on-demand course

Learning Path: R Programming for Data Analysts

with Jared P. Lander

May 2016

Beginner to advanced

15h 11m

English

Addison-Wesley Professional

Closed Captioning available in English, Japanese, Korean, Chinese (Simplified), Chinese (Traditional)

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Course outline

Part 1: R as a Tool—Introduction
3m 24s
Learning objectives
28s
1.1 Download and Install R
6m 23s
1.2 Work in the R Environment
18m 50s
1.3 Install and load packages
4m 49s
Learning objectives
26s
2.1 Use R as a calculator
3m 43s
2.2 Work with variables
4m 11s
2.3 Understand the different data types
11m 33s
2.4 Store data in vectors
16m 36s
2.5 Call functions
4m 3s
Learning objectives
25s
3.1 Create and access information in data.frames
17m 20s
3.2 Create and access information in lists
10m 57s
3.3 Create and access information in matrices
8m 2s
Learning objectives
26s
4.1 Read a CSV into R
5m 58s
4.2 Read an Excel Spreadsheet into R
4m 39s
4.3 Read from databases
5m 59s
4.4 Read data files from other statistical tools
1m 17s
4.5 Load binary R files
4m 40s
4.6 Load data included with R
1m 49s
4.7 Scrape data from the web
2m 28s
4.8 Read XML data
27m 23s
Learning objectives
34s
5.1 Find the diamonds in the data
1m 13s
5.2 Make histograms with base graphics
1m 30s
5.3 Make scatterplots with base graphics
2m 1s
5.4 Make boxplots with base graphics
1m 39s
5.5 Get familiar with ggplot2
2m 30s
5.6 Plot histograms and densities with ggplot2
3m 52s
5.7 Make scatterplots with ggplot2
5m 12s
5.8 Make boxplots and violin plots with ggplot2
4m 24s
5.9 Make line plots
8m 21s
5.10 Create small multiples
4m 1s
5.11 Control colors and shapes
1m 19s
5.12 Add themes to graphs
2m 18s
5.13 Use Web graphics
29m 48s
Learning objectives
26s
6.1 Write the classic "Hello, World!" example
2m 5s
6.2 Understand the basics of function arguments
10m 32s
6.3 Return a value from a function
2m 47s
6.4 Gain flexibility with do.call
3m 46s
6.5 Use "if" statements to control program flow
2m 8s
6.6 Stagger "if" statements with "else"
5m 33s
6.7 Check multiple statements with switch
3m 52s
6.8 Run checks on entire vectors
5m 17s
6.9 Check compound statements
5m 41s
6.10 Iterate with a for loop
6m 7s
6.11 Iterate with a while loop
1m 31s
6.12 Control loops with break and next
2m 5s
Learning objectives
34s
7.1 Repeat an operation on a matrix using apply
4m 46s
7.2 Repeat an operation on a list
3m 5s
7.3 Apply a function over multiple lists with mapply
4m 34s
7.4 Perform group summaries with the aggregate function
5m 27s
7.5 Do group operations with the plyr Package
17m 18s
7.6 Combine datasets
3m 51s
7.7 Join datasets
5m 56s
7.8 Switch storage paradigms
5m 11s
7.9 Use tidyr
2m 50s
7.10 Get faster group operations
22m 3s
Learning objectives
22s
8.1 Use tbl
1m 49s
8.2 Use select to choose columns
3m 8s
8.3 Use filter to choose rows
3m 39s
8.4 Use slice to choose rows
1m 9s
8.5 Use mutate to change or create columns
2m 40s
8.6 Use summarize for quick computation on tbl
1m 35s
8.7 Use group_by to split the data
2m 35s
8.8 Apply arbitrary functions with do
6m 50s
Learning objectives
20s
9.1 Combine strings together
7m 28s
9.2 Extract text
32m 1s

Content preview from Learning Path: R Programming for Data Analysts

19.2 Robustly cluster, even with categorical data, with PAM

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Publisher Resources

ISBN: 9780134661445

Learning Path: R Programming for Data Analysts

with Jared P. Lander

Introduction

Lesson 1: Getting Started with R

Lesson 2: The Basic Building Blocks in R

Lesson 3: Advanced Data Structures in R

Lesson 4: Reading Data into R

Lesson 5: Making Statistical Graphs

Lesson 6: Basics of Programming

Lesson 7: Data Munging

Lesson 8: In-Depth with dplyr

Lesson 9: Manipulating Strings

Lesson 10: Reports and Slideshows with knitr

Lesson 11: Include HTML Widgets in HTML Documents

Lesson 12: Shiny

Lesson 13: Package Building

Lesson 14: Rcpp for Faster Code

Summary

Introduction

Lesson 15: Basic Statistics

Lesson 16: Linear Models

Lesson 17: Other Models

Lesson 18: Time Series

Lesson 19: Clustering

Lesson 20: More Machine Learning

Lesson 21: Network Analysis

Lesson 22: Automatic Parameter Tuning with Caret

Lesson 23: Fit a Bayesian Model with RStan

Summary

Content preview from Learning Path: R Programming for Data Analysts

19.2 Robustly cluster, even with categorical data, with PAM

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.