# Data Carpentry

Data Carpentry has contributed to the materials listed below.## Course Lessons

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:

## Work with date - time formats in R - time series data

Learn how to work with date and time fields 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.

## How to handle missing data or no data values in R - NA and NAN

Learn how to import spreadsheet files into R that contain missing data values. Also learn how to properly perform calculations on these data in R.

## How to import, work with and plot spreadsheet (tabular) data 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.

## The syntax of the R scientific programming language - Data science for scientists 101

This lesson introduces the basic syntax associated with the R scientific programming language. We will introduce 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. We will work with precipitation and stream discharge data for Boulder County.

## Get help with R - data science for scientists 101

This tutorial covers ways to get help when you are not sure how to perform a task in R.

## Write clean code - expressive or literate programming in R - data science for scientists 101

This lesson covers the basics of clean coding meaning that we ensure that the code that we write is easy for someone else to understand. We will briefly cover style guides, consistent spacing, literate object naming best practices.

## Install & use packages in R

Learn what a package is in R and how to install packages to work with your data.

## Get to know RStudio

Learn how to work with R using the RStudio application.

## Install & set up R and RStudio on your computer

Learn how to download and install R and RStudio on your computer.

## Data tutorials

*Nothing to list here yet!*