Develop a report on all 7 previous programs.

Develop a script in R to calculate and visualize a correlation matrix for a given dataset, with color-coded cells indicating the strength and direction of correlations, using ggplot2’s geom_tile function.

Write an R program to create multiple dot plots for grouped data, comparing the distributions of variables across different categories, using ggplot2’s position_dodge function.

Write a program to create a violin plot using ggplot2 in R that displays the distribution of a continuous variable with separate violins for each group using an in-built dataset.

Generate a basic box plot using ggplot2, enhanced with notches and outliers, and grouped by a categorical variable using an in-built dataset in R.

Develop an R function to draw a density curve representing the probability density function of a continuous variable, with separate curves for each group, using ggplot2.

Create multiple histograms using ggplot2::facet_wrap() to visualize how a variable (e.g., Sepal.Length) is distributed across different groups (e.g., Species) in a built-in R dataset.

Develop a function in R to plot a function curve based on a mathematical equation provided as input, with different curve styles for each group, using ggplot2.

Write an R script to construct a box plot showcasing the distribution of a continuous variable, grouped by a categorical variable, using ggplot2's fill aesthetic.

Implement an R program to create a histogram illustrating the distribution of a continuous variable, with overlays of density curves for each group, using ggplot2.

Develop a script in R to produce a bar graph displaying the frequency distribution of categorical data in a given dataset, grouped by a specific variable, using ggplot2.

Implement an R function to generate a line graph depicting the trend of a time-series dataset, with separate lines for each group, utilizing ggplot2's group aesthetic.

Write an R script to create a scatter plot, incorporating categorical analysis through color-coded data points representing different groups, using ggplot2.

Develop an R program to quickly explore a given data set, including categorical analysis using the group_by command, and visualize the findings using ggplot2 features.