Learning R

Learning R
Learning R
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 51m | 485 MB

If you want to participate in the data revolution, you need the right tools and skills. R is a free, open-source language for data science that is among the most popular platforms for professional analysts. Learn the basics of R and get started finding insights from your own data, in this course with professor and data scientist Barton Poulson. The lessons explain how to get started with R, including installing R, RStudio, and code packages that extend R’s power. You also see first-hand how to use R and RStudio for beginner-level data modeling, visualization, and statistical analysis. By the end of the course, you’ll have a thorough introduction to the power and flexibility of R, and understand how to leverage this tool to explore and analyze a wide variety of data.

Topics include:

  • Installing R and RStudio
  • Navigating the RStudio environment
  • Importing data from a spreadsheet
  • Working with the tidyverse
  • Piping commands with %>%
  • Visualizing data with R base graphics and ggplot2
  • Visualizing hierarchical clusters
  • Selecting cases and subgroups
  • Recoding variables
  • Calculating frequencies
  • Calculating descriptives
  • Calculating correlations
  • Computing a linear regression
Table of Contents

Introduction
1 R for data science
2 Using the exercise files

What Is R
3 R in context

Getting Started
4 Installing R
5 Environments for R
6 Installing RStudio
7 Navigating the RStudio environment
8 Entering data
9 Data types and structures
10 Comments and headers
11 Packages for R
12 The tidyverse
13 Piping commands with
14 Sample datasets
15 Importing data from a spreadsheet

Data Visualization
16 Using colors in R
17 Creating bar charts
18 Creating histograms
19 Creating box plots
20 Creating scatterplots
21 Creating line charts
22 Creating cluster charts

Data Wrangling
23 Selecting cases and subgroups
24 Recoding variables
25 Computing new variables

Data Analysis
26 Computing frequencies
27 Computing descriptives
28 Computing correlations
29 Computing a linear regression
30 Computing contingency tables

Conclusion
31 Next steps