In this data lesson, you explore and visualize stream discharge time series data collected by the United States Geological Survey (USGS). You will use everything that you learned in the previous lessons to create your plots. You will use these plots in the report that you submit for your homework.

Note: this page just shows you what the plots should look like. You will need to use your programming skills to create the plots!

About the Data - USGS Stream Discharge Data

The USGS has a distributed network of aquatic sensors located in streams across the United States. This network monitors a suit of variables that are important to stream morphology and health. One of the metrics that this sensor network monitors is Stream Discharge, a metric which quantifies the volume of water moving down a stream. Discharge is an ideal metric to quantify flow, which increases significantly during a flood event.

As defined by USGS: Discharge is the volume of water moving down a stream or river per unit of time, commonly expressed in cubic feet per second or gallons per day. In general, river discharge is computed by multiplying the area of water in a channel cross section by the average velocity of the water in that cross section.

Read more about stream discharge data collected by USGS.

As you can imagine, stream gages can be sensitive to high flows and in the case of an extreme event like a flood are sometimes damaged. However, during the 2013 floods, one stream gage in Boulder, Colorado remained in tact. USGS stream gauge 06730200 located on Boulder Creek at North 75th St. collected data that you will use in the lesson below!

Work with USGS Stream Gage Data

Let’s begin by loading your libraries and setting your working directory.

# set your working directory
# setwd("working-dir-path-here")

# load packages
library(ggplot2) # create efficient, professional plots
library(dplyr) # data manipulation

# set strings as factors to false
options(stringsAsFactors = FALSE)

Import USGS Stream Discharge Data into R

Let’s first import your data using the read.csv() function.

discharge <- read.csv("data/week-02/discharge/06730200-discharge-daily-1986-2013.csv",
                      header = TRUE)

# view first 6 lines of data
head(discharge)
##   agency_cd site_no datetime disValue qualCode
## 1      USGS 6730200  10/1/86       30        A
## 2      USGS 6730200  10/2/86       30        A
## 3      USGS 6730200  10/3/86       30        A
## 4      USGS 6730200  10/4/86       30        A
## 5      USGS 6730200  10/5/86       30        A
## 6      USGS 6730200  10/6/86       30        A

Your plot should look something like the one below: