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Oreilly R Programming LiveLessons Video Training Fundamentals to Advanced
Course modified date: 28 Jan 2025
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Oreilly R Programming LiveLessons Video Training Fundamentals to Advanced
Course content
Sections:
17
•
Activities:
0
•
Resources:
102
- 0 Introduction to R Programming LiveLessons
- 0 Learning objectives
- 1 11 Download and install R
- 2 12 Work in The R environment
- 3 13 Install and load packages
- 0 Learning objectives
- 1 21 Use R as a calculator
- 2 22 Work with variables
- 4 24 Store data in vectors
- 5 25 Call functions
- 0 Learning objectives
- 1 31 Create and access information in dataframes
- 2 32 Create and access information in lists
- 3 33 Create and access information in matrices
- 4 34 Create and access information in arrays
- 0 Learning objectives
- 1 41 Read a CSV into R
- 2 42 Understand that Excel is not easily readable into R
- 3 43 Read from databases
- 4 44 Read data files from other statistical tools
- 5 45 Load binary R files
- 6 46 Load data included with R
- 7 47 Scrape data from the web
- 0 Learning objectives
- 10 510 Create small multiples
- 11 511 Control colors and shapes
- 12 512 Add themes to graphs
- 2 52 Make histograms with base graphics
- 3 53 Make scatterplots with base graphics
- 4 54 Make boxplots with base graphics
- 5 55 Get familiar with ggplot2
- 6 56 Plot histograms and densities with ggplot2
- 7 57 Make scatterplots with ggplot2
- 8 58 Make boxplots and violin plots with ggplot2
- 9 59 Make line plots
- 0 Learning objectives
- 10 610 Iterate with a for loop
- 11 611 Iterate with a while loop
- 12 612 Control loops with break and next
- 2 62 Understand the basics of function arguments
- 3 63 Return a value from a function
- 4 64 Gain flexibility with docall
- 5 65 Use if statements to control program flow
- 6 66 Stagger if statements with else
- 7 67 Check multiple statements with switch
- 8 68 Run checks on entire vectors
- 9 69 Check compound statements
- 0 Learning objectives
- 1 71 Repeat an operation on a matrix using apply
- 2 72 Repeat an operation on a list
- 3 73 The mapply
- 4 74 The aggregate function
- 5 75 The plyr package
- 6 76 Combine datasets
- 7 77 Join datasets
- 8 78 Switch storage paradigms
- 0 Learning objectives
- 1 81 Combine strings together
- 2 82 Extract text
- 0 Learning objectives
- 1 91_ Draw numbers from probability distributions
- 2 92_ Calculate averages standard deviations and correlations
- 3 93_ Compare samples with t tests and analysis of variance
- 0 Learning objectives
- 10 1010 Estimate uncertainty with the bootstrap
- 1 101 Fit simple linear models
- 11 1011 Choose variables using stepwise selection
- 2 102 Explore the data
- 3 103 Fit multiple regression models
- 4 104 Fit logistic regression
- 5 105 Fit Poisson regression
- 6 106 Analyze survival data
- 7 107 Assess model quality with residuals
- 8 108 Compare models
- 9 109 Judge accuracy using cross validation
- 0 Learning objectives
- 1 111 Select variables and improve predictions with the elastic net
- 2 112 Decrease uncertainty with weakly informative priors
- 3 113 Fit nonlinear least squares
- 4 114 Splines
- 5 115 GAMs
- 6 116 Fit decision trees to make a random forest
- 0 Learning objectives
- 1 121 Understand ACF and PACF
- 2 122 Fit and assess ARIMA models
- 4 124 Use GARCH for better volatility modeling
- 0 Learning objectives
- 1 131_ Partition data with K means
- 2 132_ Robustly cluster even with categorical data with PAM
- 3 133_ Perform hierarchical clustering
- 0 Learning objectives
- 1 141_ Understand the basics of LaTeX
- 2 142_ Weave R code into LaTeX using knitr
- 3 143_ Understand the basics of Markdown
- 4 144_ Weave R code into Markdown using knitr
- 5 145_ Use pandoc to convert from Markdown to HTML5 slideshow
- 0 Learning objectives
- 1 151_ Understand the folder structure and files in a package
- 2 152_ Write and document functions
- 3 153_ Check and build a package
- 4 154_ Submit a package to CRAN
- 0 Summary of R Programming LiveLessons