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Table of contents
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Part 1: R as a Tool — Introduction
3m
Worksheet
Introduction
3m
1: Getting Started with R
30m
Worksheet
Learning objectives
0m
1.1 Download and Install R
6m
1.2 Work in the R Environment
18m
1.3 Install and load packages
4m
2: The Basic Building Blocks in R
40m
Worksheet
Learning objectives
0m
2.1 Use R as a calculator
3m
2.2 Work with variables
4m
2.3 Understand the different data types
11m
2.4 Store data in vectors
16m
2.5 Call functions
4m
3: Advanced Data Structures in R
36m
Worksheet
Learning objectives
0m
3.1 Create and access information in data.frames
17m
3.2 Create and access information in lists
10m
3.3 Create and access information in matrices
8m
4: Reading Data into R
54m
Worksheet
Learning objectives
0m
4.1 Read a CSV into R
5m
4.2 Read an Excel Spreadsheet into R
4m
4.3 Read from databases
5m
4.4 Read data files from other statistical tools
1m
4.5 Load binary R files
4m
4.6 Load data included with R
1m
4.7 Scrape data from the web
2m
4.8 Read XML data
27m
5: Making Statistical Graphs
1h 8m
Worksheet
Learning objectives
0m
5.1 Find the diamonds in the data
1m
5.2 Make histograms with base graphics
1m
5.3 Make scatterplots with base graphics
1m
5.4 Make boxplots with base graphics
1m
5.5 Get familiar with ggplot2
2m
5.6 Plot histograms and densities with ggplot2
3m
5.7 Make scatterplots with ggplot2
5m
5.8 Make boxplots and violin plots with ggplot2
4m
5.9 Make line plots
8m
5.10 Create small multiples
4m
5.11 Control colors and shapes
1m
5.12 Add themes to graphs
2m
5.13 Use Web graphics
29m
6: Basics of Programming
51m
Worksheet
Learning objectives
0m
6.1 Write the classic "Hello, World!" example
2m
6.2 Understand the basics of function arguments
10m
6.3 Return a value from a function
2m
6.4 Gain flexibility with do.call
3m
6.5 Use "if" statements to control program flow
2m
6.6 Stagger "if" statements with "else"
5m
6.7 Check multiple statements with switch
3m
6.8 Run checks on entire vectors
5m
6.9 Check compound statements
5m
6.10 Iterate with a for loop
6m
6.11 Iterate with a while loop
1m
6.12 Control loops with break and next
2m
7: Data Munging
1h 15m
Worksheet
Learning objectives
0m
7.1 Repeat an operation on a matrix using apply
4m
7.2 Repeat an operation on a list
3m
7.3 Apply a function over multiple lists with mapply
4m
7.4 Perform group summaries with the aggregate function
5m
7.5 Do group operations with the plyr Package
17m
7.6 Combine datasets
3m
7.7 Join datasets
5m
7.8 Switch storage paradigms
5m
7.9 Use tidyr
2m
7.10 Get faster group operations
21m
8: In-Depth with dplyr
23m
Worksheet
Learning objectives
0m
8.1 Use tbl
1m
8.2 Use select to choose columns
3m
8.3 Use filter to choose rows
3m
8.4 Use slice to choose rows
1m
8.5 Use mutate to change or create columns
2m
8.6 Use summarize for quick computation on tbl
1m
8.7 Use group_by to split the data
2m
8.8 Apply arbitrary functions with do
6m
9: Manipulating Strings
39m
Worksheet
Learning objectives
0m
9.1 Combine strings together
7m
9.2 Extract text
31m
10: Reports and Slideshows with knitr
36m
Worksheet
Learning objectives
0m
10.1 Understand the basics of LaTeX
7m
10.2 Weave R code into LaTeX using knitr
5m
10.3 Understand the basics of Markdown
2m
10.4 Understand the basics of RMarkdown
4m
10.5 Weave R code into Markdown using knitr
2m
10.6 Convert Markdown files to Word
1m
10.7 Convert Markdown to PDF
1m
10.8 Create slideshows with RMarkdown
3m
10.9 Write equations with RMarkdown
7m
