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

Bar Charts in R

Bar charts are a popular way to represent categorical data. In this tutorial, we'll walk through the basics of creating bar charts in R using the ggplot2 package, a part of the tidyverse. If you're using base R graphics, barplot() is the go-to function, but ggplot2 offers more flexibility and customization.

1. Install and Load ggplot2

First, if you haven't already installed ggplot2, do so:

install.packages("ggplot2")

Then load the library:

library(ggplot2)

2. Basic Bar Chart

Let's use the built-in mtcars dataset to visualize the number of cars with each number of gears.

data(mtcars)

# Count the number of cars with each number of gears
gear_counts <- table(mtcars$gear)

# Convert to data frame for ggplot
gear_df <- as.data.frame(gear_counts)

# Plot
ggplot(gear_df, aes(x=Var1, y=Freq)) + 
  geom_bar(stat="identity") +
  labs(title="Number of Cars by Gears", x="Gears", y="Number of Cars")

Note: In ggplot2, the geom_bar() function by default expects categorical data on the x-axis and computes counts. By using stat="identity", we're telling ggplot to use the provided y-values as heights for the bars.

3. Grouped Bar Chart

To create a grouped (or clustered) bar chart, you need two categorical variables. Let's compare the number of cars with automatic (am=1) vs. manual (am=0) transmissions across different numbers of gears.

ggplot(mtcars, aes(x=as.factor(gear), fill=as.factor(am))) + 
  geom_bar(position="dodge") +
  labs(title="Transmission Type by Gears", x="Gears", y="Number of Cars", fill="Transmission (0=Manual, 1=Automatic)")

4. Stacked Bar Chart

Instead of grouping, you can stack the bars:

ggplot(mtcars, aes(x=as.factor(gear), fill=as.factor(am))) + 
  geom_bar(position="stack") +
  labs(title="Transmission Type by Gears", x="Gears", y="Number of Cars", fill="Transmission (0=Manual, 1=Automatic)")

5. Customizing Appearance

You can easily customize the appearance using theme() and other functions:

ggplot(gear_df, aes(x=Var1, y=Freq)) + 
  geom_bar(stat="identity", fill="steelblue") +
  labs(title="Number of Cars by Gears", x="Gears", y="Number of Cars") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle=45, hjust=1))

6. Horizontal Bar Chart

To make a horizontal bar chart, you can use coord_flip():

ggplot(gear_df, aes(x=Var1, y=Freq)) + 
  geom_bar(stat="identity") +
  labs(title="Number of Cars by Gears", x="Gears", y="Number of Cars") +
  coord_flip()

Conclusion

ggplot2 provides a powerful and customizable platform for creating various types of bar charts. You can adjust themes, colors, labels, and orientations to create effective visualizations of your categorical data.

1. Creating bar plots in R programming:

   # Creating a basic bar plot
   data <- c(3, 5, 2, 8, 6)
   barplot(data)
   

2. Bar chart customization in R:

   # Customizing bar chart appearance
   barplot(data, col = "skyblue", main = "Customized Bar Chart", xlab = "Categories", ylab = "Values")
   

3. Bar graphs with ggplot2 in R:

   # Using ggplot2 for a bar graph
   library(ggplot2)
   df <- data.frame(categories = c("A", "B", "C", "D", "E"), values = c(3, 5, 2, 8, 6))
   ggplot(df, aes(x = categories, y = values)) + geom_bar(stat = "identity")
   

4. Stacked bar charts in R:

   # Creating a stacked bar chart
   data_matrix <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 2)
   barplot(data_matrix, beside = TRUE, legend = TRUE)
   

5. Grouped bar charts in R:

   # Creating a grouped bar chart
   group_data <- matrix(c(3, 5, 2, 8, 6, 4), nrow = 2)
   barplot(group_data, beside = TRUE, col = c("skyblue", "salmon"), legend = TRUE)
   

6. Horizontal bar charts in R:

   # Creating a horizontal bar chart
   barplot(data, horiz = TRUE, col = "lightgreen", main = "Horizontal Bar Chart", ylab = "Categories", xlab = "Values")
   

7. Adding labels and annotations to bar charts in R:

   # Adding labels and annotations
   barplot(data, col = "orange", main = "Bar Chart with Labels", xlab = "Categories", ylab = "Values")
   text(1:5, data + 0.2, labels = data)
   

8. Bar chart color customization in R:

   # Customizing bar chart colors
   bar_colors <- c("red", "blue", "green", "purple", "yellow")
   barplot(data, col = bar_colors, main = "Colored Bar Chart", xlab = "Categories", ylab = "Values")