplot2 package provides a grammar-based approach to data visualization. It uses a consistent syntax for constructing plots.

ggplot2 Advanced

• Customizing plots: Various arguments control aesthetics, themes, facets, and scales within ggplot2, allowing for flexible plot customization.

dplyr Basics

• Data manipulation: The dplyr package streamlines data transformations with functions like:
  • filter(): Selects rows based on conditions.
  • select(): Selects specific columns.
  • mutate(): Creates or modifies columns.
  • arrange(): Orders rows based on column values.

dplyr Advanced

• Grouping and summarizing: Dplyr offers functions for aggregating data by groups, creating summary tables.
• Joins: Merging data from different data frames using different join types (inner, left, right, full).

tidyr Basics

• Data tidying: tidyr functions help reshape data for more efficient analysis and visualization.
  • pivot_longer(): Converts wide data into longer format.
  • pivot_wider(): Converts long data into wider format.
  • separate(): Splits a column into multiple columns based on a separator.

Data Transformation with reshape2

• Melting and casting: The reshape2 package provides functions (melt() and cast()) for reshaping data between wide and long formats.

Working with Databases

• Connecting to databases: R packages like DBI and RMySQL enable connectivity to various databases, including MySQL, allowing data analysis and querying.

Data Aggregation

• Using aggregate(): The aggregate() function provides a concise method for aggregating data based on a grouping variable.
• Other summarization functions: Functions like tapply() or custom functions can be used for more specialized data aggregation tasks.

Handling Missing Data

• Detection: Missing values are often represented by NA (Not Available) in R. Functions like is.na() can identify missing values.
• Imputation: Different methods can be used to fill in missing values, including mean imputation, median imputation, or more advanced techniques.

Regression Analysis

• Simple linear regression: A model describes the relationship between a dependent variable (response) and a single independent variable (predictor). The lm() function fits linear regression models.
• Multiple regression: Extends linear regression to multiple independent variables, allowing for analysis of complex relationships.

Logistic Regression

• Binary classification: Logistic regression is used for predicting binary outcomes (e.g., yes/no) based on independent variables.
• Model evaluation: Metrices like accuracy, precision, recall, and AUC evaluate the predictive performance.

Time Series Analysis

• Basics: Time series data is sequential data indexed by time. It can be analyzed using techniques like moving averages, decomposition, and auto-regressive approaches.
• forecast package: The forecast package provides tools for forecasting future values from time series data.

ARIMA Models

• Fitting and forecasting: ARIMA (Autoregressive Integrated Moving Average) models are commonly used for time series forecasting.

Clustering Techniques

• K-means: An unsupervised clustering algorithm that partitions data points into distinct clusters based on their proximity.
• Hierarchical clustering: Creates a hierarchical structure of clusters, allowing for visualization of relationships between data points.

Principal Component Analysis (PCA)

• Dimensionality reduction: PCA transforms data into a lower-dimensional space, preserving most of the variance.
• Visualization: PCA allows for visualization of high-dimensional data, often using scatter plots of the principal components.

Decision Trees

• Building and evaluating: Decision trees are used for classification and regression. The rpart package provides functions for building and evaluating decision trees.

Random Forests

• Implementing models: Random forests improve prediction by creating multiple decision trees and combining their predictions. The randomForest package implements this approach.

Data Resampling Techniques

• Bootstrap: Resampling with replacement is used to create multiple datasets from the original data, allowing for estimation of model variability.
• Cross-validation: Splits data into training and testing sets, repeatedly to assess model performance across different folds.

Model Evaluation Metrics

• AUC: Area Under the Curve (ROC) – measures the overall performance of a classification model.
• ROC: Receiver Operating Characteristic – plots the true positive rate against the false positive rate.
• Confusion matrix: Summarizes the performance of a classification model by showing the number of true positives, true negatives, false positives, and false negatives.
• Accuracy: Overall percentage of correct predictions.

RMarkdown

• RMarkdown: An authoring format for creating reproducible reports and presentations, combining R code and output with text, tables, and figures.

Shiny Basics

• Building interactive web apps: Shiny allows for creation of interactive web applications in R.

Shiny Advanced

• Customizing dashboards: Shiny apps can be customized with user inputs, interactive elements, and dynamic output.

APIs in R

• httr package: Provides functions for interacting with web APIs, allowing data retrieval and manipulation.

Text Mining Basics

• tm and tidytext packages: These packages provide tools for text cleaning, preprocessing, and analysis.

Sentiment Analysis

• Analyzing sentiment: Techniques are used to extract sentiment (positive, negative, neutral) from text data.

Regular Expressions

• Pattern matching: Regular expressions are used to define search patterns for finding and manipulating text.

Parallel Computing in R

• parallel and foreach packages: These packages allow for parallel computing tasks, potentially speeding up data processing.

Version Control with Git

• Integrating R projects with GitHub: Version control tools like Git help track changes, manage multiple versions, and collaborate on R projects.

Object-Oriented Programming in R

• S3, S4, and R6 classes: R supports object-oriented programming principles. These classes offer different levels of object organization and inheritance.

Package Development

• Creating R packages: Packages provide a way to organize functions, data, and documentation, sharing them with others.

Spatial Data Analysis

• sf and sp packages: These packages are used for working with spatial data, including geographic coordinates.

Integrating R with Python

• reticulate package: Enables interaction between R and Python code within the same environment.

R Basics

• Data types: Numerical, character, logical, complex.
• Data structures: Vectors, matrices, arrays, lists, data frames.

R Environment

• Installing packages: Use install.packages() function.
• Managing libraries: Load packages with library() function.

Data Input/Output

• Reading data: Use functions like read.csv(), read.xlsx(), read.table()
• Writing data: Use functions like write.csv(), write.xlsx(), write.table()

Vectors

• Creation: Use c() function or : operator.
• Manipulation: Subsetting with indices, sorting with sort(), removing elements with remove()
• Operations: +, -, *, /, etc.

Matrices

• Creation: Use matrix() function.
• Indexing: Use row and column indices, e.g., matrix[i, j].
• Operations: Matrix multiplication with %*%, transposition with t().

Lists

• Working with lists: Access elements with [[]], modify lists with [[ <- ]
• Nested lists: Lists within lists.

Data Frames

• Creation: Use data.frame() function.
• Manipulation: Adding columns, rows, and modifying values.
• Indexing: Use row and column names or indices.

