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library(dplyr)library(plotly)
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Problem Statement
Plastic production continues to increase by millions of tons annually, with 18% of it coming solely from North America and 46% from Asia (Thomas, et al. 2024).
With this sheer amount of plastic production, there is worry about managing plastic waste. While there are multiple types, one key factor of most plastics is their persistence; Plastic bags take about 100 years to decompose effectively (Dey, et al. 2023). Currently, the most common factors of plastic waste management are decomposition, incineration, and land filling (Dey, et al. 2023). Each of these methods create harmful byproducts for environments and the atmosphere. Land filling and discarded plastic in particular have detrimental effects on coastal ecosystems: specifically coral. When coral reefs come into contact with plastic, “likelihood of disease increases from 4% to 89%” (Lamb, et al. 2018).
One question that may arise: What about recycling? Journalists from the Guardian found that most of the plastic recycled in the US ends up shipped to other countries and sold for profit. In 2015, about 1.6m tons of recycled plastic was sent to China and Hong Kong for processing, however the majority was contaminated or non-recyclable, adding to the plastic waste issue (McCormick, et al. 2019).
We plan on looking at this data of exported plastic from the US and compare it to both quality of life in these countries as well as the health of coral reefs along their coasts. We hope to answer our hypothesis:
Is there a relationship between US plastic exports and coral bleaching from microplastics?
US Plastic Exports
Sums of Plastic Exports
We want to get the sum of plastic exports in each country, to ultimately find which countries are taking in the most total exports.
Looking at the plot, what interesting changes do you see? One general trend is how other countries seem to increase after China’s sharp decrease. But as of 2025, each country seems to be decreasing how much plastic they are accepting from the US.
Difference in 2025
We can also see if these are still the top 10 countries in 2025. Let’s get the 2025 top 10 below.
They are still the same! Even with stricter import laws in Hong Kong and China, they are still at the top of the list for US plastic imports, although the actual placements in the top 10 may have changed.
Quality of Life
Combining data
We have data for the GDP and Life expectancy in each country over time, lets combine that with our plastic data to see if there are any trends with quality of life and how much plastic is imported from the US.
Warning: There was 1 warning in `mutate()`.
ℹ In argument: `Time = as.integer(Time)`.
Caused by warning:
! NAs introduced by coercion
qualityPlastic <-left_join(x = quality, y = plastics, by=join_by(Entity == Country, Year == Time))
Now we have added a column in our country data to include the kilograms of plastic imported from the US in each country.
Plotting the data
Lets start by plotting our top 10 countries using the most recent data we have in 2022. We can size each point by how much plastic it imported from the US in that year.
# Following: https://plotly.com/ggplot2/animations/qualityPlastic$Vessel.Total.Exports.SWT..kg.[is.na(qualityPlastic$Vessel.Total.Exports.SWT..kg.)] <-3# Make sure US is not size 0p <- qualityPlastic |>filter(Entity %in% countries & Year >2002& Year <2023) plot <- p|>ggplot(aes(x = gdp_per_capita,y = life_expectancy_0,color = Entity)) +scale_color_manual(values =c("China"="red","India"="orange","Indonesia"="yellow","South Korea"="green","Malaysia"="cyan","Mexico"="blue","Taiwan"="purple","Thailand"="magenta","Turkey"="brown","Vietnam"="black","United States"="gray")) +geom_point(aes(size=Vessel.Total.Exports.SWT..kg., frame = Year)) +geom_point(data=qualityPlastic |>filter(Entity=="United States"& Year >2002& Year <2023), aes(frame = Year))
While there are dots all over this plot, we can see each of our countries had a lower GDP than the small gray dot of the US. Surprisingly, though, there are a wide variety of life expectancies among the top 10 countries to take in plastic imported from the US.
Surprising Regressions!
While it may seem like large amounts of plastic imports would detriment rather than help a country’s wellbeing, there is actually a strong positive trend between kilograms of plastic imports and both GDP and life expectancy!
What do you think contributes to this relationship?
GDPRel <-lm(Vessel.Total.Exports.SWT..kg. ~ gdp_per_capita, data = qualityPlastic)summary(GDPRel)
Call:
lm(formula = Vessel.Total.Exports.SWT..kg. ~ gdp_per_capita,
data = qualityPlastic)
Residuals:
Min 1Q Median 3Q Max
-3.714e+08 -2.750e+07 -1.751e+07 -1.507e+07 2.183e+10
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.182e+07 3.888e+06 3.04 0.00237 **
gdp_per_capita 2.247e+03 3.053e+02 7.36 1.91e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.81e+08 on 21584 degrees of freedom
(42972 observations deleted due to missingness)
Multiple R-squared: 0.002503, Adjusted R-squared: 0.002457
F-statistic: 54.17 on 1 and 21584 DF, p-value: 1.906e-13
Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
dat <- vroom(...)
problems(dat)
Rows: 41361 Columns: 62
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (52): Data_Source, Ocean_Name, Reef_ID, Realm_Name, Ecoregion_Name, Cou...
dbl (9): Site_ID, Sample_ID, Latitude_Degrees, Longitude_Degrees, Turbidit...
date (1): Date
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Bleaching <- Bleaching |>filter(Date_Month =="12")Bleaching$Country_Name <-replace(Bleaching$Country_Name, Bleaching$Country_Name=="Japan", "South Korea")# Japan and South Korea share coral reefs hence above codeBleaching$Percent_Bleaching <-replace(Bleaching$Percent_Bleaching, Bleaching$Percent_Bleaching=="nd", "0")countries_use <-c("China", "India", "Indonesia", "South Korea", "Malaysia", "Mexico","Taiwan", "Thailand", "Vietnam")# explained by https://www.statology.org/r-select-rows-by-condition/clean_bleaching <- Bleaching[Bleaching$Country_Name %in% countries_use,c("Country_Name", "Percent_Bleaching", "Date_Year")]clean_bleaching <- clean_bleaching |> dplyr::mutate(Percent_Bleaching = dplyr::na_if(Percent_Bleaching, "nd"),Percent_Bleaching =as.numeric(Percent_Bleaching) )view(clean_bleaching)
This filters out the countries that were included in the top 10 plastic imports.
Rows: 22530 Columns: 36
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (20): Ocean, Region, Subregion, Country, State, Beach Location, Marine S...
dbl (16): OBJECTID, Latitude (degree), Longitude(degree), Ocean Bottom Depth...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
countries_use <-c( "China", "India", "Indonesia", "South Korea", "Malaysia", "Mexico", "Taiwan", "Thailand", "Vietnam" ) MarineMicroplastics <- MarineMicroplastics %>%#https://www.statology.org/is-na/mutate(Country =case_when(is.na(Country) & Region =="Bay of Bengal"~"India",is.na(Country) & Region =="Java Sea"~"Indonesia",is.na(Country) & Region =="East China Sea"~"South Korea",is.na(Country) & Region =="Malacca Strait"~"Malaysia",is.na(Country) & Region =="South China Sea"~"Vietnam",TRUE~ Country ) )clean_microplastics <- MarineMicroplastics[MarineMicroplastics$Country %in% countries_use, c("Country", "Region", "Concentration class range")]view(clean_microplastics)
ggplot(clean_microplastics,aes(x = Country, fill =`Country`)) +geom_bar() +scale_fill_manual(values =c("China"="red","India"="orange","Indonesia"="yellow","Malaysia"="cyan","Mexico"="blue","Taiwan"="purple","Thailand"="magenta","Vietnam"="black","South Korea"="green" )) +labs(title ="Microplastic Concentration Class Ranges by Country",x ="Country",y ="Count",fill ="Country" )
