Introduction to R
About this course
Your journey of Data Anylsis starts here. Master the basics by learning common data structures like vectors, matrices, and data frames.
4 hours
Go to courseR is one of the most commonly used programming languages in data science. Whether you’re a total beginner or a seasoned pro, pick a learning path today and start unlocking endless opportunities with R.
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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.Learn how to code like a programmer in this beginner’s track. First, you’ll learn how to work with common data structures in R like vectors, matrices, and data frames before expanding your skills by mastering conditional statements, loops, and vectorized functions.
Your journey of Data Anylsis starts here. Master the basics by learning common data structures like vectors, matrices, and data frames.
4 hours
Go to courseTake the next step to mastering R here. Learn the loops, functions and conditional statements to power your own R scripts.
4 hours
Go to courseUnlock the secrets of writing efficient R code here. Discover benchmarking and profiling and how they can be utilized in parallel programming.
4 hours
Go to courseSharpen your R skills by learning to write reusable, efficient functions.
4 hours
Go to courseSpecify your relationships between functions by learning object-oriented programming.
4 hours
Go to courseBecome a master of the data by learning how to use R to import, clean, and manipulate data. Create data visualizations, learn about the most popular R packages and the tidyverse, and answer complex questions with the help of dplyr. You’ll learn to write your own R functions and you’ll perform analysis on real historical data from the United Nations.
Learn about the powerful collection of R tools, Tidyverse, and explore how you can manipulate and visualize data using the tools dplyr and ggplot 2.
4 hours
Go to coursePractice your knowledge of the tidyverse toolset and learn strategies to solve data errors via the rlang package.
4 hours
Go to courseDeepen your understanding dplyr and complex data questions by learning to combine data across multiple tables.
4 hours
Go to courseMaster the grammar of graphics and create meaningful and beautiful data visualizations with ggplot2.
4 hours
Go to courseBuild on your knowledge of ggplot2 and learn how to create meaningful explanatory plots using facets, coordinate systems, and statistics.
4 hours
Go to courseLearn to create dynamic reports with R Markdown.
4 hours
Go to courseLearn to use tools like readxl and data.table. to read differently formatted data and import into R.
3 hours
Go to courseBuild your skills and learn to dissect and analyze data in any format.
3 hours
Go to courseLearn to quickly and accurately clean data using R.
4 hours
Go to courseLearn to manipulate and analyse Date and Time data using R.
4 hours
Go to courseLearn the basics of machine learning for classification before turning your hand to predicting events using linear regression, clustering, and dimensionality reduction with R. You’ll also learn how to use the R tidyverse to generate and evaluate machine learning models, perform cluster analysis and much more.
Get started learning the basics of machine learning for classification.
4 hours
Go to courseLearn future earn prediction using linear regression, generalized additive models, random forests, and xgboost.
4 hours
Go to courseThis course provides an intro to Gain your first insights into clustering and dimensionality reduction in R from a machine learning viewpoint.
4 hours
Go to courseDiscover how to perform linear and logistic regression with multiple explanatory variables.
4 hours
Go to courseGet a clear understanding of how hierarchical and k-means clustering work and how to apply this understanding to extract insights from your data.
4 hours
Go to courseIn this course, you'll be knee-deep in learning the machine learning big idea such as how to build and evaluate predictive models.
4 hours
Go to courseLearn how to ensure your machine learning workflows are streamlined with tidymodels.
4 hours
Go to courseIn this course you will gain the knowledge required to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
4 hours
Go to courseDiscover the working of the support vector machine (SVM) using an intuitive, visual approach.
4 hours
Go to courseDiscover and digest the fundamentals of Bayesian data analysis. How it works, and why it's one of the most versatile and useful tools to have in your data science toolkit.
4 hours
Go to courseIf you're new to R, make sure you start here with our most popular course for beginners.
Taking your first course in R is just the beginning of a journey - if you’d like to create an excellent data science resumé and portfolio, you can start a skill or career track and work towards gaining a professional certification in data science or data analysis.
Our certifications are based on in-depth analysis of data science jobs and their requirements, so the assessments are designed to show that you’re ready for a demanding and lucrative job as a data scientist or analyst.
You can start your assessments straight away if you already have strong R experience, or take a certification preparation track if you’d prefer to brush up on your skills first.
It depends on your end goal and why you want to learn R.
Let’s say you already work in finance and you’d like to learn how to manipulate and analyze financial data. You should learn how to use R as a calculator alongside R vectors, matrices, and data frames—all of which are covered in DataCamp’s Introduction to R for Finance.
If you want to become a data scientist or analyst, you should learn how to import, clean, and visualize data with R. You’ll also want to learn how to navigate and use the tidyverse and popular R packages such as ggplot2.
Or perhaps you want to learn R for marketing analytics, in which case you’ll learn how to measure user engagement, analyze your business competitors, and glean intel from social media with the help of R.
When you learn R with DataCamp, you can take advantage of our skilled instructors and our tried and tested learning method.
We've fine-tuned this method over many years, and we know how to make learning R immersive, engaging, and most importantly, easy to retain. If you're starting from scatch and want to become an expert, we've designed Tracks to help you build a well-rounded skill set in R.
Skill Tracks such as R Programming will teach you now to code like a programmer and prepare you for complex tasks like advanced data visualization. If you're focused on career goals rather than specific skills, Data Scientist with R, and R Programmer will get you career-ready and teach you how perform key tasks for your chosen role.
DataCamp is home to all the R resources you need to support your learning. From R cheat sheets that make importing data easy all the way through to coding and data analysis competitions with cash prizes, we’ve got you covered.
And with more than nine million learners worldwide, there’s plenty of support from our bustling community. DataCamp’s R resources include:
It doesn’t matter whether you’re just getting started with R or grappling with object-oriented programming in R, DataCamp has the resources to support you.
Benchmark your skills against your R peers. Determine your R strengths and weaknesses, whilst receiving personalized R learning recommendations. Take a 10-minute skill assessment today.