For this project, I wanted to compare military spending between the US and China over time. I used SIPRI data from 1949 to 2025 and cleaned and analyzed it using R. My goal was to see how China’s military spending has grown and how it compares to the US.
I pulled the data from https://www.sipri.org/databases/milex?utm, you are allowed to download the data set straight from the website. The original SIPRI Military Expenditure dataset (Constant (2024) US$ worksheet) contains, 193 rows (including regional headings and a blank row), 80 columns (including 77 year columns from 1949–2025).
In terms of transforming from wide to long, I ran into issues with my code after filtering for China and the US because when using “-country” it takes everything that’s not a country and put’s them in to the rows therefore “Notes” and “…2” fell into those rows. I had to specify “cols = ‘1949’:‘2025’” in order to only pull numerical years into rows. From there I got rid of the irrelevant columns. I then chose to round the numbers to two decimals and then check for missing values, which China had 40. I then removed those 40 NA’s from the dataset.
Finally, I created a line graph and a average using ggplot2 and the mean function in R to compare military spending trends over time and better understand China’s growing military expenditure relative to the United States.
library(readxl)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(tidyr)
library(ggplot2)sipri_wide <- read_excel(
"SIPRI-Milex-data-1949-2025_v1.2.xlsx",
sheet = "Constant (2024) US$",
skip = 5
)New names:
• `` -> `...2`head(sipri_wide)# A tibble: 6 × 80
Country ...2 Notes `1949` `1950` `1951` `1952` `1953` `1954` `1955` `1956`
<chr> <lgl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 <NA> NA <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
2 Africa NA <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
3 North Afr… NA <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA> <NA>
4 Algeria NA §4 xxx xxx xxx xxx xxx xxx xxx xxx
5 Libya NA द16 xxx xxx ... ... ... ... ... ...
6 Morocco NA §17 xxx xxx xxx xxx xxx xxx xxx 122.2…
# ℹ 69 more variables: `1957` <chr>, `1958` <chr>, `1959` <chr>, `1960` <chr>,
# `1961` <chr>, `1962` <chr>, `1963` <chr>, `1964` <chr>, `1965` <chr>,
# `1966` <chr>, `1967` <chr>, `1968` <chr>, `1969` <chr>, `1970` <chr>,
# `1971` <chr>, `1972` <chr>, `1973` <chr>, `1974` <chr>, `1975` <chr>,
# `1976` <chr>, `1977` <chr>, `1978` <chr>, `1979` <chr>, `1980` <chr>,
# `1981` <chr>, `1982` <chr>, `1983` <chr>, `1984` <chr>, `1985` <chr>,
# `1986` <chr>, `1987` <chr>, `1988` <chr>, `1989` <chr>, `1990` <chr>, …sipri_clean <- sipri_wide %>%
filter(Country %in% c("United States of America", "China"))
head(sipri_clean)# A tibble: 2 × 80
Country ...2 Notes `1949` `1950` `1951` `1952` `1953` `1954` `1955` `1956`
<chr> <lgl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 United St… NA 44 18109… 19462… 41642… 58659… 60395… 52110… 49326… 49972…
