MyPage is a personalized page based on your interests.The page is customized to help you to find content that matters you the most.


I'm not curious

R Programming LiveLessons

Course Summary

Fundamentals to Advanced


  • +

    Course Syllabus

    • Introduction
      • Introduction to R Programming LiveLessons
    • Lesson 1: Getting Started with R
      • Learning objectives
      • 1.1 Download and install R
      • 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
      • 4.3 Read from databases
      • 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
      • 7.8 Switch storage paradigms
    • 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

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

Attended this course?

Back to Top

Awards & Accolades for MyTechLogy
Winner of
REDHERRING
Top 100 Asia
Finalist at SiTF Awards 2014 under the category Best Social & Community Product
Finalist at HR Vendor of the Year 2015 Awards under the category Best Learning Management System
Finalist at HR Vendor of the Year 2015 Awards under the category Best Talent Management Software
Hidden Image Url

Back to Top