Leah Wasser

Learn how to calculate seasonal summary values for MACA 2 climate data using xarray and region mask in open source Python.

Climate datasets stored in netcdf 4 format often cover the entire globe or an entire country. Learn how to subset climate data spatially and by time slices using xarray and regionmask in open source python.

Historic and projected climate data are most often stored in netcdf 4 format. Learn how to open and process MACA version 2 climate data for the Continental United States using the open source python package, xarray.

MACA V2 climate data provides but historica and future predictions of climate variables using different models. Learn how to download netcdf 4 format programatically using open source Python and open the data with xarray.

In this lesson you will learn the basics of what CMIP5 and MACA v 2 data are and how global climate data are downscaled to higher resolutions to support regional analysis.

In this lesson you will learn about that netcdf 4 data format which is a format, commonly used to store climate data. In later lessons you will learn how to open climate data using open source Python tools.

Loops can be used to automate data tasks in Python by iteratively executing the same code on multiple data structures. Practice using loops to automate certain functionality in Python.

A list comprehensions in Python is a type of loop that is often faster than traditional loops. Learn how to create list comprehensions to automate data tasks in Python.

Practice your skills creating maps of raster and vector data using open source Python.

Practice your skills plotting time series data stored in Pandas Data Frames in Python.

Practice your skills plotting time series data stored in Pandas Data Frames in Python.

Complete these exercises to practice the skills you learned in the file formats chapters.

Vector data is one of the two most common spatial data types. Learn to work with vector data for earth data science.

Raster data is one of the two most common spatial data types. Learn to work with raster data for earth data science.

Two of the major spatial data formats used in earth data science are vector and raster data. Learn about these two common spatial data formats for earth data science workflows in this chapter.

In this lesson you will learn how to parse a JSON file containing twitter data to better understand the 2013 Colorado Floods using Open Source Python tools.

MODIS is remote sensing data that is stored in the HDF4 file format. Learn how to open and use MODIS data in HDF4 form in Open Source Python.

MODIS is remote sensing data that is stored in the HDF4 file format. Learn how to view and explore HDF4 files (and their metadata) using the free HDF viewer provided by the HDF group.

Learn how to find and download MODIS data from the USGS Earth Explorer website.

MODIS is a satellite remote sensing instrument that collects data daily across the globe at 250-500 m resolution. Learn how to import, clean up and plot MODIS data in Python.

A set of activities for you to practice your skills using Landsat Data in Open Source Python.

Learn how to find and download Landsat 8 remote sensing data from the USGS Earth Explorer website.

Most remote sensing data sets contain no data values that represent pixels that contain invalid data. Learn how to handle no data values in Python for better raster processing.

Landsat remote sensing data often has pixels that are covered by clouds and cloud shadows. Learn how to remove cloud covered landsat pixels using open source Python.

Learn how to open up and create a stack of Landsat images and crop them to a certain extent using open source Python.

Landsat 8 data are downloaded in tif file format. Learn how to open and manipulate Landsat 8 data in Python. Also learn how to create RGB and color infrared Landsat image composites.

The Normalized Burn Index is used to quantify the amount of area that was impacted by a fire. Learn how to calculate the normalized burn index and classify your data using Landsat 8 data in Python.

A vegetation index is a single value that quantifies vegetation health or structure. Learn how to calculate the NDVI vegetation index using NAIP data in Python.

A vegetation index is a value that quantifies vegetation health or structure. Learn how to calculate the NDVI and NBR vegetation indices to study vegetation health and wildfire impacts in Python.

An activity to practice all of the skills you just learned in .

When you plot time series data using the matplotlib package in Python, you often want to customize the date format that is presented on the plot. Learn how to customize the date format on time series plots created using matplotlib.

Sometimes you need to take time series data collected at a higher resolution (for instance many times a day) and summarize it to a daily, weekly or even monthly value. This process is called resampling in Python and can be done using pandas dataframes. Learn how to resample time series data in Python with Pandas.

Sometimes you have data over a longer time span than you need for your analysis or plot. Learn how to subset your data using a begin and end date in Python.

Python provides a datetime object for storing and working with dates. Learn how you can convert columns in a pandas dataframe containing dates and times as strings into datetime objects for more efficient analysis and plotting.

Python provides a datetime object for storing and working with dates. Learn how you can convert columns in a pandas dataframe containing dates and times as strings into datetime objects for more efficient analysis and plotting.

Learn how to create a cumulative sum plot in Pandas to better understand stream discharge in a watershed

Learn how to calculate exceedance probability and return periods associated with a flood in Python.

A function is a reusable block of code that performs a specific task. Learn how to write functions that can take multiple as well as optional parameters in Python to eliminate repetition and improve efficiency in your code.

