R: Data Analysis with R - Step-by-Step Tutorial!: 3-in-1
4.4 (17 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
94 students enrolled

R: Data Analysis with R - Step-by-Step Tutorial!: 3-in-1

An all inclusive guide to get well versed with classifying and clustering data with R!
4.4 (17 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
95 students enrolled
Created by Packt Publishing
Last updated 9/2018
English
English [Auto-generated]
Current price: $129.99 Original price: $199.99 Discount: 35% off
15 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 5 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
Training 5 or more people?

Get your team access to 4,000+ top Udemy courses anytime, anywhere.

Try Udemy for Business
What you'll learn
  • Get to know a set of techniques for importing data, manipulating data, performing statistical analysis, and producing useful data synthesis.
  • Build decision tree model for classification and prediction
  • Understand time-series decomposition, forecasting, clustering, and classification.
  • Master essential text data visualization with R.
  • Carry out cluster analysis using visualization methods such as Dendrogram and Silhouette plots.
  • Delve into network analysis of tweets with R.
  • Perform density-based clustering and clustering of tweets.
Course content
Expand all 44 lectures 05:03:32
+ Learn R programming
17 lectures 01:29:14

This video provides an overview of the entire course. 

Preview 02:09

In this video, we will see how to download and install RStudio, and set it up as an R editing environment.

  • Understand the R language, software, and RStudio      editing environment

  • Download and install RStudio

  • Set up the RStudio Source and Console panes

Setting Up RStudio
04:10

In this video, we will Learn how to write, run, save and load R scripts in the RStudio source pane.

  • Writing code in the RStudio source pane

  • Running code from the RStudio source pane

  • Saving and loading R scripts

Writing, Running, and Saving R Scripts
04:20

In this video, we will understand how to use numbers and perform arithmetic operations in R.

  • Perform basic arithmetic operations

  • Use the exponent and modulus operators

  • Understand the order of operations and parentheses

Preview 04:36

The aim of this video is to make us understand how to create and use R variables, and the basics of vectors and vectorised operations.

  • Create and use variables

  • Understand vectors

  • Perform vectorised operations

Working with Variables and Vectors
05:45

In this video, we will understand how to find and use functions.

  • Understand calling functions

  • Explore nesting and vectorising functions

  • Understand function arguments and finding function      documentation

Using Functions and Reading Function Documentation
06:41

In this video, we will see what data types are and how to work with vectors of different data types.           

  • Understand the logical and character data types

  • Learn how to subset vectors

  • Learn how to name vector elements

Exploring Vectors in Depth and Understanding Data Types
10:01

This video explains us what is the purpose and properties of matrices and arrays and how to create them, and how to subset elements from them.

  • Understand what matrices and arrays are

  • Learn how to create matrices and arrays

  • Learn how to subset elements from matrices and arrays

Working with Matrices and Arrays
07:16

The aim of this video is to make us understand what list data structure is and how does list differ from vectors.

  • Understand what a list is

  • Learn how to create and modify lists

  • Learn how to subset list elements

Discovering Lists
07:57

In this video, we will understand how to use data frame as a flexible way to represent and work with tabular data in R.

  • Understand what a data frame is

  • Learn how to create data frames

  • Learn how to subset data frames

Discovering Data Frames
05:35

This video explains us why factors exist and how to use them in base R.

  • Understand what factors are and why they exist

  • Learn to create factors and work with factor levels

  • Understand the stringsAsFactors argument

Exploring Factors
03:30

Datasets are often provided to you in a delimited format such as CSV (comma-separated value). In this video, we will learn how to load data from this and other delimited formats into R.

  • Understand the CSV format

  • Learn how to read CSV files with read.csv()

  • Learn how to read any delimited format with      read.table()

Reading Data from a File
03:40

When working with data, it’s often useful to subset a data frame by value. In this video, we will learn how to combine logical operators with data frame subsetting to subset datasets by value.

  • Understand the six most important logical operators

  • Apply logical operators to perform logical subsetting      of data frames

  • Manage missing data with the is.na() function

Subsetting Data Frames
06:16

Large data sets can be difficult to understand at a glance. This video aims to explain how to apply a range of statistical summary functions to condense key statistical properties from dataset variables.

  • Apply the summary() and table() functions

  • Apply the min(), max(), range(), and unique() functions     

  • Apply the mean(), median(), and sd() functions

Statistical Summaries of Data
03:46

Although there are hundreds of statistical tests that can be performed in R, many of them are applied according to a similar pattern. In this video, we will learn how to perform three common statistical tests in two different ways.

  • Perform a test with vector arguments and with formulas

  • Perform a Mann-Whitney test with vector arguments and      with formulas

  • Calculate a Spearman rank correlation between variables     

Statistical Tests on Data
05:59

Data sets will not always contain all the information you need. In this video, we will learn how to manipulate and combine variables to reshape a data set for your application.

  • Combine character variables with paste()

  • Replace text substrings with sub() and factor levels      with levels()

  • Create new data frame column and replace existing      columns

Manipulating Data
05:52

When you finish working with a data frame, you need to write it back to file to work with it later or pass to somebody else. In this video, we will learn how to write a data frame to file.

