Gain the career-building R skills you need to succeed as a data scientist. No prior coding experience required.

In this track, you’ll learn how this versatile language allows you to import, clean, manipulate, and visualize data—all integral skills for any aspiring data professional or researcher. Through interactive exercises, you’ll get hands-on with some of the most popular R packages, including ggplot2 and tidyverse packages like dplyr and readr. You’ll then work with real-world datasets to learn the statistical and machine learning techniques you need to write your own functions and perform cluster analysis. Start this track, grow your R skills, and begin your journey to becoming a confident data scientist.

From data manipulation to machine learning, you'll gain the career-building R skills you need to succeed in the exciting field of data science!

with R

Data Scientist

Take your first steps with R. In this chapter, you will learn how to use the console as a calculator and how to assign variables. You will also get to know the basic data types in R. Let's get started.

Intro to basics

We take you on a trip to Vegas, where you will learn how to analyze your gambling results using vectors in R. After completing this chapter, you will be able to create vectors in R, name them, select elements from them, and compare different vectors.

Vectors

In this chapter, you will learn how to work with matrices in R. By the end of the chapter, you will be able to create matrices and understand how to do basic computations with them. You will analyze the box office numbers of the Star Wars movies and learn how to use matrices in R. May the force be with you!

Matrices

Data often falls into a limited number of categories. For example, human hair color can be categorized as black, brown, blond, red, grey, or white—and perhaps a few more options for people who color their hair. In R, categorical data is stored in factors. Factors are very important in data analysis, so start learning how to create, subset, and compare them now.

Factors

Most datasets you will be working with will be stored as data frames. By the end of this chapter, you will be able to create a data frame, select interesting parts of a data frame, and order a data frame according to certain variables.

Data frames

As opposed to vectors, lists can hold components of different types, just as your to-do lists can contain different categories of tasks. This chapter will teach you how to create, name, and subset these lists.

Lists

In  Introduction to R, you will master the basics of this widely used open source language, including factors, lists, and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. Oracle estimated over 2 million R users worldwide in 2012, cementing R as a leading programming language in statistics and data science. Every year, the number of R users grows by about 40%, and an increasing number of organizations are using it in their day-to-day activities. Begin your journey to learn R with us today!

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 Course

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

Introduction to R

Programming

In this chapter, you'll learn about relational operators for comparing R objects, and logical operators like "and" and "or" for combining TRUE and FALSE values.  Then, you'll use this knowledge to build conditional statements.

Conditionals and Control Flow

Loops can come in handy on numerous occasions. While loops are like repeated if statements, the for loop is designed to iterate over all elements in a sequence. Learn about them in this chapter.

Loops

Functions are an extremely important concept in almost every programming language, and R is no different. Learn what functions are and how to use them—then take charge by writing your own functions.

Functions

Whenever you're using a for loop, you may want to revise your code to see whether you can use the lapply function instead. Learn all about this intuitive way of applying a function over a list or a vector, and how to use its variants, sapply and vapply.

The apply family

Mastering R programming is not only about understanding its programming concepts. Having a solid understanding of a wide range of R functions is also important. This chapter introduces you to many useful functions for data structure manipulation, regular expressions, and working with times and dates.

Utilities

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.

DataCamp's Intermediate R course shows you how learn statistics with R through interactive video tutorials. See why over 300,000 people use DataCamp today!

Intermediate R

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

R Programming

Defining and manipulating R data structures

data-structures

Controlling function flow and creating loops

loops-controlflow

functional-prog

Object-oriented programming (S3 and R6 classes)

package

functions

Writing efficient code, including profiling and benchmarking

efficient-coding

In this chapter, you'll learn to do three things with a table: filter for particular observations, arrange the observations in a desired order, and mutate to add or change a column. You'll see how each of these steps allows you to answer questions about your data.

