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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.
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Training 2 or more people?
Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more- 1
The Magic of ggplot2
FreeLearn how ggplot2 turns variables into statistical graphics
Quick Data Frame Introduction100 xpThinking about aesthetics100 xpMapping variables to produce geometric plots100 xpMore about aes50 xpPoints versus lines100 xpGeoms are layers on a ggplot100 xpQuick review about ggplot2100 xpFinal Challenge: Recreate this Gapminder Plot300 xpWhat you learned in this chapter0 xp - 2
ggplot2 and categorical data
FreeMore on plotting using ggplot2
Review of factors100 xpA Basic Barplot using `geom_bar()`100 xpStacked Bars100 xpQuick Quiz50 xpProportional Barchart100 xpDodge those bars!100 xpFaceting a graph100 xpSuper Quick Review100 xpYour Task: Bar Charts300 xpBoxplots100 xpTry out geom_boxplot() yourself100 xpViolin Plots100 xpYour task: How heavy are our pets?300 xpWhat you learned in this chapter100 xp - 3
Introduction to dplyr
FreeLearn how to manipulate data into a ggplot2 friendly format
Introduction to dplyr0 xpA Little Bit about assignment100 xpLet's look at some data and ways to manipulate it.100 xpdplyr::filter()100 xpComparison operators and chaining comparisons100 xpQuick Quiz about Chaining Comparisons50 xpThe %in% operator100 xpRemoving Missing Values100 xpdplyr::mutate()100 xpYou can use mutated variables right away!100 xpAnother Use for `mutate()`50 xpThe difference between `filter()` and `mutate()`50 xpThe Pipe Operator: %>%200 xpgroup_by()/summarize()100 xpCounting Stuff50 xparrange()100 xpselect()100 xpChester Ismay's Mantra50 xpPutting it all together: Challenge 1300 xpChallenge 2: Show your stuff300 xpChallenge 3: Putting together what we know about ggplot2 and dplyr300 xpWhat you learned in this chapter0 xp - 4
The Whys and Hows of Tidy Data
FreeWhy we need tidy data and using `tidyr` to make messy data tidy
- 5
Simple Stats and Modeling with broom
FreeNow we have tidy data, let's start doing some statistics!
We've built a foundation. Now to stats!0 xpLet's explore the fishermen mercury dataset100 xpVisualize Mean of Total Mercury by Fisherman Status100 xpCompute Means with group_by100 xpIs there a difference?50 xpT-test of means for fisherman status100 xpSweep up that output with Broom100 xpLet's delve deeper into the data100 xpLinear Regression100 xpBroom with linear regression100 xpBroom with linear regression: glance100 xpCompare our models100 xpPrediction of mercury50 xpChallenge 1: augment + ggplot2100 xpChallenge 2: Proportions of fishpart by fisherman status100 xpWhat you learned in this chapter100 xp
Ted Laderas
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Bioinformatics Developer and Assistant Professor at OHSU. Collaborative Informaticist and R/Data Science evangelist. Plays well with others.
Jessica Minnier
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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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