R Basics for Data Analysis (2024)

skill Path

Learn how to analyze data using R, a powerful programming language widely used for statistical computing and kick-start your data career. You’ll learn the fundamentals of R to prepare, explore and analyze data.

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45,042 learners enrolled in this path.

  • Beginner friendly
  • 1 month (5 hrs/week)
  • Self paced
  • 4 Courses
  • 4 projects

R Basics for Data Analysis (1)

“I wish I found Dataquest earlier. The guided projects gave me a challenge and the confidence to know I could do this on my own. The companies I interviewed with were very impressed by my portfolio.”

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Senior Data Scientist @Aera Technology

R Basics for Data Analysis

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Path overview

In this path, you’ll explore the basics of R and work through the entire data analysis workflow , learn how to use packages and why they are essential in any data analysis process, and how to repeat code efficiently with iterations.

Best of all, you’ll learn by doing — you’ll write code and get feedback directly in the browser. You’ll apply your skills to several guided projects involving realistic business scenarios to build your portfolio and prepare for your next interview.

Key skills

  • Programming with R
  • Performing data analysis
  • Using packages in the data analysis workflow
  • Exploring how to use and install RStudio to improve your data analysis workflow

Path outline

Introduction to Data Analysis in R 3h

Objectives

  • Define R programming syntax
  • Define variable use and naming rules
  • Perform calculations using arithmetic operators

Data Structures in R 6h

Objectives

  • Create a data structure
  • Index a data structure
  • Perform operations over a data structure

Control Flow, Iteration, and Functions in R 4h

Objectives

  • Employ control flow with if-else statements
  • Replicate your code using iteration
  • Write functions

Specialized Data Processing in R 4h

Objectives

  • Manipulate strings from the stringr package
  • Manipulate strings from the lubridate package
  • Employ the map function from the purrr package

The Dataquest guarantee

R Basics for Data Analysis (2)

Dataquest has helped thousands of people start new careers in data. If you put in the work and follow our path, you’ll master data skills and grow your career.

R Basics for Data Analysis (3)

We believe so strongly in our paths that we offer a full satisfaction guarantee. If you complete a career path on Dataquest and aren’t satisfied with your outcome, we’ll give you a refund.

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R Basics for Data Analysis (4)

Go from zero to job-ready

Learn exactly what you need to achieve your goal. Don’t waste time on unrelated lessons.

R Basics for Data Analysis (5)

Build your project portfolio

Build confidence with our in-depth projects, and show off your data skills.

R Basics for Data Analysis (6)

Challenge yourself with exercises

Work with real data from day one with interactive lessons and hands-on exercises.

R Basics for Data Analysis (7)

Showcase your path certification

Share the evidence of your hard work with your network and potential employers.

Projects in this path

Project: Install RStudio

For this project, we’ll step into the role of aspiring data scientists, setting up our programming environment by installing R and RStudio. We’ll explore RStudio’s key features for efficient R programming and data analysis.

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Guided Project: Investigating COVID-19 Virus Trends

For this project, you’ll be a data analyst investigating a real COVID-19 dataset. You’ll use R to manipulate and analyze the data, building in-demand skills as you find countries with the highest positive test rates.

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Guided Project: Creating An Efficient Data Analysis Workflow

For this project, you’ll act as a data analyst for a company selling programming books to analyze sales data and extract useful insights to determine the most profitable titles.

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Guided Project: Creating An Efficient Data Analysis Workflow, Part 2

For this project, you’ll be a data analyst at a book company using R to clean sales data and analyze if a new program successfully boosted purchases and review sentiment.

View Project

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R Basics for Data Analysis (8)

Aaron Melton

Business Analyst at Aditi Consulting

“Dataquest starts at the most basic level, so a beginner can understand the concepts. I tried learning to code before, using Codecademy and Coursera. I struggled because I had no background in coding, and I was spending a lot of time Googling. Dataquest helped me actually learn.”

R Basics for Data Analysis (9)

Jessica Ko

Machine Learning Engineer at Twitter

“I liked the interactive environment on Dataquest. The material was clear and well organized. I spent more time practicing then watching videos and it made me want to keep learning.”

