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

Reading Files in R

Reading data files is one of the foundational tasks in R. Given that data can come in various formats, R offers a suite of functions to import data from different sources. In this tutorial, we'll focus on reading common file formats in R.

1. Reading Text Files

1.1. read.table and read.csv

These are the most basic functions for reading data files in R.

• read.table: Reads a general delimited file.

• read.csv: Specifically for reading comma-separated files. It's a specialized version of read.table.

data <- read.table("path/to/your/data.txt", header=TRUE, sep="\t")

# For CSV files
csv_data <- read.csv("path/to/your/data.csv", header=TRUE)

Note: header=TRUE indicates that the first row of the file contains column names.

1.2. readLines

This function reads a text file into a character vector, with each element of the vector representing a line from the file.

lines <- readLines("path/to/your/textfile.txt", n=10)  # Reads the first 10 lines

2. Reading Excel Files

You can use the readxl package.

install.packages("readxl")
library(readxl)

data <- read_excel("path/to/your/data.xlsx")

3. Reading Databases

R can connect to various databases using the DBI package and specific database driver packages. For instance, to connect to a SQLite database:

install.packages(c("DBI", "RSQLite"))
library(DBI)

con <- dbConnect(RSQLite::SQLite(), dbname="path/to/database.sqlite")

data <- dbGetQuery(con, "SELECT * FROM table_name")

4. Reading Files from Other Statistical Software

4.1. SPSS, SAS, and Stata

The haven package can be used.

install.packages("haven")
library(haven)

# For SPSS
data_spss <- read_sav("path/to/data.sav")

# For SAS
data_sas <- read_sas("path/to/data.sas7bdat")

# For Stata
data_stata <- read_dta("path/to/data.dta")

5. Reading from the Web

5.1. Reading tables from HTML pages

Using the rvest package:

install.packages("rvest")
library(rvest)

url <- "https://www.example.com"
web_data <- url %>%
  read_html() %>%
  html_table(fill = TRUE)

data <- web_data[[1]]  # Assumes the first table on the page

5.2. Reading JSON from APIs

Use the jsonlite package:

install.packages("jsonlite")
library(jsonlite)

json_data <- fromJSON("https://api.example.com/data")

Conclusion

The ability to read data from various sources is crucial for data analysis in R. Given R's extensive ecosystem, there are many other packages for reading different types of data. Always check the function documentation (?function_name) for detailed options and nuances, especially regarding options for handling missing values, specifying data types, and other potential intricacies of your data.

1. R code for reading CSV files:

   # Reading a CSV file
   my_data <- read.csv("my_data.csv")
   

2. Importing data from Excel files in R:

   # Install and load the readxl package
   install.packages("readxl")
   library(readxl)
   
   # Reading an Excel file
   excel_data <- read_excel("my_data.xlsx")
   

3. Reading text files in R programming:

   # Reading a text file
   text_data <- readLines("my_text_file.txt")
   

4. R read.table function examples:

   # Using read.table to read a tab-delimited file
   table_data <- read.table("my_table_file.txt", header = TRUE, sep = "\t")
   

5. Using readr package for efficient file reading in R:

   # Install and load the readr package
   install.packages("readr")
   library(readr)
   
   # Reading a CSV file with readr
   readr_data <- read_csv("my_data.csv")
   

6. Reading and parsing JSON files in R:

   # Install and load the jsonlite package
   install.packages("jsonlite")
   library(jsonlite)
   
   # Reading a JSON file
   json_data <- fromJSON("my_data.json")
   

7. Importing data from databases in R:

   # Install and load DBI and RSQLite packages
   install.packages(c("DBI", "RSQLite"))
   library(DBI)
   library(RSQLite)
   
   # Connecting to a SQLite database
   con <- dbConnect(RSQLite::SQLite(), "my_database.db")
   
   # Reading data from a table
   db_data <- dbGetQuery(con, "SELECT * FROM my_table")
   

8. R code for reading and processing XML files:

   # Install and load the XML package
   install.packages("XML")
   library(XML)
   
   # Reading an XML file
   xml_data <- xmlParse("my_data.xml")
   
   # Extracting information from XML
   nodes <- getNodeSet(xml_data, "//node")