Factors

• Categorical data: Store and analyze categorical variables.
• Manipulation: Creating, modifying, and converting factors.

Logical Operators

• AND: &
• OR: |
• NOT: !
• Conditional statements: if, else, else if.

Loops

• For loop: Iterate over a sequence of values.
• While loop: Repeat code while a condition is true.
• Repeat loop: Execute code until a condition is met.

Apply Family Functions

• Apply: Apply a function over the margins of an array.
• Lapply: Apply a function to each element of a list.
• Sapply: Apply a function to each element of a list and simplify results.
• Tapply: Apply a function to each group of a factor.
• Vapply: Apply a function with pre-defined output type.

Functions

• Creating: Use function() function.
• Using: Call functions with arguments.
• Documentation: Add comments to functions.

String Manipulation

• Stringr library: Offers functions like str_replace(), str_locate(), str_trim().
• Base R: Functions like substr(), nchar(), gsub().

Dates and Times

• Lubridate package: Provides functions like ymd(), hms(), today().
• Date operations: Calculations and conversions.

Basic Statistics

• Mean: Calculate with mean().
• Median: Calculate with median().
• Mode: Find the most frequent value (no built-in function in base R).
• Variance: Calculate with var().
• Standard deviation: Calculate with sd().

Probability Distributions

• Generating: Use functions like rnorm(), rbinom(), rpois().
• Visualizing: Use hist(), plot(), and other plotting functions.

Data Visualization Basics

• Base R plotting: Functions like plot(), hist(), boxplot(), barplot().

ggplot2 Basics

• ggplot2 library: Provides a grammar of graphics for plotting.
• Basic syntax: ggplot(data, aes(x, y)) + geom_point().

ggplot2 Advanced

• Customizing: Themes, facets, scales, annotations.

dplyr Basics

• Data manipulation: filter(), select(), mutate(), arrange().

dplyr Advanced

• Grouping: group_by().
• Summarizing: summarize().
• Joins: inner_join(), left_join().

tidyr Basics

• Data tidying: pivot_longer(), pivot_wider(), separate().

Data Transformation with reshape2

• Melting: Reshape data from wide to long format.
• Casting: Reshape data from long to wide format.

Working with Databases

• DBI package: Provides a connection interface for databases.
• RMySQL package: Connect to MySQL databases.

Data Aggregation

• Aggregate function: Summarize data based on grouping variables.
• Other functions: tapply(), by().

Handling Missing Data

• Detection: Use functions like is.na().
• Imputation: Replace missing values with estimated values.

Regression Analysis

• Simple linear regression: Fit a line to data with one explanatory variable.
• Multiple Regression: Fit a model with multiple explanatory variables.

Logistic Regression

• Binary classification: Predict the probability of a binary outcome.
• Model evaluation: AUC, ROC, confusion matrix, accuracy.

Time Series Analysis

• Time series data: Data collected over time.
• Forecast package: Provides functions for time series analysis and forecasting.

ARIMA Models

• Fitting: Identify and fit ARIMA models.
• Forecasting: Predict future values based on fitted models.

Clustering Techniques

• K-means: Partition data into clusters based on distance.
• Hierarchical clustering: Create a hierarchy of clusters based on similarity.

Principal Component Analysis (PCA)

• Dimensionality reduction: Reduce the number of variables while capturing most of the data's variance.
• Visualization: Plot principal components to understand data structure.

Decision Trees

• Building: Create tree models based on recursive partitioning.
• Evaluating: Assess model performance with metrics like accuracy and precision.

Random Forests

• Implementing: Create ensemble models by combining multiple decision trees.
• Benefits: Improved accuracy and robustness to overfitting.

Data Resampling Techniques

• Bootstrap: Resample data with replacement to estimate uncertainty.
• Cross-validation: Split data into training and test sets to evaluate model generalization.

Model Evaluation Metrics

• AUC: Area under the ROC curve.
• ROC: Receiver operating characteristic curve.
• Confusion matrix: Summarize classification performance.
• Accuracy: Proportion of correctly classified instances.

RMarkdown

• Reproducible reports: Create reports with code and output.
• Presentations: Generate presentations with embedded code and visualizations.

Shiny Basics

• Interactive web apps: Build dynamic web applications with R.
• Basic structure: App layout, input widgets, output elements, server logic.

Shiny Advanced

• Customizing: Use more advanced UI elements, integrate JavaScript, enhance interactivity.

APIs in R

• Httr package: Connect to and retrieve data from APIs.
• API endpoints: Specific URLs that provide data or services.

Text Mining Basics

• Tm and tidytext packages: Provide tools for text analysis.
• Text processing: Cleaning, tokenization, stemming, lemmatization.

Sentiment Analysis

• Analyzing sentiment: Determine the emotional tone of text data.
• Sentiment scores: Measure positive, negative, and neutral sentiment.

Regular Expressions

• Pattern matching: Search for specific patterns within text.
• Text manipulation: Extract, replace, and modify text based on patterns.

Parallel Computing in R

• Parallel and foreach packages: Execute code on multiple cores.
• Speed up computations: Improve performance for large datasets or complex calculations.

Version Control with Git

• Integrating R projects: Use Git to track changes and collaborate on projects.
• GitHub: Host and share R projects online.

Object-Oriented Programming in R

• S3, S4, and R6 classes: Implement object-oriented programming principles in R.
• Encapsulation, inheritance, polymorphism: Key concepts of object-oriented programming.

Package Development

• Creating R packages: Develop reusable code libraries in R.
• Package structure: Organize code, data, documentation, and tests.

Spatial Data Analysis

• Sf and sp packages: Provide tools for working with spatial data.
• Geographic data: Data associated with locations on the earth's surface.

Integrating R with Python

• Reticulate package: Connect R and Python for cross-language work.
• Combining strengths: Leverage the strengths of both languages in a single workflow.

R Basics

• Data Types: R supports various data types including numeric, character, logical, and complex numbers.
• Data Structures: Common data structures include vectors, matrices, lists, and data frames.
• Vectors: Ordered sequences of elements of the same data type.
• Matrices: Two-dimensional arrays of elements of the same data type.
• Lists: Ordered collections of elements that can be of different data types.
• Data Frames: Two-dimensional data structures that represent tabular data with rows and columns.
• Factors: Represent categorical data with predefined levels.

R Environment

• Installing Packages: Use install.packages("package_name") to install packages from CRAN or other repositories.
• Managing Libraries: Use library(package_name) to load packages into your current R session.

