## Sentiment Analysis of Colorado Flood Tweets in R

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

*last updated: 10 Jan 2018*

In Earth data science, often the greatest amount of time is spent figuring out how to open, clean up and explore your data. Once the data are cleaned up, you can then begin to visualize and analyze them. In the lessons below, learn the basic skills needed to open, clean up, plot and analyze scientific data.

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

*last updated: 10 Jan 2018*

This lesson provides an example of modularizing code in R.

*last updated: 10 Jan 2018*

This lesson provides an example of modularizing code in R.

*last updated: 10 Jan 2018*

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.

*last updated: 10 Jan 2018*

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.

*last updated: 10 Jan 2018*

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.

*last updated: 10 Jan 2018*

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.

*last updated: 10 Jan 2018*

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.

*last updated: 10 Jan 2018*

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.

*last updated: 10 Jan 2018*

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

*last updated: 10 Jan 2018*

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!

*last updated: 10 Jan 2018*

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: 30 Jul 2018*

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: 10 Jan 2018*

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

*last updated: 10 Jan 2018*

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

*last updated: 30 Jul 2018*

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 Jul 2018*

Learn how to plot data and customize your plots using ggplot in R.

*last updated: 10 Jan 2018*