2 China NA 55 ... ... ... ... ... ... ... ...
# ℹ 69 more variables: `1957` <chr>, `1958` <chr>, `1959` <chr>, `1960` <chr>,
# `1961` <chr>, `1962` <chr>, `1963` <chr>, `1964` <chr>, `1965` <chr>,
# `1966` <chr>, `1967` <chr>, `1968` <chr>, `1969` <chr>, `1970` <chr>,
# `1971` <chr>, `1972` <chr>, `1973` <chr>, `1974` <chr>, `1975` <chr>,
# `1976` <chr>, `1977` <chr>, `1978` <chr>, `1979` <chr>, `1980` <chr>,
# `1981` <chr>, `1982` <chr>, `1983` <chr>, `1984` <chr>, `1985` <chr>,
# `1986` <chr>, `1987` <chr>, `1988` <chr>, `1989` <chr>, `1990` <chr>, …sipri_long <- sipri_clean %>%
pivot_longer(
cols = -Country,
names_to = "Year",
values_to = "Military_Expenditure"
)
# Ran into some issues with the columns
head(sipri_long)# A tibble: 6 × 3
Country Year Military_Expenditure
<chr> <chr> <chr>
1 United States of America ...2 <NA>
2 United States of America Notes 44
3 United States of America 1949 181095.54595961623
4 United States of America 1950 194625.01922071318
5 United States of America 1951 416423.76619009074
6 United States of America 1952 586599.33539518306 # Looking at the columns
names(sipri_wide) [1] "Country" "...2" "Notes" "1949" "1950" "1951" "1952"
[8] "1953" "1954" "1955" "1956" "1957" "1958" "1959"
[15] "1960" "1961" "1962" "1963" "1964" "1965" "1966"
[22] "1967" "1968" "1969" "1970" "1971" "1972" "1973"
[29] "1974" "1975" "1976" "1977" "1978" "1979" "1980"
[36] "1981" "1982" "1983" "1984" "1985" "1986" "1987"
[43] "1988" "1989" "1990" "1991" "1992" "1993" "1994"
[50] "1995" "1996" "1997" "1998" "1999" "2000" "2001"
[57] "2002" "2003" "2004" "2005" "2006" "2007" "2008"
[64] "2009" "2010" "2011" "2012" "2013" "2014" "2015"
[71] "2016" "2017" "2018" "2019" "2020" "2021" "2022"
[78] "2023" "2024" "2025" # Going for it again after fixing my code
sipri_long <- sipri_clean %>%
pivot_longer(
cols = `1949`:`2025`,
names_to = "Year",
values_to = "Military_Expenditure"
)
# Preview the transformed data
head(sipri_long)# A tibble: 6 × 5
Country ...2 Notes Year Military_Expenditure
<chr> <lgl> <chr> <chr> <chr>
1 United States of America NA 44 1949 181095.54595961623
2 United States of America NA 44 1950 194625.01922071318
3 United States of America NA 44 1951 416423.76619009074
4 United States of America NA 44 1952 586599.33539518306
5 United States of America NA 44 1953 603952.7491342905
6 United States of America NA 44 1954 521100.55414618237 We are here to simply compare military spending for both the US and China, the “Notes” and “..2” columns are not relevant therefore will be removed.
sipri_long <- sipri_long %>%
select(-`...2`, -Notes)
sipri_long <- sipri_long %>%
mutate(
Military_Expenditure = round(as.numeric(Military_Expenditure), 2)
)Warning: There was 1 warning in `mutate()`.
ℹ In argument: `Military_Expenditure = round(as.numeric(Military_Expenditure),
2)`.
Caused by warning:
! NAs introduced by coercionhead(sipri_long)# A tibble: 6 × 3
Country Year Military_Expenditure
<chr> <chr> <dbl>
1 United States of America 1949 181096.
2 United States of America 1950 194625.
3 United States of America 1951 416424.
4 United States of America 1952 586599.
5 United States of America 1953 603953.
6 United States of America 1954 521101.China has 40 years of missing values because they were not recording their military spending at the time.
sipri_long %>%
group_by(Country) %>%
summarise(
Missing_Values = sum(is.na(Military_Expenditure))
)# A tibble: 2 × 2
Country Missing_Values
<chr> <int>
1 China 40
2 United States of America 0sipri_long <- sipri_long %>%
filter(!is.na(Military_Expenditure))
