In this post, I explore visualizing continuous variables by combining a histogram (showing the frequency of values within specific intervals) and a density plot (illustrating probability distribution)
In this post I explore different ways of reading data from multiple Excel sheets and converting them into individual data frames in R using lapply() and purrr::map() funciton.
In this post you will learn how to build a linear regression, interpret the result, test its assumptions, and use the regression equation for predictions.
The geom_histogram() function from ggplot2 package is used to create a histogram plot. For example, let’s plot the distribution of Sepal.Length from iris data.
library(ggplot2) theme_set(theme_bw()) ggplot(iris, aes(Sepal.Length)) + geom_histogram(fill = "orange") To add a vertical line to show the mean value of Sepal.