11: Include HTML Widgets in HTML Documents
22m
Worksheet
Learning objectives
0m
11.1 Work with datatables of tabular data
6m
11.2 Use rbokeh
8m
11.3 Use Leaflet for mapping
7m
12: Shiny
22m
Worksheet
Learning objectives
0m
12.1 Use shiny objects in a markdown document
13m
12.2 Work with ui.r and server.r files
8m
13: Package Building
23m
Worksheet
Learning objectives
0m
13.1 Understand the folder structure and files in a package
5m
13.2 Write and document functions
7m
13.3 Check and build a package
2m
13.4 Test R code
6m
13.5 Submit a package to CRAN
0m
14: Rcpp for Faster Code
33m
Worksheet
Learning objectives
0m
14.1 Understand the basics of C++ with R
1m
14.2 Write a C++ function for R
4m
14.3 Use Rcpp syntactic sugar
5m
14.4 Sum in C++
5m
14.5 Write a package in R
9m
14.6 Write a package with C++ code
6m
Part 1 - Summary
1m
Worksheet
Part 1: R as a Tool--Summary
1m
Part 2: R for Statistics, Modeling and Machine Learning — Introduction
2m
Worksheet
Introdution
2m
15: Basic Statistics
56m
Worksheet
Learning objectives
0m
15.1 Draw numbers from probability distributions
21m
15.2 Calculate averages, standard deviations and correlations
16m
15.3 Compare samples with t-tests and analysis of variance
18m
16: Linear Models
1h 38m
Worksheet
Learning objectives
0m
16.1 Fit simple linear models
10m
16.2 Explore the data
8m
16.3 Fit multiple regression models
19m
16.4 Fit logistic regression
10m
16.5 Fit Poisson regression
7m
16.6 Analyze survival data
12m
16.7 Assess model quality with residuals
5m
16.8 Compare models
7m
16.9 Judge accuracy using cross-validation
9m
16.10 Estimate uncertainty with the bootstrap
6m
16.11 Choose variables using stepwise selection
2m
17: Other Models
47m
Worksheet
Learning objectives
0m
17.1 Select variables and improve predictions with the elastic net
14m
17.2 Decrease uncertainty with weakly informative priors
8m
17.3 Fit nonlinear least squares
5m
17.4 Use Splines
6m
17.5 Use GAMs
5m
17.6 Fit decision trees to make a random forest
6m
18: Time Series
30m
Worksheet
Learning objectives
0m
18.1 Understand ACF and PACF
7m
18.2 Fit and assess ARIMA models
5m
18.3 Use VAR for multivariate time series
8m
18.4 Use GARCH for better volatility modeling
9m
19: Clustering
20m
Worksheet
Learning objectives
0m
19.1 Partition data with k-means
12m
19.2 Robustly cluster, even with categorical data, with PAM
2m
19.3 Perform hierarchical clustering
5m
20: More Machine Learning
26m
Worksheet
Learning objectives
0m
20.1 Build a recommendation engine with RecommenderLab
13m
20.2 Mine text with RTextTools
9m
20.3 Perform matrix factorization using irlba
4m
21: Network Analysis
36m
Worksheet
Learning objectives
0m
21.1 Get started with igraph
8m
21.2 Read edgelists
7m
21.3 Understand common graph metrics
10m
21.4 Use centrality measures
5m
21.5 Utilize more graph operations
4m
22: Automatic Parameter Tuning with Caret
10m
Worksheet
Learning objectives
0m
22.1 Establish optimal tree depth for rpart
6m
22.2 Choose the best number of trees for a random forest
3m
23: Fit a Bayesian Model with RStan
15m
Worksheet
Learning objectives
0m
23.1 Understand the Stan computing paradigm
1m
23.2 Fit a simple regression model
6m
23.3 Fit a multilevel model with Stan
6m
Part 2 - Summary
Coming soon
Worksheet
Part 2: R for Statistics, Modeling and Machine Learning--Summary
0m
7: Data Munging
7.8 Switch storage paradigms
7: Data Munging
7.8 Switch storage paradigms - Video Tutorials & Practice Problems
Video duration:
5m
Play a video:
Previous Topic: 7.7 Join datasets
Next Topic: 7.9 Use tidyr
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