Section 1 : Introduction

 		Lecture 1 Make your data make sense 0:32
Lecture 2 Using the exercise files 0:48

Section 2 : What Is R

 		Lecture 3 R in context 6:46
Lecture 4 Data science with R A case study 11:46

Section 3 : Getting Started

 		Lecture 5 Installing R 1:25
Lecture 6 Environments for R 3:31
Lecture 7 Installing RStudio 1:17
Lecture 8 Navigating the RStudio environment 6:4
Lecture 9 Entering data 7:5
Lecture 10 Data types and structures 12:24
Lecture 11 Comments and headers
Lecture 12 Packages for R 4:46
Lecture 13 The tidyverse 3:4
Lecture 14 Piping commands with %% 4:33

Section 4 : Importing Data

 		Lecture 15 R's built-in datasets 4:58
Lecture 16 Exploring sample datasets with pacman
Lecture 17 Importing data from a spreadsheet 5:39
Lecture 18 Importing XML data 5:32
Lecture 19 Importing JSON data 5:40
Lecture 20 Saving data in native R formats 6:50

Section 5 : Visualizing Data with ggplot2

 		Lecture 21 Introduction to ggplot2 4:39
Lecture 22 Using colors in R 5:3
Lecture 23 Using color palettes 8:5
Lecture 24 Creating bar charts 9:22
Lecture 25 Creating histograms
Lecture 26 Creating box plots 5:24
Lecture 27 Creating scatterplots 5:58
Lecture 28 Creating multiple graphs 4:6
Lecture 29 Creating cluster charts 8:34

Section 6 : Wrangling Data

 		Lecture 30 Creating tidy data 9:46
Lecture 31 Using tibbles 4:51
Lecture 32 Using data 4:57
Lecture 33 Converting data from wide to tall and from tall to wide 4:13
Lecture 34 Converting data from tables to rows 5:2
Lecture 35 Working with dates and times 6:20
Lecture 36 Working with list data 5:13
Lecture 37 Working with XML data 5:22
Lecture 38 Working with categorical variables 6:29
Lecture 39 Filtering cases and subgroups

Section 7 : Recoding Data

 		Lecture 40 Recoding categorical data 9:46
Lecture 41 Recoding quantitative data 7:10
Lecture 42 Transforming outliers 8:49
Lecture 43 Creating scale scores by counting 5:35
Lecture 44 Creating scale scores by averaging