**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

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