Data Input/Output

• Reading CSV: read.csv("file_path.csv")
• Writing CSV: write.csv(data, "file_path.csv")
• Reading Excel: Use the readxl package.
• Writing Excel: Use the writexl package .

Vectors

• Creation: Use c() to combine elements.
• Manipulation: Subset using indexing (e.g., vector[2] for the second element).
• Operations: Perform mathematical operations on vectors element-wise.

Matrices

• Creation: Use matrix() with dimensions and data.
• Indexing: Use [row, column] for element access.
• Operations: Perform mathematical operations on matrices, including multiplication and transposition.

Lists

• Working with Lists: Use list() to create and manipulate lists.
• Nested Lists: Lists can contain other lists, allowing for complex data structures.

Data Frames

• Creation: Use data.frame() to create data frames from vectors or lists.
• Manipulation: Use column names for selection (data_frame$column_name).
• Indexing: Use [row, column] or [row, ] for subsetting.

Factors

• Handling Categorical Data: Use factor() to convert character vectors into factors.
• Manipulation: Change levels and order using levels() and relevel().

Logical Operators

• AND: &
• OR: |
• NOT: !
• Conditional Statements: Use if, else, and else if for conditional execution.

Loops

• For: Iterate over a sequence.
• While: Execute a block of code while a condition is true.
• Repeat: Execute a block of code repeatedly until a condition is met.

Apply Family Functions

• Apply: Apply a function to the rows or columns of a matrix or array.
• Lapply: Apply a function to each element of a list.
• Sapply: Apply a function to each element of a list and simplify the output.
• Tapply: Apply a function to a subset of data based on factors.
• Vapply: Apply a function to elements of a list with type checking.

Functions

• Creating User-Defined Functions: Use function() to define functions.
• Using Functions: Call functions by name with arguments.

String Manipulation

• Using Stringr: Leverages functions like str_trim(), str_replace(), and str_detect().
• Base R: Use functions like substr(), gsub(), and nchar().

Dates and Times

• Handling Dates: Use the lubridate package for functions like ymd(), today(), and weekdays().

Basic Statistics

• Mean: mean(data)
• Median: median(data)
• Mode: Use the modeest package.
• Variance: var(data)
• Standard Deviation: sd(data)

Probability Distributions

• Generating Distributions: Use functions like rnorm() for the normal distribution, rbinom() for the binomial distribution, and runif() for the uniform distribution.
• Visualizing Distributions: Use plotting functions like hist() and boxplot().

Data Visualization Basics

• Base R Plotting: Use functions like plot(), boxplot(), and hist() for basic plots.

ggplot2 Basics

• Introduction: A powerful package for creating aesthetically pleasing and customizable plots.
• Key Components: ggplot(), geom_point(), aes(), theme(), and facet_wrap().

ggplot2 Advanced

• Customizing Plots: Use themes and scales for customization.
• Facets: Create multiple plots based on grouping variables using facet_wrap() and facet_grid().

dplyr Basics

• Data Manipulation: Use functions like filter(), select(), mutate(), and arrange() for data transformation and filtering.

dplyr Advanced

• Grouping and Summarizing: Use group_by() and summarise() for aggregation and summary statistics.
• Joins: Use functions like inner_join(), left_join(), and full_join() to merge data frames.

tidyr Basics

• Data Tidying: Use functions like pivot_longer() and pivot_wider() for reshaping data.
• Separating and Combining Columns: Use separate() and unite() for managing columns.

Data Transformation with reshape2

• Melting and Casting: Use melt() and cast() for reshaping data.

Working with Databases

• Connecting to Databases: Use the DBI package for interacting with databases.
• RMySQL: Utilize the RMySQL package to connect to MySQL databases.

Data Aggregation

• Using Aggregate: Use the aggregate() function for grouping and summarization.
• Other Summarization Functions: Explore functions like tapply() and by() for data summarization.

Handling Missing Data

• Detection: Use functions like is.na() to identify missing values.
• Imputation Methods: Replace missing values with sensible estimates using techniques like mean imputation or k-nearest neighbors.

Regression Analysis

• Simple Linear Regression: Fit a linear model to predict a dependent variable based on an independent variable using lm() and interpret coefficients.

Multiple Regression

• Fitting Models: Fit multiple linear regression models using lm() with multiple predictors.
• Interpreting Coefficients: Interpret the coefficients and assess their significance.

Logistic Regression

• Binary Classification: Predict a binary outcome using glm() with a family of binomial.
• Model Evaluation: Evaluate model performance using metrics like accuracy, precision, recall, and AUC.

Time Series Analysis

• Basics: Understand the concepts of time series data, seasonality, trend, and autocorrelation.
• Forecast Package: Learn how to use the forecast package for time series analysis.

ARIMA Models

• Fitting: Fit ARIMA models to time series data using auto.arima().
• Forecasting: Use fitted models to generate forecasts for future time points.

Clustering Techniques

• K-means: Partition data points into clusters based on proximity.
• Hierarchical Clustering: Create a hierarchy of clusters based on distance calculations.

Principal Component Analysis (PCA)

• Dimensionality Reduction: Reduce the number of variables in a dataset while preserving most of the variance.
• Visualization: Visualize data in lower dimensions using PCA plots.

Decision Trees

• Building Trees: Use the rpart package to construct decision trees.
• Evaluating Trees: Assess the performance of decision trees using measures like accuracy and AUC.

Random Forests

• Implementation: Use the randomForest package to build random forest models.
• Ensemble Learning: Combine multiple decision trees to improve prediction accuracy and robustness.

Data Resampling Techniques

• Bootstrap: Resample data with replacement to estimate model uncertainty.
• Cross-validation: Split data into training and testing sets to evaluate model generalization.

Model Evaluation Metrics

• AUC: Area under the Receiver Operating Characteristic curve.
• ROC: Receiver Operating Characteristic curve.
• Confusion Matrix: Table summarizing classification results.
• Accuracy: Overall proportion of correct predictions.

RMarkdown

• Reproducible Reports: Create dynamic reports with code, output, and text using RMarkdown.
• Presentations: Create slide shows with RMarkdown.

Shiny Basics

• Interactive Web Apps: Build interactive dashboards and web applications with the Shiny package.

Shiny Advanced

• Customizing Dashboards: Add custom inputs and outputs, control user interactions, and integrate with external data sources.

APIs in R

• Using httr: Use the httr package for making requests to web APIs.
• Retrieving Data from APIs: Extract data from API responses and process it in R.