Data wrangling

Often a better way to understand and present data as a graph. In this chapter, you'll learn the essential skills of data visualization using the ggplot2 package, and you'll see how the dplyr and ggplot2 packages work closely together to create informative graphs.

Data visualization

So far you've been answering questions about individual country-year pairs, but you may be interested in aggregations of the data, such as the average life expectancy of all countries within each year. Here you'll learn to use the group by and summarize verbs, which collapse large datasets into manageable summaries.

Grouping and summarizing

In this chapter, you'll learn how to create line plots, bar plots, histograms, and boxplots. You'll see how each plot requires different methods of data manipulation and preparation, and you’ll understand how each of these plot types plays a different role in data analysis.

Types of visualizations

Gapminder

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.

Introduction to the Tidyverse

Learn verbs you can use to transform your data, including select, filter, arrange, and mutate. You'll use these functions to modify the counties dataset to view particular observations and answer questions about the data.

Transforming Data with dplyr

Now that you know how to transform your data, you'll want to know more about how to aggregate your data to make it more interpretable. You'll learn a number of functions you can use to take many observations in your data and summarize them, including count, group_by, summarize, ungroup, and top_n.

Aggregating Data

Learn advanced methods to select and transform columns. Also learn about select helpers, which are functions that specify criteria for columns you want to choose, as well as the rename and transmute verbs.

Selecting and Transforming Data

Work with a new dataset that represents the names of babies born in the United States each year. Learn how to use grouped mutates and window functions to ask and answer more complex questions about your data. And use a combination of dplyr and ggplot2 to make interesting graphs to further explore your data.

Case Study: The babynames Dataset

counties

babynames

Say you've found a great dataset and would like to learn more about it. How can you start to answer the questions you have about the data? You can use dplyr to answer those questions—it can also help with basic transformations of your data. You'll also learn to aggregate your data and add, remove, or change the variables. Along the way, you'll explore a dataset containing information about counties in the United States. You'll finish the course by applying these tools to the babynames dataset to explore trends of baby names in the United States.

Learn to transform and manipulate your data using dplyr. 

Data Manipulation with dplyr

Data Manipulation

Get started with your first joining verb: inner-join! You'll learn to join tables together to answer questions about the LEGO dataset, which contains information across many tables about the sets, parts, themes, and colors of LEGOs over time.  


Joining Tables

Learn two more mutating joins, the left and right join, which are mirror images of each other! You'll learn use cases for each type of join as you explore parts and colors of LEGO themes. Then, you'll explore how to join tables to themselves to understand the hierarchy of LEGO themes in the data. 

Left and Right Joins

In this chapter, you'll cover three more joining verbs: full-join, semi-join, and anti-join. You'll then use these verbs to answer questions about the similarities and differences between a variety of LEGO sets. 

Full, Semi, and Anti Joins

Put together all the types of join you learned in this course to analyze a new dataset: Stack Overflow questions, answers, and tags. This includes calculating and visualizing trends for some notable tags like dplyr and ggplot2. You'll also master one more method for combining tables, the bind_rows verb, which stacks tables on top of each other.

Case Study: Joins on Stack Overflow Data

sets

themes

parts

part_categories

inventories

inventory_parts

colors

questions

question_tags

answers

Often in data science, you'll encounter fascinating data that is spread across multiple tables. This course will teach you the skills you'll need to join multiple tables together to analyze them in combination. You'll practice your skills using a fun dataset about LEGOs from the Rebrickable website. The dataset contains information about the sets, parts, themes, and colors of LEGOs, but is spread across many tables. You'll work with the data throughout the course as you learn a total of six different joins! You'll learn four mutating joins: inner join, left join, right join, and full join, and two filtering joins: semi join and anti join. In the final chapter, you'll apply your new skills to Stack Overflow data, containing each of the almost 300,000 Stack Oveflow questions that are tagged with R, including information about their answers, the date they were asked, and their score. Get ready to take your dplyr skills to the next level!

Learn to combine data across multiple tables to answer more complex questions with dplyr.

