R Tutorial

• Introduction to R Programming Language
• R vs Python
• Environments in R Programming
• Introduction to R Studio
• How to Install R Studio on Windows and Linux?
• Creation and Execution of R File in R Studio
• Clear the Console and the Environment in R Studio
• Hello World in R Programming

Fundamentals of R

• Basic Syntax
• Comments
• Operators
• Keywords
• Data Types

Variables

• Introduction to Variables
• Scope of Variable
• Dynamic Scoping
• Lexical Scoping
• Lexical Scoping vs Dynamic Scoping

Input and Output

• Taking Input from User
• Printing Output of R Program
• Print the Argument to the Screen - print() Function

Decision Making

• Decision Making - if, if-else, if-else-if ladder, nested if-else, and switch
• if statement
• if-else statement
• Switch case

Control Flow

• Introduction to Control Statements
• Loops (for, while, repeat)
• For loop
• while loop
• Repeat loop
• goto statement
• Break and Next statements
• Next Statement

Functions

• Introduction to Functions
• Function Arguments
• Types of Functions
• Recursive Functions
• Conversion Functions

Strings

• Introduction to Strings
• Working with Text
• String Manipulation
• Concatenate Two Strings
• String Matching
• How to find a SubString?
• Finding the length of string - nchar() method
• Adding elements in a vector - append() method
• Convert string from Lowercase to Uppercase - toupper() function
• Convert String from Uppercase to Lowercase - tolower() method
• Splitting Strings - strsplit() method
• Print a Formatted string - sprintf() Function

Vectors

• Introduction to Vectors
• Operations on Vectors
• Append Operation on Vectors
• Dot Product of Vectors
• Types of Vectors
• Assigning Vectors 
• Getting and Setting Length of the Vectors - length() Function
• Creating a Vector of sequenced elements - seq() Function
• Get the Minimum and Maximum element of a Vector - range() Function
• Formatting Numbers and Strings - format() Function
• Replace the Elements of a Vector - replace() Function
• Sorting of a Vector - sort() Function
• Convert elements of a Vector to Strings - toString() Function
• Extracting Substrings from a Character Vector - substring() Function

Lists

• Introduction to Lists
• Two Dimensional List
• Operations on Lists
• List of Vectors
• List of Dataframes
• Named List
• Check if the Object is a List - is.list() Function
• Convert an Object to List - as.list() Function
• Check if an Object of the Specified Name is Defined or not - exists() Function
• Apply a Function over a List of elements - lapply() Function
• Performing Operations on Multiple Lists simultaneously - mapply() Function

Arrays

• Introduction to Arrays
• Multidimensional Array
• Array Operations
• Sorting of Arrays
• Convert values of an Object to Logical Vector - as.logical() Function
• Performing different Operations on Two Arrays - outer() Function
• Intersection of Two Objects - intersect() Function
• Get Exclusive Elements between Two Objects - setdiff() Function

Matrices

• Introduction to Matrices
• Create Matrix from Vectors
• Operations on Matrices
• Matrix Multiplication
• Algebraic Operations on a Matrix
• Combining Matrices
• Matrix Transpose
• Inverse of Matrix
• Working with Sparse Matrices
• Check if the Object is a Matrix - is.matrix() Function
• Convert an Object into a Matrix - as.matrix() Function
• Get or Set Dimensions of a Matrix - dim() Function
• Calculate Cumulative Sum of a Numeric Object - cumsum() Function
• Compute the Sum of Rows of a Matrix or Array - rowSums Function

Factors

• Introduction to Factors
• Level Ordering of Factors
• Convert Factor to Numeric and Numeric to Factor
• Check if a Factor is an Ordered Factor - is.ordered() Function
• Convert an Unordered Factor to an Ordered Factor  - as.ordered() Function
• Checking if the Object is a Factor - is.factor() Function
• Convert a Vector into Factor - as.factor() Function

DataFrames

• Introduction to Data Frames
• Matrix vs Dataframe
• DataFrame Operations
• DataFrame Manipulation
• Joining of Dataframes
• The Factor Issue in a DataFrame
• Data Reshaping
• Creating a Data Frame from Vectors
• Data Wrangling - Data Transformation
• Data Wrangling - Working with Tibbles
• Melting and Casting
• Subsetting of DataFrames
• Handling Missing Values
• Convert an Object to Data Frame - as.data.frame() Function
• Get the number of columns of an Object - ncol() Function
• Get the number of rows of an Object - nrow() Function
• Get Addition of the Objects passed as Arguments - sum() Function
• Create Subsets of a Data frame - subset() Function