R Basics for Data Analysis (10)

Victoria E. Guzik

Associate Data Scientist at Callisto Media

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R Basics for Data Analysis (2024)

FAQs

How do I start data analysis in R? ›

  1. Data Analysis with R.
  2. Getting Set Up. Accessing RStudio. Basic Scripting. ...
  3. 1 Data Visualization. 1.1 Scatter Plots. ...
  4. 2 Data Transformation. 2.1 Filtering a Data Set. ...
  5. 3 Importing and Cleaning Data. 3.1 Importing Tabular Data. ...
  6. 4 Linear Regression. 4.1 Simple Linear Regression. ...
  7. 5 Programming in R. 5.1 Print to Screen. ...
  8. Final Project.

What is the R method of data analysis? ›

R analytics is data analytics using R programming language, an open-source language used for statistical computing or graphics. This programming language is often used in statistical analysis and data mining. It can be used for analytics to identify patterns and build practical models.

Is R hard to learn? ›

Learning R can be tough, especially for beginners. Let's explore why many struggle and how to overcome these challenges. R's unique syntax and steep learning curve often surprise new learners. Its complex data structures and error messages can be overwhelming, particularly for those new to programming.

How long does it take to learn R for data analysis? ›

Brand new programmers may take six weeks to a few months to become comfortable with the R language. Three months is generally enough time for any new programmer to use the language and start applying it in their professional life. By setting a goal with Pluralsight's Skills app, you learn at your own pace.

Is Python or R better for data analysis? ›

If this is your first foray into computer programming, you may find Python code easier to learn and more broadly applicable. However, if you already have some understanding of programming languages or have specific career goals centered on data analysis, R language may be more tailored to your needs.

What is the R tool for data analysis? ›

R is a free, open source statistical programming language. It is useful for data cleaning, analysis, and visualization. It complements workflows that require the use of other software. You can read more about the language and find documentation on the R Project Website.

How can a beginner learn R? ›

No one starting point will serve all beginners, but here are 6 ways to begin learning R.
  1. Install , RStudio, and R packages like the tidyverse. ...
  2. Spend an hour with A Gentle Introduction to Tidy Statistics In R. ...
  3. Start coding using RStudio. ...
  4. Publish your work with R Markdown. ...
  5. Learn about some power tools for development.

What are the 4 parts of R? ›

RStudio is divided into 4 “Panes”:
  • the Source for your scripts and documents (top-left, in the default layout),
  • the R Console (bottom-left),
  • your Environment/History (top-right), and.
  • your Files/Plots/Packages/Help/Viewer (bottom-right).

Is R the easiest language to learn? ›

R is considered one of the more difficult programming languages to learn due to how different its syntax is from other languages like Python and its extensive set of commands. It takes most learners without prior coding experience roughly four to six weeks to learn R. Of course, this depends on several factors.

What are the 4 pillars of data analysis? ›

That's why it's important to understand the four levels of analytics: descriptive, diagnostic, predictive and prescriptive.

What are the 5 W's of data analysis? ›

The "Five Ws" is a simple, easy-to-remember device to help you evaluate research sources. For any source you choose--including websites, news publications, books, magazines, journals, or other types of content--you can ask yourself: Who, What, Where, When, and Why?

What are the 7 stages of data analysis? ›

Why Data Analytics?
  • Step 1: Understanding the business problem. ...
  • Step 2: Analyze data requirements. ...
  • Step 3: Data understanding and collection. ...
  • Step 4: Data Preparation. ...
  • Step 5: Data visualization. ...
  • Step 6: Data analysis. ...
  • Step 7: Deployment.

Where do I start with data analysis? ›

How do I become a data analyst? A step-by-step guide
  • Get a foundational education. ...
  • Build your technical skills. ...
  • Work on projects with real data. ...
  • Develop a portfolio of your work. ...
  • Practice presenting your findings. ...
  • Get an entry-level data analyst job. ...
  • Consider certification or an advanced degree.
Apr 19, 2024

How do you start a data analysis section? ›

1 Define the problem and the purpose of the analysis

This section should provide the background and context of the issue, the main question or objective of the analysis, and the scope and limitations of the data and methods. You should also explain why the analysis is relevant and valuable for the intended audience.

How do I get started with R statistics? ›

No one starting point will serve all beginners, but here are 6 ways to begin learning R.
  1. Install , RStudio, and R packages like the tidyverse. ...
  2. Spend an hour with A Gentle Introduction to Tidy Statistics In R. ...
  3. Start coding using RStudio. ...
  4. Publish your work with R Markdown. ...
  5. Learn about some power tools for development.

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