  • Write a data frame to file with write.csv()

  • Create a complete data analysis script finishing with      write.csv()

  • Review data analysis concepts

Writing Data to File
01:41
Learn R programming
5 questions
+ Classifying and Clustering Data with R
19 lectures 02:29:43

This video gives an overview of entire course.

Preview 02:29

This video covers data preparation for clustering.

  • Discuss iris data as an example

  • Show how to normalize data

  • Show how to calculate distances

Iris Data
08:29

This video covers clustering using dendrogram.

  • Show dendrogram with complete and average linkages

  • Learn characterization of clusters

  • Show how to make Silhouette plot

Hierarchical Clustering Using Dendrogram with R
08:51

In this video, k-means clustering is covered.

  • Show steps to do k-means in R

  • Provide output interpretation

  • Show steps for making scree plot

Nonhierarchical K-means Clustering with R
11:16

In this video, we will see how to do data preparation for density based clustering.

  • Use iris data as an example

  • Show what key packages need to be installed

  • Show steps for obtaining optimal eps value

Preparing Data and Packages for Density-based Clustering
05:39

In this video, we will see how to do density based clustering.

  • See how to use fps package

  • Learn how to use dbscan package

  • Show how to do cluster visualization

Density-based Clustering with R
07:22

In this video, we will see how to prepare for text data clustering.

  • Show steps to read text file and build corpus

  • Show steps to do term document matrix

  • Show steps to plot frequent terms

Text Data Preparation for Clustering
07:00

In this video, we will see how to do clustering for words or tweets.

  • Show steps for hierarchical clustering

  • Provide interpretation of dendrogram

  • Show steps for k-means clustering

Clustering Words or Tweets with R
08:56

This video shows how to do discriminant analysis in R.

  • Discuss iris data, correlations, and scatter plot

  • Show how to do data partition

  • Show how to do linear discriminant analysis

Discriminant Analysis with R
07:28

This video shows how to do model interpretation.

  • Discuss coefficients of linear discriminants

  • Discuss proportion of trace

  • Discuss prediction

Model Interpretation
07:40

This video shows how to do visualizations in discriminant analysis.

  • Show steps to do stacked histograms

  • Show steps to do bi-plot

  • Show steps to do partition plots

Visualization
09:01

This video shows how to do model assessment.

  • Show steps to do confusion matrix and accuracy      calculations for training data

  • Show steps to do confusion matrix and accuracy      calculations for testing data

  • Discuss interpretations

Model Assessment
06:11

This video shows how to do time series decomposition in R.

  • Discuss an example of time series data

  • Show how to do log transformation of data

  • Show how to do decomposition of additive time series

Time Series Decomposition with R
07:55

This video shows how to do time series forecasting in R.

  • Show steps to develop ARIMA model

  • Show steps for ACF, PACF, and residual plots

  • Show steps to do forecast using the model

Time Series Forecasting with R
09:23

This video shows how to do time series clustering in R.

  • Show steps to do data partitioning

  • Show steps to calculate distances

  • Show steps to do hierarchical clustering

Time Series Clustering with R
11:38

This video shows how to do time series classification in R.

  • Show steps to do data preparation

  • Shows steps to do classification using decision tree

  • Show how to do classification performance assessment

Time Series Classification with R
07:33

This video shows how to do decision tree in R.

  • Discuss an example using iris data

  • Show how to do data partition

  • Show how to develop decision tree model

Decision Tree with R
06:05

This video shows how to do visualize decision tree.

  • Show steps to plot decision tree

  • Discuss interpretation of the tree model

  • Discuss categorical versus numerical dependent variable     

Visualization of Decision Trees
09:02

This video shows how to assess classification performance.

  • Show steps to do prediction

  • Show steps to create confusion matrix for training data     

  • Show steps to create confusion matrix for testing data

Prediction and Misclassification Errors
07:45
Classifying and Clustering Data with R
4 questions
+ Bringing Order to Unstructured Data with R
8 lectures 01:04:35

This video gives an overview of entire course.

Preview 02:56

This video covers steps for obtaining Twitter data.

  • Show how to register API using Twitter account

  • Show how to use information from earlier step to get      tweets

  • Show how to create a csv file

Obtaining Twitter Data Using R
06:23

This video covers steps for data cleaning and preparation.

  • Show how to read the CSV file

  • Show how to build corpus and clean text

  • Show how to create term document matrix

Data Cleaning and Preparation with R
10:46

In this video, visualization of text data is covered.

  • Show steps to do bar plot of most frequent words

  • Show steps to do wordcloud

  • Show steps for improving wordcloud

Visualization of Text Data with R
07:25

In this video, we will see how to do sentiment analysis using Twitter data.

  • Show what packages are needed

  • Show how to obtain sentiment scores

  • Show steps for plotting sentiment scores

Sentiment Analysis with R
08:17

This video covers steps for network analysis using tweets.

