Udemy
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
  •  
Development
Web Development Data Science Mobile Development Programming Languages Game Development Database Design & Development Software Testing Software Engineering Software Development Tools No-Code Development
Business
Entrepreneurship Communication Management Sales Business Strategy Operations Project Management Business Law Business Analytics & Intelligence Human Resources Industry E-Commerce Media Real Estate Other Business
Finance & Accounting
Accounting & Bookkeeping Compliance Cryptocurrency & Blockchain Economics Finance Finance Cert & Exam Prep Financial Modeling & Analysis Investing & Trading Money Management Tools Taxes Other Finance & Accounting
IT & Software
IT Certifications Network & Security Hardware Operating Systems & Servers Other IT & Software
Office Productivity
Microsoft Apple Google SAP Oracle Other Office Productivity
Personal Development
Personal Transformation Personal Productivity Leadership Career Development Parenting & Relationships Happiness Esoteric Practices Religion & Spirituality Personal Brand Building Creativity Influence Self Esteem & Confidence Stress Management Memory & Study Skills Motivation Other Personal Development
Design
Web Design Graphic Design & Illustration Design Tools User Experience Design Game Design 3D & Animation Fashion Design Architectural Design Interior Design Other Design
Marketing
Digital Marketing Search Engine Optimization Social Media Marketing Branding Marketing Fundamentals Marketing Analytics & Automation Public Relations Paid Advertising Video & Mobile Marketing Content Marketing Growth Hacking Affiliate Marketing Product Marketing Other Marketing
Lifestyle
Arts & Crafts Beauty & Makeup Esoteric Practices Food & Beverage Gaming Home Improvement & Gardening Pet Care & Training Travel Other Lifestyle
Photography & Video
Digital Photography Photography Portrait Photography Photography Tools Commercial Photography Video Design Other Photography & Video
Health & Fitness
Fitness General Health Sports Nutrition & Diet Yoga Mental Health Martial Arts & Self Defense Safety & First Aid Dance Meditation Other Health & Fitness
Music
Instruments Music Production Music Fundamentals Vocal Music Techniques Music Software Other Music
Teaching & Academics
Engineering Humanities Math Science Online Education Social Science Language Learning Teacher Training Test Prep Other Teaching & Academics
Web Development JavaScript React Angular CSS Node.Js PHP HTML5 Vue JS
AWS Certification Microsoft Certification AWS Certified Solutions Architect - Associate AWS Certified Cloud Practitioner CompTIA A+ Amazon AWS Cisco CCNA CompTIA Security+ Microsoft AZ-900
Microsoft Power BI SQL Tableau Data Modeling Business Analysis Business Intelligence MySQL Qlik Sense Data Analysis
Unity Unreal Engine Game Development Fundamentals C# 3D Game Development C++ Unreal Engine Blueprints 2D Game Development Mobile Game Development
Google Flutter iOS Development Android Development Swift React Native Dart (programming language) Kotlin Mobile App Development SwiftUI
Graphic Design Photoshop Adobe Illustrator Drawing Digital Painting Canva InDesign Character Design Procreate Digital Illustration App
Life Coach Training Personal Development Neuro-Linguistic Programming Personal Transformation Life Purpose Mindfulness Sound Therapy Coaching CBT Cognitive Behavioral Therapy
Business Fundamentals Entrepreneurship Fundamentals Freelancing Business Strategy Startup Business Plan Online Business Blogging Leadership
Digital Marketing Social Media Marketing Marketing Strategy Internet Marketing Google Analytics Copywriting Email Marketing Startup YouTube Marketing

DevelopmentData ScienceR (programming language)

R Data Pre-Processing & Data Management - Shape your Data!

Learn how to prepare your data for great analytics in R.
Rating: 4.6 out of 54.6 (617 ratings)
4,538 students
Created by R-Tutorials Training
Last updated 11/2018
English
English [Auto]

What you'll learn

  • import data into R in several ways while also beeing able to identify a suitable import tool
  • select and implement a proper object class (data.frame, data.table, data_frame)
  • convert your data into (and understand) a tidy data format
  • filter and query your data based on a wide range of parameters
  • join 2 data tables together with dplyr 2 table verb syntax
  • use SQL code within R
  • translate basic R into SQL
  • work with dates and time
  • work with strings using regular expressions
  • detecting outliers in datasets

Requirements

  • Computer with R and RStudio ready to use
  • You should have basic R / RStudio knowledge
  • Required add on packages will be listed in the course orientation video

Description

Let’s get your data in shape!

