R programming a study guide for beginners | R Tutorial for Beginners: Learn R Programming Language
What is R Programming Language? Introduction to R | Step-by-step guide to R programming | Learn to work with data using R from scratch | R Data Skills for Data engineers | Statistical Inference via Data Science: A ModernDive into R | advanced R programming | Statistical Models in R
by Lipa Bunton
R is a programming language that has so many features as an open-source language that it can be difficult to create a study plan around it. Where do you begin? What are the learning objects? Models for learning? Taking on data frames? Everything becomes more complicated as these concepts become entangled. R for data science is an invaluable tool since it can handle many data types. RStudio is an integrated development environment (IDE) for R. RStudio is known for making excellent tools and packages for R programming. Many data scientists find that r tutors can support students or professional academics and researchers, and so choose to work with an r coding tutor online to learn the basics of R.
R code has an advantage for beginners. It’s arguably simpler than Python because most people learn it to do one of three things:
- Data Analytics
- Data Science
- Data Visualization
Python can be used to do other things (more related to software engineering), such as back-end development or an automation project, which makes the language more complex than R.
Data Science with R Tutorials— aimed at people who want to build a career in predictive modeling and data science. This post is your introduction to R, Data Analysis and Visualization Using R. This R tutorial was created after a lot of iteration on my R for Absolute Beginners Course and incorporating a lot of feedback from my students (kudos to them!). Lets learn how to start modeling and machine learning using r.
These are the six major topics in the R package that I recommend you study in order:
- R Basic Objects
- R Data Frame
- Modelling
- Functions
- Libraries
- Plotting
Let’s dive deeper into them now.
R Basic Objects for Data Science
Objects are the R language’s fundamental building blocks. Basic examples include:
- Vectors
- Lists
- Arrays
- Matrices
You will come into contact with two key characteristics as you study them that will define how you can interact with them, namely:
- uni-type vs multi-type objects
- uni-dimensional vs multidimensional objects.
Why not jump immediately into the main R object, the Data Frame?
Because certain crucial operations can only be accomplished by mastering the related objects, consider these two instances:
- Vectors can be used with the %in% command to subset multiple instances in a filter.
- Lists are the only object in R that lets you nest objects.
Fundamentals of r programming logic revolves around these fundamental objects.
To improve your chances of developing into a skilled programmer, you should study them before taking on other tasks. You can learn more about objects by clicking here or by starting at the beginning of my R Programming Course.
Nailing the Data Frame Object in R programming language
Manipulating data frames will be the most crucial skill to add to your toolkit if you work with data science or data analysis.
This object will be a paradigm shift if you are used to working with other two-dimensional formats, such as the SQL table. You must immerse yourself in the following to master data frames:
- Indexing rows and columns in R;
- Sorting objects;
- Aggregating by a specific key;
- Filtering;
When constructing our data for analysis or modelling, these operations are extremely common. If you truly comprehend how to exchange data frames back and forth, you will only be able to accelerate the development of your code.
A nice tutorial about them can be found on W3 Schools!
Functions of R Code
Functions are responsible for making your code reusable and clean. We couldn’t call multiple methods on different objects without them because they are the foundation of proper R scripts.
Do you realise that you begin interacting with functions as soon as you start the R language? For instance, when you use the c() function to create a vector, you are interacting with the c function, which combines the objects you pass as arguments. Don’t you trust me? Use the help (c) function on your R console.
Everyone would be writing tedious and repetitive code that would be impossible to maintain and debug if functions were not present.
Because most people are used to doing scripting, you might be a little confused when you first come across building your own function (particularly if you are not from a software engineering background). Your coding skills will advance if you can write them. When you learn R programming, you can take on other coding paradigms and programming languages.
More information on them is available in my R Programming Course and in this blog post’s list of best practices.
Step into Libraries
Working with other people’s code is the only way to become a true R developer. How do you go about doing that? Making use of libraries!
The main advantage of using R is its libraries (or packages) (when compared with other non open-source languages). Learning how to install, load, and debug package code gives you access to millions of lines of code written by the community.
What libraries can you start with? Here are some suggestions:
- The built-in rpart library that trains decision trees.
- dplyr a really cool data wrangling library.
- ggplot2 the most famous plotting library in R. You should leave this one for a bit later in your learning.
If you want to see some library recommendations, you should go to this blog post.
Modelling data science with R
The most common mistake people make when they first start using R is to dive right into modelling.
You will most likely have a frustrating experience if you begin here without having basic programming skills and understanding the basic objects and functions.
Why?
First, you won’t be able to manipulate your models’ output as well because different models require different objects and may even output in different formats.
Second, it will be challenging for you to comprehend how the arguments for the modelling functions operate. You don’t want to be limited to R’s base modelling; you want to be able to train your own advanced models with a caret, h2o, or other stand-alone libraries like ranger. Each of these libraries has its own set of quirks and features. They all require various types of arguments, objects, and specifics.
Each model is ultimately a function unto itself, with its own set of inputs and outputs. And there are three critical things you must understand in order to work with them effectively:
- How to manipulate functions.
- What type of objects do the arguments expect.
- How you can improve your training process by using external libraries with faster or more accurate models.
Join my R Data Science Bootcamp when you’re ready to start modelling to learn the theory and practice of building machine learning models in R.
Plotting
Plotting is the final item on this list. When it comes to visualisation libraries, you have a lot of options with ggplot2, plotly, and altair. They can all produce really intriguing plots that can illustrate your data in a narrative fashion.
It is not easy to become a Data Visualization expert. The libraries I’ve mentioned have literally hundreds of parameters and settings that can be tweaked to improve them. I recommend that you begin by creating a baseline of the following plots:
- A simple scatter plot.
- A historical data line plot.
- A box plot.
- A pie chart.
You can perform one of these plots in each of the libraries listed above.
Understanding their major differences and complexities will give you more flexibility when creating your own data-driven storytelling. Another important point is that I recommend you avoid using base R plotting because it is very limited when compared to any of the packages mentioned above.
That’s all! I hope you enjoyed this post and that it has helped you better plan your learning journey.
Following this journey has resulted in noticeable improvements in people’s coding skills, which has helped me train thousands of people who want to learn R around the world.
This obviously does not imply that you should only move on to the subsequent concept once you have mastered the previous one in its entirety. After completing a few practical exercises and writing some code, move on to the next component after mastering the fundamentals.
The important thing is that you feel confident in each skill set before moving on to the next.