  • Show how to create term document matrix of tweets

  • Show how to develop network of terms using igraph      package in R

  • Show how to create a network diagram

Network Analysis of Tweets with R
09:01

This video covers steps for term network visualization.

  • Show how to visualize network of terms in communities      using various algorithms

  • Show how to visualize hubs and authorities

  • Show how to highlight degrees in the network diagrams

Visualization and Interpretation – One
09:48

This video covers steps for tweet network visualization.

  • Show steps to visualize network of tweets

  • Show steps to delete vertices that have low degrees

  • Show steps to delete edges to improve visualization of      network

Visualization and Interpretation – Two
09:59
Bringing Order to Unstructured Data with R
6 questions
Requirements
  • Prior basic understanding R programming language will be useful.
Description

Are you looking forward to get well versed with classifying and clustering data with R? Then this is the perfect course for you!

There’s an increase in the number of data being produced every day which has led to the demand for skilled professionals who can analyze these data and make decisions. R is a programming language and environment used in statistical computing, data analytics and scientific research. Due to its expressive syntax and easy-to-use interface, it has grown in popularity in recent years.

This comprehensive 3-in-1 course takes a practical and incremental approach. Analyze and manage large volumes of data using advanced techniques. Attain a greater understanding of the fundamentals of applied statistics. Load, manipulate, and analyze data from different sources! Develop decision tree model for classification and prediction. Know how to use hierarchical cluster analysis using visualization methods such as Dendrogram and Silhouette plots!

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Learn R programming, covers R programming to create data structures and perform extensive statistical data analysis and synthesis. You’ll work with powerful R tools and techniques. Boost your productivity with the most popular R packages and tackle data structures such as matrices, lists, and factors. Create vectors, handle variables, and perform other core functions. You’ll be able to tackle issues with data input/output and will learn to work with strings and dates. Explore more advanced concepts such as metaprogramming with R and functional programming. Finally, you’ll learn to tackle issues while working with databases and data manipulation.

The second course, Classifying and Clustering Data with R, covers classifying and clustering Data with R. This video course provides the steps you need to carry out classification and clustering with R/RStudio software. You’ll understand hierarchical clustering, non-hierarchical clustering, density-based clustering, and clustering of tweets. It also provides steps to carry out classification using discriminant analysis and decision tree methods.In addition, we cover time-series decomposition, forecasting, clustering, and classification.

By the end the course, you will be well-versed with clustering and classification using Cluster Analysis, Discriminant Analysis, Time-series Analysis, and decision trees.

The third course, Bringing Order to Unstructured Data with R, covers obtaining, cleansing, and visualizing data with R. This video course will demonstrate the steps for analyzing unstructured data with the R/R Studio software.

At the end the video course you’ll have mastered obtaining and visualizing data with R. You’ll also be confident with data cleaning, preparation, and sentiment analysis with R.

By the end of the course, you’ll be able to classify as well as cluster data and bring order to unstructured data with R.

About the Authors

  • Dr. David Wilkins has been writing R for over a decade. He is the author of a number of popular open-source R packages, two previous Packt Publishing courses on the R language, and over a dozen scientific publications involving R analyses. He holds a Bachelor's degree in Science and a PhD in molecular genetics. David has a particular passion for creating beautiful and informative statistical graphics, and enjoys teaching people to use R to find and express insights in their own datasets.


  • Dr. Bharatendra Rai is Professor of Business Statistics and Operations Management in the Charlton College of Business at UMass Dartmouth. He received his Ph.D. in Industrial Engineering from Wayne State University, Detroit. His two master's degrees include specializations in quality, reliability, and OR from Indian Statistical Institute and another in statistics from Meerut University, India. He teaches courses on topics such as Analyzing Big Data, Business Analytics and Data Mining, Twitter and Text Analytics, Applied Decision Techniques, Operations Management, and Data Science for Business. He has over twenty years' consulting and training experience, including industries such as automotive, cutting tool, electronics, food, software, chemical, defense, and so on, in the areas of SPC, design of experiments, quality engineering, problem solving tools, Six-Sigma, and QMS. His work experience includes extensive research experience over five years at Ford in the areas of quality, reliability, and six-sigma. His research publications include journals such as IEEE Transactions on Reliability, Reliability Engineering & System Safety, Quality Engineering, International Journal of Product Development, International Journal of Business Excellence, and JSSSE. He has been keynote speaker at conferences and presented his research work at conferences such as SAE World Conference, INFORMS Annual Meetings, Industrial Engineering Research Conference, ASQs Annual Quality Congress, Taguchi's Robust Engineering Symposium, and Canadian RAMS. Dr. Rai has won awards for Excellence and exemplary teamwork at Ford for his contributions in the area of applied statistics. He also received an Employee Recognition Award by FAIA for his Ph.D. dissertation in support of Ford Motor Company. He is certified as ISO 9000 lead assessor from British Standards Institute, ISO 14000 lead assessor from Marsden Environmental International, and Six Sigma Black Belt from ASQ.

Who this course is for:
  • Data scientist or a data analyst who want to master the art of Data Analysis and Statistics using the R programming language.