Complete Guide to R: Wrangling, Visualizing, and Modeling Data

Course details

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 course with data analytics expert Barton Poulson 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. Barton 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.

Concepts

0. Introduction

01 - Make your data make sense
02 - Using the exercise files

1. What Is R

03 - R in context
04 - Data science with R - A case study

2. Getting Started

05 - Installing R
06 - Environments for R
07 - Installing RStudio
08 - Navigating the RStudio environment
09 - Entering data
10 - Data types and structures
11 - Comments and headers
12 - Packages for R
13 - The tidyverse
14 - Piping commands with

3. Importing Data

15 - R's 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

4. 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

5. 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

6. 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

7. An R for Data Science Case Study

45 - Data science with R - A case study

8. Exploring Data

46 - Computing frequencies
47 - Computing descriptive statistics
48 - Computing correlations
49 - Creating contingency tables
50 - Conducting a principal component analysis
51 - Conducting an item analysis
52 - Conducting a confirmatory factor analysis

9. Analyzing Data

53 - Comparing proportions
54 - Comparing one mean to a population - One-sample t-test
55 - Comparing paired means - Paired samples t-test
56 - Comparing two means - Independent samples t-test
57 - Comparing multiple means - One-factor analysis of variance
58 - Comparing means with multiple categorical predictors - Factorial analysis of variance

10. Predicting Outcomes

59 - Predicting outcomes with linear regression
60 - Predicting outcomes with lasso regression
61 - Predicting outcomes with quantile regression
62 - Predicting outcomes with logistic regression
63 - Predicting outcomes with Poisson or log-linear regression
64 - Assessing predictions with blocked-entry models

11. Clustering and Classifying Cases

65 - Grouping cases with hierarchical clustering
66 - Grouping cases with k-means clustering
67 - Classifying cases with k-nearest neighbors
68 - Classifying cases with decision tree analysis
69 - Creating ensemble models with random forest classification

Conclusion

70 - Next steps