head(sipri_long)# A tibble: 6 × 3
Country Year Military_Expenditure
<chr> <chr> <dbl>
1 United States of America 1949 181096.
2 United States of America 1950 194625.
3 United States of America 1951 416424.
4 United States of America 1952 586599.
5 United States of America 1953 603953.
6 United States of America 1954 521101.sipri_long %>%
group_by(Country) %>%
summarise(
Average_Expenditure = round(mean(Military_Expenditure), 2)
)# A tibble: 2 × 2
Country Average_Expenditure
<chr> <dbl>
1 China 126177.
2 United States of America 695115.ggplot(sipri_long, aes(
x = as.numeric(Year),
y = Military_Expenditure,
color = Country
)) +
geom_line() +
scale_y_continuous(labels = scales::comma) +
labs(
title = "U.S. vs. China Military Expenditure",
x = "Year",
y = "Military Expenditure (In Millions USD)",
color = "Country"
)
As you can see the US military has significantly spent more than China. Although China only has half the amount of data in years that the US has, the trend for the US still exceeds that of China. In 2025 the US exceeded Chinese military spending by 600 billion (US 900 billion, China 300 billion). Another point is that the US expenditure fluctuates up and down quite a lot through out the years but trends upwards.
The analysis shows that the United States has consistently spent more on its military than China. The US on average has spent 695 billion compared to China’s 126 million. However, China’s military spending has steadily increased over time, reflecting its growing influence as a global power.
For this project, I will use World Bank GDP data to compare the economic growth of the United States and China. The original dataset contains GDP figures organized by year in wide format, which I will transform into long format using tidyr. I will then analyze GDP trends over time to better understand how China’s economy has grown relative to the United States.
I downloaded the GDP dataset from the World Bank’s World Development Indicators database https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=US-CN. The original dataset was in wide format with 2 rows and 70 columns, including yearly GDP data from 1960 to 2025.
I used read_excel() to import the data and skipped the first three rows to correctly identify the column names. I then removed unnecessary columns using select() and transformed the data from wide to long format using pivot_longer(). The final dataset contains 132 rows and 3 columns: Country Name, Year, and GDP.
I checked for missing values and found none. Finally, I used group_by() and summarise() to calculate average GDP for both countries and created a line graph with ggplot2 to compare their economic growth over time.
library(readxl)
library(dplyr)
library(tidyr)
library(ggplot2)# Import World Bank GDP data
gdp_wide <- read_excel("api_gdp_v2.xls", skip = 3)
# Preview the dataset
head(gdp_wide)# A tibble: 2 × 70
`Country Name` `Country Code` `Indicator Name` `Indicator Code` `1960`
<chr> <chr> <chr> <chr> <dbl>
1 China CHN GDP (current US$) NY.GDP.MKTP.CD 59846235025.
2 United States USA GDP (current US$) NY.GDP.MKTP.CD 541988586207.
