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

Ultimately, data analysis is about understanding relationships among variables. Exploring data with multiple variables requires new, more complex tools, but enables a richer set of comparisons. In this course, you will learn how to describe relationships between two numerical quantities. You will characterize these relationships graphically, in the form of summary statistics, and through simple linear regression models.

Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.

Learn how to describe relationships between two numerical quantities and characterize these relationships graphically through simple linear regression models.

Correlation and Regression in R

In this course, you will become an expert in fitting ARIMA models to time series data using R.  First, you will explore the nature of time series data using the tools in the R stats package.  Next, you learn how to fit various ARMA models to simulated data (where you will know the correct model) using the R package astsa.  Once you have mastered the basics, you will learn how to fit integrated ARMA models, or ARIMA models to various real data sets. You will learn how to check the validity of an ARIMA model and you will learn how to forecast time series data.  Finally, you will learn how to fit ARIMA models to seasonal data, including forecasting using the astsa package.

Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

In this course, you will become an expert in fitting ARIMA models to time series data using R, from fitting to simulated data and seasonal data to using astsa.

ARIMA Models in R

As the saying goes, “A chart is worth a thousand words”. This is why visualization is the most used and powerful way to get a better understanding of your data. After this course you will have a very good overview of R time series visualisation capabilities and you will be able to better decide which model to choose for subsequent analysis. You will be able to also convey the message you want to deliver in an efficient and beautiful way.

Learn how to visualize time series in R, then practice with a stock-picking case study.

Visualizing Time Series Data in R

Forecasting involves making predictions about the future. It is required in many situations: deciding whether to build another power generation plant in the next ten years requires forecasts of future demand; scheduling staff in a call centre next week requires forecasts of call volumes; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments), or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an important aid to effective and efficient planning. This course provides an introduction to time series forecasting using R.

Learn how to make predictions about the future using time series forecasting in R.

Make predictions about the future using time series forecasting in R. You will learn how to plot data, observe patterns, benchmark, forecast, and more.

Forecasting in R

From a machine learning perspective, regression is the task of predicting numerical outcomes from various inputs. In this course, you'll learn about different regression models, how to train these models in R, how to evaluate the models you train and use them to make predictions.

In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

Supervised Learning in R: Regression

Linear regression serves as a workhorse of statistics, but cannot handle some types of complex data. A generalized linear model (GLM) expands upon linear regression to include non-normal distributions including binomial and count data. Throughout this course, you will expand your data science toolkit to include GLMs in R. As part of learning about GLMs, you will learn how to fit model binomial data with logistic regression and count data with Poisson regression. You will also learn how to understand these results and plot them with ggplot2.

The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

Learn to fit model binomial data with logistic regression to count data with Poisson regression, and how to understand and plot the results with ggplot2.

Generalized Linear Models in R

Statistics is the study of how to collect, analyze, and draw conclusions from data. It’s a hugely valuable tool that you can use to bring the future into focus and infer the answer to tons of questions. For example, what is the likelihood of someone purchasing your product, how many calls will your support team receive, and how many jeans sizes should you manufacture to fit 95% of the population? In this course, you'll use sales data to discover how to answer questions like these as you grow your statistical skills and learn how to calculate averages, use scatterplots to show the relationship between numeric values, and calculate correlation. You'll also tackle probability, the backbone of statistical reasoning, and learn how to conduct a well-designed study to draw your own conclusions from data.

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

Learn how to work with variables, plotting, and standard deviation in R with this free statistics course. It covers histograms, distributions and more.

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