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")

Student Survey Data Frame

data <- data.frame( Communication = c(5, 4, 3, 2, 1), Teamwork = c(4, 5, 2, 3, 1), ProblemSolving = c(3, 4, 5, 2, 1), Creativity = c(2, 3, 4, 5, 1) ) data <- rbind(rep(5, 4), rep(0, 4), data) radarchart(data, title = "Radar Chart: Employee Performance", pcol = "purple", pfcol = rgb(0.5, 0, 0.5, 0.3), plwd = 2)

data <- data.frame( Product1 = c(5, 4, 3, 2, 1), Product2 = c(4, 5, 2, 3, 1), Product3 = c(3, 4, 5, 2, 1) ) data <- rbind(rep(5, 3), rep(0, 3), data) radarchart(data, title = "Radar Chart: Comparing Products", pcol = c("blue", "red", "green"), pfcol = c(rgb(0, 0, 1, 0.3), rgb(1, 0, 0, 0.3), rgb(0, 1, 0, 0.3)), plwd = 2)