# ℹ 65 more variables: `1961` <dbl>, `1962` <dbl>, `1963` <dbl>, `1964` <dbl>,
# `1965` <dbl>, `1966` <dbl>, `1967` <dbl>, `1968` <dbl>, `1969` <dbl>,
# `1970` <dbl>, `1971` <dbl>, `1972` <dbl>, `1973` <dbl>, `1974` <dbl>,
# `1975` <dbl>, `1976` <dbl>, `1977` <dbl>, `1978` <dbl>, `1979` <dbl>,
# `1980` <dbl>, `1981` <dbl>, `1982` <dbl>, `1983` <dbl>, `1984` <dbl>,
# `1985` <dbl>, `1986` <dbl>, `1987` <dbl>, `1988` <dbl>, `1989` <dbl>,
# `1990` <dbl>, `1991` <dbl>, `1992` <dbl>, `1993` <dbl>, `1994` <dbl>, …# Check column names and dimensions
names(gdp_wide) [1] "Country Name" "Country Code" "Indicator Name" "Indicator Code"
[5] "1960" "1961" "1962" "1963"
[9] "1964" "1965" "1966" "1967"
[13] "1968" "1969" "1970" "1971"
[17] "1972" "1973" "1974" "1975"
[21] "1976" "1977" "1978" "1979"
[25] "1980" "1981" "1982" "1983"
[29] "1984" "1985" "1986" "1987"
[33] "1988" "1989" "1990" "1991"
[37] "1992" "1993" "1994" "1995"
[41] "1996" "1997" "1998" "1999"
[45] "2000" "2001" "2002" "2003"
[49] "2004" "2005" "2006" "2007"
[53] "2008" "2009" "2010" "2011"
[57] "2012" "2013" "2014" "2015"
[61] "2016" "2017" "2018" "2019"
[65] "2020" "2021" "2022" "2023"
[69] "2024" "2025" dim(gdp_wide)[1] 2 70# Keep country names and yearly GDP values
gdp_clean <- gdp_wide %>%
select(-`Country Code`, -`Indicator Name`, -`Indicator Code`)
# Preview cleaned data
head(gdp_clean)# A tibble: 2 × 67
`Country Name` `1960` `1961` `1962` `1963` `1964` `1965` `1966` `1967`
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 China 5.98e10 5.02e10 4.73e10 5.08e10 5.98e10 7.06e10 7.69e10 7.30e10
2 United States 5.42e11 5.62e11 6.04e11 6.37e11 6.84e11 7.42e11 8.13e11 8.60e11
# ℹ 58 more variables: `1968` <dbl>, `1969` <dbl>, `1970` <dbl>, `1971` <dbl>,
# `1972` <dbl>, `1973` <dbl>, `1974` <dbl>, `1975` <dbl>, `1976` <dbl>,
# `1977` <dbl>, `1978` <dbl>, `1979` <dbl>, `1980` <dbl>, `1981` <dbl>,
# `1982` <dbl>, `1983` <dbl>, `1984` <dbl>, `1985` <dbl>, `1986` <dbl>,
# `1987` <dbl>, `1988` <dbl>, `1989` <dbl>, `1990` <dbl>, `1991` <dbl>,
# `1992` <dbl>, `1993` <dbl>, `1994` <dbl>, `1995` <dbl>, `1996` <dbl>,
# `1997` <dbl>, `1998` <dbl>, `1999` <dbl>, `2000` <dbl>, `2001` <dbl>, …# Transform yearly GDP columns into long format
gdp_long <- gdp_clean %>%
pivot_longer(
cols = `1960`:`2025`,
names_to = "Year",
values_to = "GDP"
)
# Preview transformed data
head(gdp_long)# A tibble: 6 × 3
`Country Name` Year GDP
<chr> <chr> <dbl>
1 China 1960 59846235025.
2 China 1961 50162299350.
3 China 1962 47310737754.
4 China 1963 50812227919.
5 China 1964 59821862703.
6 China 1965 70565994356.# Count missing GDP values by country
gdp_long %>%
group_by(`Country Name`) %>%
summarise(
Missing_Values = sum(is.na(GDP))
)# A tibble: 2 × 2
`Country Name` Missing_Values
<chr> <int>
1 China 0
2 United States 0gdp_long %>%
group_by(`Country Name`) %>%
summarise(
Average_GDP_Trillions = round(mean(GDP) / 1e12, 2)
)# A tibble: 2 × 2
`Country Name` Average_GDP_Trillions
<chr> <dbl>
1 China 3.83
2 United States 9.2 GDP Trends Over Time
ggplot(gdp_long, aes(
x = as.numeric(Year),
y = GDP / 1e12,
color = `Country Name`
)) +
geom_line() +
labs(
title = "U.S. vs. China GDP (1960–2025)",
x = "Year",
y = "GDP (Trillions of USD)",
color = "Country"
)
The United States has maintained a higher GDP than China throughout the period, but China’s economy has grown rapidly since the early 2000s. China’s GDP increased from around $1 trillion in 2000 to nearly $20 trillion in 2025, while the U.S. reached approximately $31 trillion. Although China has significantly narrowed the economic gap compared with earlier decades, the United States still maintains a substantial lead.