A function is a reusable block of code that performs a specific task. Learn how to write functions in Python to eliminate repetition and improve efficiency in your code.

A function is a reusable block of code that performs a specific task. Learn how functions can be used to write efficient and DRY (Do Not Repeat Yourself), code in Python.

Loops can be an important part of creating a data workflow in Python. Use loops to go from raw data to a finished project more effeciently.

Loops can be used to automate data tasks in Python by iteratively executing the same code on multiple data structures. Learn how to automate data tasks in Python using data structures such as lists, numpy arrays, and pandas dataframes.

Loops can help reduce repetition in code by iteratively executing the same code on a range or list of values. Learn about the basic types of loops in Python and how they can be used to write Do Not Repeat Yourself, or DRY, code in Python.

Conditional statements in Python can be written to check for alternative conditions or combinations of multiple conditions. Learn how to write conditional statements in Python that choose betweeen alternative conditions or check for combinations of conditions before executing code.

Conditional statements help you to control the flow of code by executing code only when certain conditions are met. Learn about the structure of conditional statements in Python and how they can be used to write Do Not Repeat Yourself, or DRY, code in Python.

A pull request allows anyone to suggest changes to a repository on GitHub that can be easily reviewed by others. Learn how to submit pull requests on GitHub.com to suggest changes to a GitHub repository.

GitHub.com can be used to store and access files in the cloud using GitHub repositories. Learn how to submit pull requests on GitHub.com to suggest changes to a GitHub repository.

An issue is a GitHub project management tool that allows anyone to identify and discuss potential changes to a repo. Learn how to create and manage GitHub issues to support collaborative open reproducible science projects.

When you are working on a forked GitHub repository you will need to update your files frequently. Learn how to update your GitHub fork using a reverse pull request.

A pull request allows anyone to suggest changes to a repository on GitHub that can be easily reviewed by others. Learn how to submit pull requests on GitHub.com to suggest changes to a GitHub repository.

GitHub is a website that supports git-based version control and collaborative project management. Learn how to use git and GitHub to collaborate on projects in support of open reproducible science.

Pandas dataframes are a commonly used scientific data structure in Python that store tabular data using rows and columns with headers. Learn how to use indexing and filtering to select data from pandas dataframes.

Pandas dataframes are a commonly used scientific data structure in Python that store tabular data using rows and columns with headers. Learn how to run calculations and summary statistics (such as mean or maximum) on columns in pandas dataframes.

Pandas dataframes are a commonly used scientific data structure in Python that store tabular data using rows and columns with headers. Learn how to import text data from .csv files into numpy arrays.

Pandas dataframes are a commonly used scientific data structure in Python that store tabular data using rows and columns with headers. Learn about the key characteristics of pandas dataframes that make them a useful data structure for storing and working with labeled scientific datasets.

Numpy arrays are an efficient data structure for working with scientific data in Python. Learn how to use indexing to slice (or select) data from one-dimensional and two-dimensional numpy arrays.

Numpy arrays are an efficient data structure for working with scientific data in Python. Learn how to run calculations and summary statistics (such as mean or maximum) on one-dimensional and two-dimensional numpy arrays.

Numpy arrays are an efficient data structure for working with scientific data in Python. Learn how to import text data from .txt and .csv files into numpy arrays.

Numpy arrays are a commonly used scientific data structure in Python that store data as a grid, or a matrix. Learn about the key characteristics of numpy arrays that make them an efficient data structure for storing and working with large scientific datasets.

The os and glob packages are very useful tools in Python for accessing files and directories and for creating lists of paths to files and directories, respectively. Learn how to manipulate and parse file and directory paths using os and glob.

Manually constructed files paths will often not run on computers with different operating systems. Learn how to construct file paths in Python that will work on Mac, Linux and Windows, in support of open reproducible science.

A directory refers to a folder on a computer that has relationships to other folders. Learn about directories, files, and paths, as they relate to creating reproducible science projects.

Packages in Python provide pre-built functionality that adds to the functionality available in base Python. Learn how to install packages in Python using conda environments.

A conda environment is a self contained Python environment that allows you to run different versions of Python (with different installed packages) on your computer. Learn how to conda environments can you help manage Python packages and dependencies.

The Python programming language provides many packages and libraries for working with scientific data. Learn about key Python packages for earth data science.

In this lesson you will review how to customize matplotlib maps created using vector data in Python. You will review how to add legends, titles and how to customize map colors.

When you plot time series data in matplotlib, you often want to customize the date format that is presented on the plot. Learn how to customize the date format in a Python matplotlib plot.

