Learning Path: R Programming

Name: Learning Path: R Programming
Rating: 4.1 (17 reviews)

Get started with Spark for large-scale distributed data science and processing

Created byPackt Publishing

Last updated 4/2017

English

What you'll learn

Create and master the manipulation of vectors, lists, dataframes, and matrices
Write conditional control structures, and debug and handle errors for efficient error handling
Handle dates using lubridate and manipulate strings with stringr package
Work with databases without having to write SQL using the dplyr package
Work on a full-scale data analysis / data munging project
Perform pre-model-building steps
Understand the working behind core machine learning algorithms
Implement unsupervised learning algorithms
Construct nice looking charts with Ggplot2
Build R packages from scratch and submit them to CRAN

Course content

2 sections • 103 lectures • 8h 58m total length

The Course Overview4:54
This video will provide you an overview of entire course.
Installing R3:45
The aim of this video is to show how to install R on our system.
Installing RStudio4:35
To run and write code in R, we first need to focus on how to get and install the IDE.
Installing Packages4:49
We have installed R and RStudio. Now let’s check out how to install the packages.
Data Types and Data Structures3:04
The aim of this video is to teach you what data types and data structures in R are.
Vectors5:43
In this video, we will see how to work with vectors in R.
Random Numbers, Rounding, and Binning4:00
The aim of this video is to show how to work with random numbers and do rounding and binning.
Missing Values2:46
Taking vectors a step ahead, let’s see how we can to handle missing values.
The which() Operator3:11
We now know a lot about how vectors work, but how do we get specific items from a vector based on any condition? Let’s check out just that in this video.
Lists4:34
This video will introduce a new data structure called list and how to work with it.
Set Operations2:08
In this video, our goal is to understand how to perform set operations in R.
Sampling and Sorting2:52
What is sampling and sorting and how to do it in R?
Check Conditions2:17
Checking conditions is often a requirement for a programmer to write maintainable code. Let’s understand how we can check conditions in R.
For Loops2:34
You may have come across several instances whilst coding where you need to perform repetitive operations through loops, right? In this video, we’ll see how to do that in R using for loops.
Dataframes8:30
Let’s explore what data frames are and how to work with them.
Importing and Exporting Data6:29
In this video, we will check out how to import and export data in R.
Matrices and Frequency Tables3:41
The aim of this video is to check out how to work with matrices and frequency tables.
Merging Dataframes2:26
Our goal in this video is to use W to merge data frames.
Aggregation2:48
How to do aggregation in R?
Melting and Cross Tabulations with dcast()3:58
In this video, we will look at how to de-aggregate data frames and create cross tabulations.
Dates5:35
In this video, we will look at how to handle date variables in R.
String Manipulation5:14
The goal of this video is to see how to perform string operations in R.
Functions5:34
Let’s learn how to avoid code replication.
Debugging and Error Handling4:29
The aim of this video is to understand how to debug and handle errors.
Fast Loops with apply()4:26
We’ll see in this video how to write fast loops with apply().
Fast Loops with sapply(), lapply() and vapply()1:59
Sometimes we’d want to iterate through lists. What do we do then? Let’s learn using fast loops with sapply, vapply and lapply to help us achieve this goal.
Creating and Customizing an R Plot7:03
How to make plots and customize them.
Drawing Plots with 2 Y Axes2:23
Sometimes, just a single Y axis is not enough. It becomes difficult to depict the variations for two variables on different scales in the same chart. To solve this, we’ll look at how to make a plot with two Y axes.
Multiplots and Custom Layouts3:07
In this video, we will learn how to make multiple plots and custom layout to get better at our analyzing skills.
Creating Basic Graph Types4:47
The aim of this video is to create different types of plots.
Univariate Analysis6:16
What are the steps and actions one needs to do as part of data analysis before jumping to predictive modeling? Let’s understand this better.
Normal Distribution, Central Limit Theorem, and Confidence Intervals5:32
The aim of this video is to teach you what normal distribution, central limit theorem, and confidence intervals are.
Correlation and Covariance3:03
In this video, we will understand correlation and Covariance, the concept behind them, and their implementation in R.
Chi-sq Statistic4:42
What is the chi-square statistic, when is it used, and how to do the chi-sq test?
ANOVA4:54
What is ANOVA, its purpose, when to use it, and how to implement it in R?
Statistical Tests5:14
What are the other commonly used statistical tests in R and how to implement them?
Project 1 – Data Munging and Summarizing11:31
All knowledge is incomplete without being put to practice. We’ve got a good taste of the core concepts that govern statistical analysis with R. Let’s solve the challenges pertaining to data manipulation in this video.
Project 2 – Visualization with Base Graphics5:42
What is data if not represented visually! We have solved challenges related to data manipulation. Now it’s time to tackle visualization in this video.
Project 3 – Statistical Inference3:50
Practice solving exercises that involve making statistical inferences.
Pipes with Magrittr5:21
The aim of this video is to introduce the magrittr package, its significance, and features such as pipe operators.
The 7 Data Manipulation Verbs5:19
Understand and use the 7 data manipulation verbs.
Aggregation and Special Functions3:36
How to group datasets by one or more variables using dplyr.
Two Table Verbs2:42
How to join two tables using the two table verbs of dplyr.
Working With Databases5:30
How to work with databases with DplyR.
Understanding Basics, Filter, and Select7:34
Understand the basics of data.table; do filter and select operations.
Understanding Syntax, Creating and Updating Columns4:06
Understand the syntax; create and update columns in a data.table.
Aggregating Data, .N, and .I4:20
Learn how to aggregate data.tables. Also learn the .N and .I operators.
data.table4:17
Understand and implement chaining, keys, functions, and .SD.
Fast Loops with set(), Keys, and Joins9:12
How to write for-loops with set, set keys, and join data.tables?

