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")

hist(iris$Sepal.Length, main = "Histogram of Sepal Length", xlab = "Sepal Length", col = "orange")

barplot(table(airquality$Month), main = "Bar Chart of Months in Airquality Dataset", xlab = "Month", ylab = "Count", col = "lightgreen")

Analysis of storm data to see which events are the most harmful in terms of public health and economic impact

barplot(table(iris$Species), main = "Bar Chart of Iris Species", xlab = "Species", ylab = "Count", col = "skyblue")

library(ggplot2) histogram <- ggplot(diamonds, aes(x = price)) + geom_histogram(binwidth = 500, fill = "blue", color = "black") + labs(title = "Histogram: Distribution of Diamond Prices", x = "Price", y = "Frequency") + theme_minimal() print(histogram)

library(ggplot2) data("diamonds") bar_chart <- ggplot(diamonds, aes(x = cut, fill = cut)) + geom_bar() + labs(title = "Bar Chart: Count of Diamonds by Cut", x = "Cut", y = "Count") + theme_minimal() print(bar_chart)

library(ggplot2) ggplot(fruit_data, aes(x = Fruit, y = Sales)) + geom_point(size = 4, color = "red") + labs(title = "Fruit Sales (Scatter Plot)", x = "Fruit", y = "Sales Count")