Matplotlib is the most commonly used plotting library in Python. Learn how to customize the colors, symbols, and labels on your plots using matplotlib.

Matplotlib is the most commonly used plotting library in Python. Learn how to create plots using the matplotlib object oriented approach.

DRY (Do Not Repeat Yourself) code supports reproducibility by removing repetition and making code easier to read. Learn about key strategies to write DRY code in Python.

Expressive variable names refer to function and variable names that describe what the variable contains or what the function does. Using expressive names makes your code easier to understand. Learn how to create expressive names for objects in your Python code.

Using a standard format and syntax when programming makes your code easier to read. Learn more about PEP 8, a set of guidelines for writing clean code in Python.

Clean code refers to writing code that runs efficiently, is not redundant and is easy for anyone to understand. Learn about the characteristics and benefits of writing clean, expressive code in Python.

Complete these exercises to practice the skills you learned in the Python fundamentals chapters.

Operators are symbols in Python that carry out a specific computation, or operation, such as arithmetic calculations. Learn how to use basic operators in Python.

A Python list is a data structure that stores a collection of values in a specified order (or sequence) and is mutable (or changeable). Learn how to create and work with lists in Python.

Variables store data (i.e. information) that you want to re-use in your code (e.g. single numeric value, path to a directory or file). Learn how to to create and work with variables in Python.

Python is a free, open source programming language that can be used to work with scientific data. Learn about using Python to develop scientific workflows.

Tabular data is common in all analytical work, most commonly seen as .txt and .csv files. Learn to work with tabular data for earth data science in this lesson.

Markdown allows you to format text using simple, plain-text syntax and can be used to document code in a variety of tools, including Jupyter Notebook. Learn how to format text in Jupyter Notebook using Markdown.

There are many text file formats that are useful for earth data science workflows including Markdown, text (.txt, .csv) files, and YAML (Yet Another Markup Language). Learn about these common text file formats for earth data science workflows.

The Jupyter ecosystem contains many useful tools for working with Python including Jupyter Notebook, an interactive coding environment. Learn useful shortcuts in Jupyter Notebook that can help you complete your tasks quickly and efficiently.

The Jupyter ecosystem contains many useful tools for working with Python including Jupyter Notebook, an interactive coding environment, and the Jupyter Notebook dashboard, which allows you to manage files and directories in your Jupyter environment. Learn how to manage Jupyter Notebook files including saving, renaming, deleting, moving, and downloading notebooks.

The Jupyter ecosystem contains many useful tools for working with Python including the Jupyter Notebook dashboard, which allows you to manage files and directories in your Jupyter environment. Learn how to create, rename, move, and delete directories using the Jupyter Notebook dashboard.

The Jupyter ecosystem contains many useful tools for working with Python including Jupyter Notebook, an interactive coding environment. Learn how to work with cells, including Python code and Markdown text cells, in Jupyter Notebook.

The Jupyter ecosystem contains many useful tools for working with Python including Jupyter Notebook, an interactive coding environment. Learn how to launch and close Jupyter Notebook sessions and how to navigate the Jupyter Dashboard to create and open Jupyter Notebook files (.ipynb).

The Jupyter ecosystem contains many useful tools for working with Python including Jupyter Notebook, an interactive coding environment. Learn how the components and functionality of Jupyter Notebook can help you implement open reproducible science workflows.

Bash or Shell is a command line tool that is used in open science to efficiently manipulate files and directories. Learn how to run useful Bash commands to access and manage directories and files on your computer.

Bash or Shell is a command line tool that is used in open science to efficiently manipulate files and directories. Learn how to use Bash to manipulate files in support of reproducible science.

In this lesson you will learn how to adjust the x and y limits of your matplotlib and geopandas map to change the spatial extent..

Open reproducible science refers to developing workflows that others can easily understand and use. Learn about best practices for organizing open reproducible science projects including the use of machine readable names.

Key tools for open reproducible science include Shell (Bash), git and GitHub, Jupyter, and Python. Learn how these tools help you implement open reproducible science workflows.

Open reproducible science refers to developing workflows that others can easily understand and use. It enables you to build on others' work rather than starting from scratch. Learn about the importance and benefits of open reproducible science.

When making maps, you often want to create legends, customize colors, adjust zoom levels, or even make interactive maps. Learn how to customize maps created using vector data in Python with matplotlib, geopandas, and folium.

Sometimes vector data are missing attribute data, and it can be helpful to clean up your data. Learn how to handle missing attribute data in Python using GeoPandas.

A spatial join is when you assign attributes from one shapefile to another based upon its spatial location. Learn how to perform spatial joins in Python.