Joining Data with dplyr

Data Manipulation with R

Using slicing and selection techniques to get to know your data with the tidyverse

understanding-your-data

Adding and updating columns and creating aggregations using dplyr

updating-and-aggregating

Using basic exploration techniques to identify characteristics of the data

exploratory-data-analysis

Creating standard exploratory visualizations for numeric and categorical data with ggplot2

visual-exploration

In this chapter we’ll get you into the right frame of mind for developing meaningful visualizations with R. You’ll understand that as a communications tool, visualizations require you to think about your audience first. You’ll also be introduced to the basics of ggplot2 - the 7 different grammatical elements (layers) and aesthetic mappings.

Introduction

Aesthetic mappings are the cornerstone of the grammar of graphics plotting concept. This is where the magic happens - converting continuous and categorical data into visual scales that provide access to a large amount of information in a very short time. In this chapter you’ll understand how to choose the best aesthetic mappings for your data.

Aesthetics

A plot’s geometry dictates what visual elements will be used. In this chapter, we’ll familiarize you with the geometries used in the three most common plot types you’ll encounter - scatter plots, bar charts and line plots. We’ll look at a variety of different ways to construct these plots.

Geometries

In this chapter, we’ll explore how understanding the structure of your data makes data visualization much easier. Plus, it’s time to make our plots pretty. This is the last step in the data viz process. The Themes layer will enable you to make publication quality plots directly in R. In the next course we'll look at some extra layers to add more variables to your plots.

Themes

Diamonds

Iris

Recession

Fish

The ability to produce meaningful and beautiful data visualizations is an essential part of your skill set as a data scientist. This course, the first R data visualization tutorial in the series, introduces you to the principles of good visualizations and the grammar of graphics plotting concepts implemented in the ggplot2 package. ggplot2 has become the go-to tool for flexible and professional plots in R. Here, we’ll examine the first three essential layers for making a plot - Data, Aesthetics and Geometries. By the end of the course you will be able to make complex exploratory plots.

Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.

Introduction to Data Visualization with ggplot2

Data Visualization

A picture paints a thousand words, which is why R ggplot2 is such a powerful tool for graphical data analysis. In this chapter, you’ll progress from simply plotting data to applying a variety of statistical methods. These include a variety of linear models, descriptive and inferential statistics (mean, standard deviation and confidence intervals) and custom functions.

Statistics

The Coordinates layers offer specific and very useful tools for efficiently and accurately communicating data. Here we’ll look at the various ways of effectively using these layers, so you can clearly visualize lognormal datasets, variables with units, and periodic data.

Coordinates

Facets let you split plots into multiple panes, each displaying subsets of the dataset. Here you'll learn how to wrap facets and arrange them in a grid, as well as providing custom labeling.

Facets

Now that you have the technical skills to make great visualizations, it’s important that you make them as meaningful as possible. In this chapter, you’ll review three plot types that are commonly discouraged in the data viz community: heat maps, pie charts, and dynamite plots. You’ll learn the pitfalls with these plots and how to avoid making these mistakes yourself.

Best Practices

This ggplot2 course builds on your knowledge from the introductory course to produce meaningful explanatory plots. Statistics will be calculated on the fly and you’ll see how Coordinates and Facets aid in communication. You’ll also explore details of data visualization best practices with ggplot2 to help make sure you have a sound understanding of what works and why. By the end of the course, you’ll have all the tools needed to make a custom plotting function to explore a large data set, combining statistics and excellent visuals.

Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.

Intermediate Data Visualization with ggplot2

In this chapter, you'll learn about the three components of a Markdown file: the code, the text, and the metadata. You'll also learn to add and modify each of these elements to your own reports, as you create your first Markdown files. 

Getting Started with R Markdown

In this chapter, you’ll use dplyr to begin to analyze the World Bank IFC datasets and include the analyses in your report. You’ll then create visualizations of the data using ggplot2 and learn to modify how the plots display in your knit report. 