The Course Overview7:44
This video gives an overview of the entire course.
Performing Univariate Analysis5:22
In this video, we will take a look at how to perform univariate analysis.
Bivariate Analysis – Correlation, Chi-Sq Test, and ANOVA5:42
The goal of this video is to perform bivariate analysis in R using three cases.
Detecting and Treating Outlier3:20
In this video, we will see how to detect and treat outliers.
Treating Missing Values with `mice`3:59
The goal of this video is to see how to treat missing values in R.
Building Linear Regressors7:35
In this video we'll see what is linear regression, its purpose, when to use it, and how to implement in R.
Interpreting Regression Results and Interactions Terms5:19
We'll see how to interpret regression results and Interaction effects in this video
Performing Residual Analysis and Extracting Extreme Observations With Cook's Dis3:25
In this video we will discuss what is residual analysis and detect multivariate outliers using Cook's Distance
Extracting Better Models with Best Subsets, Stepwise Regression, and ANOVA4:39
The goal of this video is to understand how to do model selection and comparison using best subsets, stepwise regression and ANOVA.
Validating Model Performance on New Data with k-Fold Cross Validation2:29
In this video we will see how to do k-fold cross validation in R.
Building Non-Linear Regressors with Splines and GAMs5:19
The goal of this video is check out how to build non-linear regression models using Splines and GAMs.
Building Logistic Regressors, Evaluation Metrics, and ROC Curve12:38
Our goal in this video would be to understand logistic regression, evaluation metrics of binary classification problems, and interpretation of the ROC curve.
Understanding the Concept and Building Naive Bayes Classifier9:23
In this video, we will understand the concept and working of naïve Bayes classifier and how to implement the R code.
Building k-Nearest Neighbors Classifier7:01
In this video, we will look at what k-nearest neighbors algorithms, how does it works and how to implement it in T.
Building Tree Based Models Using RPart, cTree, and C5.06:32
The goal of this video is to understand how decision trees work, what they are used for, and how to implement then.
Building Predictive Models with the caret Package8:11
The goal of this video is know what the various features of the caret package are and how to build predictive models.
Selecting Important Features with RFE, varImp, and Boruta5:19
The goal of this video is to know how to do feature selection before building predictive models.
Building Classifiers with Support Vector Machines8:03
In this video, we will look at how support vector machines work.
Understanding Bagging and Building Random Forest Classifier5:06
In this video, we will look at the concept behind bagging and random forests and how to implement it to solve problems.
Implementing Stochastic Gradient Boosting with GBM5:18
Let's understand what boosting is and how stochastic gradient boosting works with GBM.
Regularization with Ridge, Lasso, and Elasticnet8:52
In this video, we will look at what regularization is, ridge and lasso regression, and how to implement it.
Building Classifiers and Regressors with XGBoost10:10
Let's look at how XG Boost works and how to implement it in this video.
Dimensionality Reduction with Principal Component Analysis5:04
Our goal in this video would be to reduce the dimensionality of data with principal components, and understand the concept and how to implement it in R.
Clustering with k-means and Principal Components3:16
In this video, we will understand the k-means clustering algorithm and implement it using the principal components.
Determining Optimum Number of Clusters5:24
In this video, we will analyze the clustering tendency of a dataset and identify the ideal number of clusters or groups.
Understanding and Implementing Hierarchical Clustering2:36
The goal of this video is to understand the logic of hierarchical clustering, types, and how to implement it in R.
Clustering with Affinity Propagation5:24
How to use affinity propagation to cluster data points? How is it different from conventional algorithms?
Building Recommendation Engines9:00
How to build recommendation engines to recommend products/movies to new and existing users?
Understanding the Components of a Time Series, and the xts Package5:41
The goal of this video is to understand what a time series is, how to create time series of various frequencies, and the enhanced facilities available in the xts package.
Stationarity, De-Trend, and De-Seasonalize4:07
The goal of this video is to understand the characteristics of a time series: stationarity and how to de-trend and de-seasonalize a time series.
Understanding the Significance of Lags, ACF, PACF, and CCF3:49
In this video, we will introduce the characteristics of time series such as ACF, PACF, and CCF; why they matter; and how to interpret them.
Forecasting with Moving Average and Exponential Smoothing2:25
Our goal in this video would be to understand moving average and exponential smoothing and use it to forecast.
Forecasting with Double Exponential and Holt Winters3:22
In this video, we will understand how double exponential smoothing and holt winter forecasting works, when to use them, and how to implement them in R.
Forecasting with ARIMA Modelling5:26
Let's look at what ARIMA forecasting is, understand the concepts, and learn how ARIMA modelling works in this video.
Scraping Web Pages and Processing Texts9:24
In this video, we'll take a look at how to scrape data from web pages and how to clean and process raw web and other textual data.
Corpus, TDM, TF-IDF, and Word Cloud9:06
Our goal in this video is to know how to process texts using tm package and understand the significance of TF-IDF and its implementation. Finally, we see how to draw a word cloud in R.
Cosine Similarity and Latent Semantic Analysis7:20
Let's see how to use cosine similarity and latent semantic analysis to find and map similar documents.
Extracting Topics with Latent Dirichlet Allocation5:07
In this video, we will see how to extract the underlying topics in a document, the keywords related to each topic and the proportion of topics in each document.
Sentiment Scoring with tidytext and Syuzhet4:23
Let's check out how to perform sentiment analysis and scoring in R.
Classifying Texts with RTextTools3:57
How to classify texts with machine learning algorithms using the RTextTools package?
Building a Basic ggplot2 and Customizing the Aesthetics and Themes7:18
The goal of this videos is to understand what is the basic structure of to make charts with ggplot, how to customize the aesthetics, and manipulate the theme elements.
Manipulating Legend, AddingText, and Annotation3:31
In this video, we will see how to manipulate the legend the way we want and how to add texts and annotation in ggplot.
Drawing Multiple Plots with Faceting and Changing Layouts3:18
The goal of this video is to understand how to plot multiple plots in the same chart and how to change the layouts of ggplot.
Creating Bar Charts, Boxplots, Time Series, and Ribbon Plots5:25
How to make various types of plots in ggplot such as bar chart, time series, boxplot, ribbon chart,and so on.
ggplot2 Extensions and ggplotly3:11
In this video, we will understand what the popular ggplot extensions are, and where to find them, and their applications.
Implementing Best Practices to Speed Up R Code5:46
We will discuss the best practices that should be followed to minimize code runtime in this video.
Implementing Parallel Computing with doParallel and foreach4:22
Let's tackle the implementation of parallel computing in R.
Writing Readable and Fast R Code with Pipes and DPlyR5:39
The goal of this video is understand how to work with DplyR and pipes.
Writing Super Fast R Code with Minimal Keystrokes Using Data.Table6:38
In this video, we will discuss how to manipulate data with the data.table package, how to achieve maximum speed, and what the various features of data.table are.
Interface C++ in R with RCpp11:09
Our main focus in this video is to understand how to write C++ code and make it work in R. Also leverage the speed of C++ in R, interface Rcpp with R, and write Rcpp code.
Understanding the Structure of an R Package5:02
We'll take a look at the components of an R package in this video.
Build, Document, and Host an R Package on GitHub7:09
In this video, we will look at how to create an R Package so that it can be submitted to CRAN.
Performing Important Checks Before Submitting to CRAN4:05
We will understand the mandatory checks and common problems faced by developers when creating R packages in this video.
Submitting an R Package to CRAN3:10
The goal of this video is to show how to submit an R package to CRAN.

