Assignment 1: R Markdown
Alex Hahnenberg - adapted from Nick Gubbins
2025-03-12
knitr::opts_chunk$set(echo = TRUE)
library(dataRetrieval)## Warning: package 'dataRetrieval' was built under R version 4.4.3library(lubridate)## Warning: package 'lubridate' was built under R version 4.4.3library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.4.3## Warning: package 'ggplot2' was built under R version 4.4.3## Warning: package 'tibble' was built under R version 4.4.3## Warning: package 'tidyr' was built under R version 4.4.3## Warning: package 'readr' was built under R version 4.4.3## Warning: package 'purrr' was built under R version 4.4.3## Warning: package 'dplyr' was built under R version 4.4.3## Warning: package 'stringr' was built under R version 4.4.3## Warning: package 'forcats' was built under R version 4.4.3Ecological Context
Temperature as a master control
Available energy in a system is the master control on which, and at what rate, chemical reactions take place. Temperature is the most common, and most easily accessible, measurement of energy in a system. While other forms of energy ( such as turbulence) are critical for understanding aquatic systems, temperature can be easily and cheaply measured without effecting the system itself. For this week’s assignment, we will be getting more familiar with R by downloading and analyzing temperature data from the USGS.
Power generation and water temperature
Rivers are often used as a water source for the production of steam in power generation. Generating stations draw in water from upstream, use it to create steam, use that steam to spin turbines, then (mostly) cool the steam, and re-release it downstream. You can clearly see the effect of this on the White River in downtown Indianapolis, IN. Station 03353000 is located just upstream of a major generating plant. Station 03353611 is just below it. The gauges are only ~2 miles apart. Let’s load in a few months of data from both and see how they differ.
site_data <- readNWISuv(siteNumbers = c( '03353000', "03353611"),
parameterCd = '00010',
startDate = '2024-10-01',
endDate = '2025-01-01')## GET:https://nwis.waterservices.usgs.gov/nwis/iv/?site=03353000%2C03353611&format=waterml%2C1.1&ParameterCd=00010&startDT=2024-10-01&endDT=2025-01-01ggplot(site_data, aes(x = dateTime, y = X_00010_00000, color = site_no))+
geom_line()+
labs(x = 'Date', y = 'Temperature (degrees C)', color = 'Site')
You can see the effect of the generating station most clearly as temperatures drop for the winter. Water is usually returned at ~20 degrees C, giving the downstream site a higher average temperature.
Dissolved oxygen and temperature
Dissolved oxygen is a key indicator of aquatic health. It is produced by photosynthesizing microbes and macrophytes. Heterotrophs, like fish and many kinds of bacteria, use dissolved oxygen in respiration. Healthy freshwater systems tend to have dissolved oxygen levels above 6 mg/L (usually around 80% saturation or higher). When dissolved oxygen levels get too low (below 6 mg/L) fish cannot spawn, as fish eggs rely on passive diffusion of dissolved oxygen to survive. Below 5 mg/L DO, you can see dramatic fish kills. These kills represent a huge disruption to the river’s food chain and a massive influx of organic matter, often leading to spiraling water quality issues.
Assignment
Read in mean daily discharge (code ‘00060’), DO (code ‘00300’), and temperature (code ‘00010’) data from the South Platte River at Englewood, CO (‘06711565’) for the 2023 water year
Let’s first look at the readNWISdv function to see what it does.