Adding Analyses and Visualizations

Now that you've learned how to add, label, and modify code chunks, you'll learn about code chunk options. You can use these to determine whether the code and results appear in the knit report. You'll also discover how to create lists and tables to include in your report. 

Improving the Report

In this final chapter, you'll learn how to customize your report by adding a table of contents and adding a CSS file to the YAML header, to personalize reports with your brand’s fonts and colors. You'll also learn how to efficiently create new reports from your data using parameters, which will save you time from manually updating existing reports to create new ones. 

Customizing the Report

Investment Services Projects

Investment Annual Summary

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

Reporting

A lot of data comes in the form of flat files: simple tabular text files. Learn how to import the common formats of flat file data with base R functions.

Importing data from flat files with utils

In addition to base R, there are dedicated packages to easily and efficiently import flat file data. We'll talk about two such packages: readr and data.table.

readr & data.table

Excel is a widely used data analysis tool. If you prefer to do your analyses in R, though, you'll need an understanding of how to import  .csv data into R. This chapter will show you how to use readxl and gdata to do so.

Importing Excel data

Beyond importing data from Excel, you can take things one step further with XLConnect. Learn all about it and bridge the gap between R and Excel.

Reproducible Excel work with XLConnect

Hotdogs

Potatoes (CSV)

Potatoes (TSV)

Swimming pools

Urban population (XLS)

Urban population (XLSX)

Importing data into R should be the easiest step in your analysis. Unfortunately, that is almost never the case. Data can come in many formats, ranging from .csv and text files, to statistical software files, to databases and HTML data. Knowing which approach to use is key to getting started with the actual analysis.
In this course, you’ll start by learning how to read .csv and text files in R. You will then cover the readr and data.table packages to easily and efficiently import flat file data. After that, you will learn how to read .xls files in R using readxl and gdata.

In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.

Introduction to Importing Data in R

Importing & Cleaning Data

Many companies store their information in relational databases. The R community has also developed R packages to get data from these architectures. You'll learn how to connect to a database and how to retrieve data from it.

Importing data from databases (Part 1)

Importing an entire table from a database while you might only need a tiny bit of information seems like a lot of unncessary work. In this chapter, you'll learn about SQL queries, which will help you make things more efficient by performing some computations on the database side.

Importing data from databases (Part 2)

More and more of the information that data scientists are using resides on the web. Importing this data into R requires an understanding of the protocols used on the web. In this chapter, you'll get a crash course in HTTP and learn to perform your own HTTP requests from inside R.

Importing data from the web (Part 1)

Importing data from the web is one thing; actually being able to extract useful information is another. Learn more about the JSON format to get one step closer to web domination.

Importing data from the web (Part 2)

Next to R, there are also other commonly used statistical software packages: SAS, STATA and SPSS. Each of them has their own file format. Learn how to use the haven and foreign packages to get them into R with remarkable ease!

Importing data from statistical software packages

Education equality data

Employee data

Florida election data

International socio-economic data

Latitude (XLS)

Latitude (XLSX)

Big Five data

Potatoes

Sales data

Sugar import data

Water data

Wine data

In this course, you will take a deeper dive into the wide range of data formats out there. More specifically, you'll learn how to import data from relational databases and how to import and work with data coming from the web. Finally, you'll get hands-on experience with importing data from statistical software packages such as SAS, STATA, and SPSS.

Learn how to parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.

Intermediate Importing Data in R

Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.

Importing & Cleaning Data with R

Working with a range of file types to import into a suitable format

import-local-data

Accessing data through APIs and scraping techniques

accessing-external-data

Performing common tidying operations such as moving between wide and long format data with tidyr

tidying

Identifying and resolving common data issues such as incorrect categories and missing data issues with stringr, tidyr, and readr

handling-data-issues

Working with multiple data sets to combine and create a relevant single data structure

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