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

Machine learning is the study and application of algorithms that learn from and make predictions on data. From search results to self-driving cars, it has manifested itself in all areas of our lives and is one of the most exciting and fast growing fields of research in the world of data science. This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for optimal performance, how to preprocess data for better results, and much more. The popular <code>caret</code> R package, which provides a consistent interface to all of R's most powerful machine learning facilities, is used throughout the course.

This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

This course teaches the big ideas in machine learning: how to build and evaluate predictive models, how to tune them for performance, and how to preprocess data

Machine Learning with caret in R

Once you've started learning tools for data manipulation and visualization like dplyr and ggplot2, this course gives you a chance to use them in action on a real dataset. You'll explore the historical voting of the United Nations General Assembly, including analyzing differences in voting between countries, across time, and among international issues. In the process you'll gain more practice with the dplyr and ggplot2 packages, learn about the broom package for tidying model output, and experience the kind of start-to-finish exploratory analysis common in data science.

Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.

Use your data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.

Case Study: Exploratory Data Analysis in R

When your dataset is represented as a table or a database, it's difficult to observe much about it beyond its size and the types of variables it contains. In this course, you'll learn how to use graphical and numerical techniques to begin uncovering the structure of your data. Which variables suggest interesting relationships? Which observations are unusual? By the end of the course, you'll be able to answer these questions and more, while generating graphics that are both insightful and beautiful.

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

Learn how to use graphical and numerical techniques for exploratory data analysis while generating insightful and beautiful graphics in R.

Exploratory Data Analysis in R

After learning the basics of using Shiny to build web applications, this course takes you to the next level by putting your newly acquired skills into practice. You'll get experience developing fun and realistic Shiny apps for different common use cases, such as using Shiny to explore a dataset, generate a customized plot, and even create a word cloud. With all this practice and new knowledge, you will be well-equipped to develop Shiny apps for your own use.

Practice your Shiny skills while building some fun Shiny apps for real-life scenarios!

Case Studies: Building Web Applications with Shiny in R

They say that a picture is worth a thousand words. Indeed, successfully promoting your data analysis is not only a matter of accurate and effective graphics, but also of aesthetics and uniqueness. This course teaches you how to leverage the power of ggplot2 themes for producing publication-quality graphics that stick out from the mass of boilerplate plots out there. It shows you how to tweak and get the most out of ggplot2 in order to produce unconventional plots that draw attention on social media. In the end, you will combine that knowledge to produce a slick and custom-styled report with RMarkdown and CSS – all of that within the powerful tidyverse.

Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.

Communicating with Data in R (Tidyverse)

Communicating with Data in the Tidyverse

Much of the biological research, from medicine to biotech, is moving toward sequence analysis. We are now generating targeted and whole genome big data, which needs to be analyzed to answer biological questions. To help you get started, you will be introduced to The Bioconductor project. Bioconductor is and builds the infrastructure to share software tools (packages), workflows and datasets for the analysis and comprehension of genomic data. Bioconductor is a great platform accessible to you, and it is a community developed open software resource. By the end of this course, you will be able to use essential Bioconductor packages and get a grasp of its infrastructure and some built-in datasets. Using BSgenome, Biostrings, IRanges, GenomicRanges, TxDB, ShortRead and Rqc with real datasets from different species is going to be an exceptional experience!

Learn to use essential bioconductor packages using datasets from virus, fungus, human and plants!

Discover Bioconductor packages, using BSgenome, Biostrings, IRanges, GenomicRanges, TxDB, ShortRead and Rqc with real datasets to analyse genomic data.

Introduction to Bioconductor in R

Functional genomic technologies like microarrays, sequencing, and mass spectrometry enable scientists to gather unbiased measurements of gene expression levels on a genome-wide scale. Whether you are generating your own data or want to explore the large number of publicly available data sets, you will first need to learn how to analyze these types of experiments. In this course, you will be taught how to use the versatile R/Bioconductor package limma to perform a differential expression analysis on the most common experimental designs. Furthermore, you will learn how to pre-process the data, identify and correct for batch effects, visually assess the results, and perform enrichment testing. After completing this course, you will have general analysis strategies for gaining insight from any functional genomics study.

Learn to use the Bioconductor package limma for differential gene expression analysis.

Discover how you can use the versatile R/Bioconductor package limma to perform a differential expression analysis on the most common experimental designs.

Differential Expression Analysis with limma in R

This course will teach you to leverage the tools in the "tidyverse" to generate, explore, and evaluate machine learning models. Using a combination of tidyr and purrr packages, you will build a foundation for how to work with complex model objects in a "tidy" way. You will also learn how to leverage the broom package to explore your resulting models. You will then be introduced to the tools in the test-train-validate workflow, which will empower you evaluate the performance of both classification and regression models as well as provide the necessary information to optimize model performance via hyperparameter tuning.

Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.

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