When you dissolve polygons, you remove the interior boundaries of a set of polygons with the same attribute value and create one new merged or combined polygon for each attribute value. Learn how to dissolve polygons in Python using GeoPandas.

Sometimes you may want to spatially clip a vector data layer to a specified boundary for easier plotting and analysis of smaller spatial areas. Learn how to clip a vector data layer in Python using GeoPandas and Shapely.

Often when spatial data do not line up properly on a plot, it is because they are in different coordinate reference systems (CRS). Learn how to reproject a vector dataset to a different CRS in Python using the to_crs() function from GeoPandas.

Often when spatial data do not line up properly on a plot, it is because they are in different coordinate reference systems (CRS). Learn how to reproject a vector dataset to a different CRS in Python using the to_crs() function from GeoPandas.

Designing and developing data workflows can help you complete your work more efficiently by allowing you to repeat and automate data tasks. Learn how to design and develop efficient workflows to automate data analyses in Python.

Pseudcode can help you design data workflows through listing out the individual steps of workflow in plain language, so the focus is on the overall data process, rather than on the specific code needed. Learn best practices for writing pseudocode for data workflows.

Identifying aspects of a workflow that can be modularized and tested can help you design efficient and effective data workflows. Learn best practices for designing efficient data workflows.

Restructured text (RST) is a text format similar to markdown that is often used to document python software. Learn how create headings, lists and code blocks in a text file using RST syntax.

Lack of documentation will limit peoples’ use of your code. In this lesson you will learn about 2 ways to document python code using docstrings and online documentation. YOu will also learn how to improve documentation in other software packages.

Open source means that you can view and contribute to software code like packages you use in Python. Learn about the ways that you can contribute without being an expert progammer.

Open source means that you can view and contribute to software code like packages you use in Python. Learn about the ways that you can contribute without being an expert progammer.

Scientists often use remote sensing methods to study the impacts of wildfire through calculations of vegetation indices before and after wildfire. Learn more about how remote sensing can be used to study wildfire impacts.

Scientists often use field survey methods to study the impacts of wildfire through measurements of biomass and soil. Learn more about how survey methods can be used to study wildfire impacts.

The Cold Springs wildfire burned a total of 528 acres of land between July 9, 2016 and July 14, 2016. Learn more about this wildfire and how scientists study wildfire using both field and remote sensing methods.

Practice your skills setting up git locally, committing changes to files and pushing and pulling files to GitHub.com

A version control system allows you to track and manage changes to your files. Learn how to undo changes in git after they have been added or committed to version control.

A version control system allows you to track and manage changes to your files. Learn how to use some basic Git commands including add, commit and push.

Learn how to setup git locally on your computer.

GitHub.com can be used to store and access files in the cloud to share with others or simply as a backup of your local files. Learn how to create a copy of files on GitHub (fork) and to download files from GitHub to your computer (clone).

A version control system allows you to track and manage changes to your files. Learn benefits of version control for scientific workflows and how git and GitHub.com support version control.

This lesson teaches you how to collaborate with others in a project, including tasks such as notifying others that an assigned task has been completed.

This lesson teaches you how to undo changes in Git after they have been added or committed.

This lesson covers how to crop a raster dataset and export it as a new raster in Python

In this lesson you review how to dissolve polygons in python. A spatial join is when you assign attributes from one shapefile to another based upon its spatial location.

Sometimes two shapefiles do not line up properly even if they cover the same area because they are in different coordinate reference systems. Learn how to reproject vector data in Python using geopandas to ensure your data line up.

This lesson introduces what vector data are and how to open vector data stored in shapefile format in Python.

Sometimes you need to manipulate multiple rasters to create a new raster output data set in Python. Learn how to create a CHM by subtracting an elevation raster dataset from a surface model dataset in Python.

This lesson introduces the raster geotiff file format - which is often used to store lidar raster data. You will learn the 3 key spatial attributes of a raster dataset including Coordinate reference system, spatial extent and resolution.

There are a suite of powerful open source python libraries that can be used to work with spatial data. Learn how to use geopandas, rasterio and matplotlib to plot and manipulate spatial data in Python.

Text editors can be used to edit code and for commit messages in git. Learn about features to look for in a text editor and how to change your default text editor at the command line.

Conda environments allow you to easily manage the Python package installations on your computer. Learn how to install a conda environment using a yml file.

This tutorial walks you through how access the shell through terminal, use basic commands in the terminal for file organization, and set up a working directory for the course.

Learn how to install Git, GitBash (a version of command line Bash) and the Miniconda Python distribution on your computer.

There are several core tools that are required to work with data. These include Shell/Bash, Git/Github and Python. Learn how to set all of these tools up on your computer so you can work with different types of data using open science workflows.

