Data CarpentryData Carpentry has contributed to the materials listed below.
Course lessons are developed as a part of a course curriculum. They teach specific learning objectives associated with data and scientific programming. Data Carpentry has contributed to the following lessons:
Times series data can be manipulated efficiently in R. Learn how to work with, plot and subset data with dates in R.
Plot Data and Customize Plots with ggplot Plots in R - Earth Analytics - Data Science for Scientists
Learn how to plot data and customize your plots using ggplot in R.
Missing data in R can be caused by issues in data collection and / or processing and presents challenges in data analysis. Learn how to address missing data values in R.
Learn how to import and plot data in R using the read_csv & qplot / ggplot functions.
Understand the Vector Data Type in R and Classes Including Strings, Numbers and Logicals - Data Science for Scientists 101
This tutorial introduces vectors in R. It also introduces the differences between strings, numbers and logical or boolean values (True / False) in R.
Creating Variables in R and the String vs Numeric Data Type or Class - Data Science for Scientists 101
This lesson covers creating variables or objects in R. It also introduces some of the basic data types or classes including strings and numbers. This lesson is designed for someone who has not used R before.
This lesson introduces the basic syntax associated with the R scientific programming language. You will learn about assignment operators (<-), comments and basic functions that are available to use in R to perform basic tasks including head(), qplot() to quickly plot data and others. This lesson is designed for someone who has not used R before. You will work with precipitation and stream discharge data for Boulder County.
This tutorial covers ways to get help when you are not sure how to perform a task in R.
This lesson covers the basics of clean coding meaning that you ensure that the code that you write is easy for someone else to understand. The lesson will briefly cover style guides, consistent spacing, literate object naming best practices.
Learn what a package is in R and how to install packages to work with your data.
Learn how to work with R using the RStudio application.
Learn how to download and install R and RStudio on your computer.