what story can i tell with graphs in Visualizing & Analyzing Data with R with the following dataset. this is the assignment i need the story for: he United States has resettled more than $600,000$ refugees from $60$ different countries since $2006.$

In this assignment, you will use R$,$ ggplot to explore where these refugees have come from. Feel free to use also graphical tools like InkscapeLinks to an external site..

Instructions

Here$$s what you need to do:

Use the Department of Homeland Security$$s annual count of people granted refugee status between $2006-2015$:

A$2\_$refugee$\_$status.csvDownload A$2\_$refugee$\_$status.csv

Create a new R Markdown file

Clean the data using the suggestions I$$ve given you below.

Summarize the data somehow. There is data for $60$ countries over $10$ years, so you$$ll probably need to aggregate or reshape the data somehow $($unless you do a $60-$country sparkline$).$ I$$ve included some examples down below.

Create an appropriate time$-$based visualization based on the data. Don$$t just calculate overall averages or totals per country$$the visualization needs to deal with change over time. Do as much polishing and refining in R$$make adjustments to the colors, scales, labels, grid lines, and even fonts, etc. and this is that code i have: # Load required packages$$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$))$