Data is everywhere, and statisticians and analysts everywhere need to handle this data efficiently and tactfully. In comes R, a powerful programming language, arming developers with the tools to cater to their needs. This course will give you everything you need to start making software that can unlock your statistics and data.
The course is broken down into three parts. The first part will introduce R Studio and the basics of R—using packages and teaching you programming concepts such as variables, vectors, arrays, loops, and matrices. By solving coding challenges, you will gain a strong foundation for data munging.
With the basics mastered, we will take you through a number of topics such as handling dates with the lubridate package, handling strings with the stringr package, writing functions, debugging, error handling, and writing an apply family of functions. When you’ve mastered data munging, we’ll focus on visualizing data using base graphics.
Naturally, the next step is to learn how to make statistical inferences. We walk you through the fundamentals of univariate and bivariate analysis, computing confidence intervals, interpreting p values, and working with statistical significance. You’ll see how and when to use some of the commonly used statistical tests. With that, you will be ready for your first full-scale data analysis project to test the skills you’ve learned.
Finally, you will glimpse two powerful packages for data munging, the dplyr and data.table, which have both seen a rise in the R community. It is imperative to learn about both of these packages because much modern R code has been written using them.
With the help of interesting examples and coding challenges, this course will ensure that you have all the hacks and tricks you need to get started with R.
About The Author
Selva Prabhakaran is a data scientist with a global e-commerce organization. During 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.
- 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 course will be a great place to start
|1.1 The course overview||00:00:00|
|1.2 Installing R||00:00:00|
|1.3 Installing RStudio||00:00:00|
|1.4 Installing Packages||00:00:00|
|Section 2: Working with vectors|
|2.1 Data Types and Data Structures||00:00:00|
|2.3 Random Numbers, Rounding and Binding||00:00:00|
|2.4 Missing Values||00:00:00|
|2.5 The Which() Operator||00:00:00|
|Section 3: R Essentials|
|3.2 Set Operations||00:00:00|
|3.3 Sampling and Sorting||00:00:00|
|3.4 Check Conditions||00:00:00|
|3.5 For Loops||00:00:00|
|Section 4: Dataframes and Matrices|
|4.2 Importing and Exporting Data||00:00:00|
|4.3 Matrices and Frequency Tables||00:00:00|
|4.4 Merging Dataframes||00:00:00|
|4.6 Melting and Cross Tabulations with dcast()||00:00:00|
|Section 5: Core Programming|
|5.2 String Manipulation||00:00:00|
|5.4 Debugging and Eror Handling||00:00:00|
|5.5 Fast Loops with apply()||00:00:00|
|5.6 Fast Loops with lapply(), sapply() and vapply()||00:00:00|
|Section 6: Making Plots with Base Graphics|
|6.1 Creating and Customizing an R Plot||00:00:00|
|6.2 Drawing Plots with 2 y Axes||00:00:00|
|6.3 Multiplots and Custom Layout||00:00:00|
|6.4 Creating Basic Graph Types||00:00:00|
|Section 7: Statistical Inference|
|7.1 Univariate Analysis||00:00:00|
|7.2 Norman Distribution, Central Limit Theorem, and Confidence Intervals||00:00:00|
|7.3 Correlation and Covariance||00:00:00|
|7.4 Chi-sq Statistic||00:00:00|
|7.6 Statistical Tests||00:00:00|
|Section 8: R Very Own Project|
|8.1 Project 1 – Data Munging and Summarizing||00:00:00|
|8.2 Project 2 – Visualization with Base Graphics||00:00:00|
|8.3 Project 3 – Statistical Inference||00:00:00|
|Section 9: DPlyR and Pipes|
|9.1 Pipes with Magrittr||00:00:00|
|9.2 The 7 Data Manipulation Verbs||00:00:00|
|9.3 Aggregation and Special Functions||00:00:00|
|9.4 Two Table Verbs||00:00:00|
|9.5 Working With Databases||00:00:00|
|Section 10: data.table|
|10.1 Understanding Basics, Filter, and Select||00:00:00|
|10.2 Understanding Syntax, Creating and Updating Columns||00:00:00|
|10.3 Aggregating Data, .N, and .I||00:00:00|
|10.4 Chaining, Functions, and .SD||00:00:00|
|10.5 Fast Loops with set(), Keys, and Joins||00:00:00|
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