The analysis shows that although the United States has maintained a larger GDP, China has experienced significant economic growth over the past several decades. China’s rapid expansion demonstrates how much its economy has developed, even though it has not surpassed the United States in total GDP measured in current U.S. dollars.
For this project, I will analyze water chemistry data collected by the National Park Service from 2006 to 2024. The original dataset is in wide format, with different chemical measurements stored in separate columns. I will use tidyr and dplyr to transform the data into long format, clean missing values, and examine how water chemistry varies across monitoring sites and over time.
I used the National Park Service’s Water Quality and Quantity Monitoring dataset (https://catalog.data.gov/dataset/water-quality-and-quantity-monitoring-data-package-for-measurements-collected-wi-2006-2024), which was shared by a classmate in Discussion 5A. The original dataset was in wide format with 3,014 rows and 51 columns, containing water chemistry measurements collected at different national parks and monitoring sites. Make sure to download Chemistry_Data_Wide_20250715.csv from the website if you want to access the original file.
I used read.csv() to import the data and select() to keep the park names, monitoring sites, sampling dates, pH, nitrogen, and phosphorus measurements. I then used pivot_longer() to transform the three measurement columns into rows, creating a tidy dataset with 9,042 rows and 5 columns.
I found 2,089 missing values: 409 for nitrogen, 725 for phosphorus, and 955 for pH. I removed these observations using filter() and is.na() because missing measurements cannot contribute to the analysis. I kept the available measurements rather than treating missing values as zero, leaving 6,953 rows for analysis.
Finally, I used group_by() and summarise() to calculate average measurements and ggplot2 to compare average pH across national parks. This helped me identify differences in water chemistry between locations.
library(dplyr)
library(tidyr)
library(ggplot2)water_wide <- read.csv("Chemistry_Data_Wide_20250715.csv")
head(water_wide) GroupCode GroupName UnitCode UnitName
1 NETN Northeast Temperate Network ACAD Acadia National Park
2 NETN Northeast Temperate Network ACAD Acadia National Park
3 NETN Northeast Temperate Network ACAD Acadia National Park
4 NETN Northeast Temperate Network ACAD Acadia National Park
5 NETN Northeast Temperate Network ACAD Acadia National Park
6 NETN Northeast Temperate Network ACAD Acadia National Park
SubUnitCode SubUnitName SiteCode SiteName SiteType EventDate
1 <NA> <NA> ACABIN Aunt Betty Pond Inlet Stream 2006-06-26
2 <NA> <NA> ACABIN Aunt Betty Pond Inlet Stream 2006-08-30
3 <NA> <NA> ACABIN Aunt Betty Pond Inlet Stream 2006-08-30