Requirements

This is for absolute beginners. No prior knowledge of R is required.

Description

Do you want to step into the ever-growing field of data science? Do you wish to equip yourself with one of the most widely used language for data science?

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

Data is on the rise and it’s the need of the hour to process it and make sense out it. Analysts and statisticians need to get this job done. It’s an art to tactfully and efficiently process data. But, as it goes an art becomes a reality only with the help of right tools and the knowledge of using these right. So, it is with data science. R is a powerful language that provides with all the tools required to build probabilistic models, perform data science, and build machine learning algorithms.

With this Learning Path, you’ll be introduced to R Studio and the basics of R. Then, you’ll taken through a number of topics such as handling dates with the lubridate package, handling strings with the stringr package, and making statistical inferences. Finally, the focus will be on machine learning concepts in depth and applying them in the real world with R.

The goal of this course to introduce you to R and have a solid knowledge of machine learning and the R language itself. You’ll also solve numerous coding challenges throughout the course.

This Learning Path is authored by one of the best in the fields.

Selva Prabhakaran Selva Prabhakaran is a data scientist with a large E-commerce organization. In his 7 years of experience in data science, he has tackled complex real-world data science problems and delivered production-grade solutions for top multinational companies. Selva lives in Bangalore with his wife.

Who this course is for:

If you are looking to start your data science career, or are already familiar with data science, statistics, and machine learning concepts, but want to switch to R, this Video Learning Path will be a great place to start.
The Learning Path follows a pragmatic approach where you’ll find step-by-step instructions of the functions, tools, and concepts, and the reason you’re learning about them. Most of the videos close with coding challenges, putting your newly learned skills into practical use immediately. You’ll get hands-on working sessions and detailed explanations.

Learning Path: R Programming

What you'll learn

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Course content

Introduction to R Programming49 lectures • 3hr 46min

Mastering R Programming54 lectures • 5hr 12min

Requirements

Description

Who this course is for: