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16+ Hours of Video Instruction

R Programming: Fundamentals to Advanced is a tour through the most important parts of R, the statistical programming language, from the very basics to complex modeling. It covers reading data, programming basics, visualization. data munging, regression, classification, clustering, modern machine learning and more.

Data scientist, Columbia University adjunct Professor, author and organizer of the New York Open Statistical Programming meetup Jared P. Lander presents the 20 percent of R functionality to accomplish 80 percent of most statistics needs. This video is based on the material in R for Everyoneand is a condensed version of the course Mr. Lander teaches at Columbia. You start with simply installing R and setting up a productive work environment. You then learn the basics of data and programming using these skills to munge and prepare data for analysis. You then learn visualization, modeling and predicting and close with generating reports and websites and building R packages.

Table of Contents

Lesson 1 Getting Started with R: R can only be used after installation, which fortunately is just as simple as installing any other program. In this lesson you learn about where to download R, how to decide on the best version, how to install it and you get familiar with its environment, using RStudio as a front end. We also take a look at the package system.

Lesson 2 The Basic Building Blocks in R: R is a flexible and robust programming language and using it requires understanding how it handles data. We learn about performing basic math in R, storing various types of data in variables—such as numeric, integer, character and time-based—and calling functions on the data.

Lesson 3 Advanced Data Structures in R: Like many other languages, R offers more complex storage mechanisms such as vectors, arrays, matrices and lists. We take a look at those, and the data.frame, a special storage type that strongly resembles a spreadsheet and is part of what makes working with data in R such a pleasure.

Lesson 4 Reading Data into R: Data is abundant in the world, so analyzing it is just a matter of getting the data into R. There are many ways of doing so, the most common being reading from a CSV or database. We cover these and also importing from other statistical tools, and scraping websites.

Lesson 5 Making Statistical Graphs: Visualizing data is a crucial part of data science both in the discovery phase and when reporting results. R has long been known for its capability to produce compelling plots, and Hadley Wickham’s ggplot2 package makes it even easier to produce better looking graphics. We cover histograms, boxplots, scatterplots, line charts and more.

Lesson 6 Basics of Programming: R has all the standard components of a programming language such as writing functions, if statements and loops, all with their own caveats and quirks. We start with the requisite “Hello, World!’ function and learn about arguments to functions, the regular if statement and the vectorized version, and how to build loops and why they should be avoided.

Lesson 7 Data Munging: Data scientists often bemoan that 80% of their work is manipulating data. As such, R has many tools for this, which are, contrary to what Python users may say, easy to use. We see how R excels at group operations using apply, lapply and the plyr package. We also take a look at its facilities for joining, combing and rearranging data.

Lesson 8 Manipulating Strings: Text data is becoming more pervasive in the world, and fortunately, R provides ways for both combing text and ripping it apart, which we walk through. We also examine R’s extensive regular expression capabilities.

Lesson 9 Basic Statistics: Naturally, R has all the basics when it comes to statistics such as means, variance, correlation, t-tests and anovas. We look at all the different ways those can be computed.

Lesson 10 Linear Models: The workhorse of statistics is regression and its extensions. This consists of linear models, generalized linear models–including logistic and Poisson regression–and survival models. We look at how to fit these models in R and how to evaluate them using measures such as mean squared error, deviance and AIC.

Lesson 11 Other Models: Beyond regression there are many other types of models that can be fit to data. Models covered include regularization with the elastic net, bayesian shrinkage, nonlinear models such as nonlinear least squares, splines and generalized additive models, decision tress and random forests.

Lesson 12 Time Series: Special care must be taken with data where there is time based correlation, otherwise known as autocorrelation. We look at some common methods for dealing with time series such as ARIMA, VAR and GARCH.

Lesson 13 Clustering: A focal point of modern machine learning is clustering, the partitioning of data into groups. We explore three popular methods: K-means, K-medoids and hierarchical clustering.

Lesson 14 Reports and Slideshows with knitr: Successfully delivering the results of an analysis can be just as important as the analysis itself, so it is important to communicate them in an effective way. This communication can take the form of a written report, a Web site of results, a slide show or a dashboard. In this lesson we focus on the first three, which are made remarkably easy using knitr, a package written by Yihui Xie.

Lesson 15 Package Building: Building packages is a great way to contribute back to the R community and doing so has never been easier thanks to Hadley Wickham’s devtools package. This lesson covers all the requirements for a package and how to go about authoring and distributing them.

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Course Curriculum

Lesson 1: Getting Started with R
2. Learning objectives 00:00:00
1_1. Download and install R 00:00:00
1_2. Work in The R environment 00:00:00
1_3. Install and load packages 00:00:00
Lesson 2: The basic building block in R
6.Learning objectives 00:00:00
2_1. Use R as a calculator 00:00:00
2_2. Work with variables 00:00:00
2_3. Understand the different data types 00:00:00
2_4. Store data in vectors 00:00:00
2_5. Call functions 00:00:00
Lesson 3: Advanced Data Structure in R
12.Learning objectives 00:00:00
3_1. Create and access information in data frames 00:00:00
3_2. Create and access information in lists 00:00:00
3_3. Create and access information in matrices 00:00:00
3_4. Create and access information in arrays 00:00:00
Lesson 4: Reading Data into R
17.Learning objectives 00:00:00
4_1. Read a CSV into R 00:00:00
4_2. Understand that Excel is not easily readable into R 00:00:00
4_3. Read from databases 00:00:00
4_4. Read data files from other statistical tools 00:00:00
4_5. Load binary R files 00:00:00
4_6. Load data included with R 00:00:00
4_7. Scrape data from the web 00:00:00
Lesson 5: Making Statistical Graphs
25.Learning objectives 00:00:00
5_1. Find the diamonds data 00:00:00
5_2. Make histograms with base graphics 00:00:00
5_3. Make scatterplots with base graphics 00:00:00
5_4. Make boxplots with base graphics 00:00:00
5_5. Get familiar with ggplot2 00:00:00
5_6. Plot histograms and densities with ggplot2 00:00:00
5_7. Make scatterplots with ggplot2 00:00:00
5_8. Make boxplots and violin plots with ggplot2 00:00:00
5_9. Make line plots 00:00:00
5_10. Create small multiples 00:00:00
5_11. Control colors and shapes 00:00:00
5_12. Add themes to graphs 00:00:00
Section 6: Basics of Programing
38.Learning objectives 00:00:00
6_1. Write the classic GÇ£Hello, World!GÇ¥ example 00:00:00
6_2. Understand the basics of function arguments 00:00:00
6_3. Return a value from a function 00:00:00
6_4. Gain flexibility with do 00:00:00
6_5. Use if statements to control program flow 00:00:00
6_6. Stagger if statements with else 00:00:00
6_7. Check multiple statements with switch 00:00:00
6_8. Run checks on entire vectors 00:00:00
6_9. Check compound statements 00:00:00
6_10. Iterate with a for loop 00:00:00
6_11. Iterate with a while loop 00:00:00
6_12. Control loops with break and next 00:00:00
Lesson 7: Data Munging
51.Learning objectives 00:00:00
7_1. Repeat an operation on a matrix using apply 00:00:00
7_2. Repeat an operation on a list 00:00:00
7_3. The mapply 00:00:00
7_4. The aggregate function 00:00:00
7_5. The plyr package 00:00:00
7_6. Combine datasets 00:00:00
7_7. Join datasets 00:00:00
7_8. Switch storage paradigms 00:00:00
Section 8: Manipulating Strings
60.Learning objectives 00:00:00
8_1. Combine strings together 00:00:00
8_2. Extract text 00:00:00
Section 9: Baisic Statistics
63.Learning objectives 00:00:00
9_1. Draw numbers from probability distributions 00:00:00
9_2. Calculate averages, standard deviations and correlations. 00:00:00
9_3. Compare samples with t-tests and analysis of variance 00:00:00
Lesson 10: Linear Models
67.Learning objectives 00:00:00
10_1. Fit simple linear models 00:00:00
10_2. Explore the data 00:00:00
10_3. Fit multiple regression models 00:00:00
10_4. Fit logistic regression 00:00:00
10_5. Fit Poisson regression 00:00:00
10_6. Analyze survival data 00:00:00
10_7. Assess model quality with residuals 00:00:00
10_8. Compare models 00:00:00
10_9. Judge accuracy using cross-validation 00:00:00
10_10. Estimate uncertainty with the bootstrap 00:00:00
10_11. Choose variables using stepwise selection 00:00:00
Lesson 11: Other Models
79.Learning objectives 00:00:00
11_1. Select variables and improve predictions with the elastic net 00:00:00
11_2. Decrease uncertainty with weakly informative priors 00:00:00
11_3. Fit nonlinear least squares 00:00:00
11_4. Splines 00:00:00
11_5. GAMs 00:00:00
11_6. Fit decision trees to make a random forest 00:00:00
Lesson 12: Time Series
86.Learning objectives 00:00:00
12_1. Understand ACF and PACF 00:00:00
12_2. Fit and assess ARIMA models 00:00:00
12_3. Use VAR for multivariate time series 00:00:00
12_4. Use GARCH for better volatility modeling 00:00:00
Section 13: Clustering
91.Learning objectives 00:00:00
13_1. Partition data with K-means 00:00:00
13_2. Robustly cluster, even with categorical data, with PAM 00:00:00
13_3. Perform hierarchical clustering 00:00:00
Section 14: Report and Slideshows with knitr
95.Learning objectives 00:00:00
14_1. Understand the basics of LaTeX 00:00:00
14_2. Weave R code into LaTeX using knitr 00:00:00
14_3. Understand the basics of Markdown 00:00:00
14_4. Weave R code into Markdown using knitr 00:00:00
14_5. Use pandoc to convert from Markdown to HTML5 slideshow 00:00:00
Lesson 15: Packlage Building
101.Learning objectives 00:00:00
15_1. Understand the folder structure and files in a package 00:00:00
15_2. Write and document functions 00:00:00
15_3. Check and build a package 00:00:00
15_4. Submit a package to CRAN 00:00:00
Summary of R programing
106. Summary of R Programming LiveLesson 00:00:00
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