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R Programming

By Jared P. Lander
    1. Overview
    2. Part 1: R as a Tool—Introduction
      3m 24s
    1. Overview
    2. Learning objectives
      28s
    3. 1.1 Download and Install R
      6m 23s
    4. 1.2 Work in the R Environment
      18m 50s
    5. 1.3 Install and load packages
      4m 49s
    6. Getting R
      19 pages
    7. R Packages
      7 pages
    1. Overview
    2. Learning objectives
      26s
    3. 2.1 Use R as a calculator
      3m 43s
    4. 2.2 Work with variables
      4m 11s
    5. 2.3 Understand the different data types
      11m 33s
    6. 2.4 Store data in vectors
      16m 36s
    7. 2.5 Call functions
      4m 3s
    8. Basics of R
      16 pages
    1. Overview
    2. Learning objectives
      25s
    3. 3.1 Create and access information in data.frames
      17m 20s
    4. 3.2 Create and access information in lists
      10m 57s
    5. 3.3 Create and access information in matrices
      8m 2s
    6. Advanced Data Structures
      15 pages
    1. Overview
    2. Learning objectives
      26s
    3. 4.1 Read a CSV into R
      5m 58s
    4. 4.2 Read an Excel Spreadsheet into R
      4m 39s
    5. 4.3 Read from databases
      5m 59s
    6. 4.4 Read data files from other statistical tools
      1m 17s
    7. 4.5 Load binary R files
      4m 40s
    8. 4.6 Load data included with R
      1m 49s
    9. 4.7 Scrape data from the web
      2m 28s
    10. 4.8 Read XML data
      27m 23s
    11. Reading Data into R
      25 pages
    1. Overview
    2. Learning objectives
      34s
    3. 5.1 Find the diamonds in the data
      1m 13s
    4. 5.2 Make histograms with base graphics
      1m 30s
    5. 5.3 Make scatterplots with base graphics
      2m 1s
    6. 5.4 Make boxplots with base graphics
      1m 39s
    7. 5.5 Get familiar with ggplot2
      2m 30s
    8. 5.6 Plot histograms and densities with ggplot2
      3m 52s
    9. 5.7 Make scatterplots with ggplot2
      5m 12s
    10. 5.8 Make boxplots and violin plots with ggplot2
      4m 24s
    11. 5.9 Make line plots
      8m 21s
    12. 5.10 Create small multiples
      4m 1s
    13. 5.11 Control colors and shapes
      1m 19s
    14. 5.12 Add themes to graphs
      2m 18s
    15. 5.13 Use Web graphics
      29m 48s
    16. Statistical Graphics
      24 pages
    1. Overview
    2. Learning objectives
      26s
    3. 6.1 Write the classic "Hello, World!" example
      2m 5s
    4. 6.2 Understand the basics of function arguments
      10m 32s
    5. 6.3 Return a value from a function
      2m 47s
    6. 6.4 Gain flexibility with do.call
      3m 46s
    7. 6.5 Use "if" statements to control program flow
      2m 8s
    8. 6.6 Stagger "if" statements with "else"
      5m 33s
    9. 6.7 Check multiple statements with switch
      3m 52s
    10. 6.8 Run checks on entire vectors
      5m 17s
    11. 6.9 Check compound statements
      5m 41s
    12. 6.10 Iterate with a for loop
      6m 7s
    13. 6.11 Iterate with a while loop
      1m 31s
    14. 6.12 Control loops with break and next
      2m 5s
    15. Writing R functions
      6 pages
    16. Control Statements
      7 pages
    17. Loops, the Un-R Way to Iterate
      3 pages
    1. Overview
    2. Learning objectives
      34s
    3. 7.1 Repeat an operation on a matrix using apply
      4m 46s
    4. 7.2 Repeat an operation on a list
      3m 5s
    5. 7.3 Apply a function over multiple lists with mapply
      4m 34s
    6. 7.4 Perform group summaries with the aggregate function
      5m 27s
    7. 7.5 Do group operations with the plyr Package
      17m 18s
    8. 7.6 Combine datasets
      3m 51s
    9. 7.7 Join datasets
      5m 56s
    10. 7.8 Switch storage paradigms
      5m 11s
    11. 7.9 Use tidyr
      2m 50s
    12. 7.10 Get faster group operations
      22m 3s
    13. Group Manipulation
      24 pages
    1. Overview
    2. Learning objectives
      22s
    3. 8.1 Use tbl
      1m 49s
    4. 8.2 Use select to choose columns
      3m 8s
    5. 8.3 Use filter to choose rows
      3m 39s
    6. 8.4 Use slice to choose rows
      1m 9s
    7. 8.5 Use mutate to change or create columns
      2m 40s
    8. 8.6 Use summarize for quick computation on tbl
      1m 35s
    9. 8.7 Use group_by to split the data
      2m 35s
    10. 8.8 Apply arbitrary functions with do
      6m 50s
    11. Faster Group Manipulation with dplyr
      36 pages
    1. Overview
    2. Learning objectives
      20s
    3. 9.1 Combine strings together
      7m 28s
    4. 9.2 Extract text
      32m 1s
    5. Manipulating Strings
      20 pages
    1. Overview
    2. Learning objectives
      28s
    3. 10.1 Understand the basics of LaTeX
      7m 16s
    4. 10.2 Weave R code into LaTeX using knitr
      5m 33s
    5. 10.3 Understand the basics of Markdown
      2m 45s
    6. 10.4 Understand the basics of RMarkdown
