English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 4h 18m | 718 MB
Trying to locate meaning and direction in big data is difficult. R can help you find your way. R is a statistical programming language to analyze and visualize the relationships between large amounts of data. This training series provides a thorough introduction to R, with detailed instruction for installing and navigating R and RStudio and hands-on examples, from exploratory graphics to neural networks. In part one, instructor Barton Poulson shows how to get R and popular R packages up and running and start importing, cleaning, and converting data for analysis. He also shows how to create visualizations such as bar charts, histograms, and scatterplots and transform categorical, qualitative, and outlier data to best meet your research questions and the requirements of your algorithms.
Topics include:
- Installing R
- Entering data
- Packages for R
- Importing XLS, XML, and JSON data
- Visualizing data with ggplot2
- Creating charts, histograms, scatterplots, and graphs
- Converting data
- Filtering cases and subgroups
- Recoding data
- Creating scale scores
Table of Contents
Introduction
1 Make your data make sense
2 Using the exercise files
What Is R
3 R in context
4 Data science with R A case study
Getting Started
5 Installing R
6 Environments for R
7 Installing RStudio
8 Navigating the RStudio environment
9 Entering data
10 Data types and structures
11 Comments and headers
12 Packages for R
13 The tidyverse
14 Piping commands with
Importing Data
15 Rs built-in datasets
16 Exploring sample datasets with pacman
17 Importing data from a spreadsheet
18 Importing XML data
19 Importing JSON data
20 Saving data in native R formats
Visualizing Data with ggplot2
21 Introduction to ggplot2
22 Using colors in R
23 Using color palettes
24 Creating bar charts
25 Creating histograms
26 Creating box plots
27 Creating scatterplots
28 Creating multiple graphs
29 Creating cluster charts
Wrangling Data
30 Creating tidy data
31 Using tibbles
32 Using data.table
33 Converting data from wide to tall and from tall to wide
34 Converting data from tables to rows
35 Working with dates and times
36 Working with list data
37 Working with XML data
38 Working with categorical variables
39 Filtering cases and subgroups
Recoding Data
40 Recoding categorical data
41 Recoding quantitative data
42 Transforming outliers
43 Creating scale scores by counting
44 Creating scale scores by averaging
Conclusion
45 Next steps
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