based on this code and dataset: # Load required packages

library$($dplyr$)$

library$($countrycode$)$

library$($lubridate$)$

library$($tidyr$)$

library$($tidyverse$)$

# Load the data

df read.csv$("$~$/$Documents$/$R and Rstudio$/$A$2\mathrm{.}1$: Refugees$/$data$/$A$2\_$refugee$\_$status.csv$")$

refugees$\_$raw read$\_$csv$("$~$/$Documents$/$R and Rstudio$/$A$2\mathrm{.}1$: Refugees$/$data$/$A$2\_$refugee$\_$status.csv$",$ na $=$ c$("-","$X$","$D$"))$

non$\_$countries c$("$Africa$",$ "Asia", "Europe", "North America", "Oceania",

"South America", "Unknown", "Other", "Total"$)$

refugees$\_$clean refugees$\_$raw $\%>\%$

# Make this column name easier to work with

rename$($origin$\_$country $=`$Continent$/$Country of Nationality$`)\%>\%$

# Get rid of non$-$countries

filter$(!($origin$\_$country $\%$in$\%$ non$\_$countries$))\%>\%$

# Convert country names to ISO$3$ codes

mutate$($iso$3=$ countrycode$($origin$\_$country, "country.name", "iso$3$c$",$

custom$\_$match $=$ c$("$Korea$,$ North" $="$PRK$")))\%>\%$

# Convert ISO$3$ codes to country names, regions, and continents

mutate$($origin$\_$country $=$ countrycode$($iso$3,$ "iso$3$c$",$ "country.name"$),$

origin$\_$region $=$ countrycode$($iso$3,$ "iso$3$c$",$ "region"$),$

origin$\_$continent $=$ countrycode$($iso$3,$ "iso$3$c$",$ "continent"$))\%>\%$

# Make this data tidy

gather$($year$,$ number, $-$origin$\_$country, $-$iso$3,-$origin$\_$region, $-$origin$\_$continent$)\%>\%$

# Make sure the year column is numeric $+$ make an actual date column for years

mutate$($year $=$ as$.$numeric$($year$),$

year$\_$date $=$ ymd$($paste$0($year$,"-01-01")))$

refugees$\_$clean

refugees$\_$countries$\_$cumulative refugees$\_$clean $\%>\%$

arrange$($year$\_$date$)\%>\%$

group$\_$by$($origin$\_$country$)\%>\%$

mutate$($cumulative$\_$total $=$ cumsum$($number$))$

refugees$\_$countries$\_$cumulative

refugees$\_$continents refugees$\_$clean $\%>\%$

group$\_$by$($origin$\_$continent, year$\_$date$)\%>\%$

summarize$($total $=$ sum$($number$,$ na$.$rm $=$ TRUE$))$

refugees$\_$continents$\_$cumulative refugees$\_$clean $\%>\%$

group$\_$by$($origin$\_$continent, year$\_$date$)\%>\%$

summarize$($total $=$ sum$($number$,$ na$.$rm $=$ TRUE$))\%>\%$

arrange$($year$\_$date$)\%>\%$

group$\_$by$($origin$\_$continent$)\%>\%$

mutate$($cumulative$\_$total $=$ cumsum$($total$))$

create graphs to support this story:

From $2006$ to $2015,$ the United States opened its arms to over $600,000$ refugees, showcasing its longstanding dedication to offering refuge to those escaping peril. This intricate mosaic of fluctuating trends, reflecting the geopolitical upheavals and humanitarian crises shaping our world during this period, extends far beyond a mere collection of numbers.

Myanmar's Surge and Retreat $($MMR$)$

In Myanmar, the sharp increase in refugees circa $2008$ aligns with Cyclone Nargis's devastating impact. The tragedy spurred a significant humanitarian crisis, propelling a wave of displacement. Fast forward to $2014,$ and we witness another peak. Here, the ongoing ethnic conflicts, a longstanding source of distress, likely drove another exodus, reflecting the nation's turbulent journey.

Iraq's Escalating Exodus $($IRQ$)$

Turning to Iraq, a gradual yet steady rise in refugees culminates in a spike around $2012.$ This pattern echoes the enduring instability and conflict following the $2003$ Iraq War, with the emergence of ISIS around $2012$ exacerbating the situation, compelling many to flee their homeland in search of safety.

Bhutan's Peaks of Departure $($BTN$)$

The story of Bhutanese refugees, marked by peaks in $2007$ and $2010,$ harks back to the ethnic Nepalis' displacement in the early $1990$s$.$ The persistence of this refugee wave underscores the protracted nature of their plight, with many spending years in Nepalese camps before finding resettlement.

Interpreting the Ebb and Flow

These graphical trends mirror pivotal historical events, offering insights into the causes behind the refugee movements. While spikes in the graph suggest periods of intensified crisis, the subsequent declines often coincide with stabilization efforts or successful asylum in host nations.

Policy Reflections and Humanizing Data

The fluctuations also mirror policy changes and international diplomacy's ebb and flow, affecting refugees' journeys to safety. However, beyond the graph lines and data points lies the core human narrative$$the uprooted families, the resilience amid tribulations, and the ongoing search for a home.

The Chronicles of Refuge and Resilience

Our exploration of the leading refugee$-$origin countries over a decade paints a vivid picture of global unrest and the unyielding spirit of humanity. More than a statistical chart, this visual narrative stands as a testament to our era, emphasizing the ongoing need for empathy and support for those who continue to seek a new life in the United States.