Project 1: The criminality narrative part 1
Eva Armendariz
October 23, 2025
Overview
This project will allow you to access data on deportations in the United States from 2003 to 2024. In your analysis, you will be able to assess several claims that have been made regarding deportation. Are common narratives about deportation sustainable given the observed data? This is what social scientists do: we make, or attempt to make, evidence-based claims. The tasks I am asking you to do here are portable to any (or most any) data set you might encounter whether it is in international relations, comparative politics, economics, sociology, and so forth. The “POL 51” aspect of this assignment is to give you hands-on experience in interpreting plots, univariate statistics, and rudimentary hypothesis testing. In class, we will cover this extensively. I have assigned an article by Patler and Jones and it is posted on Canvas. It is expected you read this article in advance of writing up your responses.
Project 1 will roll out in two parts. This is the first and it is worth 400 points. The second part will be worth 400 points for a total of 800 points for full project. You will submit the HTML file you generate on Canvas by October 20. The second part of the project will be due by October 31.
Part 1: The Criminality Narrative
A common trope in the immigration debate is that undocumented immigrants commit, at high rates, violent crimes. Therefore, the supposition is that migrants who are deported are migrants who have committed serious criminal infractions. This idea is prevalent in political rhetoric surrounding the issue of deportation. But is the claim consistent with the actual data?
Part 1 of this assignment is asking you to analyze real-world data on deportations in the United States between the years 2003 and 2024. The data you access records annual ICE removals (deportations) based on what ICE records as the “Most Serious Criminal Conviction” for someone who is deported. The following information is from TRAC (Transactional Records Access Clearinghouse) and describes what the classification levels mean:
“Seriousness Level of MSCC Conviction. ICE classifies National Crime Information Center (NCIC) offense codes into three seriousness levels. The most serious (Level 1) covers what ICE considers to be”aggravated felonies.” Level 2 offenses cover other felonies, while Level 3 offenses are misdemeanors, including petty and other minor violations of the law. TRAC uses ICE’s “business rules” to group recorded NCIC offense codes into these three seriousness levels.”
Essentially what this loosely means is that “Level 1” convictions are the most serious and “Level 3” convictions are generally minor legal infractions. In addition to Levels 1-3, there is a fourth category called “NoneER” denoting that the deportee had no criminal convictions.
This chunk of code will access the data set.
reasons="https://raw.githubusercontent.com/mightyjoemoon/POL51/main/ICE_reasonforremoval.csv"
reasons<-read_csv(url(reasons))
summary(reasons)## Year President All None
## Min. :2003 Length:22 Min. : 56882 Min. : 19495
## 1st Qu.:2008 Class :character 1st Qu.:178149 1st Qu.: 85446
## Median :2014 Mode :character Median :238765 Median :106426
## Mean :2014 Mean :248987 Mean :122287
## 3rd Qu.:2019 3rd Qu.:356423 3rd Qu.:165287
## Max. :2024 Max. :407821 Max. :253342
## Level1 Level2 Level3 Undocumented
## Min. : 9819 Min. : 3846 Min. : 11045 Min. :10100000
## 1st Qu.:38485 1st Qu.: 9056 1st Qu.: 34978 1st Qu.:10500000
## Median :46743 Median :17480 Median : 63186 Median :11050000
## Mean :46534 Mean :15601 Mean : 64541 Mean :11015455
## 3rd Qu.:57148 3rd Qu.:20343 3rd Qu.: 90950 3rd Qu.:11375000
## Max. :75590 Max. :29436 Max. :130251 Max. :12200000
## ER_Non
## Min. : 4018
## 1st Qu.:28563
## Median :41647
## Mean :38980
## 3rd Qu.:50230
## Max. :71686Task 1: Creation and interpretation of a line plot
The following is a shell of a line plot of the four levels of criminality (Levels 1-3 and None).
First add code to produce a publication-quality plot.
Second, provide a thorough substantive interpretation of the plot. This interpretation will require proper citation of research relevant to the criminality narrative.
If you were conveying the information from this plot to an audience interested in understanding deportation, what would you say? And perhaps, more importantly, what would you not say?
Mechanical answers, short answers, or answers that do not display a substantive understanding of the issue will receive low scores even if the mechanical interpretation is correct.
At minimum, I would expect a serious, substantive interpretation of this plot to be 2 to 3 paragraphs minimum.
The quality of the plot is worth 100 points and write-up/analysis is worth 100 points. This gives a total of 200 points.
#This is the chunk where you will edit this plot to produce something that looks useful
colors <- c("a. No criminal conviction" = "forestgreen",
"b. Level 1 conviction" = "purple",
"c. Level 2 conviction" = "blue",
"d. Level 3 conviction"="coral4")
figure1<-ggplot(reasons, aes(x = Year)) +
geom_line(aes(y = None, color="a. No criminal conviction"), size=1) +
geom_line(aes(y = Level1, color="b. Level 1 conviction"), size=1) +
geom_line(aes(y = Level2, color="c. Level 2 conviction"), size=1) +
geom_line(aes(y = Level3, color="d. Level 3 conviction"), size=1) +
scale_color_manual(values = colors) +
scale_y_continuous(labels = label_comma()) +
labs(title="",
y="Total Number of Deportations", x="Year",
color="") +
theme_classic()+
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=9, hjust = .7),
plot.caption=element_text(hjust=.2, size=10),
legend.position=c(.85,.98),
legend.justification=c("right", "top"),
legend.title = element_text(size = 6),
legend.text = element_text(size = 8),
plot.title = element_text(size=12))
figure1
The graph of the number of deportations per year shows notable rises and falls, different for each level of conviction, between 2003 and 2024. From the beginning of the 21st century, Most deportations, depicted by the green line significantly above the others, were those labeled as “no criminal conviction.” This is followed by the red and purple line that are closer to one another, Level 1 and Level 3 respectively, hovering just below 50,000. The last of the lines, depicted in blue, are the Level 2 convictions. Despite these numbers coming out a time period in the 1990s labeled as “crimmigration,” (Patler and Jones, 2025) they are relatively low deportation levels compared to the rest of the graph.