Object Oriented Programming

• Introduction to Object-Oriented Programming
• Classes
• Objects
• Encapsulation
• Polymorphism
• Inheritance
• Abstraction
• Looping over Objects
• Creating, Listing, and Deleting Objects in Memory
• S3 class
• Explicit Coercion
• R6 Classes
• Getting attributes of Objects - attributes() and attr() Function
• Get or Set names of Elements of an Object - names() Function
• Get the Minimum element of an Object - min() Function
• Get the Maximum element of an Object - max() Function

Error Handling

• Introduction to Error Handling
• Condition Handling
• Debugging in R Programming

File Handling

• Introduction to File Handling
• Reading Files
• Writing to Files
• Read Lines from a File - readLines() Function
• Working with Binary Files

Packages in R

• Introduction to Packages
• dplyr Package
• ggplot2 package
• Grid and Lattice Packages
• Shiny Package
• tidyr Package
• What Are the Tidyverse Packages?
• Data Munging

Data Interfaces

• Data Handling
• Importing Data in R Script
• How To Import Data from a File?
• Exporting Data from scripts 
• Working with CSV files 
• Working with XML Files
• Working with Excel Files
• Working with JSON Files 
• Reading Tabular Data from files
• Working with Databases
• Database Connectivity
• Manipulate Data Frames Using SQL

Data Visualization

• Graph Plotting
• Graphical Models
• Plotting Graphs using Two Dimensional List
• Data Visualization
• Charts and Graphs
• Add Titles to a Graph
• Adding Colors to Charts
• Adding Text to Plots
• Adding axis to a Plot
• Set or View the Graphics Palette
• Plotting of Data using Generic plots
• Bar Charts
• Line Graphs
• Adding Straight Lines to a Plot
• Addition of Lines to a Plot
• Histograms
• Pie Charts
• Scatter plots
• Create One Dimensional Scatterplots
• Create a Plot Matrix of Scatterplots
• Create Dot Charts
• Boxplots in R Language
• Stratified Boxplot
• Create a Heatmap
• Pareto Chart
• Waffle Chart
• Draw a Quantile-Quantile Plot
• Creating 3D Plots
• Describe Parts of a Chart in Graphical Form
• Principal Component Analysis
• Social Network Analysis

Statistics

• Introduction to Statistics
• Calculate the Mean, Median, and Mode
• Calculate the Average, Variance, and Standard Deviation
• Homogeneity of Variance Test
• Covariance and Correlation 
• Correlation Matrix
• Visualize correlation matrix using correlogram 
• Distance Matrix by GPU 
• Descriptive Analysis
• Normal Distribution 
• Binomial Distribution 
• Compute the Negative Binomial Density
• Poisson Functions
• ANOVA Test
• MANOVA Test
• Naive Bayes Classifier
• K-NN Classifier
• Central Tendency
• Variability
• Skewness and Kurtosis
• Absolute and Relative Frequency
• Permutation Hypothesis Test
• AB Testing
• Completely Randomized Design
• Randomized Block Design
• Bartlett’s Test
• Tree Entropy
• Compute Summary Statistics of Subsets
• Hypothesis Testing
• Bootstrapping
• Time Series Analysis
• T-Test Approach

Machine Learning with R

• Introduction to Machine Learning
• Setting up Environment for Machine Learning
• Supervised and Unsupervised Learning
• Classification
• Regression and its Types
• Regression Analysis
• Decision Tree
• Random Forest Approach
• Root-Mean-Square Error
• Clustering
• Hierarchical Clustering
• DBScan Clustering
• Deep Learning
• Building a Simple Neural Network
• How Neural Networks are used for Regression?
• Multi Layered Neural Networks
• Survival Analysis
• Stem and Leaf Plots

Conversion Functions in R

In R, data conversion between different types is common. Here's a tutorial that provides an overview of various data conversion functions in R:

1. Numeric to Character and Vice Versa

• as.numeric(): Converts to numeric type.
• as.character(): Converts to character type.

