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# R Programming LiveLessons

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### Course Syllabus

• Introduction
• Introduction to R Programming LiveLessons
• Lesson 1: Getting Started with R
• Learning objectives
• 1.2 Work in The R environment
• 1.3 Install and load packages
• Lesson 2: The Basic Building Blocks in R
• Learning objectives
• 2.1 Use R as a calculator
• 2.2 Work with variables
• 2.3 Understand the different data types
• 2.4 Store data in vectors
• 2.5 Call functions
• Lesson 3: Advanced Data Structures in R
• Learning objectives
• 3.1 Create and access information in data.frames
• 3.2 Create and access information in lists
• 3.3 Create and access information in matrices
• 3.4 Create and access information in arrays
• Lesson 4: Reading Data into R
• Learning objectives
• 4.1 Read a CSV into R
• 4.2 Understand that Excel is not easily readable into R
• 4.4 Read data files from other statistical tools
• 4.5 Load binary R files
• 4.6 Load data included with R
• 4.7 Scrape data from the web
• Lesson 5: Making Statistical Graphs
• Learning objectives
• 5.1 Find the diamonds data
• 5.2 Make histograms with base graphics
• 5.3 Make scatterplots with base graphics
• 5.4 Make boxplots with base graphics
• 5.5 Get familiar with ggplot2
• 5.6 Plot histograms and densities with ggplot2
• 5.7 Make scatterplots with ggplot2
• 5.8 Make boxplots and violin plots with ggplot2
• 5.9 Make line plots
• 5.10 Create small multiples
• 5.11 Control colors and shapes
• 5.12 Add themes to graphs
• Lesson 6: Basics of Programming
• Learning objectives
• 6.1 Write the classic “Hello, World!” example
• 6.2 Understand the basics of function arguments
• 6.3 Return a value from a function
• 6.4 Gain flexibility with do.call
• 6.5 Use if statements to control program flow
• 6.6 Stagger if statements with else
• 6.7 Check multiple statements with switch
• 6.8 Run checks on entire vectors
• 6.9 Check compound statements
• 6.10 Iterate with a for loop
• 6.11 Iterate with a while loop
• 6.12 Control loops with break and next
• Lesson 7: Data Munging
• Learning objectives
• 7.1 Repeat an operation on a matrix using apply
• 7.2 Repeat an operation on a list
• 7.3 The mapply
• 7.4 The aggregate function
• 7.5 The plyr package
• 7.6 Combine datasets
• 7.7 Join datasets
• Lesson 8: Manipulating Strings
• Learning objectives
• 8.1 Combine strings together
• 8.2 Extract text
• Lesson 9: Basic Statistics
• Learning objectives
• 9.1: Draw numbers from probability distributions
• 9.2: Calculate averages, standard deviations and correlations
• 9.3: Compare samples with t-tests and analysis of variance
• Lesson 10: Linear Models
• Learning objectives
• 10.1 Fit simple linear models
• 10.2 Explore the data
• 10.3 Fit multiple regression models
• 10.4 Fit logistic regression
• 10.5 Fit Poisson regression
• 10.6 Analyze survival data
• 10.7 Assess model quality with residuals
• 10.8 Compare models
• 10.9 Judge accuracy using cross-validation
• 10.10 Estimate uncertainty with the bootstrap
• 10.11 Choose variables using stepwise selection
• Lesson 11: Other Models
• Learning objectives
• 11.1 Select variables and improve predictions with the elastic net
• 11.2 Decrease uncertainty with weakly informative priors
• 11.3 Fit nonlinear least squares
• 11.4 Splines
• 11.5 GAMs
• 11.6 Fit decision trees to make a random forest
• Lesson 12: Time Series
• Learning objectives
• 12.1 Understand ACF and PACF
• 12.2 Fit and assess ARIMA models
• 12.3 Use VAR for multivariate time series
• 12.4 Use GARCH for better volatility modeling
• Lesson 13: Clustering
• Learning objectives
• 13.1: Partition data with K-means
• 13.2: Robustly cluster, even with categorical data, with PAM
• 13.3: Perform hierarchical clustering
• Lesson 14: Reports and Slideshows with knitr
• Learning objectives
• 14.1: Understand the basics of LaTeX
• 14.2: Weave R code into LaTeX using knitr
• 14.3: Understand the basics of Markdown
• 14.4: Weave R code into Markdown using knitr
• 14.5: Use pandoc to convert from Markdown to HTML5 slideshow
• Lesson 15: Package Building
• Learning objectives
• 15.1: Understand the folder structure and files in a package
• 15.2: Write and document functions
• 15.3: Check and build a package
• 15.4: Submit a package to CRAN
• Summary
• Summary of R Programming LiveLessons

Course Fee:
USD 299

Self-Study

### Course Status:

Active

1 - 4 hours / week

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