Density plot tutorial in R

OLSRR package for residual diagnostics in R

Moving beyond the basics with pie charts

Customize background in box plots with R

Adding summary stats to boxplots in R

Maybe you've heard the saying that a picture is worth a thousand words, but guess what? Words themselves are pretty important also! Especially when it comes to visualizing data, putting labels on your

R is a popular open-source programming language for data analysis. Its interactive programming environment and data visualization capabilities make R an ideal tool for creating a wide variety of data

R programming and in particular RStudio is a great tool for data processing and statistical analysis. However the data visualization part is what R is best to. Its ggplot2 package can make very beautiful

A line diagram is useful for visualizing how a certain continous variable changes in time. If you have many variables and you want to compare them then a multiple lines diagram is waht you are lookin for

There are many packages in R. On the one hand, it is difficult to orient yourself among all those packages. On the other, it is impossible that you can learn how to use all of them. So it becomes handy

This guide shows how to confront the attributes of different objects in order to understand how those objects are similar (based on their attributes) or on the other hand differ one from the other. By

There are many cases in which visualizing the data in a certain way helps in making comparisons and confrontations in order to understand better the different orders of magnitude that exist between variables.

R programming is great for doing different types of statistical analysis but mainly for doing data visualization. In this guide, datatreemap will show you how to make a scatterplot of labels starting from

A table plot is a simple thought powerful data visualization method that gives a quick summary of the variables (columns) of a table. In this example, we visualize a table of data from a census taking

When data collected has a geographic dimension it may be interesting to visualize it on a map by using the R library map. In this example, we visualize the murder rates by US state back in 1973. That's

There are many cases in which visualizing the data in a certain way helps in making different considerations. For example, you have a population of individuals and you want to divide them per age groups

A guide on how to with data structures such as Vector, Matrix, Array, Data frame and List in R programming language. Lets see those data structures one by one: Vector It can be defined with the combine