Text Mining Basics

• Using tm and tidytext: Use the tm and tidytext packages for basic text analysis.
• Term Frequency-Inverse Document Frequency (TF-IDF): Measure term importance in a corpus.

Sentiment Analysis

• Analyzing Sentiment: Use libraries like sentiment and syuzhet to gauge sentiment in text data.

Regular Expressions

• Pattern Matching and Manipulation: Use regular expressions to find and extract specific patterns from text data.

Parallel Computing in R

• Using parallel and foreach: Take advantage of multi-core processors for parallel computations.

Version Control with Git

• Integrating R Projects with GitHub: Utilize Git and GitHub for version control and collaboration on R projects.

Object-Oriented Programming in R

• S3, S4, and R6 Classes: Understand and use object-oriented programming concepts in R.

Package Development

• Basics: Learn how to create your own R packages for sharing code and functions.

Spatial Data Analysis

• Using sf and sp: Use these packages for spatial data handling and analysis.

Integrating R with Python

• Using reticulate: Access Python libraries and functions from within R using the reticulate package.

R Basics

• Data Types: R handles various data types including numeric, character, logical, and complex.
• Data Structures: R offers fundamental data structures like vectors, matrices, lists, and data frames.
• Vectors: One-dimensional arrays holding elements of the same data type.
• Matrices: Two-dimensional arrays with rows and columns.
• Lists: Ordered collection of objects, allowing for diverse data types.
• Data Frames: Tabular data representation with rows and columns, commonly used for analysis.
• Factors: Categorical data type for representing factors, useful for analysis.
• Logical Operators: AND, OR, NOT operators for logical evaluations and conditional statements.
• Loops: for, while, and repeat loops execute code blocks repeatedly.
• Apply Family Functions: Functions like apply, lapply, sapply, tapply, and vapply apply functions to objects.
• Functions: Create custom functions for specific operations within your code.

Data Input/Output

• Data File Handling: R can read and write data from various file formats:
  • CSV (Comma-Separated Values)
  • Excel spreadsheets
  • Other file types like text files, JSON, and XML.

R Environment & Libraries

• Installing Packages: Use install.packages() to install R packages from the CRAN repository or other sources.
• Managing Libraries: Load packages into your current R session using library() or require().

String Manipulation

• stringr Package: A dedicated package for working with strings, offering functions for subsetting, pattern matching, and manipulation.

Dates and Times

• lubridate Package: A powerful library for working with dates and times, providing functions to manipulate and format dates.

Basic Statistics

• Statistical Measures: R offers functions to calculate key statistics like mean, median, mode, variance, and standard deviation.

Probability Distributions

• Generating Distributions: Generate random samples from various statistical distributions, including normal, binomial, and others.
• Visualizing Distributions: Create plots like histograms, boxplots, and density curves to visualize distributions.

Data Visualization

• Base R Plotting: Utilize base R graphics for creating plots and charts.
• ggplot2: A comprehensive and powerful package for creating visually appealing and customizable plots.

Data Manipulation

• dplyr Package: A versatile package for data manipulation tasks like filtering, selecting, mutating, and arranging data.
• tidyr Package: A package for data tidying, reshaping, and organizing data.

Data Transformation

• reshape2 Package: Tools like melt() and cast() for transforming data into different formats.

Working with Databases

• DBI Package: Provides a unified interface for interacting with relational databases.
• RMySQL Package: Facilitates connecting and interacting with MySQL databases.

Data Aggregation

• aggregate() Function: Summarizes data based on grouping variables.

Handling Missing Data

• Detection: Identifying missing values in your data using is.na().
• Imputation: Filling in missing values with various strategies like mean, median, or model-based imputation.

Statistical Models & Analyses

• Regression Analysis: Building linear regression models to predict a dependent variable from independent variables.
• Logistic Regression: Predicting binary outcomes (e.g., success/failure) using a logistic regression model.
• Time Series Analysis: Analyzing data that changes over time.
• ARIMA Models: Autoregressive integrated moving average (ARIMA) models for time series forecasting.
• Clustering Techniques: Clustering algorithms like k-means and hierarchical clustering for grouping similar data points.
• Principal Component Analysis (PCA): A dimensionality reduction technique to simplify high-dimensional data while preserving as much information as possible.
• Decision Trees: Building tree-based models with rpart package.
• Random Forests: Using randomForest package to build ensemble models with decision trees, often improving predictive accuracy.

Data Resampling Techniques

• Bootstrap: A resampling method for estimating statistics or assessing model variability.
• Cross-validation: A resampling technique used for model selection and evaluation.

Model Evaluation

• Metrics: Evaluate the performance of models using metrics like AUC (Area Under the Curve), ROC (Receiver Operating Characteristic), confusion matrix, and accuracy.

RMarkdown and Shiny

• RMarkdown: Creating reports, presentations, and reproducible documents with embedded R code.
• Shiny: Developing interactive web applications using R, allowing users to interact with data and analysis results.

APIs and Text Mining

• APIs: Connecting to APIs using httr for retrieving data from external sources.
• Text Mining: Analyzing text data using packages like tm and tidytext.
• Sentiment Analysis: Extracting sentiment from text data.
• Regular Expressions: Pattern matching and manipulation of text.

Parallel Computing and Git

• Parallel Computing: Speeding up computations using parallel processing with packages like parallel and foreach.
• Git: Using Git for version control and collaboration on R projects.

Object-Oriented Programming

• S3, S4, and R6 classes: Object-oriented approaches for organizing code and creating reusable components.

Package Development

• Creating Packages: Creating and distributing your own R packages.

Spatial Data Analysis

• sf and sp packages: Working with spatial data and performing geographic analyses.

Integrating R with Python

• reticulate package: Connecting and interacting with Python code from within R.

R Basics

• Data types: R handles various data types: numeric, character, logical, and complex.
• Data structures: Common structures include vectors (single data type, ordered elements), matrices (2D array of same data type), lists (ordered collection of diverse data types), data frames (tabular data with columns of different data types), and factors (categorical data with levels).
• Vectors: Created using "c()" function; manipulated using operators like "+" for addition, "-" for subtraction, "*" for multiplication, and "/" for division.

R Environment

• Installing packages: Use "install.packages()" function to install packages from CRAN (Comprehensive R Archive Network) or GitHub.
• Managing libraries: Once installed, use "library()" function to load packages for use in your script.

