Fundamentals of Bayesian Data Analysis in R

Experience the whole data science pipeline from importing and tidying data to wrangling and visualizing data to modeling and communicating with data. Gain exposure to each component of this pipeline from a variety of different perspectives in this tidyverse R track.

Import and tidy data, wrangle and visualize data, and model and communicate with data in R with the tidyverse.

with R

Tidyverse Fundamentals

In this chapter, you'll learn to do three things with a table: filter for particular observations, arrange the observations in a desired order, and mutate to add or change a column. You'll see how each of these steps allows you to answer questions about your data.

Data wrangling

Often a better way to understand and present data as a graph. In this chapter, you'll learn the essential skills of data visualization using the ggplot2 package, and you'll see how the dplyr and ggplot2 packages work closely together to create informative graphs.

Data visualization

So far you've been answering questions about individual country-year pairs, but you may be interested in aggregations of the data, such as the average life expectancy of all countries within each year. Here you'll learn to use the group by and summarize verbs, which collapse large datasets into manageable summaries.

Grouping and summarizing

In this chapter, you'll learn how to create line plots, bar plots, histograms, and boxplots. You'll see how each plot requires different methods of data manipulation and preparation, and you’ll understand how each of these plot types plays a different role in data analysis.

Types of visualizations

Gapminder

This is an introduction to the programming language R, focused on a powerful set of tools known as the Tidyverse. You'll learn the intertwined processes of data manipulation and visualization using the tools dplyr and ggplot2. You'll learn to manipulate data by filtering, sorting, and summarizing a real dataset of historical country data in order to answer exploratory questions. You'll then learn to turn this processed data into informative line plots, bar plots, histograms, and more with the ggplot2 package. You’ll get a taste of the value of exploratory data analysis and the power of Tidyverse tools. This is a suitable introduction for those who have no previous experience in R and are interested in performing data analysis.

Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.

Introduction to the Tidyverse

Programming

You'll be introduced to the concept of tidy data which is central to this course. In the first two lessons, you'll jump straight into the action by separating messy character columns into tidy variables and observations ready for analysis. In the final lesson, you'll learn how to overwrite and remove missing values.

Tidy data

This chapter is all about pivoting data from a wide to long format and back again using the `pivot_longer()` and `pivot_wider()` functions. You'll need these functions when variables are hidden in messy column names or when variables are stored in rows instead of columns. You'll learn about space dogs, nuclear bombs, and planet temperatures along the way.

From wide to long and back

Values can often be missing in your data, and sometimes entire observations are absent too. In this chapter, you'll learn how to complete your dataset with these missing observations. You'll add observations with zero values to counted data, expand time series to a full sequence of intervals, and more!

Expanding data

In the final chapter, you'll learn how to turn nested data structures such as JSON and XML files into tidy, rectangular data. This skill will enable you to process data from web APIs. You'll also learn how nested data structures can be used to write elegant modeling pipelines that produce tidy outputs.

Rectangling data

Nuclear explosions data

Planet data

Star Wars data

Netflix data

ANSUR II data

Olympic medals data

Data in the wild can be scary—when confronted with a complicated and messy dataset you may find yourself wondering, where do I even start? The tidyr package allows you to wrangle such beasts into nice and tidy datasets. Inaccessible values stored in column names will be put into rows, JSON files will become data frames, and missing values will never go missing again. You'll practice these techniques on a wide range of messy datasets, learning along the way how many dogs the Soviet Union sent into space and what bird is most popular in New Zealand. With the tidyr package in your tidyverse toolkit, you'll be able to transform almost any dataset in a tidy format which will pay-off during the rest of your analysis.

Data Manipulation with dplyr 

Transform almost any dataset into a tidy format to make analysis easier.

Reshaping Data with tidyr

Data Manipulation

Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.


In 1847, the Hungarian physician Ignaz Semmelweis made a breakthough discovery:
he discovers handwashing. Contaminated hands was a major cause of childbed fever
and by enforcing handwashing at his hospital he saved hundreds of lives. 


1. Meet Dr. Ignaz Semmelweis

2. The alarming number of deaths

3. Death at the clinics

4. The handwashing begins

5. The effect of handwashing

6. The effect of handwashing highlighted

7. More handwashing, fewer deaths?

8. A statistical analysis of Semmelweis handwashing data

9. The fate of Dr. Semmelweis

Dr. Semmelweis and the Discovery of Handwashing

Once you’ve got some data, you’ll often want to reshape it, move it around, and merge it with other data. Learn the basics of data manipulation using today’s most powerful and popular tools.

Creating visual representations of your data is one of the quickest and most effective ways to understand it, discover surprising results, and communicate those results to others. Learn the basics of data visualization using state-of-the-art tools.

Data Visualization

Data science is a vast and rapidly evolving field. For this reason, it’s important for you to put your skills to the test by solving real-world problems once you’ve mastered the basics.

Case Studies

This chapter will introduce you to some background theory and terminology for modeling, in particular, the general modeling framework, the difference between modeling for explanation and modeling for prediction, and the modeling problem. Furthermore, you'll start performing your first exploratory data analysis, a crucial first step before any formal modeling.

Introduction to Modeling

Equipped with your understanding of the general modeling framework, in this chapter, we'll cover basic linear regression where you'll keep things simple and model the outcome variable y as a function of a single explanatory/ predictor variable x. We'll use both numerical and categorical x variables. The outcome variable of interest in this chapter will be teaching evaluation scores of instructors at the University of Texas, Austin.