Data Pre-Processing is the very first step in data analytics. You cannot escape it, it is too important. Unfortunately this topic is widely overlooked and information is hard to find.

With this course I will change this!

Data Pre-Processing as taught in this course has the following steps:

1.       Data Import: this might sound trivial but if you consider all the different data formats out there you can imagine that this can be confusing. In the course we will take a look at a standard way of importing csv files, we will learn about the very fast fread method and I will show you what you can do if you have more exotic file formats to handle.

2.       Selecting the object class: a standard data.frame might be fine for easy standard tasks, but there are more advanced classes out there like the data.table. Especially with those huge datasets nowadays, a data.frame might not do it anymore. Alternatives will be demonstrated in this course.

3.       Getting your data in a tidy form: a tidy dataset has 1 row for each observation and 1 column for each variable. This might sound trivial, but in your daily work you will find instances where this simple rule is not followed. Often times you will not even notice that the dataset is not tidy in its layout. We will learn how tidyr can help you in getting your data into a clean and tidy format.

4.       Querying and filtering: when you have a huge dataset you need to filter for the desired parameters. We will learn about the combination of parameters and implementation of advanced filtering methods. Especially data.table has proven effective for that sort of querying on huge datasets, therefore we will focus on this package in the querying section.

5.       Data joins: when your data is spread over 2 different tables but you want to join them together based on given criteria, you will need joins for that. There are several methods of data joins in R, but here we will take a look at dplyr and the 2 table verbs which are such a great tool to work with 2 tables at the same time.

6.       Integrating and interacting with SQL: R is great at interacting with SQL. And SQL is of course the leading database language, which you will have to learn sooner or later as a data scientist. I will show you how to use SQL code within R and there is even a R to SQL translator for standard R code. And we will set up a SQLite database from within R. 

7.  Outlier detection: Datasets often contain values outside a plausible range. Faulty data generation or entry happens regularly. Statistical methods of outlier detection help to identify these values. We will take a look at the implemention of these.

8. Character strings as well as dates and time have their own rules when it comes to pre-processing. In this course we will also take a look at these types of data and how to effectively handle it in R.

How do you best prepare yourself for this course?

You only need a basic knowledge of R to fully benefit from this course. Once you know the basics of RStudio and R you are ready to follow along with the course material. Of course you will also get the R scripts which makes it even easier.

The screencasts are made in RStudio so you should get this program on top of R. Add on packages required are listed in the course.

Again, if you want to make sure that you have proper data with a tidy format, take a look at this course. It will make your analytics with R much easier!

Who this course is for:

  • Data pre-processing is a crucial step of data related work - therefore this course is intended for all R users

Featured review

Maria Caro
Maria C.
35 courses
10 reviews
Rating: 4.5 out of 52 years ago
Well structured course with clear explanations and examples. In my opinion this is how an online course should be taught. However, you need to have some knowledge of R to better follow up the instructor.

Instructor

R-Tutorials Training
Data Science Education
R-Tutorials Training
  • 4.5 Instructor Rating
  • 31,280 Reviews
  • 254,911 Students
  • 24 Courses

  R-Tutorials is your provider of choice when it comes to analytics training courses! Try it out – our 100,000+ students love it. 

        We focus on Data Science tutorials. Offering several R courses for every skill level, we are among Udemy's top R training provider. On top of that courses on Tableau, Excel and a Data Science career guide are available.

        All of our courses contain exercises to give you the opportunity to try out the material on your own. You will also get downloadable script pdfs to recap the lessons. 

        The courses are taught by our main instructor Martin – trained biostatistician and enthusiastic data scientist / R user. 

        Should you have any questions, you are invited to check out our website, you can open a discussion in the course or you can simply drop us a pm. 

        We are here to help you boost your career with analytics training – Just learn and enjoy. 

Top companies choose Udemy Business to build in-demand career skills.
NasdaqVolkswagenBoxNetAppEventbrite
  • Udemy Business
  • Teach on Udemy
  • Get the app
  • About us
  • Contact us
  • Careers
  • Blog
  • Help and Support
  • Affiliate
  • Investors
  • Impressum Kontakt
  • Terms
  • Privacy policy
  • Cookie settings
  • Sitemap
  • Accessibility statement
Udemy
© 2022 Udemy, Inc.