R: Data Analysis with R - Step-by-Step Tutorial!: 3-in-1
- 5 hours on-demand video
- 1 downloadable resource
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- 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.
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
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
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()
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
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
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
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
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
- Prior basic understanding R programming language will be useful.
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.
- Data scientist or a data analyst who want to master the art of Data Analysis and Statistics using the R programming language.