4 <NA> <NA> ACABIN Aunt Betty Pond Inlet Stream 2008-05-13
5 <NA> <NA> ACABIN Aunt Betty Pond Inlet Stream 2008-08-04
6 <NA> <NA> ACABIN Aunt Betty Pond Inlet Stream 2010-05-04
EventCode Project QCtype LabCode SampleTime SampleDepth_m SampleType pH_Lab
1 484 NETN_LS ENV NWQL 16:30:00 0.1 G NA
2 460 NETN_LS ENV NWQL 12:45:00 0.1 G NA
3 460 NETN_LS REP NWQL 12:45:00 0.1 G NA
4 638 NETN_LS ENV SECRL 14:50:00 0.1 G 6.35
5 601 NETN_LS ENV SECRL 14:06:15 0.1 G 6.06
6 105 NETN_LS ENV SECRL 14:27:00 0.2 G 6.68
pH_Lab_Method AppColor_PCU AppColorFlag ANC_ueqL ANCFlag Cl_ueqL ClFlag
1 <NA> 40 <NA> 172.18 <NA> NA <NA>
2 <NA> 60 <NA> 172.98 <NA> NA <NA>
3 <NA> 60 <NA> 169.26 <NA> NA <NA>
4 ANCpH 41 <NA> 81.60 <NA> NA <NA>
5 ANCpH 70 <NA> 101.00 <NA> NA <NA>
6 ANCpH 51 <NA> 139.00 <NA> NA <NA>
SO4_ueqL SO4Flag NO3_ueqL NO3Flag NO2.NO3_mgL NO2.NO3Flag NO2_mgL NO2Flag
1 NA <NA> NA <NA> NA <MRL 0.06 NA <MRL 0.002
2 NA <NA> NA <NA> NA <MRL 0.016 NA <MRL 0.002
3 NA <NA> NA <NA> NA <MRL 0.016 NA <MRL 0.002
4 NA <NA> NA <NA> NA <MRL 0.005 NA <MRL 0.005
5 NA <NA> NA <NA> NA <MRL 0.005 NA <MRL 0.005
6 NA <NA> NA <NA> 0.017 <NA> NA <MRL 0.005
TotDissN_mgL TotDissNFlag NH3_mgL NH3Flag TN_mgL TNFlag TP_ugL
1 NA <NA> 0.006 E, <MRL 0.01 0.430 <NA> 10.0
2 NA <NA> 0.015 <NA> 0.470 <NA> 14.0
3 NA <NA> 0.016 <NA> 0.480 <NA> 12.0
4 NA <MRL 0.1 NA <MRL 0.08 0.167 <NA> 6.0
5 0.321 <NA> NA <MRL 0.08 0.318 <NA> 9.6
6 0.203 <NA> NA <MRL 0.08 0.247 <NA> 13.0
TPFlag TotDissP_ugL TotDissPFlag PO4_ugL PO4Flag ChlA_ugL ChlAFlag
1 E, <MRL 20 NA <NA> NA <MRL 6 NA <NA>
2 <NA> NA <NA> NA <MRL 6 NA <NA>
3 <NA> NA <NA> 4.0 E, <MRL 6 NA <NA>
4 <NA> 5.4 <NA> 1.4 <NA> NA <NA>
5 <NA> 6.6 <NA> NA <MRL 1 NA <NA>
6 <NA> 6.4 <NA> 1.4 <NA> NA <NA>
DOC_mgL DOCFlag Comments IsEventCUI
1 NA <NA> <NA> FALSE
2 NA <NA> <NA> FALSE
3 NA <NA> <NA> FALSE
4 NA <NA> <NA> FALSE
5 NA <NA> <NA> FALSE
6 NA <NA> <NA> FALSEnames(water_wide) [1] "GroupCode" "GroupName" "UnitCode" "UnitName"
[5] "SubUnitCode" "SubUnitName" "SiteCode" "SiteName"
[9] "SiteType" "EventDate" "EventCode" "Project"
[13] "QCtype" "LabCode" "SampleTime" "SampleDepth_m"
[17] "SampleType" "pH_Lab" "pH_Lab_Method" "AppColor_PCU"
[21] "AppColorFlag" "ANC_ueqL" "ANCFlag" "Cl_ueqL"
[25] "ClFlag" "SO4_ueqL" "SO4Flag" "NO3_ueqL"
[29] "NO3Flag" "NO2.NO3_mgL" "NO2.NO3Flag" "NO2_mgL"
[33] "NO2Flag" "TotDissN_mgL" "TotDissNFlag" "NH3_mgL"
[37] "NH3Flag" "TN_mgL" "TNFlag" "TP_ugL"
[41] "TPFlag" "TotDissP_ugL" "TotDissPFlag" "PO4_ugL"
[45] "PO4Flag" "ChlA_ugL" "ChlAFlag" "DOC_mgL"
[49] "DOCFlag" "Comments" "IsEventCUI" dim(water_wide)[1] 3014 51water_clean <- water_wide %>%
select(UnitName, SiteName, EventDate,
pH_Lab, TN_mgL, TP_ugL)
head(water_clean) UnitName SiteName EventDate pH_Lab TN_mgL TP_ugL
1 Acadia National Park Aunt Betty Pond Inlet 2006-06-26 NA 0.430 10.0