In this lesson you will review how to find and download USDS NAIP imagery from the USGS Earth Explorere website.

Learn how to open up a multi-band raster layer or image stored in .tiff format in Python using Rasterio. Learn how to plot histograms of raster values and how to plot 3 band RGB and color infrared or false color images.

Learn the basics of how addidative colors models are used to render RGB images in Python.

Multispectral remote sensing data can be in different resolutions and formats and often has different bands. Learn about the differences between NAIP, Landsat and MODIS remote sensing data as it is used in Python.

Interactive maps allow you to easily explore data. Learn how to create interative leaflet maps embedded in a Jupyter Notebook using Python and folium.

When making maps, sometimes you want to zoom in to a specific area in your map. Learn how to adjust the x and y limits of your matplotlib and geopandas map to change the spatial extent that is displayed.

When making maps, you often want to add legends and customize the map colors. Learn how to customize legends and colors in matplotlib maps created using vector data in Python.

When plotting raster and vector data together, the extent of the plot needs to be defined for the data to overlay with each other correctly. Learn how to define plotting extents for Python Matplotlib Plots.

Sometimes you want to customize the colorbar and range of values plotted in a raster map. Learn how to create breaks to plot rasters in Python.

Folium is a Python package that can be used to create interactive maps in Jupyter Notebook. Learn how to create interactive maps with raster overlays in Python using Folium.

A hillshade is a representation of the earth's surface as it would look with shade and shadows from the sun. Learn how to overlay raster data on top of a hillshade in Python.

When plotting rasters, you often want to overlay two rasters, add a legend, or make the raster interactive. Learn how to make a map of raster data that has these attributes using Python.

This lesson defines 3 lidar data products: the digital elevation model (DEM), the digital surface model (DSM) and the canopy height model (CHM).

Rasters are gridded data composed of pixels that store values, such as an image or elevation data file. Learn how a lidar data point cloud is converted to a raster format such as a GeoTIFF.

This lesson covers what a lidar point cloud is. You will use the free plas.io point cloud viewer to explore a point cloud.

This lesson reviews what Lidar remote sensing is, what the lidar instrument measures and discusses the core components of a lidar remote sensing system.

A flood event often changes the terrain as water moves sediment and debris across the landscape. Learn how terrain changes are measured using lidar remote sensing data.

The amount and/or duration of rainfall can impact how severe a flood is. Learn how rainfall is measured and used to understand flood impacts.

Learn more about the stream discharge data that is used in this data story.

Changes in the atmosphere, including how quickly a storm moves can impact the severity of a flood. Learn more about how atmospheric conditions impact flood events.

The 2013 flood event caused significant damage throughout the state of Colorado, USA. Learn about what caused the 2013 floods in Colorado and also some of the impacts.

One way to analyze Twitter data is to analyze attitudes (or sentiment) in the tweet text. Learn how to analyze sentiments in Twitter data using open source Python.

One common way to analyze Twitter data is to identify the co-occurrence and networks of words in Tweets. Learn how to analyze word co-occurrence (i.e. bigrams) and networks of words using Python.

One common way to analyze Twitter data is to calculate word frequencies to understand how often words are used in tweets on a particular topic. To complete any analysis, you need to first prepare the data. Learn how to clean Twitter data and calculate word frequencies using Python.

You can use the Twitter RESTful API to access tweet data from Twitter. Learn how to use tweepy to download and work with twitter social media data in Python.

You can access twitter social media data using the twitter API automatically in Python. Learn about the basics of downloading twitter data using open source Python.

This lesson walks through the process of retrieving and manipulating surface water data housed in the Colorado Information Warehouse. These data are stored in JSON format with spatial x, y information that support mapping.

This lesson introduces how to work with the JSON data structure using Python using the JSON and Pandas libraries to create and convert JSON objects.

JSON is a powerful text based data format that contains hierarchical data. JSON and GeoJSON are common data formats that are returned when accessing automatically data using an API. Learn more about JSON and GeoJSON data.

API's allow you to automate access and downloading data in your code to support open reproducible science. Learn how how to use API's to download data from the internet using open source python.

Sometimes you will work with multiple rasters that are not in the same projections, and thus, need to reproject the rasters, so they are in the same coordinate reference system. Learn how to reproject raster data in Python using Rasterio.

Sometimes a raster dataset covers a larger spatial extent than is needed for a particular purpose. In these cases, you can crop a raster file to a smaller extent. Learn how to crop raster data using a shapefile and export it as a new raster in open source Python

Reclassifying raster data allows you to use a set of defined values to organize pixel values into new bins or categories. Learn how to classify a raster dataset and export it as a new raster in Python.