      4m 55s
    7. 10.5 Weave R code into Markdown using knitr
      2m 53s
    8. 10.6 Convert Markdown files to Word
      1m 30s
    9. 10.7 Convert Markdown to PDF
      1m 25s
    10. 10.8 Create slideshows with RMarkdown
      3m 10s
    11. 10.9 Write equations with RMarkdown
      7m 14s
    12. Reproducibility and Reports with knitr
      11 pages
    1. Overview
    2. Learning objectives
      27s
    3. 11.1 Work with datatables of tabular data
      6m 10s
    4. 11.2 Use rbokeh
      8m 31s
    5. 11.3 Use Leaflet for mapping
      7m 11s
    1. Overview
    2. Learning objectives
      22s
    3. 12.1 Use shiny objects in a markdown document
      13m 57s
    4. 12.2 Work with ui.r and server.r files
      8m 21s
    5. Interactive Dashboards with Shiny
      21 pages
    1. Overview
    2. Learning objectives
      23s
    3. 13.1 Understand the folder structure and files in a package
      5m 25s
    4. 13.2 Write and document functions
      7m 32s
    5. 13.3 Check and build a package
      2m 10s
    6. 13.4 Test R code
      6m 58s
    7. 13.5 Submit a package to CRAN
      46s
    8. Building R Packages
      26 pages
    1. Overview
    2. Learning objectives
      29s
    3. 14.1 Understand the basics of C++ with R
      1m 47s
    4. 14.2 Write a C++ function for R
      4m 35s
    5. 14.3 Use Rcpp syntactic sugar
      5m 51s
    6. 14.4 Sum in C++
      5m 35s
    7. 14.5 Write a package in R
      9m 37s
    8. 14.6 Write a package with C++ code
      6m 5s
    1. Overview
    2. Part 1: R as a Tool—Summary
      1m 2s
    1. Overview
    2. Part 2: R for Statistics, Modeling and Machine Learning—Introduction
      2m 14s
    1. Overview
    2. Learning objectives
      19s
    3. 15.1 Draw numbers from probability distributions
      21m 10s
    4. 15.2 Calculate averages, standard deviations and correlations
      16m 13s
    5. 15.3 Compare samples with t-tests and analysis of variance
      18m 59s
    6. Basic Statistics
      29 pages
    1. Overview
    2. Learning objectives
      28s
    3. 16.1 Fit simple linear models
      10m 15s
    4. 16.2 Explore the data
      8m 33s
    5. 16.3 Fit multiple regression models
      19m 17s
    6. 16.4 Fit logistic regression
      10m 6s
    7. 16.5 Fit Poisson regression
      7m 5s
    8. 16.6 Analyze survival data
      12m 1s
    9. 16.7 Assess model quality with residuals
      5m 16s
    10. 16.8 Compare models
      7m 18s
    11. 16.9 Judge accuracy using cross-validation
      9m 6s
    12. 16.10 Estimate uncertainty with the bootstrap
      6m 23s
    13. 16.11 Choose variables using stepwise selection
      2m 42s
    14. Linear Models
      31 pages
    1. Overview
    2. Learning objectives
      27s
    3. 17.1 Select variables and improve predictions with the elastic net
      14m 15s
    4. 17.2 Decrease uncertainty with weakly informative priors
      8m 53s
    5. 17.3 Fit nonlinear least squares
      5m 16s
    6. 17.4 Use Splines
      6m 49s
    7. 17.5 Use GAMs
      5m 24s
    8. 17.6 Fit decision trees to make a random forest
      6m 34s
    9. Nonlinear Models
      28 pages
    1. Overview
    2. Learning objectives
      20s
    3. 18.1 Understand ACF and PACF
      7m 16s
    4. 18.2 Fit and assess ARIMA models
      5m 14s
    5. 18.3 Use VAR for multivariate time series
      8m 6s
    6. 18.4 Use GARCH for better volatility modeling
      9m 24s
    7. Time Series and Autocorrelation
      25 pages
    1. Overview
    2. Learning objectives
      20s
    3. 19.1 Partition data with k-means
      12m 26s
    4. 19.2 Robustly cluster, even with categorical data, with PAM
      2m 13s
    5. 19.3 Perform hierarchical clustering
      5m 38s
    6. Clustering
      27 pages
    1. Overview
    2. Learning objectives
      20s
    3. 20.1 Build a recommendation engine with RecommenderLab
      13m 13s
    4. 20.2 Mine text with RTextTools
      9m 13s
    5. 20.3 Perform matrix factorization using irlba
      4m 5s
    1. Overview
    2. Learning objectives
      18s
    3. 21.1 Get started with igraph
      8m 16s
    4. 21.2 Read edgelists
      7m 12s
    5. 21.3 Understand common graph metrics
      10m 12s
    6. 21.4 Use centrality measures
      6m
    7. 21.5 Utilize more graph operations
      4m 16s
    1. Overview
    2. Learning objectives
      19s
    3. 22.1 Establish optimal tree depth for rpart
      6m 18s
    4. 22.2 Choose the best number of trees for a random forest
      3m 35s
    5. Model Fitting with Caret
      10 pages
    1. Overview
    2. Learning objectives
      25s
    3. 23.1 Understand the Stan computing paradigm
      1m 33s
    4. 23.2 Fit a simple regression model
      6m 53s
    5. 23.3 Fit a multilevel model with Stan
      6m 43s
    1. Overview
    2. Part 2: R for Statistics, Modeling and Machine Learning—Summary
      49s