2 Acadia National Park Aunt Betty Pond Inlet 2006-08-30 NA 0.470 14.0
3 Acadia National Park Aunt Betty Pond Inlet 2006-08-30 NA 0.480 12.0
4 Acadia National Park Aunt Betty Pond Inlet 2008-05-13 6.35 0.167 6.0
5 Acadia National Park Aunt Betty Pond Inlet 2008-08-04 6.06 0.318 9.6
6 Acadia National Park Aunt Betty Pond Inlet 2010-05-04 6.68 0.247 13.0water_long <- water_clean %>%
pivot_longer(
cols = c(pH_Lab, TN_mgL, TP_ugL),
names_to = "Measurement",
values_to = "Value"
)
head(water_long)# A tibble: 6 × 5
UnitName SiteName EventDate Measurement Value
<chr> <chr> <chr> <chr> <dbl>
1 Acadia National Park Aunt Betty Pond Inlet 2006-06-26 pH_Lab NA
2 Acadia National Park Aunt Betty Pond Inlet 2006-06-26 TN_mgL 0.43
3 Acadia National Park Aunt Betty Pond Inlet 2006-06-26 TP_ugL 10
4 Acadia National Park Aunt Betty Pond Inlet 2006-08-30 pH_Lab NA
5 Acadia National Park Aunt Betty Pond Inlet 2006-08-30 TN_mgL 0.47
6 Acadia National Park Aunt Betty Pond Inlet 2006-08-30 TP_ugL 14 water_long %>%
group_by(Measurement) %>%
summarise(
Missing_Values = sum(is.na(Value))
)# A tibble: 3 × 2
Measurement Missing_Values
<chr> <int>
1 TN_mgL 409
2 TP_ugL 725
3 pH_Lab 955water_long <- water_long %>%
filter(!is.na(Value))
head(water_long)# A tibble: 6 × 5
UnitName SiteName EventDate Measurement Value
<chr> <chr> <chr> <chr> <dbl>
1 Acadia National Park Aunt Betty Pond Inlet 2006-06-26 TN_mgL 0.43
2 Acadia National Park Aunt Betty Pond Inlet 2006-06-26 TP_ugL 10
3 Acadia National Park Aunt Betty Pond Inlet 2006-08-30 TN_mgL 0.47
4 Acadia National Park Aunt Betty Pond Inlet 2006-08-30 TP_ugL 14
5 Acadia National Park Aunt Betty Pond Inlet 2006-08-30 TN_mgL 0.48
6 Acadia National Park Aunt Betty Pond Inlet 2006-08-30 TP_ugL 12 water_long %>%
group_by(Measurement) %>%
summarise(
Average_Value = round(mean(Value), 2)
)# A tibble: 3 × 2
Measurement Average_Value
<chr> <dbl>
1 TN_mgL 0.51
2 TP_ugL 22.9
3 pH_Lab 6.87water_ph <- water_long %>%
filter(Measurement == "pH_Lab") %>%
group_by(UnitName) %>%
summarise(
Average_pH = round(mean(Value), 2)
)
ggplot(water_ph, aes(
x = UnitName,
y = Average_pH
)) +
geom_col() +
coord_flip() +
labs(
title = "Average pH by National Park",
x = "National Park",
y = "Average pH"
)
The results show that average water pH varies across the national parks, ranging from approximately 6.4 to 8.1. Saratoga National Historical Park has the highest average pH, while Acadia National Park has the lowest. This suggests that water chemistry differs depending on the location, with some parks having more acidic water than others.
The analysis shows that water chemistry varies across national parks, with average pH ranging from approximately 6.4 to 8.1. These differences highlight the importance of monitoring water quality and understanding how environmental conditions vary between locations.