Data Input/Output

• CSV: Read and write data from/to Comma-Separated Values (CSV) files using "read.csv()" and "write.csv()" functions.
• Excel: Utilize the "readxl" package for reading Excel (.xlsx) files and "writexl" for writing.
• Other file types: R can work with other file types like JSON, XML, and plain text using specialized packages.

Matrices

• Creation: Create matrices using the function "matrix()"; specify data, number of rows, columns, and optionally byrow argument for filling.
• Indexing: Retrieve elements using "[ , ]" brackets; e.g., "my_matrix[2,3]" accesses the element at row 2, column 3.
• Basic Operations: Arithmetic operations on matrices are element-wise unless using matrix multiplication (%*%).

Lists

• Working with lists: Store a collection of elements of varying types (including other lists). Access elements by position or name.
• Nested lists: Lists within lists are used for complex data structures. Access elements using double square brackets "[" and "][".

Data Frames

• Creation: Use "data.frame()" to create data frames.
• Manipulation: Manipulate data frames using "[ , ]", "$" for column access.
• Indexing: Select rows or columns based on conditions using logical vectors.

Factors

• Categorical data: Factors represent categorical data with predefined levels.
• Manipulation: Convert character vectors to factors using "as.factor()" to control order and labels.

Logical Operators

• AND: "&&"
• OR: "||"
• NOT: "!"

Loops

• For loop: Executes a block of code for every element in a sequence.
• While loop: Executes a block of code repeatedly as long as a condition is TRUE.
• Repeat loop: Executes a block of code repeatedly until a specific condition is met using a "break" statement inside the loop.

Apply Family Functions

• Apply family: Provides efficient ways to perform operations on data structures:
  • apply(): Apply a function to the rows or columns of a matrix or array.
  • lapply(): Apply a function to each element of a list.
  • sapply(): Same as lapply but simplifies output if possible.
  • tapply(): Apply a function to each group of elements defined by a factor.
  • vapply(): Similar to sapply but specifies the output type for type checking.

Functions

• Creating functions: Define functions using the function keyword and specifying arguments.
• Using functions: Call functions by name and pass in the specified arguments.
• Returning values: Functions return a value using the "return()" function or implicitly return the last expression evaluated.

String Manipulation

• Stringr package: Provides powerful functions for string manipulation (e.g., "str_replace", "str_extract").
• Base R: Functions like "substr", "grep" are available in base R but can be less user-friendly.

Dates and Times

• lubridate package: Provides functions for working with dates and times (e.g., "ymd", "hms", "difftime").

Basic Statistics

• Mean: Average of a set of numbers.
• Median: Middle value when numbers are sorted.
• Mode: Most frequent value.
• Variance: Measure of data spread around the mean.
• Standard deviation: Square root of the variance.

Probability Distributions

• Generating distributions: Use functions like "rnorm" to generate random samples from distributions.
• Visualizing distributions: Use plotting functions (hist, density) to visualize data from distributions.

Data Visualization Basics

• Base R: Use functions "plot", "hist", "boxplot" for creating basic visualizations.

ggplot2 Basics

• ggplot2: Powerful and flexible data visualization library.
• Core elements:
  • ggplot(): Creates a blank plotting environment.
  • aes(): Specifies aesthetic mappings between data columns and visual properties.
  • Geom layers: Defines the type of geometric objects (points, lines, bars) to be plotted.

ggplot2 Advanced

• Customizing plots: Modify plot appearance using themes, facets for subplots, and scales for customizing axes and legends.

dplyr Basics

• Data manipulation: Powerful data manipulation package.
• Key verbs:
  • filter(): Subset rows based on a condition.
  • select(): Select specific columns.
  • mutate(): Add new columns or modify existing columns.
  • arrange(): Sort rows by one or more columns.

dplyr Advanced

• Group by: Group rows based on factor variables using "group_by()".
• Summarize: Calculate summary statistics within groups using "summarise()".
• Joins: Combine data frames based on shared columns using "left_join", "right_join", and "inner_join".

tidyr Basics

• Data tidying: Package for manipulating messy data to a tidy format.
• Key functions:
  • pivot_longer(): Transform data from wide to long format.
  • pivot_wider(): Transform data from long to wide format.
  • separate(): Split a single column into multiple columns.

Data Transformation with reshape2

• Melting and casting: Functions like "melt" and "cast" are used for reshaping data from wide to long and vice versa.

Working with Databases

• DBI package: Provides a generic interface for connecting to databases.
• RMySQL package: Enables connecting to MySQL databases.

Data Aggregation

• Aggregate function: Used for summarizing data within groups.
• Other functions: "tapply", "by" also provide data aggregation capabilities.

Handling Missing Data

• Detection: Identify missing values (NA) using "is.na()".
• Imputation methods: Use techniques like mean imputation, median imputation, or more complex models to fill missing values.

Regression Analysis

• Simple linear regression: Models the relationship between a single predictor variable and a response variable.
• Multiple regression: Extends the model to include multiple predictor variables.

Logistic Regression

• Binary classification: Predicts the probability of a binary outcome based on predictor variables.
• Model evaluation: Calculate accuracy, precision, recall, and AUC to assess the model's performance.

Time Series Analysis

• Time series data: Data collected at regular intervals over time.
• Forecast package: Provides tools for forecasting time series data.

ARIMA Models

• ARIMA models: Autoregressive Integrated Moving Average models represent the time series based on past values and random noise components.

Clustering Techniques

• K-means clustering: Partitioning data into K clusters based on minimizing within-cluster variance.
• Hierarchical clustering: Creating a hierarchical structure of clusters based on distances or similarities between data points.

Principal Component Analysis (PCA)

• Dimensionality reduction: Transforms data into a smaller number of uncorrelated variables called principal components.
• Visualization: Provides reduced-dimensionality visualization of data.

Decision Trees

• rpart package: Provides tools for building and evaluating decision trees.

Random Forests

• randomForest package: Implements random forest models, which combine multiple decision trees for improved prediction performance.

Data Resampling Techniques

• Bootstrap: Resampling data with replacement to create multiple datasets for model training and assessment.
• Cross-validation: Partitioning data into training and testing sets for robust model evaluation.

Model Evaluation Metrics

• AUC: Area Under the Curve (ROC curve) measures the model's ability to discriminate between classes.
• ROC: Receiver Operating Characteristic curve plots the true positive rate against the false positive rate at different classification thresholds.
• Confusion matrix: Summarizes the classification performance of a model by showing correctly and incorrectly classified cases.
• Accuracy: Proportion of correctly classified cases.