Coming out of the “crimmigration” era, the terrorist attacks on September 11, 2001 prompted then President Bush to create the Department of Homeland Security, which was further empowered with the creation and subsequent transferring under DHS, of Immigration and Customs Enforcement, or ICE (Patler and Jones, 2025). Right around this time, Level 3 convictions increase significantly, making them much closer to the green line than the purple line. Level 3 and No Criminal Conviction also see a rise in the quantity of deportations around 2005 so that not only do they make up the majority of deportations, but the majority is rising in number. It is interesting to note that in 2005 “Operation Steamline” was introduced, which allowed people to be deported in larger quantities (Patler and Jones, 2025).
Just before 2015, while the levels kept their order of No Criminal Conviction, Level 3, Level 1 and then Level 2, all four lines slope downward reaching a distinct fall in 2015. It is interesting to note that in 2014, a change to the immigration policy of the United States was introduced, called the Priority Enforcement Program. This program, as implied by the name, allowed immigration officers to focus their efforts on illegal immigrants with records of multiple misdemeanors and felonies (Patler and Jones, 2025). Though this correlation seems to assume a prediction on Level 2 and Level 1 deportations becoming the majority of deportations, they remain below both No Criminal Convictions and Level 3 (the least serious crimes) convictions.
In 2020, the Coronavirus Virus epidemic seized each country by storm, leaving President Trump and then Biden to decide the emergency procedure for United States immigration. Migration rules and regulations became stricter, and deportations overall saw a serious decline. The graph reflects this with each line plunging down, but especially the green and red lines which were able to decrease more dramatically because they were much higher to begin with. The red line, representing Level 3 or the least serious convictions, drops below Level 1 convictions for the first time since before 2010. Though it rises to meet the purple line later on, it has failed to resume its superior position since the Coronavirus epidemic.
By looking at the lines for No Criminal conviction or Level 3 convictions, it is observed that they are nearly always above the other types of convictions, No criminal conviction deportations and the least serious conviction deportations remain, with some exeptions, perpetually higher than the most serious conviction levels of deportation, leading to a contradiction of the claim that most deportations are of violent criminals.
Task 2: Understanding deportations and criminal offenses
For this task you will create three new variables from existing ones in the data set, create a plot, and provide a substantive interpretation of the plot. The write-up of this plot I would expect to be 1-2 well-written paragraphs. The quality of the plot is worth 100 points and the write-up is worth 100 points for 200 points total.
Task 2.1, create a new variable called minor that sums all deportations associated with no criminal conviction (“None”) and Level 3 convictions. These are the deportations associated with minor or no criminal activity. Enter the code to do this part of the task in this chunk.
#Insert code for 2.1 here
reasons$minor <- reasons$None + reasons$Level3Task 2.2, compute the percentage of all deportations that are “minor” deportations (i.e. \(100 \times \frac{None + Level~3}{None + Level~1 + Level~2 + Level~3}\)). Call this new variable percent_minor.
#Insert the code for task 2.2 here
reasons$percent_minor <- 100 * (reasons$minor/(reasons$None + reasons$Level1 + reasons$Level2 + reasons$Level3)) Task 2.3, in the chunk below, create a presentation-grade plot of the variable percent_minor and provide a thorough and substantive interpretation of the plot. Quality of the plot will be scored on a 100-point scale and quality of the write-up will be scored on a 100-point scale.
ggplot(reasons, aes(x = Year)) +
geom_line(aes(y = percent_minor), color="blue") +
scale_color_manual(values = colors) +
scale_y_continuous(labels = label_comma()) +
labs(title="Percentage of Deportations that are No Criminal Conviction and Level 1 Convictions",
y="Total Number of Deportations", x="Year",
color="") +
theme_classic()+
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
axis.text.y = element_text(size=9),
axis.text.x = element_text(size=9, hjust = .7),
plot.caption=element_text(hjust=.2, size=10),
legend.position=c(.85,.98),
legend.justification=c("right", "top"),
legend.title = element_text(size = 6),
legend.text = element_text(size = 8),
plot.title = element_text(size=12))
In the second graph, the data for no criminal convictions and level 1 convictions combined are shown as a percentage of total deportations in the United States during the years 2003 to 2024. Although significantly high throughout the graph, significant rises and falls occur, correlating with certain policies and departmental changes in United States immigration policy. Rising until shortly before 2010, the rate declines steadily until 2015. With a slight rise and fall between 2015 and 2020, the percentage suddenly plummets at 2020, corresponding with the Corona virus epidemic. Almost as steeply, the percentage shoots up after 2020 and continues to rise for the duration of the given data.
The percentage of least serious convictions or no convictions corresponds to two major events which may have affected United States immigration policy. The first, in 2015 was the Priority Enforcement Program. This makes sense coupled with a fall in the ratio of less serious or no criminal convictions to serious convictions, as it allowed immigration authorities to focus on individuals with more misdemeanors or felonies. The second, as previously mentioned, is the Corona virus epidemic, which limited resource allocation concerning immigration, automatically prioritizing the most urgent criminal deportations.
It is important to note that even when the percentage line plummets to the lowest it ever appears from 2003 to 2024, the percentage of no criminal convictions or level 3 convictions remains close to 50%. It can be implied from this graph, then, that even when the ratio is most weighted towards felons and repeat offenders, they only make up about half of overall deportations. This implication and the data from the graph does not seem to support the claim that most deportations are Level 1 or Level 2 convictions.