In this introduction to R course, you'll master the basics of this widely used open source language&mdash;including vectors, factors, lists, and data frames. With the coding skills you'll gain in this course, you'll be ready to undertake your own data analysis in R. There are millions of R users worldwide, cementing it as a leading programming language in statistics and data science. More and more organizations are using R, so begin your coding journey in one of DataCamp's most popular courses today!

Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.

DataCamp's Into to R training course teaches you how to use R programming for data science at your own pace with video tutorials & interactive challenges.

Introduction to R Online

Introduction to R

Intermediate R is the next stop on your journey in mastering the R programming language. In this R training, you will learn about conditional statements, loops, and functions to power your own R scripts. Next, make your R code more efficient and readable using the apply functions. Finally, the utilities chapter gets you up to speed with regular expressions in R, data structure manipulations, and times and dates. This course will allow you to take the next step in advancing your overall knowledge and capabilities while programming in R.

Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.

Discover conditional statements, loops, and functions to power your own R scripts, and learn to make your R code more efficient using the apply functions.

Intermediate R

This is an introduction to the programming language R, focused on a powerful set of tools known as the Tidyverse. You'll learn the intertwined processes of data manipulation and visualization using the tools dplyr and ggplot2. You'll learn to manipulate data by filtering, sorting, and summarizing a real dataset of historical country data in order to answer exploratory questions. You'll then learn to turn this processed data into informative line plots, bar plots, histograms, and more with the ggplot2 package. You’ll get a taste of the value of exploratory data analysis and the power of Tidyverse tools. This is a suitable introduction for those who have no previous experience in R and are interested in performing data analysis.

Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.

Discover the fundamentals of the Tidyverse, and learn all about renaming and reordering variables, while becoming familiar with binomial distribution.

Introduction to the Tidyverse

R Markdown is an easy to use formatting language you can use to reveal insights from data and author your findings as a PDF, HTML file, or Shiny app. In this course, you'll learn how to create and modify each element of a Markdown file, including the code, text, and metadata. You'll analyze data with dplyr, create visualizations with ggplot2, and author your analyses and plots as reports. You’ll gain hands-on experience of building reports as you work with real-world data from the International Finance Corporation (IFC)—learning how to efficiently organize reports using code chunk options, create lists and tables, and include a table of contents. By the end of the course, you'll have the skills you need to add your brand’s fonts and colors using parameters and Cascading Style Sheets (CSS), to make your reports stand out.

R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.

Reporting with R Markdown

This beginner-level introduction to machine learning covers four of the most common classification algorithms. You will come away with a basic understanding of how each algorithm approaches a learning task, as well as learn the R functions needed to apply these tools to your own work.

In this course you will learn the basics of machine learning for classification.

Supervised Learning in R: Classification

In this course, you'll learn to work with data using tools from the tidyverse in R. By data, we mean your own data, other people's data, messy data, big data, small data - any data with rows and columns that comes your way! By work, we mean doing most of the things that sound hard to do with R, and that need to happen before you can analyze or visualize your data. But work doesn't mean that it is not fun - you will see why so many people love working in the tidyverse as you learn how to explore, tame, tidy, and transform your data. Throughout this course, you'll work with data from a popular television baking competition called "The Great British Bake Off."

Learn to work with data using tools from the tidyverse, and master the important skills of taming and tidying your data.

Working with Data in the Tidyverse

In this course you'll learn how to work with and visualize network data. You'll use the <code>igraph</code> package to create networks from edgelists and adjacency matrices. You'll also learn how to plot networks and their attributes. Then, you'll learn how to identify important vertices using measures like betweenness and degree. Next, this course covers network structures, including triangles and cliques. Next, you'll learn how to identify special relationships between vertices, using metrics like assortativity. Finally, you'll see how to create interactive network plots using threejs.

In this course you'll learn to analyze and visualize network data with the igraph package.

Network Analysis in R

Now that you're familiar with the basics of network analysis it's time to see how to apply those concepts to large real-world data sets. You'll work through three different case studies, each building on your previous work. These case studies are working with the kinds of data you'll see in both academic and industry settings. We'll explore some of the computational and visualization challenges you'll face and how to overcome them. Your knowledge of igraph will continue to grow, but we'll also leverage other visualization libraries that will help you bring your visualizations to the web.

Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.

Case Studies: Network Analysis in R

In this course, you will learn to perform state-of-the art predictive analytics using networked data in R.  The aim of network analytics is to predict to which class a network node belongs, such as churner or not, fraudster or not, defaulter or not, etc.  To accomplish this, we discuss how to leverage information from the network and its underlying structure in a predictive way.  More specifically, we introduce the idea of featurization such that network features can be added to non-network features as such boosting the performance of any resulting analytical model.  In this course, you will use the igraph package to generate and label a network of customers in a churn setting and learn about the foundations of network learning. Then, you will learn about homophily, dyadicity and heterophilicty, and how these can be used to get key exploratory insights in your network. Next, you will use the functionality of the igraph package to compute various network features to calculate both node-centric as well as neighbor based network features.  Furthermore, you will use the Google PageRank algorithm to compute network features and empirically validate their predictive power. Finally, we teach you how to generate a flat dataset from the network and analyze it using logistic regression and random forests.

Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network

Predictive Analytics using Networked Data in R

If you've ever wanted to understand more about social networks, information networks, or even the neural networks of our brains, then you need to know network science! It will demonstrate network analysis using several R packages, including dplyr, ggplot2, igraph, ggraph as well as visNetwork. You will take on the role of Interpol Analyst and investigate the terrorist network behind the Madrid train bombing in 2004. Following the course, you will be able to analyse any network with basic centrality and similarity measures and create beautiful and interactive network visualizations.

Learn how to analyze and visualize network data in the R programming language using the tidyverse approach.

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