#?readNWISdvNow we can will in the function inputs to get the data we want
data <- readNWISdv(siteNumbers = '06711565',
parameterCd = c('00060', '00300', '00010'),
startDate = '2023-10-01',
endDate = '2024-9-30')## GET:https://waterservices.usgs.gov/nwis/dv/?site=06711565&format=waterml%2C1.1&ParameterCd=00060%2C00300%2C00010&StatCd=00003&startDT=2023-10-01&endDT=2024-9-30summary(data)## agency_cd site_no Date X_00010_00003
## Length:366 Length:366 Min. :2023-10-01 Min. : 0.000
## Class :character Class :character 1st Qu.:2023-12-31 1st Qu.: 6.125
## Mode :character Mode :character Median :2024-03-31 Median :11.750
## Mean :2024-03-31 Mean :12.028
## 3rd Qu.:2024-06-30 3rd Qu.:18.600
## Max. :2024-09-30 Max. :23.100
## NA's :12
## X_00010_00003_cd X_00060_00003 X_00060_00003_cd X_00300_00003
## Length:366 Min. : 43.9 Length:366 Min. : 6.300
## Class :character 1st Qu.: 69.5 Class :character 1st Qu.: 7.700
## Mode :character Median : 100.0 Mode :character Median : 8.950
## Mean : 222.5 Mean : 8.973
## 3rd Qu.: 242.8 3rd Qu.:10.200
## Max. :1270.0 Max. :11.500
## NA's :16
## X_00300_00003_cd
## Length:366
## Class :character
## Mode :character
##
##
##
## Q1 (10 pts):
summary(data)## agency_cd site_no Date X_00010_00003
## Length:366 Length:366 Min. :2023-10-01 Min. : 0.000
## Class :character Class :character 1st Qu.:2023-12-31 1st Qu.: 6.125
## Mode :character Mode :character Median :2024-03-31 Median :11.750
## Mean :2024-03-31 Mean :12.028
## 3rd Qu.:2024-06-30 3rd Qu.:18.600
## Max. :2024-09-30 Max. :23.100
## NA's :12
## X_00010_00003_cd X_00060_00003 X_00060_00003_cd X_00300_00003
## Length:366 Min. : 43.9 Length:366 Min. : 6.300
## Class :character 1st Qu.: 69.5 Class :character 1st Qu.: 7.700
## Mode :character Median : 100.0 Mode :character Median : 8.950
## Mean : 222.5 Mean : 8.973
## 3rd Qu.: 242.8 3rd Qu.:10.200
## Max. :1270.0 Max. :11.500
## NA's :16
## X_00300_00003_cd
## Length:366
## Class :character
## Mode :character
##
##
##
## ggplot(data, aes(x = Date, y = X_00060_00003, color = site_no))+
geom_line()+
labs(x = 'Date', y = 'Temperature (degrees C)', color = 'Site')
ggplot(data, aes(x = Date, y = X_00010_00003, color = site_no))+
geom_line()+
labs(x = 'Date', y = 'Temperature (degrees C)', color = 'Site')
ggplot(data, aes(x = Date, y = X_00300_00003, color = site_no))+
geom_line()+
labs(x = 'Date', y = 'Temperature (degrees C)', color = 'Site')
Look at the structure of the data in any way you choose.
Q2 (10 pts):
Rename your columns to something sensible, omit rows without complete observations (NAs), and reduce your data frame to only include the date and your data of interest.
library(ggpubr)## Warning: package 'ggpubr' was built under R version 4.4.3cleandata<-data%>%
rename(temp = X_00010_00003,
do = X_00300_00003,
discharge=X_00060_00003)%>%
na.omit()
summary(cleandata)## agency_cd site_no Date temp
## Length:348 Length:348 Min. :2023-10-01 Min. : 0.000
## Class :character Class :character 1st Qu.:2023-12-26 1st Qu.: 6.075
## Mode :character Mode :character Median :2024-03-30 Median :11.550
## Mean :2024-03-29 Mean :11.871
## 3rd Qu.:2024-06-29 3rd Qu.:18.425
## Max. :2024-09-30 Max. :23.100
## X_00010_00003_cd discharge X_00060_00003_cd do
## Length:348 Min. : 43.90 Length:348 Min. : 6.300
## Class :character 1st Qu.: 68.88 Class :character 1st Qu.: 7.675
## Mode :character Median : 88.80 Mode :character Median : 8.900
## Mean : 220.74 Mean : 8.963
## 3rd Qu.: 242.25 3rd Qu.:10.200
## Max. :1270.00 Max. :11.500
## X_00300_00003_cd
## Length:348
## Class :character
## Mode :character
##
##
## plot1<-ggplot(cleandata, aes(x = Date, y = discharge, color = site_no))+
geom_line()+
labs(x = 'Date', y = 'Discharge', color = 'Site')
plot2<-ggplot(cleandata, aes(x = Date, y = temp, color = site_no))+
geom_line()+
labs(x = 'Date', y = 'Temperature (degrees C)', color = 'Site')
plot3<-ggplot(cleandata, aes(x = Date, y = do, color = site_no))+
geom_line()+
labs(x = 'Date', y = 'Dissolved Oxygen', color = 'Site')
ggarrange(plot1,plot2,plot3,ncol=1)
## Q3 (10pts):
Plot a time series of all three parameters in the same graph.
ggplot(data, aes(x = Date, y = X_00060_00003, color = site_no))+
geom_line(aes(x = Date, y = X_00010_00003 ),color = "blue")+
geom_line(aes(x = Date, y = X_00300_00003),color = "green")+
geom_line(aes(x = Date, y = X_00060_00003 ),color = "red")+
labs(x = 'Date', y = 'Temperature (degrees C)', color = 'Site')+
theme_classic()
## Q4 (10 pts):
This time series graph is not very useful to us. Why not? Give two examples of design choices you could make to improve its usefulness.
I think that the plots are not as useful as they could be because they are all the same color. It would be more useful to have them be different colors for each variable. Also, they could be more helpful with each variable having its own individual axis.
