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.
The aim of this video is to show how to install R on our system.
To run and write code in R, we first need to focus on how to get and install the IDE.
We have installed R and RStudio. Now let’s check out how to install the packages.
The aim of this video is to teach you what data types and data structures in R are.
In this video, we will see how to work with vectors in R.
The aim of this video is to show how to work with random numbers and do rounding and binning.
Taking vectors a step ahead, let’s see how we can to handle missing values.
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.
This video will introduce a new data structure called list and how to work with it.
In this video, our goal is to understand how to perform set operations in R.
What is sampling and sorting and how to do it in R?
Checking conditions is often a requirement for a programmer to write maintainable code. Let’s understand how we can check conditions in R.
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.
In this video, we will check out how to import and export data in R.
The aim of this video is to check out how to work with matrices and frequency tables.
Our goal in this video is to use W to merge data frame
How to do aggregation in R?
In this video, we will look at how to de-aggregate data frames and create cross tabulations.
The goal of this video is to see how to perform string operations in R.
Let’s learn how to avoid code replication.
The aim of this video is to understand how to debug and handle errors.
We’ll see in this video how to write fast loops with apply().
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.
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.
In this video, we will learn how to make multiple plots and custom layout to get better at our analyzing skills.
The aim of this video is to create different types of plots.
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.
The aim of this video is to teach you what normal distribution, central limit theorem, and confidence intervals are.
In this video, we will understand correlation and Covariance, the concept behind them, and their implementation in R. c
What is the chi-square statistic, when is it used, and how to do the chi-sq test?
What is ANOVA, its purpose, when to use it, and how to implement it in R?
What are the other commonly used statistical tests in R and how to implement them?
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.
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.
Practice solving exercises that involve making statistical inference
The aim of this video is to introduce the magrittr package, its significance, and features such as pipe operators.
Understand and use the 7 data manipulation verbs.
How to group datasets by one or more variables using dplyr.
How to join two tables using the two table verbs of dplyr.
How to work with databases with DplyR.
Understand the basics of data.table; do filter and select operations
Understand the syntax; create and update columns in a data.table.
Learn how to aggregate data.tables. Also learn the .N and .I operators.
Understand and implement chaining, keys, functions, and .SD.
How to write for-loops with set, set keys, and join data.tables?
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