Mastering the Basics of R Programming and Navigating RStudio

Welcome back to our fourth tutorial! In this post, we will be teaching you how to use basic R commands and navigate RStudio. If you’re joining us, we have a project file open with an R markdown file, which you can follow by checking out our YouTube channel @cradletograver.

Recommended Books

To further enhance your understanding of R programming and data manipulation, we recommend the following books (as an Amazon Associate, I may earn a small commission from these links):

  1. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
  2. Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street
  3. The Kaggle Book: Data analysis and machine learning for competitive data science
  4. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

Understanding Vectors in R

In R, a vector is a sequence of elements of the same type, such as numbers or bits of text, but not a combination of both. The power of R is that most functions can use a vector directly as input, which simplifies coding in many applications because you avoid using for loops.

To create a vector, we can use the c() function. For example:

number1 <- c(1, 3, 4, 7, 10, 11)

This will create a vector called number1 with the given elements.

Useful Functions with Vectors

With vectors, we can perform various operations and use different functions. Some of them are:

  • sum(): Calculate the sum of the elements in a vector.
  • mean(): Calculate the mean (average) of the elements in a vector.
  • length(): Find the number of elements in a vector.
  • min(): Find the minimum value in a vector.
  • max(): Find the maximum value in a vector.
  • unique(): Extract the unique values in a vector.
  • sort(): Sort the elements of a vector.

For example, to find the sum of the elements in number1, we can use the sum() function:


This will return the sum of the elements in number1.

Random Facts about R

  1. Ross Ihaka and Robert Gentleman created R at the University of Auckland, New Zealand, and is an implementation of the S programming language.
  2. R is open-source software that is freely available and can be modified and redistributed.
  3. Statisticians, data scientists, and researchers widely use R for statistical analysis, visualization, and machine learning tasks.

More on R Functions

In R, functions can have default values for their arguments. For example, the round() function has an argument called digits with a default value of 0. If you want to round a number to a certain number of decimal places, you can override the default value by specifying the digits argument:

round(3.14159, digits = 2)

This will return the value of 3.14.

To learn more about a function and its arguments, start typing the function name and opening the parenthesis. A yellow box will pop up with information about the function’s arguments and their default values.

Recommended Resources

To dive deeper into R programming and its applications, check out these books:

  1. Machine Learning with R
  2. Extending Power BI with Python and R: Ingest, transform, enrich, and visualize data using the power of analytical languages
  3. The Book of R – A First Course in Programming and Statistics

We hope you found this tutorial helpful! Please comment below and subscribe to our YouTube channel to stay updated with more videos like this.

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