If you’re a data scientist (or an aspiring one), don’t wait – learn R programming!
As the demand for data analysis and data scientists grows, there is one programming language that stands at the forefront: R. Used predominantly in the world of statistics, it’s become an essential tool for anyone looking to dive into data analysis or research. Don’t worry if you’re new to coding, everyone starts somewhere and R is a brilliant language for beginners to pick up.
Table of contents
What is R Programming?
R is a language developed specifically for data manipulation and statistical analysis. It’s extremely popular in academia and is rapidly gaining favor in commercial situations thanks to its powerful statistical analysis capabilities. It offers an abundance of statistical and graphical techniques, including linear and nonlinear modelling, statistical tests, time-series analysis, and more.
Likewise, it’s become ever more important to learn R programming when dealing with these elements to stay ahead of the curve.
Why Should You Learn R Programming?
While R might have started as a niche language, it’s becoming more mainstream. Plenty of industries, ranging from healthcare to fintech, are looking for experts who know how to use this powerful tool, making it a highly valuable skill to have on your CV. Here’s why you should be learning R:
- A boon for data scientists: R is extensive and pertinent for all areas of data science, including visualization, data manipulation, statistical modelling, machine learning, and more.
- Great for beginners: While R has lots of advanced features, it’s also very beginner-friendly, and there are many resources available for new learners.
- Community support: R has a massive community of active users who are constantly developing packages and features.
You might have a bit of a learning curve, especially for true coding beginners, to learn R programming, but thanks to its versatile nature and wide usage, it’s definitely worth the effort. Stay tuned to learn more about how to code in R!
Getting Started with R
First up, let’s see how simple it is to print out “Hello, world!” in R. Code example is as follows:
print("Hello, world!")To execute this code, you simply have to run it like any other R script.
Variable Assignment in R
Variables are a fundamental concept in any programming language. In this example, we’ll create a variable called `x` and assign it the value of 10.
x <- 10 print(x)
In R, we use the `<-` operator to assign values to variables. Running this script will print out the number 10.
Doing Maths with R
Performing calculations in R is straightforward. In this example, we’ll assign two variables, `x` and `y`, then calculate their sum using the `+` operator:
x <- 5 y <- 20 z <- x + y print(z)
This produces the result `25` when we run the script.
Working with Vectors in R
One of the key structures in R is the vector. Vectors are one-dimensional arrays that can hold numeric data, character data, or logical data. In other words, a vector is a simple tool to store data. For example, you might want to store the age of every person in a group.
ages <- c(25, 34, 29, 40) print(ages)
In this script, `ages` is a vector that contains four numbers.
Conditions and Control Structures in R
Much like other languages, R uses control structures like `if`, `else`, and `for` loops. Here’s a simple example of an `if` statement in R:
x <- 10
if(x>5) {
print("x is greater than 5")
} else {
print("x is not greater than 5")
}In this script, because `x` (which we set to 10) is greater than 5, the output would be “x is greater than 5”.
Data Frames in R
Data frames are another crucial data structure in R and one of the most critical points to master when you learn R programming. They’re utilized for storing data tables, and you can think of them as a table in Excel, or a CSV file.
names <- c("Alice", "Bob", "Charlie")
ages <- c(25, 34, 29)
persons <- data.frame(names, ages)
print(persons)In this script, `persons` is a data frame that holds our names and ages vectors. It will display the data structured as a table.
Accessing Data Frame Elements
Just like in other data structures, you can access elements of a data frame. Here’s how you can access the data of a specific column:
names <- persons$names print(names)
In this example, `persons$names` returns the names column from our `persons` data frame.
Introducing R Functions
Enumerating over a data structure using a loop is a common task in data processing. But R provides a more efficient way to manage this. Let’s sum a vector of numbers with a function:
numbers <- c(1, 2, 3, 4, 5) sum_result <- sum(numbers) print(sum_result)
Here, we used the `sum` function to add up the elements of the `numbers` vector.
Creating Your Own Functions
While there are many built-in functions, you can also define your own. Here’s how to define a function that adds two numbers together:
add_numbers <- function(a, b) {
return(a + b)
}
result <- add_numbers(5, 10)
print(result)Functions are defined using the `function` keyword. Functions can take parameters and return a value using the `return` keyword.
Working with Libraries
In R, there are thousands of libraries that you can use to extend the functionality of the language. You can include them in your project using the `library` function, like so:
install.packages('ggplot2')
library(ggplot2)In this example, the `install.packages` function installs the `ggplot2` library, and `library(ggplot2)` makes the library available for use in our script.
Creating Basic Plots
A great feature of R is its ability to create graphics and plots easily. Let’s create a basic plot of some data:
data <- c(1,2,3,4,5,6,7,8,9,10) plot(data)
In this example, the `plot` function generates a basic line plot of our `data` vector.
Where to Go Next?
Now that you’ve dipped your toes into R’s waters, you’re undoubtedly eager to explore further. Luckily, there is a world of possibilities for you to venture into when it comes to data science that go beyond even R programming. In otherwords, don’t just learn R programming – explore the other data science principles as well.
Zenva’s Data Science Mini-Degree
While you can pick up bits and pieces of the language by yourself, if you’re really serious about seeking a career in data science, comprehensive education is key. One fantastic place to turn for that comprehensive education is our Data Science Mini-Degree.
Our Data Science Mini-Degree offered by Zenva Academy is a focused and strategic collection of courses. While you won’t learn R programming here, you will into data science with Python, the other most admired language in the field. Perfectly suitable for beginners yet suitable equally for our more advanced students, this path covers everything from the basics of Python, thorough web scraping and handling databases to significant data manipulation, analysis and visualization techniques. More importantly, all these aspects are taught via hands-on projects.
Each course is designed to imbue learners with the knowledge and skillset necessary to navigate the dynamic field of data science, a field perpetually in hot demand across multiple industries.
As a learner, you can set your course pace to suit your own needs and will also have access to expert mentor support when needed. Furthermore, we periodically update our courses to remain relevant against the ever-progressing tech landscape.
Intermediate-Level Learners
For those already past the basics stage, a range of more advanced courses is readily available. They can be accessed at our data science courses catalogue. Each course has been created to foster your professional development and push you one step closer to meeting your career goals.
Why Choose Zenva
Our courses span a vast array of subjects right from programming to game development and AI. Over 250 different career-boosting courses are available. Zenva’s academic offering ranges from beginner to professional levels, meaning you can start where you are comfortable and make your way up the ranks at your own pace.
Furthermore, Zenva offers certificates for the completion of our courses, providing evidence of your commitment and your new capabilities. Join us here at Zenva and make your journey from beginner to professional as seamless as possible.
The tools to take the next step and build a career in data science are at your disposal. The question is, will you take advantage of our mini-degree or intermediate courses, or will you choose to take on the challenge alone either way, we’re excited to see where your journey in R will take you!
Conclusion
By now, you should have a basic understanding of how to learn R programming. With its extensive statistical capabilities, R has proven itself an invaluable tool in the world of data science, boasting immense potential for both new and experienced coders alike.
Embracing R’s brilliance can be a game-changer for your career, putting you one step ahead in the data-driven world. Are you ready to embark on your journey with R and uncover the power of data? If R isn’t your speed, you can also join us at Zenva Academy and transform into a skilled data scientist with Python, exploring new frontiers and unlocking exciting career opportunities in the rapidly evolving realm of data science.
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