Often you need to process two raster datasets together to create a new raster output and then save that output as a new file. Learn how to subtract rasters and create a new GeoTIFF file using open source Python.

You can perform the same raster processing steps in Python that you would in a tool like ArcGIS. Learn how to process spatial raster data using Open Source Python.

Raster data are gridded data composed of pixels that store values, such as an image or elevation data file. Learn how to open, plot, and explore raster files in Python using Rasterio.

Challenge your skills. Practice opening, cleaning and plotting raster data in Python

Metadata describe the key characteristics of a dataset such as a raster. For spatial data, these characteristics including the coordinate reference system (CRS), resolution and spatial extent. Learn about the use of TIF tags or metadata embedded within a GeoTIFF file to explore the metadata programatically.

Raster metadata includes the coordinate reference system (CRS), resolution, and spatial extent. Learn about these metadata and how to access them in Python

Histograms of raster data provide the distribution of pixel values in the dataset. Learn how to explore and plot the distribution of values within a raster using histograms.

Raster data are gridded data composed of pixels that store values, such as an image or elevation data file. Learn how to open, plot, and explore raster files in Python.

Rasters are gridded data composed of pixels that store values. Learn more about the structure of raster data and how to use them to store data, such as imagery or elevation values.

Coordinate Reference System (CRS) information is often stored in three key formats, including proj.4, EPSG and WKT. Learn more about the ways that coordinate reference system data are stored including proj4, well known text (wkt) and EPSG codes.

Geographic coordinate systems span the entire globe (e.g. latitude / longitude), while projected coordinate systems are localized to minimize visual distortion in a particular region (e.g. Robinson, UTM, State Plane). Learn more about key differences between projected vs. geographic coordinate reference systems.

A coordinate reference system (CRS) defines the translation between a location on the round earth and that same location, on a flattened, 2 dimensional coordinate system. Learn how to explore and reproject data into geographic and projected CRS in Python.

Vector data are composed of discrete geometric locations (x, y values) known as vertices that define the shape of the spatial object. Learn more about the structure of vector data and how to open vector data stored in shapefile format in Python.

Practice interpreting data on plots that show rainfall (precipitation) and stream flow (discharge) as it changes over time.

Connecting data to analysis and outputs is an important part of open reproducible science. In this lesson you will explore that value of a well documented workflow.

Learn how to use the time series feature in Google Earth to view before and after images of a location.

This lesson contains a series of challenges that require using tidyverse functions in R to process data.

When you are programming, it can be easy to copy and paste code that works. However this approach is not efficient. Learn how to create for-loops to process multiple files in R.

Learn how to handle missing data in the R programming language.

Learn how to write pseudocode to plan our your approach to working with data. Then use tidyverse functions including group_by and summarise to implement your plan.

Learn...

When working with data, you often spend the most amount of time cleaning your data. Learn how to write more efficient code using the tidyverse in R.

Learn how to create and submit a pull request to another repo.

Learn how to fork a repository using the GitHub website.

Learn how to find a repo on the GitHub website using the search. Also get to know the GitHub repo interface.

Learn how to undo changes in git after they have been added or committed.

Learn basic git commands, including clone, add, commit, and push.

Learn what version control is, and how Git and GitHub are used in a typical version control workflow.

In this lesson you learn the steps to create a map in R using ggplot.

You can use R as a GIS. Learn how to create a map in R using ggplot in this R maps tutorial.

Learn how to perform a basic sentiment analysis using the tidytext package in R.

This lesson provides an example of modularizing code in R.

This lesson provides an example of modularizing code in R.

Text mining is used to extract useful information from text - such as Tweets. Learn how to use the Tidytext package in R to analyze twitter data.

You can use the Twitter RESTful API to access data about Twitter users and tweets. Learn how to use rtweet to download and analyze twitter social media data in R.

This lesson will discuss some of the challenges associated with working with social media data in science. These challenges include working with non standard text, large volumes of data, API limitations, and geolocation issues.

This lesson covers the basics of creating an interactive map using the leaflet API in R. We will import data from the Colorado Information warehouse using the SODA RESTful API and then create an interactive map that can be published to an HTML formatted file using knitr and rmarkdown.

This lesson walks through the process of retrieving and manipulating surface water data housed in the Colorado Information Warehouse. These data are stored in JSON format with spatial x, y information that support mapping.

This lesson covers namespaces in R and how we can tell R where to get a function from (what code to use) in R.

This lesson covers accessing data via the Colorado Information Warehouse SODA API in R.