RMarkdown

• Reproducible reports: Create dynamic, reproducible reports and documents using RMarkdown.

Shiny Basics

• Interactive web apps: Develop interactive web applications using Shiny.
• Core components:
  • UI: Defines the user interface elements of the app.
  • Server: Handles calculations and data manipulation based on user input.

Shiny Advanced

• Customizing dashboards: Use inputs and outputs to create interactive dashboards with dynamic visualizations and functionality.

APIs in R

• httr package: Provides tools for accessing and retrieving data from web APIs using HTTP requests.

Text Mining Basics

• tm and tidytext: Libraries for text analysis, including tokenization, stemming, and stop word removal.

Sentiment Analysis

• Analyzing sentiment: Extract sentiment (positive, negative, neutral) from text data.

Regular Expressions

• Pattern matching: Use regular expressions to search for patterns in text strings.
• Text manipulation: Perform complex text manipulations using regular expressions.

Parallel Computing in R

• parallel and foreach packages: Enable parallel processing to speed up computationally intensive tasks.

Version Control with Git

• GitHub integration: Use Git for version control to track changes and collaborate on R projects.

Object-Oriented Programming in R

• S3, S4, and R6 classes: Implement object-oriented programming concepts in R using different class systems.

Package Development

• Creating R packages: Develop and distribute reusable R packages for specific functionalities.

Spatial Data Analysis

• sf and sp packages: Work with spatial data (geographical shapes, locations) for analysis and visualization.

Integrating R with Python

• reticulate package: Enables seamless integration of R with Python for using Python libraries within R scripts.

R Basics: Data Types and Structures

• Data Types: R utilizes various data types to represent different kinds of data. Common types include numeric (integers and decimals), character (text), logical (TRUE/FALSE), and complex (numbers with imaginary components).
• Data Structures: R offers several data structures for organizing and manipulating data. These include:
  • Vectors: Ordered sequences of elements of the same data type.
  • Matrices: Two-dimensional arrays with rows and columns, containing elements of the same type.
  • Arrays: Multi-dimensional generalizations of matrices, allowing for more complex data organization.
  • Lists: Flexible structures holding elements of different data types.
  • Data Frames: Tabular data structures similar to spreadsheets, often used for storing datasets.
  • Factors: Categorical data types, representing discrete groups.

R Environment: Installing Packages and Managing Libraries

• Packages: R packages are collections of functions, data, and other resources that extend the core functionalities of R.
• CRAN (Comprehensive R Archive Network): A primary repository for R packages, offering a wide range of packages for various purposes.
• Installing Packages: You can install packages from CRAN using the install.packages() function.
• Loading Libraries: Once installed, packages can be loaded into your R session using the library() function, making their functions accessible for use.

Data Input/Output: Reading and Writing CSV, Excel, and Other File Types

• CSV (Comma-Separated Values): A common file format for storing data in tabular form.
• Reading CSV Files: R provides functions like read.csv() and read.table() for reading CSV files into data frames.
• Writing CSV Files: Use the write.csv() function to save data frames as CSV files.
• Excel Files: R can work with Excel files using packages like readxl and openxlsx.
• Other File Types: R supports various file formats, including JSON, XML, and text files.

Vectors: Creation, Manipulation, and Operations

• Creating Vectors: Use the c() function to combine elements into a vector, or use functions like seq() (sequences), rep() (repeating elements), and numeric() (creating vectors of numbers).
• Manipulating Vectors: R provides functions to modify vectors, including sort(), rev(), unique(), length(), which(), and head().
• Vector Operations: Arithmetic operations are performed element-wise on vectors, allowing for efficient calculations.

Matrices: Creation, Indexing, and Basic Operations

• Creating Matrices: Use the matrix() function or the cbind() (column binding) and rbind() (row binding) functions to create matrices.
• Indexing Matrices: Matrix elements are accessed using square brackets [], specifying row and column indices.
• Basic Operations: Arithmetic operations can be applied element-wise on matrices. Functions like t() (transpose), dim() (dimensions), and colSums() (column sums) are useful for matrix manipulation.

Lists: Working with Lists and Nested Lists

• Lists: Lists can contain objects of different types, including vectors, matrices, data frames, other lists, and more.
• Accessing Elements: Use double square brackets [[]] to access elements within lists.
• Nested Lists: Lists can be nested, enabling complex data structures.

Data Frames: Creation, Manipulation, and Indexing

• Data Frames: Data frames are highly versatile, resembling tabular data with rows (observations) and columns (variables).
• Creation: Use the data.frame() function to create data frames.
• Accessing Elements: Individual cells can be accessed using row and column indices. The $ operator is used to extract specific columns.
• Manipulation: R functions like subset(), merge(), and transform() allow for filtering, combining, and modifying data frames.

Factors: Categorical Data Handling and Manipulation

• Factors: Factors represent categorical data, which can be grouped into levels. Examples include colors, genders, or ratings.
• Creating Factors: Use the factor() function to create factors, assigning labels to each level.
• Manipulation: R offers functions like levels(), nlevels(), and reorder() to work with factor levels.

Logical Operators: AND, OR, NOT, and Conditional Statements

• Logical Operators: R provides logical operators to perform comparisons and truth evaluations:
  • & (AND): Evaluates to TRUE if both operands are TRUE.
  • | (OR): Evaluates to TRUE if at least one operand is TRUE.
  • ! (NOT): Negates the logical value of the operand.
• Conditional Statements: R uses if, else, and else if statements for conditional execution of code based on logical conditions.

Loops: For, While, and Repeat Loops

• For Loops: Repeat a block of code for each element in a sequence.
• While Loops: Execute a block of code repeatedly as long as a certain condition remains TRUE.
• Repeat Loops: Execute a block of code indefinitely until explicitly stopped.

Apply Family Functions: Apply, lapply, sapply, tapply, vapply

• apply() Family Functions: Provide efficient ways to apply functions to data structures, particularly to vectors, matrices, and arrays.
• apply(): Applies a function to rows or columns of a matrix or array.
• lapply(): Applies a function to each element of a list, returning a list of results.
• sapply(): Similar to lapply(), but attempts to simplify the returned list, often into a vector or matrix.
• tapply(): Applies a function to a vector, grouping based on another factor, and then summarizes the results by group.
• vapply(): Like sapply(), but enforces type and length constraints on the output, providing more control and error checking.

