(R Tutorials for Citizen Data Scientist)
R Visualisation for Beginners – Boxplots
Boxplots are a measure of how well distributed is the data in a data set. It divides the data set into three quartiles. This graph represents the minimum, maximum, median, first quartile and third quartile in the data set. It is also useful in comparing the distribution of data across data sets by drawing boxplots for each of them.
Boxplots are created in R by using the boxplot() function.
Syntax
The basic syntax to create a boxplot in R is −
boxplot(x, data, notch, varwidth, names, main)
Following is the description of the parameters used −
- x is a vector or a formula.
- data is the data frame.
- notch is a logical value. Set as TRUE to draw a notch.
- varwidth is a logical value. Set as true to draw width of the box proportionate to the sample size.
- names are the group labels which will be printed under each boxplot.
- main is used to give a title to the graph.
Example
We use the data set “mtcars” available in the R environment to create a basic boxplot. Let’s look at the columns “mpg” and “cyl” in mtcars.
input <- mtcars[,c('mpg','cyl')] print(head(input))
When we execute above code, it produces following result −
mpg cyl Mazda RX4 21.0 6 Mazda RX4 Wag 21.0 6 Datsun 710 22.8 4 Hornet 4 Drive 21.4 6 Hornet Sportabout 18.7 8 Valiant 18.1 6
Creating the Boxplot
The below script will create a boxplot graph for the relation between mpg (miles per gallon) and cyl (number of cylinders).
# Give the chart file a name. png(file = "boxplot.png") # Plot the chart. boxplot(mpg ~ cyl, data = mtcars, xlab = "Number of Cylinders", ylab = "Miles Per Gallon", main = "Mileage Data") # Save the file. dev.off()
When we execute the above code, it produces the following result −
Boxplot with Notch
We can draw boxplot with notch to find out how the medians of different data groups match with each other.
The below script will create a boxplot graph with notch for each of the data group.
# Give the chart file a name. png(file = "boxplot_with_notch.png") # Plot the chart. boxplot(mpg ~ cyl, data = mtcars, xlab = "Number of Cylinders", ylab = "Miles Per Gallon", main = "Mileage Data", notch = TRUE, varwidth = TRUE, col = c("green","yellow","purple"), names = c("High","Medium","Low") ) # Save the file. dev.off()
When we execute the above code, it produces the following result −
Applied Data Science Coding in Python: How to visualise data with Boxplot
R Visualisation for Beginners – Boxplots
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.
Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners
Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R:
All Notebooks in One Bundle: Data Science Recipes and Examples in Python & R.
End-to-End Python Machine Learning Recipes & Examples.
End-to-End R Machine Learning Recipes & Examples.
Applied Statistics with R for Beginners and Business Professionals
Data Science and Machine Learning Projects in Python: Tabular Data Analytics
Data Science and Machine Learning Projects in R: Tabular Data Analytics
Python Machine Learning & Data Science Recipes: Learn by Coding
R Machine Learning & Data Science Recipes: Learn by Coding
Comparing Different Machine Learning Algorithms in Python for Classification (FREE)
There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $29.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.