Border Crossing Project

Exploration of US-Canada and US-Mexico Border Crossings

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.6     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.9
## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1

## Warning: package 'ggplot2' was built under R version 4.1.3

## Warning: package 'tidyr' was built under R version 4.1.3

## Warning: package 'readr' was built under R version 4.1.3

## Warning: package 'dplyr' was built under R version 4.1.3

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

setwd("C:/Users/rsaidi/Dropbox/Rachel/MontColl/Datasets/Datasets")
bc <- read_csv("border_crossing.csv")

## Rows: 355511 Columns: 7

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (5): Port Name, State, Border, Date, Measure
## dbl (2): Port Code, Value
## 
## 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.

Clean the headers

names(bc) <- tolower(names(bc))
names(bc) <- gsub(" ", "_", names(bc))

Adjust the format of the dates to be readable

bc$date <- as.Date(bc$date, format = "%m/%d/%Y")

Isolate just the months and just the years of the border crossings

bc2 <- bc %>%
  mutate(year = year(date)) %>%
  mutate(month = month(date))
head(bc2)

## # A tibble: 6 x 9
##   port_name      state port_code border     date       measure value  year month
##   <chr>          <chr>     <dbl> <chr>      <date>     <chr>   <dbl> <dbl> <dbl>
## 1 Alcan          AK         3104 US-Canada~ 2020-02-01 Person~  1414  2020     2
## 2 Alcan          AK         3104 US-Canada~ 2020-02-01 Person~   763  2020     2
## 3 Alcan          AK         3104 US-Canada~ 2020-02-01 Truck ~   412  2020     2
## 4 Alcan          AK         3104 US-Canada~ 2020-02-01 Truck ~   122  2020     2
## 5 Alcan          AK         3104 US-Canada~ 2020-02-01 Trucks    545  2020     2
## 6 Alexandria Bay NY          708 US-Canada~ 2020-02-01 Bus Pa~  1174  2020     2

Compare Number of Border Crossings Between US-Canada and US-Mexico

plot1 <- ggplot(bc2) +
  geom_bar(aes(state, fill=border)) +
  ylab("Total Crossings") +
  ggtitle("Total Bording Crossing Per State")
plot1

Briefly describe what this plot shows

Compare Border Crossing by Method

plot2 <- ggplot(bc2, aes(border, fill=measure)) + 
  geom_bar(position="dodge") +
  ylab("Count") +
  ggtitle("US-Canada vs US-Mexico Border Crossing Count") +
  scale_fill_brewer(palette = "Set3") +
  guides(fill=guide_legend(title="Method"))
plot2

Briefly describe what this plot shows

Determine How Many Unique Ports

length(unique(bc2$port_name))

## [1] 116

new <- bc2 %>%
  group_by(border, date, measure) %>%
  summarise(total = sum(value)) %>%
  filter(measure %in% c("Personal Vehicle Passengers", "Personal Vehicles", "Pedestrians"))

## `summarise()` has grouped output by 'border', 'date'. You can override using
## the `.groups` argument.

head(new)

## # A tibble: 6 x 4
## # Groups:   border, date [2]
##   border           date       measure                       total
##   <chr>            <date>     <chr>                         <dbl>
## 1 US-Canada Border 1996-01-01 Pedestrians                   18537
## 2 US-Canada Border 1996-01-01 Personal Vehicle Passengers 6136398
## 3 US-Canada Border 1996-01-01 Personal Vehicles           2489564
## 4 US-Canada Border 1996-02-01 Pedestrians                   22856
## 5 US-Canada Border 1996-02-01 Personal Vehicle Passengers 6540141
## 6 US-Canada Border 1996-02-01 Personal Vehicles           2567875

Line Graph of Border Crossings by Method

options(scipen = 999)
plot3 <- new %>%
  filter(date >= "2005-01-01") %>%
  ggplot(aes(date, total, color = measure)) +
  geom_point() +
  scale_color_brewer(palette = "Set1") +
  facet_wrap(~border) +
  theme_bw()
plot3

Explore 1 or 2 more plots, possibly add a Tableau vis include hyperlink

Conclusion of the border crossing exploration

Text here