R Tutorial

Fundamentals of R

Variables

Input and Output

Decision Making

Control Flow

Functions

Strings

Vectors

Lists

Arrays

Matrices

Factors

DataFrames

Object Oriented Programming

Error Handling

File Handling

Packages in R

Data Interfaces

Data Visualization

Statistics

Machine Learning with R

Data Visualization in R

Data visualization is a critical component in the data analysis process. In R, there are several tools available for visualizing data, but this tutorial will primarily focus on the ggplot2 package, which is part of the tidyverse collection. The ggplot2 package is based on the Grammar of Graphics, a system for data visualization.

1. Installing and Loading Required Packages:

install.packages("tidyverse")
library(tidyverse)

2. Basic Plotting:

Scatter Plot:

data(mpg)
ggplot(data = mpg, aes(x = displ, y = hwy)) + 
  geom_point()

3. Adding Layers:

Aesthetic Mappings:

Differentiate points based on a third variable.

ggplot(data = mpg, aes(x = displ, y = hwy, color = class)) + 
  geom_point()

Faceting:

Display multiple plots based on a factor.

ggplot(data = mpg, aes(x = displ, y = hwy)) + 
  geom_point() + 
  facet_wrap(~ class)

4. Other Types of Plots:

Histogram:

ggplot(data = mpg, aes(x = hwy)) + 
  geom_histogram(binwidth = 3)

Boxplot:

ggplot(data = mpg, aes(x = class, y = hwy)) + 
  geom_boxplot()

Bar Plot:

ggplot(data = mpg, aes(x = class)) + 
  geom_bar()

5. Customizing the Appearance:

Theme:

ggplot(data = mpg, aes(x = displ, y = hwy)) + 
  geom_point() + 
  theme_minimal()

Labels:

ggplot(data = mpg, aes(x = displ, y = hwy)) + 
  geom_point() + 
  labs(title = "Engine Displacement vs. Highway MPG", x = "Displacement (L)", y = "Highway MPG")

6. Saving the Plot:

p <- ggplot(data = mpg, aes(x = displ, y = hwy)) + 
  geom_point()

ggsave(filename = "scatterplot.png", plot = p, width = 6, height = 4)

7. Extending ggplot2:

There are various extension packages available. For example, ggmap allows for spatial visualizations on maps, and gganimate lets you create animated visuals.

Conclusion:

This tutorial offers a concise introduction to data visualization in R using the ggplot2 package. Given the package's versatility and the importance of visualization in data analysis, it's worth diving deeper into ggplot2 to explore its full potential. The ggplot2 documentation, available online, provides comprehensive details on its capabilities and usage.

R base graphics examples:

Base graphics in R provide a simple way to create plots using functions like plot, hist, and boxplot.

# Basic scatter plot using base graphics
plot(x = c(1, 2, 3, 4), y = c(2, 4, 1, 3), main = "Scatter Plot", xlab = "X-axis", ylab = "Y-axis")

Interactive data visualization in R:

Use interactive visualization libraries like plotly for dynamic plots.

# Interactive scatter plot using plotly
library(plotly)
plot_ly(x = c(1, 2, 3, 4), y = c(2, 4, 1, 3), type = "scatter", mode = "markers")

Data visualization packages in R:

R offers various visualization packages such as ggplot2, plotly, ggvis, and more.

# Using ggplot2 for creating a bar plot
library(ggplot2)
ggplot(data = iris, aes(x = Species, y = Sepal.Length)) +
  geom_bar(stat = "identity", position = "dodge", fill = "steelblue") +
  labs(title = "Bar Plot", x = "Species", y = "Sepal Length")

Customizing plots in R:

Customize plots using parameters like color, labels, and titles.

# Customized scatter plot using base graphics
plot(x = c(1, 2, 3, 4), y = c(2, 4, 1, 3), main = "Customized Scatter Plot", 
     xlab = "X-axis", ylab = "Y-axis", col = "red", pch = 16)

Heatmap in R:

Create a heatmap using packages like heatmap or heatmap.2.

# Heatmap using base heatmap function
heatmap(data_matrix, col = cm.colors(256), scale = "column", main = "Heatmap")

Time series visualization in R:
- Visualize time series data using functions like plot or specialized time series packages.
```
# Time series plot using base graphics
plot(ts_data, main = "Time Series Plot", xlab = "Time", ylab = "Values")
```