Summary Activity for Time Series Data
An activity to practice all of the skills you just learned in .
last updated: 11 Sep 2020
Use Time Series Data in R and Python to Understand Change: Lessons
About Time Series Data
Time series data are used to understand changes over time in our environment. For instance, you can collect temperature data over time to track how temperature fluctuates, hourly, daily monthly and even annually. However often working with dates and times in tools like R and Python can be tricky given different date and time formats and time zones. Learn how to work with, clean up and plot time series data in the R programming language in the lessons below. Stay tuned for Python lessons!
Resample or Summarize Time Series Data in Python With Pandas - Hourly to Daily Summary
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.
last updated: 11 Sep 2020
Subset Time Series By Dates Python Using 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.
last updated: 11 Sep 2020
Work With Datetime Format in Python - Time Series Data
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.
last updated: 11 Sep 2020
Work With Datetime Format in Python - Time Series Data
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.
last updated: 19 Jan 2022
Why A Hundred Year Flood Can Occur Every Year. Calculate Exceedance Probability and Return Periods in Python
Learn how to calculate exceedance probability and return periods associated with a flood in Python.
last updated: 11 Sep 2020
Measure Changes in the Terrain Caused by a Flood Using Lidar Data
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.
last updated: 11 Sep 2020
Rain: a Driver of the 2013 Colorado Floods
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.
last updated: 11 Sep 2020
About the Stream Discharge Data Used in this Data Story
Learn more about the stream discharge data that is used in this data story.
last updated: 11 Sep 2020
How the Atmosphere Drives Floods: The 2013 Colorado Floods
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.
last updated: 11 Sep 2020
An Overview of the 2013 Colorado Floods
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.
last updated: 11 Sep 2020
Explore Precipitation and Stream Flow Data Using Interactive Plots: The 2013 Colorado Floods
Practice interpreting data on plots that show rainfall (precipitation) and stream flow (discharge) as it changes over time.
last updated: 06 Mar 2020
Create Data Driven Reports using Jupyter Notebooks | 2013 Colorado Flood Data
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.
last updated: 06 Mar 2020
Use Google Earth Time Series Images to View Flood Impacts
Learn how to use the time series feature in Google Earth to view before and after images of a location.
last updated: 27 Jan 2022
Subset & Aggregate Time Series Precipitation Data in R Using mutate(), group_by() and summarise()
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.
last updated: 03 Sep 2019
Homework Challenge: Plot USGS Stream Discharge Data in R
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.
last updated: 03 Sep 2019
Summarize Time Series Data by Month or Year Using Tidyverse Pipes in R
Learn how to summarize time series data by day, month or year with Tidyverse pipes in R.
last updated: 03 Sep 2019
Use Tidyverse Pipes to Subset Time Series Data in R
Learn how to extract and plot data by a range of dates using pipes in R.
last updated: 03 Sep 2019
Time Series Data: Work with Dates in R
Times series data can be manipulated efficiently in R. Learn how to work with, plot and subset data with dates in R.
last updated: 30 Mar 2020
Explore Precipitation and Stream Flow Data Using Interactive Plots: The 2013 Colorado Floods
Practice interpreting data on plots that show rainfall (precipitation) and stream flow (discharge) as it changes over time.
last updated: 03 Sep 2019
Work With Precipitation Data in R: 2013 Colorado Floods
Learn why documentation is important when analyzing data by evaluating someone elses report on the Colorado floods.
last updated: 03 Sep 2019
Use Google Earth Time Series Images to Explore Flood Impacts
Learn how to use the time series feature in Google Earth to view before / after images of a location.
last updated: 30 Mar 2020