This lesson covers the JSON data structure. JSON is a powerful text based format that supports hierarchical data structures. It is the core structure used to create geoJSON which is a spatial version of json that can be used to create maps. JSON is preferred for use over .csv files for data structures as it has been proven to be more efficient - particulary as data size becomes large.

This lesson reviews how to use functions within the RCurl package to access data on a secure (https) server in R.

This lesson provides an example of modularizing code in R.

In this module, you learn various ways to access, download and work with data programmatically. These methods include downloading text files directly from a website onto your computer and into R, reading in data stored in text format from a website, into a data.frame in R and finally, accessing subsets of particular data using REST API calls in R.

Learn how to take code in a for loop and convert it to be used in an apply function. Make your R code more efficient and expressive programming.

Learn how to combine if statements, functions and for loops to process sets of text files.

Learn how to write a for loop to process a set of .csv format text files in R.

Learn how to work with function arguments in the R programming language..

This lesson introduces the function environment and documenting functions in R. When you run a function intermediate variables are not stored in the global environment. This not only saves memory on your computer but also keeps our environment clean, reducing the risk of conflicting variables.

Learn how to write a function in the R programming language.

This lesson will cover the basic principles of using functions and why they are important.

In this lesson you will explore how to import and work with MODIS remote sensing data in raster geotiff format in R. You will cover importing many files using regular expressions and cleaning raster stack layer names for nice plotting.

In this lesson you review how to calculate difference normalized burn ratio using pre and post fire NBR rasters in R. You finally will classify the dNBR raster.

In this lesson you review the normalized burn ratio (NBR) index which can be used to identify the area and severity of a fire. Specifically you will calculate NBR using Landsat 8 spectral remote sensing data in raster, .tif format.

Often data have missing or bad data values that you need to replace. Learn how to replace missing or bad data values in a raster, with values from another raster in the same pixel location using the cover function in R.

In this lesson you will review how to find and download Landsat imagery from the USGS Earth Explorere website.

In this lesson, you will learn how to deal with clouds when working with spectral remote sensing data. You will learn how to mask clouds from landsat and MODIS remote sensing data in R using the mask() function. You will also discuss issues associated with cloud cover - particular as they relate to a research topic.

Learn how to convert a day of year value to a normal date format in R.

In this lesson you will review how to adjust the extent of a spatial plot in R using the ext() or extent argument and the extent of another layer.

In this lesson you learn to use the par() or parameter settings in R to plot several raster RGB plots in R in a grid.

In this lesson you review how to remove those pesky borders from a raster plot using base plot in R. We also cover adding legends to your plot outside of the plot extent.

Learn how to source a function in R. Learn how to import functions that are stored in a separate file into a script or R Markdown file.

In this lesson you will cover the basics of using Landsat 7 and 8 in R. You will learn how to import Landsat data stored in .tif format - where each .tif file represents a single band rather than a stack of bands. Finally you will plot the data using various 3 band combinations including RGB and color-infrared.

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NDVI is calculated using near infrared and red wavelengths or types of light and is used to measure vegetation greenness or health. Learn how to calculate remote sensing NDVI using multispectral imagery in R.

In this lesson you will learn the basics of using Landsat 7 and 8 in R. You will learn how to import Landsat data stored in .tif format - where each .tif file represents a single band rather than a stack of bands. Finally you will plot the data using various 3 band combinations including RGB and color-infrared.

In this lesson you learn how to open up a multi-band raster layer or image stored in .tiff format in R. You are introduced to the stack() function in R which can be used to import more than one band into a stack object in R. You also review using plotRGB to plot a multi-band image using RGB, color-infrared to other band combinations.

Multispectral imagery can be provided at different resolutions and may contain different bands or types of light. Learn about spectral vs spatial resolution as it relates to spectral data.

It is important to compare differences between tree height measurements made by humans on the ground to those estimated using lidar remote sensing data. Learn how to perform this analysis and calculate error or uncertainty in R.

This lesson reviews how to extract pixels from a raster dataset using a vector boundary. You can use the extracted pixels to calculate mean and max tree height for a study area (in this case a field site where tree heights were measured on the ground. Finally you will compare tree heights derived from lidar data compared to tree height measured by humans on the ground.

There are difference sources of error when you measure tree height using Lidar. Learn about accuracy, precision and the sources of error associated with lidar remote sensing data.

In this lesson you break down the steps required to create a custom legend for spatial data in R. You learn about creating unique symbols per category, customizing colors and placing your legend outside of the plot using the xpd argument combined with x,y placement and margin settings.

In this lesson you learn how to reproject a vector dataset using the spTransform() function in R.

This lesson discusses ways that coordinate reference system data are stored including proj4, well known text (wkt) and EPSG codes.