Q5 (10pts):
plot1<-ggplot(cleandata, aes(x = Date, y = discharge, color = site_no))+
geom_line(aes(x = Date, y = discharge),color = "blue")+
labs(x = 'Date', y = 'Discharge', color = 'Site')
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## - attr(*, "validate")= logi TRUEplot2<-ggplot(cleandata, aes(x = Date, y = temp, color = site_no))+
geom_line(aes(x = Date, y = discharge),color = "purple")+
labs(x = 'Date', y = 'Temperature (degrees C)', color = 'Site')
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geom_line(aes(x = Date, y = discharge),color = "green")+
labs(x = 'Date', y = 'Dissolved Oxygen', color = 'Site')
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## - attr(*, "validate")= logi TRUEggarrange(plot1,plot2,plot3,ncol=1)
Implement both of your proposed changes.
Q6 (10pts):
Scatterplots are a great way to compare correlations. Create two scatterplots. One of DO vs Q, the other of DO vs temperature.
plot1<-ggplot(cleandata, aes(x = do, y = discharge, color = site_no))+
geom_point()+
labs(x = 'do', y = 'Discharge', color = 'Site')
plot1
plot2<-ggplot(cleandata, aes(x=do,y=temp, color=site_no))+
geom_point()+
labs(x='do',y='temp',color='Site')
plot2
Q7 (10pts):
Use the ‘cor()’ function to test which two variables are the most closely related (look at the ‘help’ window for documentation). Which relationship is it? Why do you think that is? Report your answers in complete sentences.
cor(cleandata$do,cleandata$discharge)## [1] -0.2888968cor(cleandata$do, cleandata$temp)## [1] -0.9810135cor(cleandata$temp, cleandata$discharge)## [1] 0.3083419The most closely related variables are dissolved oxygen and temperatrure. The cor number value at -0.9810135 is the highest ratio for the relationship of those 2 variables. This makes sense becaue from a physics standpoint we already know that temperature affects the amount that water can hold dissolved gasses. ## Q8 (30 pts):
Imagine the city of Englewood is exploring building a coal-fired power plant using the South Platte as its water source and you have been hired on as a consultant. Write a paragraph brief for the city. Given that the benefits to the city are concrete, what issues would you raise? What effects do you think the plant would have on water temperatures and water quality? What data/assurances would you need to see before thinking the project should go ahead?
Dear City of Englewood engineers and planners. The proposed power plant stands to affect the quality of the water in the river. Increases in temperature stand to possibly decrease the levels of dissolved oxyen in the water. Decreasing the levels of dissolved oxygen in the river stand to negatively affect any fish or other aquatic species that live in the river. Certain species also do not do well living in water that is too high a temperture either as they are native cold water species. There is even the possibilty of killing many fish either through the temperatures being too high, or from decreased levels of dissolved oxygen. I would not recommend the power plant be allowed to operate unless the water temperature issues are solved. I would possibly work to build some kind of holding ponds or any other method that would allow the water to sit and cool before being discharged into the river.
Thank you, Alex HahnenbergBonus Q (Ungraded):
Pull in daily discharge (code ‘00060’) data for the two USGS sites on the Cache la Poudre River in Fort Collins, CO (‘06752260’ and ‘06752280’) for the 2023 water year (2023-10-01 - 2024-09-30). Plot the data in a way that shows the difference between the two sites. What do you think is causing the difference?
##Since I can not load the data with my attempts shown below here I would guess that there is a temperature diference between the measurements between the two sites. If I had to guess at a cause I would asssume that there is a point source discharging warm water. However, I am just guessing. I was never able to get the readNWIS command to work either here or at the beginning of the assignment.
##data <- readNWISuv(siteNumbers = '06752260',
##parameterCd = c('00060', '00300', '00010'),
##startDate = '2023-10-01',
##endDate = '2024-9-30')
##summary(data_2)
##summary(site_data)
##data_3<- readNWISdv(siteNumbers = '06752280',
##parameterCd = c('00060', '00300', '00010'),
##startDate = '2023-10-01',
##endDate = '2024-9-30')
##summary(data3)
##ggplot(data_2, aes(x = dateTime, y = X_00060, color = site_no))+
##geom_line()+
##labs(x = 'Date', y = 'Temperature (degrees C)', color = 'Site')
##df<-data2::data_3
##site_data_2 <- readNWISuv(siteNumbers = c( '06752260', "06752280"),
##parameterCd = '00010',
##startDate = '2024-10-01',
##endDate = '2025-01-01')
##ggplot(site_data_2, aes(x = dateTime, y = '00060', color = site_no))+
##geom_line()+
##labs(x = 'Date', y = 'Temperature (degrees C)', color = 'Site')