Functions: Creating and Using User-Defined Functions

• User-Defined Functions: You can define your own functions in R using the function({}) syntax. Functions take arguments (inputs) and perform a series of operations, returning an output.
• Structure: A function consists of a name, its arguments, and a body containing code.
• Example:

my_sum <- function(x, y) {
  return(x + y)
}

• Calling Functions: Once defined, functions can be invoked by providing the arguments.

String Manipulation: Using stringr and base R for string operations

• Base R Functions: R provides a range of built-in functions for string manipulation, including substr(), nchar(), tolower(), toupper(), and grep().
• stringr Package: The stringr package offers a cleaner and more comprehensive set of tools for string manipulation. Its functions include str_length(), str_sub(), str_extract(), and str_replace().

Dates and Times: Handling dates and times with lubridate package

• lubridate Package: The lubridate package simplifies working with dates and times in R.
• Key Functions: Functions like ymd(), mdy(), dmy(), today(), now() create date and time objects. Functions like year(), month(), day(), and hour() extract components from date and time objects.

Basic Statistics: Mean, median, mode, variance, standard deviation

• Mean: The average of a set of values.
• Median: The middle value when data is sorted.
• Mode: The most frequent value in a dataset.
• Variance: A measure of how spread out the data is from the mean.
• Standard Deviation: The square root of the variance, providing a more intuitive measure of dispersion.
• R Functions: Functions mean(), median(), mode(), var(), and sd() are used to calculate these statistics in R.

Probability Distributions: Generating and Visualizing Distributions (normal, binomial, etc.)

• Probability Distributions: Mathematical functions that describe the likelihood of different outcomes in a random experiment.
• Normal Distribution: A bell-shaped curve, commonly used to model many natural phenomena.
• Binomial Distribution: Represents the probability of successes in a series of independent trials.
• R Functions: rnorm() (generate random values from a normal distribution), dbinom() (calculate probabilities from a binomial distribution).
• Visualization: Visualize these distributions using hist() for histograms or plot() for plotting density curves.

Data Visualization Basics: Using base R for plotting

• Base R Graphics: R provides a set of built-in functions for creating basic plots.
• plot() Function: For creating scatterplots, line plots, and other basic graphics.
• hist() Function: For creating histograms to visualize the distribution of data.
• boxplot() Function: For creating boxplots to show the distribution of data points.

ggplot2 Basics: Introduction to ggplot2 for data visualization

• ggplot2 Package: A powerful and versatile package for creating elegant and customizable visualizations.
• Grammar of Graphics: ggplot2 uses a grammar of graphics approach, allowing you to layer components (geoms, stats, scales, etc.) to build plots.
• Basic Structure: The ggplot() function initializes a blank plot, and then subsequent functions such as geom_point(), geom_line(), and geom_bar() are used to add layers.

ggplot2 Advanced: Customizing plots with themes, facets, and scales

• Themes: Themes define the overall appearance of a ggplot2 plot, controlling elements such as font styles, colors, borders, background, and more.
• Facets: Facets are used to split a plot into multiple subplots based on variables.
• Scales: Scales control how data is represented visually in plots. They can be adjusted to adjust color palettes, axis limits, and other visual properties.

dplyr Basics: Data manipulation using filter, select, mutate, arrange

• dplyr Package: A package for data manipulation, offering functions for filtering, transforming, and summarizing data.
• Key Functions:
  • filter(): Extracts rows matching specific conditions.
  • select(): Selects specific columns.
  • mutate(): Creates new columns or modifies existing ones.
  • arrange(): Sorts rows based on column values.

dplyr Advanced: Grouping, summarizing, and joins

• Grouping: dplyr allows for grouping data based on factors or variables for applying calculations and transformations.
• Summarizing: Functions like summarise() and group_by() provide tools for aggregating and summarizing data grouped by specific categories.
• Joins: dplyr supports joining data frames based on common columns to combine related data.

tidyr Basics: Data tidying with pivot_longer, pivot_wider, separate

• tidyr Package: A package specifically designed for tidying and transforming messy data into a neat and consistent format.
• Key Functions:
  • pivot_longer(): Converts columns into rows, creating a longer format.
  • pivot_wider(): Converts rows into columns, creating a wider format.
  • separate(): Splits a single column into multiple columns based on delimiters.

Data Transformation with reshape2: Melting and casting data

• reshape2 Package: Offers functions for reshaping and transforming data, particularly for transforming between wide and long formats.
• melt() Function: Converts a wide data frame to a long format, with a single column for the variables.
• dcast() Function: Casts data from a long format to a wide format, creating columns based on the values of a specified variable.

Working with Databases: Connecting R to databases using DBI and RMySQL

• DBI Package: Provides a common interface for working with databases, allowing you to connect to different database systems.
• RMySQL Package: Provides specific functionality for connecting to MySQL databases.
• Connects to Databases: You can use R functions to connect to databases, execute queries, fetch results, and manipulate data directly from the database.

Data Aggregation: Using aggregate and other summarization functions

• aggregate() Function: Provides a convenient way to summarize data based on grouping variables.
• tapply() Function: Similar to aggregate(), but may be easier to use when summarizing a single variable.
• Other Functions: R offers additional functions for aggregation and summarization, including summary(), table(), and by().

Handling Missing Data: Detection and imputation methods

• Missing Data: Data points that are missing or unrecorded.
• Detection: Use functions like is.na() to identify missing values.
• Imputation: Replacing missing values with estimated values based on existing data. Common methods include mean imputation, median imputation, and using predictive models.

Regression Analysis: Simple linear regression

• Simple Linear Regression: Models the relationship between a dependent variable and a single independent variable.
• Equation: The relationship is modeled using a straight line: y = mx + b (where y is the dependent variable, x is the independent variable, m is the slope, and b is the intercept).
• R Functions: lm() function fits regression models.

Multiple Regression: Fitting and interpreting multiple linear regression models

• Multiple Regression: Extends simple linear regression to include multiple independent variables, allowing for more complex models.
• Equation: The relationship is modeled as: y = b0 + b1x1 + b2x2 + ... + bnxn (where y is the dependent variable, x1...xn are the independent variables, and b0...bn are the coefficients).
• Interpretation: Understand the impact of each independent variable on the dependent variable.

