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Development Programming Languages R

Get started with R -Introduction to R-programming #Beginners

Learn and harness the power of R and RStudio, R-programming for FREE . This course is ideal 4 Beginners in data science
Rating: 4.1 out of 54.1 (74 ratings)
3,438 students
Created by Kate Alison
Published 11/2020
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • This is a R crash course for anyone who previously had no or very little contact with script-based programming in R
  • Learn how to install R and RStudio on your computer
  • You will learn Package Management in R & RStudio
  • You will be able to learn all the fundamentals of R-programming in 2 hours!
  • All data types in R-language
  • Learn about functions, control structures and for loops in R & RStudio

Course content

5 sections • 18 lectures • 1h 26m total length

  • Preview03:00
  • What is R and RStudio?
    02:43
  • How to install R and RStudio in 2020
    05:33
  • Get started with R in RStudio
    09:37

  • Lab: Installing Packages and Package Management in R
    04:19
  • Calculations in R
    01:46
  • Variables in R and assigning Variables in R
    01:45
  • Data types and data structures in R
    09:57

  • Overview of Data types and Structures in R
    08:19
  • Vectors in R
    07:37
  • Factors
    02:32
  • Lists
    07:00
  • Matrices
    04:50
  • Dataframes
    03:21

  • Lab: Functions in R
    04:11
  • Control Functions in R
    04:50
  • For Loops in R
    03:56

  • Bonus Lecture
    01:04

Requirements

  • Computer & interenet connection

Description

This is a R crash course for anyone who previously had no or very little contact with script-based programming in R.

The main goal is to establish the basic understanding needed for more advanced courses that uses R language, RStudio and R-programming for example, for data science, machine learning or statistical analysis in R.


The best about this course that is a very concise manner (less than2 hours!) you will be able to learn all the fundamentals of R-programming that will allow you to get started with R!


The course is ideal for professionals such as data scientists, statisticians, geographers, programmers, social scientists, geologists, and all other experts who need to use statistics & data science in their field.

This course is NOT for you if you an intermediate or advanced user of R and don't need an introduction to R programming!


Who this course is for:

  • The course is ideal for professionals such as data scientists, statisticians, geographers, programmers, social scientists, geologists, and all other experts who need to use statistics & data science in their field.
  • This course is NOT for you, if you an intermediate or advanced user of R and don't need an introduction to R programming!

Instructor

Kate Alison
Remote Sensing/GIS Expert & Data Scientist
Kate Alison
  • 4.3 Instructor Rating
  • 1,172 Reviews
  • 39,112 Students
  • 26 Courses

I am a passionate data scientist, Earth Observation (EO), and GIS expert and educator. I received my M.Sc. in Earth Observation and applied data science from the University of Southampton (United Kingdom) and I also hold a Ph.D. Degree in EO from Germany. I do regular teaching and training all over the world as well as do regularly consultancies on the mentioned topic. I have thousands of satisfied clients all over the world! And now I will be glad if I can teach also you these interesting, highly applied, and exciting topics!


For GIS & Remote Sensing students:

If you would like to learn comprehensively geospatial data analysis, here is a preferred order for how to take my courses:

Option 1: Take all individual courses that have more details on specific subjects, more lectures, and more labs in the following order:

1. Get started with GIS & Remote Sensing in QGIS #Beginners

2. Remote Sensing in QGIS: Fundamentals of Image Analysis 2020

3. Core GIS: Land Use and Land Cover & Change Detection in QGIS

4. Machine Learning in GIS: Understand the Theory and Practice

5. Machine Learning in GIS: Land Use/Land Cover Image Analysis

6. Machine Learning in ArcGIS: Map Land Use/ Land Cover in GIS

7. Object-based image analysis & classification in QGIS/ArcGIS

8. ArcGIS: Learn Deep Learning in ArcGIS to advance GIS skills

8. Google Earth Engine for Big GeoData Analysis: 3 Courses in 1

10. Google Earth Engine for Machine Learning & Change Detection

11. QGIS & Google Earth Engine for Environmental Applications

Option 2: Take my ‘joint’ courses that contain summarized information from the above courses, though in fewer details (labs, videos):

1. Geospatial Data Analyses & Remote Sensing: 4 Classes in 1

2. Machine Learning in GIS and Remote Sensing: 5 Courses in 1

3. Google Earth Engine for Big GeoData Analysis: 3 Courses in 1

4. Google Earth Engine for Machine Learning & Change Detection

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