# Time Series

## 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 we will aggregate data by day to get daily precipitation totals for Boulder during the 2013 floods.

last updated: 12 May 2017

## Plot Stream Discharge Data in R

This lesson is a challenge exercise that asks you to use all of the skills used during the week 2 set of lessons in the earth analytics course. Here you will import data and subset it to create a final plot of stream discharge over time.

last updated: 12 May 2017

## Subset time series data in R - introduction to dplyr pipes and tidyverse coding approaches - Flooding & erosion data

This lesson walks through extracting temporal subsets of time series data using dplyr pipes. In the previous lesson we learned how to convert data containing a data field into a data class. In this lesson we use pipes to extract temporal subsets so that we can refine our time series data analysis. Finally we plot the data using ggplot.

last updated: 12 May 2017

## Work With Date - Time formats in R - Time Series Data

This lesson covers how to deal with dates in R. It reviews how to apply the as.Date() function to a column containing date or data-time data. This function converts a field containing dates in a standard format, to a date class that R can understand and plot efficiently.

last updated: 12 May 2017

## Explore time series data using interactive plots - The 2013 Colorado Floods

In this lesson, we will explore 2 different types of time series data that can be used to better understand a flood event: precipitation data and stream discharge data which quantifies the volume and velocity of water moving through a stream channel. Students will explore these data using interactive plot.ly plots. No programming experience is required to complete this lesson.

last updated: 21 Apr 2017

## Work with Precipitation Data in R - 2013 Colorado Floods

This lesson provides students with an example of a data driven report to emphasize the importance of connecting data, documentation and results.

last updated: 12 May 2017

## Using before / after remote sensing images to better understand the impacts of flooding & erosion - Google Earth and the 2013 Colorado floods

In this lesson, we will use time series imagery from Google Earth to look at the impacts of the floods in Boulder, Colorado. Specifically we will look at spectral remote sensing data before and after the flood to see what changed int he landscape. This lesson requires doesn't require any programming!

last updated: 21 Apr 2017