Modeling with Basic Regression

In the previous chapter, you learned about basic regression using either a single numerical or a categorical predictor. But why limit ourselves to using only one variable to inform your explanations/predictions? You will now extend basic regression to multiple regression, which allows for incorporation of more than one explanatory or one predictor variable in your models. You'll be modeling house prices using a dataset of houses in the Seattle, WA metropolitan area. 

Modeling with Multiple Regression

In the previous chapters, you fit various models to explain or predict an outcome variable of interest. However, how do we know which models to choose? Model assessment measures allow you to assess how well an explanatory model "fits" a set of data or how accurate a predictive model is. Based on these measures, you'll learn about criteria for determining which models are "best". 

Model Assessment and Selection

In this course, you will learn to model with data. Models attempt to capture the relationship between an outcome variable of interest and a series of explanatory/predictor variables. Such models can be used for both explanatory purposes, e.g. "Does knowing professors' ages help explain their teaching evaluation scores?", and predictive purposes, e.g., "How well can we predict a house's price based on its size and condition?" You will leverage your tidyverse skills to construct and interpret such models. This course centers around the use of linear regression, one of the most commonly-used and easy to understand approaches to modeling. Such modeling and thinking is used in a wide variety of fields, including statistics, causal inference, machine learning, and artificial intelligence.

Working with Data in the Tidyverse

Explore Linear Regression in a tidy framework. 

Modeling with Data in the Tidyverse

Probability & Statistics

In this chapter, you will have a first look at the data you're going to work with throughout this course: the relationship between weekly working hours and monetary compensation in European countries, according to the International Labour Organization (ILO). After that, you'll dive right in and discover a stunning correlation by employing an exploratory visualization. You will then apply a custom look to that graphic – you'll turn an ordinary plot into an aesthetically pleasing and unique data visualization.

Custom ggplot2 themes

Barcharts, scatter plots, and histograms are probably the most common and effective data visualizations. Yet, sometimes, there are even better ways to visually highlight the finding you want to communicate to your audience. So-called "dot plots" make us better grasp and understand changes in data: development over time, for example. In this chapter, you'll build a custom and unique visualization that emphasizes and explains exactly one aspect of the story you want to tell.

Creating a custom and unique visualization

Back in the old days, researchers and data analysts used to generate plots in R and then tediously copy them into their LaTeX or Word documents. Nowadays, whole reports can be produced and reproduced from within R and RStudio, using the RMarkdown language – combining R chunks, formatted prose, tables and plots. In this chapter, you'll take your previous findings, results, and graphics and integrate them into such a report to tell the story that needs to be told.

 Introduction to RMarkdown

Your boss, your client, or your professor usually expects your results to be accurate and presented in a clear and concise structure. However, coming up with a nicely formatted and unique report on top of that is certainly a plus and RMarkdown can be customized to accomplish this. In this last chapter, you'll take your report from the last chapter and brand it with your own custom and unique style.


Customizing your RMarkdown report

Hourly Compensation (ILO)

Weekly Working Hours (ILO)

They say that a picture is worth a thousand words. Indeed, successfully promoting your data analysis is not only a matter of accurate and effective graphics, but also of aesthetics and uniqueness. This course teaches you how to leverage the power of ggplot2 themes for producing publication-quality graphics that stick out from the mass of boilerplate plots out there. It shows you how to tweak and get the most out of ggplot2 in order to produce unconventional plots that draw attention on social media. In the end, you will combine that knowledge to produce a slick and custom-styled report with RMarkdown and CSS – all of that within the powerful tidyverse.

Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.

Communicating with Data in R (Tidyverse)

Communicating with Data in the Tidyverse

In this chapter, you’ll learn all about factors. You’ll discover the difference between categorical and ordinal variables, how R represents them, and how to inspect them to find the number and names of the levels. Finally, you’ll find how forcats, a tidyverse package, can improve your plots by letting you quickly reorder variables by their frequency.

Introduction to Factor Variables

You’ll continue to dive into the forcats package, learning how to change the order and names of levels and even collapse them into one another.

Manipulating Factor Variables

Having gotten a good grasp of forcats, you’ll expand out to the rest of the tidyverse, learning and reviewing functions from dplyr, tidyr, and stringr. You’ll refine graphs with ggplot2 by changing axes to percentage scales, editing the layout of the text, and more.

Creating Factor Variables

In this final chapter, you’ll take all that you’ve learned and apply it in a case study. You’ll learn more about working with strings and summarizing data, then replicate a publication quality 538 plot.

Case Study on Flight Etiquette

538 Flying Etiquette survey

Kaggle multiple choice responses

As a data scientist, you will often find yourself working with non-numerical data, such as job titles, survey responses, or demographic information. R has a special way of representing them, called factors, and this course will help you master working with them using the tidyverse package forcats. We’ll also work with other tidyverse packages, including ggplot2, dplyr, stringr, and tidyr and use real world datasets, such as the fivethirtyeight flight dataset and Kaggle’s State of Data Science and ML Survey. Following this course, you’ll be able to identify and manipulate factor variables, quickly and efficiently visualize your data, and effectively communicate your results. Get ready to categorize!

Get ready to categorize! In this course,  you will work with non-numerical data, such as job titles or survey responses, using the Tidyverse landscape.

Learning Experience

Business Features

Academics

Tidyverse Fundamentals with R

Create Your Free Account

Loved by learners at thousands of companies

Instructors

Loved by learners at thousands of companies

Instructors

Join over .css-w0wpus-TrackShowPage{color:#03ef62;}7,700,000 learners and start Tidyverse Fundamentals with R today!

Create Your Free Account

Join over 7,700,000 learners and start Tidyverse Fundamentals with R today!