Geographic coordinate reference systems are often used to make maps of the world. Projected coordinate reference systems are use to optimize spatial analysis for a region. Learn about WGS84 and UTM Coordinate Reference Systems as used in R.

Coordinate reference systems are used to convert locations on the earth which is round, to a two dimensional (flat) map. Learn about the differences between coordinate reference systems.

This lesson reviews the core files that are required to use a shapefile including: shp, shx and dbf. It also covers the .prj format which is used to define the coordinate reference system (CRS) of the data.

This lesson introduces what vector data are and how to open vector data stored in shapefile format in R.

You can clip a raster to a polygon extent to save processing time and make image sizes smaller. Learn how to crop a raster dataset in R.

This lesson presents how to classify a raster dataset and export it as a new raster in R.

A canopy height model contains height values trees and can be used to understand landscape change over time. Learn how to use LIDAR elevation data to calculate canopy height and change in terrain over time.

A GeoTIFF is a standard file format with spatial metadata embedded as tags. Use the raster package in R to open geotiff files and spatial metadata programmatically.

This lesson introduces the raster geotiff file format - which is often used to store lidar raster data. You learn the 3 key spatial attributes of a raster dataset including Coordinate reference system, spatial extent and resolution.

This lesson introduces the raster geotiff file format - which is often used to store lidar raster data. You learn the 3 key spatial attributes of a raster dataset including Coordinate reference system, spatial extent and resolution.

This lesson reviews how a lidar data point cloud is converted to a raster format such as a geotiff.

This lesson covers what a lidar point cloud is. We will use the free plas.io point cloud viewer to explore a point cloud.

This lesson reviews what lidar remote sensing is, what the lidar instrument measures and discusses the core components of a lidar remote sensing system.

This lesson covers how to overlay raster data on a hillshade in R using baseplot and layer opacity arguments.

This lesson covers creating a basemap with the ggmap package in R. Given some ongoing bugs with ggmap it also covers the map package as a backup!

Learn how to create interactive reports using plotly and dygraphs in R for plotting.

This lesson introduces the mutate() and group_by() dplyr functions - which allow you to aggregate or summarize time series data by a particular field - in this case you will aggregate data by day to get daily precipitation totals for Boulder during the 2013 floods.

This lesson illustrated what your final stream discharge homework plots should look like for the week. Use all of the skills that you've learned in the previous lessons to complete it.

Learn how to summarize time series data by day, month or year with Tidyverse pipes in R.

Learn how to extract and plot data by a range of dates using pipes in R.

Times series data can be manipulated efficiently in R. Learn how to work with, plot and subset data with dates in R.

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.

This tutorial introduces vectors in R. It also introduces the differences between strings, numbers and logical or boolean values (True / False) in R.

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.

You often want to understand the relationships between two different types of data. Learn how to use regression to determine whether there is a relationship between two variables.

To explore uncertainty in remote sensing data, it is helpful to compare ground-based measurements and data that are collected via airborne instruments or satellites. Learn how to create scatter plots that compare values across two datasets.

For many scientific analyses, it is helpful to be able to select raster pixels based on their relationship to a vector dataset (e.g. locations, boundaries). Learn how to extract data from a raster dataset using a vector dataset.

Uncertainty quantifies a range of values within which a measurement value could be within, considering a specified level of confidence. Learn about the types of uncertainty that you can expect when working with tree height data both derived from lidar remote sensing and human measurements and learn about sources of error including systematic vs. random error.

Practice interpreting data on plots that show rainfall (precipitation) and stream flow (discharge) as it changes over time.

Learn why documentation is important when analyzing data by evaluating someone elses report on the Colorado floods.

Learn how to use the time series feature in Google Earth to view before / after images of a location.

Find resources that will help you use the R Markdown format.

Learn how to use bookdown in R to add citations and cross references to your data-driven reports.

This lesson covers how to use markdown to add images to a report. It also discusses good file management practices associated with saving images within your project directory to avoid losing them if you have to go back and work on the report in the future.

Knitr can be used to convert R Markdown files to different formats, including web friendly formats. Learn how to convert R Markdown to PDF or HTML in RStudio.

Code chunks in an R Markdown document are used to separate code from text in a Rmd file. Learn how to create reports using R Markdown.

Learn how to write using the markdown syntax in an R Markdown document.

Learn about the format of a R Markdown file including a YAML header, R code and markdown formatted text.

Learn what open science is and how R Markdown can help you document your work.

Learn how to create a well-organized working directory to store your course data.

Learn key principles for naming and organizing files and folders in a working directory.

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.

A hands-on activity where students review a project for readability, organization, etc and identify key elements that would make it more usable and readily reproducible.

Instructor notes for the open science lesson.