Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
First steps in data analysis with R
Rating: 4.4 out of 5(14 ratings)
71 students

First steps in data analysis with R

Data analysis from zero to hero
Created byMarco Plebani, PhD
Last updated 5/2022
English

What you'll learn

  • Develop a reliable and versatile data analysis framework
  • Visualise your data with publication-ready figures
  • Master general linear models: regression, ANOVA, etc.
  • Refresh your statistical knowledge in a visual, intuitive way
  • Learn and apply the principles of hypothesis testing and model selection
  • Introduction to generalised linear models and to non-linear modelling

Course content

16 sections • 30 lectures • 2h 58m total length

  • Introduction1:19

    In attachment: a few examples of the figures that you will learn to produce as part of the data visualisation sections of this course.

  • Installing R5:08

    Download all the scripts and datasets used throughout the course from here, or download them one by one from each lecture.

  • Writing code and creating object in the R console0:58

    Capture the result of a function like log(3) by assigning it to a named object such as log_three, then retrieve it to perform further operations.

  • Your first R script5:21

    Create and save your first R script, add comments with hashtags to document functions, then build a numeric vector with c(), and apply log to compute logarithms for multiple values.

  • Your first graph1:49

    Learn to create your first scatter plot in R with input as X and output as Y using plot, and rerun the code after restarting to fix input not found.

  • Your second graph6:11
  • Saving figures on a Windows machine1:59

    Learn how to save figures on a Windows machine by running a script, adjusting the interface for readability, and saving the figure as a JPEG on the desktop.

Requirements

  • No programming experience needed. You'll learn how to use R from absolute 0.
  • You should be familiar with statistical concepts covered in any introductory statistics course, such as: Normal distribution, model parameters, variance, standard deviation, standard error, F-test, p-value.

Description

This course is aimed at those that already have a theoretical understanding of statistical concepts and want to learn the practical side of data analysis.

Learning how to analyse data can be a daunting test. Applying the statistical knowledge learned from books to real-world scenarios can be challenging, and it's often made harder by seemingly complicated data analysis softwares.

This course will help you to develop a reliable data analysis pipeline, creating a solid basis that will make it easy for you to further your data analysis skills throughout your career.

We will use R, a free, state-of-the-art software environment for modelling, data handling, data analysis, and data visualisation.

We will start from installing R and taking baby steps to become familiar with the R programming language. We will then learn how to load data in R, how to visualise them with publication-level quality graphs, and how to analyse them.

I will provide you with the scripts that I use throughout the course, so that you can easily use them and adapt them to your own research objectives.

We will learn R one small step at a time, starting from absolute zero:

· how to enter data in R

· how to visualise data using function plot() and package ggplot2

· how to fit, interpret, and evaluate general linear models for a variety of study designs, including t test, ANOVA, regression, ANCOVA, and multiple regression scenarios

· how to fit polynomial regression

· an introduction to user-defined non-linear models

· an introduction to generalised linear models for non-normally distributed data (case study: count data)

· optimal data organisation and "data wrangling" - merging, subsetting, and summarising data

Who this course is for:

  • Junior researchers moving their first steps into practical research in natural sciences, biology, medicine, etc.

Report abuse