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

Convert an Object to Data Frame - as.data.frame() Function in R

The as.data.frame() function in R is a fundamental function used to convert various types of objects into data frames. Let's dive into how to use this function with different R objects:

1. Basic Usage with Matrices

If you have a matrix, you can use the as.data.frame() function to convert it into a data frame:

mat <- matrix(1:4, ncol=2)
print(mat)

df <- as.data.frame(mat)
print(df)

2. Using with List

You can also convert lists to data frames. If the list elements have the same length, they will become the columns of the data frame:

lst <- list(a = c(1, 2), b = c(3, 4))
df_from_lst <- as.data.frame(lst)
print(df_from_lst)

3. Using with Vectors

When converting a vector to a data frame, the vector becomes a column in the resulting data frame:

vec <- c(1, 2, 3, 4)
df_from_vec <- as.data.frame(vec)
print(df_from_vec)

4. Factors in Data Frames

By default, character vectors are converted to factors in data frames. To keep them as characters, you can set the stringsAsFactors argument to FALSE:

char_vec <- c("apple", "banana", "cherry")
df_char <- as.data.frame(char_vec, stringsAsFactors = FALSE)
print(df_char)

5. Handling Different Data Types

When converting a list with different data types to a data frame, each list element will become a column with its respective data type:

lst_mixed <- list(numbers = c(1, 2), text = c("x", "y"))
df_mixed <- as.data.frame(lst_mixed)
print(df_mixed)

6. Using with Tables

You can also convert tables (like those produced by the table() function) to data frames:

table_data <- table(c("A", "B", "A", "C", "B"))
df_from_table <- as.data.frame(table_data)
print(df_from_table)

Key Takeaways:

• as.data.frame() is a versatile function that can convert matrices, lists, vectors, and other objects into data frames in R.
• By default, character vectors are converted into factors in a data frame, unless stringsAsFactors = FALSE is set.
• When converting different objects to data frames, always check the resulting structure to ensure it's what you expect.

This tutorial provides a basic understanding of how to use the as.data.frame() function in R. The function is foundational in R, and having a good grasp of its behavior is crucial when working with various data structures.

1. R as.data.frame() Function Example:

   Use as.data.frame() to convert objects to data frames.

   # as.data.frame() function in R
   x <- c(1, 2, 3)
   data_frame_x <- as.data.frame(x)
   

2. How to Use as.data.frame() in R:

   Convert various objects to data frames using as.data.frame().

   # Using as.data.frame() in R
   matrix_data <- matrix(1:6, nrow = 2)
   data_frame_data <- as.data.frame(matrix_data)
   

3. Converting Matrices to Data Frames in R:

   Convert matrices to data frames using as.data.frame().

   # Converting matrices to data frames in R
   matrix_data <- matrix(1:6, nrow = 2)
   data_frame_data <- as.data.frame(matrix_data)
   

4. Using as.data.frame() with Lists in R:

   Convert lists to data frames using as.data.frame().

   # Using as.data.frame() with lists in R
   list_data <- list(A = c(1, 2), B = c("x", "y"))
   data_frame_data <- as.data.frame(list_data)
   

5. Data Frame Conversion for Vectors in R:

   Convert vectors to data frames using as.data.frame().

   # Data frame conversion for vectors in R
   vector_data <- c(1, 2, 3)
   data_frame_data <- as.data.frame(vector_data)
   

6. Handling Factors in as.data.frame() in R:

   Handle factors when converting to a data frame using as.data.frame().

   # Handling factors in as.data.frame() in R
   factor_data <- factor(c("A", "B", "C"))
   data_frame_data <- as.data.frame(factor_data)
   

7. Conditional Data Frame Conversion in R:

   Conditionally convert objects to data frames using as.data.frame().

   # Conditional data frame conversion in R
   x <- c(1, 2, 3)
   data_frame_x <- if (length(x) > 1) as.data.frame(x) else data.frame(x)
   

8. Converting Tibbles to Data Frames in R:

   Convert tibbles to data frames using as.data.frame().

   # Converting tibbles to data frames in R
   library(tibble)
   tibble_data <- tibble(A = c(1, 2), B = c("x", "y"))
   data_frame_data <- as.data.frame(tibble_data)
   

9. Assembling Data Frames with as.data.frame() in R:

   Assemble data frames using as.data.frame().

   # Assembling data frames with as.data.frame() in R
   df1 <- data.frame(A = c(1, 2), B = c("x", "y"))
   df2 <- data.frame(A = c(3, 4), B = c("z", "w"))
   assembled_df <- as.data.frame(list(df1, df2))