box_plot <- ggplot(diamonds, aes(x = cut, y = price, fill = cut)) + geom_boxplot() + labs(title = "Box Plot: Price Distribution by Cut", x = "Cut", y = "Price") + theme_minimal() print(box_plot)

library(wordcloud) library(fmsb) library(waffle) library(reshape2) color_counts <- diamonds %>% group_by(color) %>% summarise(count = n()) waffle_chart <- waffle(color_counts$count, rows = 5, title = "Waffle Chart: Proportion of Diamonds by Color") print(waffle_chart)

library(wordcloud) library(fmsb) library(waffle) library(reshape2) wordcloud(words = unique(diamonds$clarity), freq = table(diamonds$clarity), colors = brewer.pal(8, "Dark2"), main = "Word Cloud: Diamond Clarity")

ggplot(airquality, aes(x = Temp, y = Ozone)) + geom_point() + geom_smooth(method = "lm", col = "red") + ggtitle("Regression Plot: Temperature vs Ozone") + xlab("Temperature") + ylab("Ozone")

plot(iris$Sepal.Length, type = "l", main = "Line Plot of Sepal Length", xlab = "Index", ylab = "Sepal Length", col = "purple")

line_plot <- ggplot(diamonds, aes(x = carat, y = price)) + geom_point(alpha = 0.3) + geom_smooth(method = "lm", color = "red") + labs(title = "Line Plot with Regression: Price Trends Over Carat", x = "Carat", y = "Price") + theme_minimal() print(line_plot)

plot(iris$Sepal.Length, iris$Sepal.Width, main = "Scatter Plot: Sepal Length vs Sepal Width", xlab = "Sepal Length", ylab = "Sepal Width", col = iris$Species, pch = 19)

library(ggplot2) library(dplyr) scatter_plot <- ggplot(diamonds, aes(x = carat, y = price, color = cut)) + geom_point(alpha = 0.5) + labs(title = "Scatter Plot: Carat vs Price", x = "Carat", y = "Price") + theme_minimal() print(scatter_plot)

pie(table(airquality$Month), main = "Pie Chart of Months in Airquality Dataset", col = rainbow(length(unique(airquality$Month))))

pie(table(iris$Species), main = "Pie Chart of Iris Species", col = rainbow(length(unique(iris$Species))))

library(ggplot2) library(dplyr) clarity_counts <- diamonds %>% group_by(clarity) %>% summarise(count = n()) %>% mutate(percentage = count / sum(count) * 100) pie_chart <- ggplot(clarity_counts, aes(x = "", y = percentage, fill = clarity)) + geom_bar(stat = "identity", width = 1) + coord_polar("y") + labs(title = "Pie Chart: Proportion of Diamonds by Clarity") + theme_void() print(pie_chart)

hist(airquality$Temp, main = "Histogram of Temperature", xlab = "Temperature", col = "pink")