x <- 123.45
y <- as.character(x)  # "123.45"
z <- as.numeric(y)    # 123.45

2. Logical to Numeric

• as.logical(): Converts to logical type. Non-zero values are TRUE; zero is FALSE.

x <- 1
y <- as.logical(x)  # TRUE

3. Factor to Character

• as.factor(): Converts to factor type.
• as.character(): Converts factor to character.

x <- factor(c("apple", "banana", "cherry"))
y <- as.character(x)  # "apple" "banana" "cherry"

4. List to Data Frame and Vice Versa

• as.data.frame(): Converts list to data frame.
• as.list(): Converts data frame to list.

mylist <- list(name = c("John", "Anna"), age = c(25, 30))
df <- as.data.frame(mylist)

mydf <- data.frame(name = c("John", "Anna"), age = c(25, 30))
lst <- as.list(mydf)

5. Matrix to Data Frame and Vice Versa

• as.data.frame(): Converts matrix to data frame.
• as.matrix(): Converts data frame to matrix.

mymatrix <- matrix(1:6, nrow=2)
df <- as.data.frame(mymatrix)

mydf <- data.frame(a = 1:3, b = 4:6)
mat <- as.matrix(mydf)

6. Date Conversions

• as.Date(): Converts character string to Date type.
• as.POSIXct(): Converts Date or character to POSIXct (date-time) type.

date_str <- "2023-09-05"
date <- as.Date(date_str)

datetime_str <- "2023-09-05 14:30:00"
datetime <- as.POSIXct(datetime_str, format="%Y-%m-%d %H:%M:%S")

7. Conversion to and from Vectors

• as.vector(): Converts an object to a vector of the specified mode.

mat <- matrix(1:6, nrow=2)
vec <- as.vector(mat)

8. Check Types

• is.character(), is.numeric(), is.factor(), etc.: These functions check if an object is of a certain type.

x <- "Hello"
is.character(x)  # TRUE

Tips:

1. Always be cautious when converting between types. There's potential for data loss, especially when coercing to simpler data types.
2. R will often try to coerce automatically, especially in functions that expect a certain type. Explicitly converting ensures that you're aware of potential issues.
3. Remember that factors are a common source of confusion in R. They look like characters, but behave differently.

Practice these conversions in different scenarios to become more proficient with them.

1. as.numeric() Function in R:

   Use as.numeric() to convert objects to numeric.

   # as.numeric() function in R
   x <- "123"
   numeric_x <- as.numeric(x)
   

2. Convert Character to Numeric in R:

   Convert character data to numeric using as.numeric().

   # Convert character to numeric in R
   character_value <- "42.5"
   numeric_value <- as.numeric(character_value)
   

3. as.character() Function in R:

   Use as.character() to convert objects to character.

   # as.character() function in R
   y <- 42.5
   character_y <- as.character(y)
   

4. Converting Factors to Characters in R:

   Convert factors to characters using as.character().

   # Converting factors to characters in R
   factor_data <- factor(c("A", "B", "C"))
   character_data <- as.character(factor_data)
   

5. as.factor() Function in R:

   Use as.factor() to convert objects to factors.

   # as.factor() function in R
   numeric_vector <- c(1, 2, 3)
   factor_vector <- as.factor(numeric_vector)
   

6. Convert Numeric to Factor in R:

   Convert numeric data to factor using as.factor().

   # Convert numeric to factor in R
   numeric_data <- c(1, 2, 3, 1, 2)
   factor_data <- as.factor(numeric_data)
   

7. as.logical() Function in R:

   Use as.logical() to convert objects to logical.

   # as.logical() function in R
   logical_value <- as.logical(1)
   

8. Convert Date to Character in R:

   Convert date data to character using as.character().

   # Convert date to character in R
   date_value <- as.Date("2023-01-01")
   character_date <- as.character(date_value)
   

9. Conversion Functions for Lists and Data Frames in R:

   Utilize various conversion functions for lists and data frames.

   # Conversion functions for lists and data frames in R
   list_data <- list(1, "abc", TRUE)
   data_frame <- data.frame(A = c(1, 2), B = c("x", "y"))
   
   list_to_vector <- unlist(list_data)
   data_frame_to_matrix <- as.matrix(data_frame)