Logistic Regression: Binary classification and model evaluation

• Logistic Regression: Used for binary classification problems, predicting the probability of a binary outcome (e.g., success/failure, yes/no).
• Equation: The logistic function maps a linear combination of independent variables to a probability between 0 and 1.
• Model Evaluation: Metrics like accuracy, precision, recall, and AUC are used to assess the performance of logistic regression models.

Time Series Analysis: Basics of time series data and forecast package

• Time Series Data: Data collected over time, ordered sequentially.
• Forecast Package: Provides functions for time series data manipulation, analysis, and forecasting.
• Components of Time Series: Trend, сезонность, цикличность, and noise.

ARIMA Models: Fitting and forecasting with ARIMA

• ARIMA Models: A class of statistical models used for time series forecasting.
• Structure: AR (Autoregressive) component, MA (Moving Average) component, and I (Integrated) component.
• auto.arima() Function: Simplifies the process of selecting the optimal ARIMA model parameters.

Clustering Techniques: K-means and hierarchical clustering

• Clustering: Grouping data points into clusters based on their similarity.
• K-means Clustering: An unsupervised learning algorithm that partitions data into k clusters, minimizing the distance within clusters and maximizing the distance between clusters.
• Hierarchical Clustering: A method that constructs a hierarchy of clusters, progressively merging or splitting clusters based on distance.

Principal Component Analysis (PCA): Dimensionality reduction and visualization

• PCA: A dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional representation while preserving as much variance as possible.
• Dimensionality Reduction: Reduces the number of variables in a dataset, simplifying analysis and visualization.
• Visualization: PCA can be used to create scatterplots visualizing the data in a lower-dimensional space.

Decision Trees: Building and evaluating tree models with rpart

• Decision Trees: A predictive model that partitions data into a tree-like structure to predict a target variable.
• rpart Package: Provides functions for constructing decision trees in R.
• Tree Structure: Nodes represent decisions, and branches represent possible outcomes.

Random Forests: Implementing random forest models with randomForest

• Random Forests: An ensemble learning method that aggregates predictions from multiple decision trees, improving prediction accuracy and reducing overfitting.
• randomForest Package: Provides tools for fitting and evaluating random forest models in R.

Data Resampling Techniques: Bootstrap and cross-validation methods

• Bootstrapping: A resampling technique that involves repeatedly sampling with replacement from the original dataset to create multiple datasets.
• Cross-validation: A resampling technique that divides the dataset into folds, using one fold for testing and the rest for training.

Model Evaluation Metrics: AUC, ROC, confusion matrix, accuracy

• AUC (Area Under the Curve): A measure of the overall performance of a classification model, particularly for binary classification.
• ROC (Receiver Operating Characteristic) Curve: Plots the sensitivity (true positive rate) against the 1-specificity (false positive rate) for different threshold values.
• Confusion Matrix: A table that summarizes the results of a classification model, displaying the number of true positives, true negatives, false positives, and false negatives.
• Accuracy: The proportion of correctly classified instances.

RMarkdown: Creating reproducible reports and presentations

• RMarkdown: A format for creating dynamic and reproducible reports, integrating R code, output, and visualizations.
• Structure: RMarkdown files are written using Markdown syntax, with code chunks embedded using the ````r` syntax.

Shiny Basics: Building interactive web apps

• Shiny Package: A package for creating interactive web applications using R.
• Structure: Shiny apps typically consist of a UI (user interface) and a server component.
• Key Features: Shiny provides a framework for building user-friendly dashboards with interactive elements like buttons, sliders, and plots.

Shiny Advanced: Customizing Shiny dashboards with inputs and outputs

• Inputs: Elements that allow users to interact with the web app, such as text boxes, dropdowns, sliders, and date pickers.
• Outputs: Visualizations, tables, and other outputs displayed to the user based on user inputs.

APIs in R: Using httr to connect and retrieve data from APIs

• APIs (Application Programming Interfaces): Allow programs to communicate and interact with each other.
• httr Package: Provides functions for interacting with APIs in R, including making requests, handling authentication, and parsing data.

Text Mining Basics: Using tm and tidytext for text analysis

• Text Mining: The process of extracting meaningful information from unstructured text data.
• tm Package: A package providing functions for text preprocessing and analysis.
• tidytext Package: A package for working with text data in a tidy (long format) way, making it easier to analyze text data with dplyr and ggplot2.

Sentiment Analysis: Analyzing sentiment with text data

• Sentiment Analysis: The task of determining the emotional tone or sentiment expressed in text data.
• Methods: Lexicon-based methods, machine learning models, and deep learning techniques.

Regular Expressions: Pattern matching and text manipulation

• Regular Expressions: Powerful tools for pattern matching and text manipulation.
• Syntax: Regular expressions use specific characters and combinations to specify patterns in text data.

Parallel Computing in R: Using parallel and foreach packages

• Parallel Computing: Running tasks concurrently to speed up computations, particularly for demanding tasks.
• parallel Package: Provides functions for parallel processing using multiple cores on your machine.
• foreach Package: Makes it easier to write loops that can be run in parallel.

Version Control with Git: Integrating R projects with GitHub

• Git: A version control system used for tracking changes to files over time.
• GitHub: A platform for hosting Git repositories, providing collaboration and sharing features.

Object-Oriented Programming in R: S3, S4, and R6 classes

• Object-Oriented Programming (OOP): A programming paradigm that organizes code around objects, which are instances of classes.
• R's OOP Systems: R offers several OOP systems:
  • S3: A flexible system based on generic functions and methods.
  • S4: A more formal system with stricter class definitions and methods.
  • R6: A more modern OOP system with features like inheritance, encapsulation, and immutability.

Package Development: Basics of creating R packages

• R Packages: Collections of functions, data, and other resources distributed for use by others.
• Structure: R packages have a specific directory structure containing necessary files and metadata.

Spatial Data Analysis: Using sf and sp packages for geographic data

• Spatial Data: Data associated with locations on the Earth’s surface.
• sf and sp Packages: Provide tools for working with spatial data, including loading, manipulating, analyzing, and visualizing geographic data.

Integrating R with Python: Using reticulate for cross-language work

• reticulate Package: Allows you to run Python code from within your R session.
• Cross-Language Work: Combine the strengths of both languages, leveraging Python libraries for specific tasks within your R workflow.

This quiz covers the fundamental concepts of R programming, including data types, structures, and the R environment. You'll explore essential functions for data input and output, helping you to manage your data effectively in R. Test your knowledge on vectors, matrices, lists, and more!