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RBootcamp

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70 Exercises
281 Participants
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Course Description

Self-directed bootcamp for learning the basics of data manipulation with the tidyverse. We start with visualization and ask fun questions of the data.

Learn how ggplot2 turns variables into statistical graphics

Learn how to manipulate data into a ggplot2 friendly format

Now we have tidy data, let's start doing some statistics!

What do other learners have to say?

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Harvard Business School

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Ronald Bowers

Decision Science Analytics @ USAA

Ted Laderas

Instructor

Bioinformatics Developer and Assistant Professor at OHSU. Collaborative Informaticist and R/Data Science evangelist. Plays well with others.

Jessica Minnier

Assistant Professor of Biostatistics at Oregon Health & Science University

Jessica is an Assistant Professor of Biostatistics in the OHSU-PSU School of Public Health at Oregon Health & Science University. Her statistical research interests include risk prediction with high dimensional data sets and the analysis of genetic and other omics data. She is passionate about teaching R and programming, reproducible research, and open science.

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Course Description

The Magic of ggplot2

Quick Data Frame Introduction

Thinking about aesthetics

Mapping variables to produce geometric plots

More about aes

Points versus lines

Geoms are layers on a ggplot

Quick review about ggplot2

Final Challenge: Recreate this Gapminder Plot

What you learned in this chapter

Introduction to dplyr

Introduction to dplyr

A Little Bit about assignment

Let's look at some data and ways to manipulate it.

dplyr::filter()

Comparison operators and chaining comparisons

Quick Quiz about Chaining Comparisons

The %in% operator

Removing Missing Values

dplyr::mutate()

You can use mutated variables right away!

Another Use for `mutate()`

The difference between `filter()` and `mutate()`

The Pipe Operator: %>%

group_by()/summarize()

Counting Stuff

arrange()

select()

Chester Ismay's Mantra

Putting it all together: Challenge 1

Challenge 2: Show your stuff

Challenge 3: Putting together what we know about ggplot2 and dplyr

What you learned in this chapter

Simple Stats and Modeling with broom

We've built a foundation. Now to stats!

Let's explore the fishermen mercury dataset

Visualize Mean of Total Mercury by Fisherman Status

Compute Means with group_by

Is there a difference?

T-test of means for fisherman status

Sweep up that output with Broom

Let's delve deeper into the data

Linear Regression

Broom with linear regression

Broom with linear regression: glance

Compare our models

Prediction of mercury

Challenge 1: augment + ggplot2

Challenge 2: Proportions of fishpart by fisherman status

What you learned in this chapter

ggplot2 and categorical data

Review of factors

A Basic Barplot using `geom_bar()`

Stacked Bars

Quick Quiz

Proportional Barchart

Dodge those bars!

Faceting a graph

Super Quick Review

Your Task: Bar Charts

Boxplots

Try out geom_boxplot() yourself

Violin Plots

Your task: How heavy are our pets?

What you learned in this chapter

The Whys and Hows of Tidy Data

What you'll learn in this Chapter:

What is Tidy Data?

tidyr::gather()

tidyr::spread()

dplyr::separate()