---
title: "EPPS 6356.501 - Final Project Dashboard"
author: "Team Maitreyi"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
---
```{r setup, include=FALSE}
library(plotly)
library(flexdashboard)
library(dplyr)
library(ggplot2)
library(tidyr)
library(gtools)
library(dplyr)
library(tidyverse)
library(ggplot2)
library(tidyr)
library(ggthemes)
library(ggdark)
library(naniar)
# Load ds
full <- read.csv('Merged_redux.csv')
# Set blanks to 'NA':
fult <- full %>% replace_with_na(replace = list(Armament.category = 0))
fult2 <- fult %>% replace_with_na(replace = list(Armament.category = ""))
# Rename cols.
names(fult2)[names(fult2) == 'Partner.Name'] <- 'Partner'
names(fult2)[names(fult2) == 'Armament.category'] <- 'Armament'
```
## Row.{.tabset .tabset-fade}
-----------------------------------------------------
### World Map of Routes
```{r, fig.width=20, fig.height=9}
#options(width = 1400)
library(ggplot2)
library(dplyr)
library(maps)
library(tibble)
trade_data <- read.csv("Merged_redux.csv")
region_mapping <- tibble(
Region = c("North America", "North America", "North America", "South and Central America", "Europe & Eurasia", "Asia", "Asia",
"South and Central America", "Asia", "Asia", "Africa", "Europe & Eurasia", "Africa", "Middle East", "Europe & Eurasia", "Middle East"),
Country = c("United States", "China", "Russia", "United States", "China", "Russia",
"United States", "China", "Russia", "United States", "China", "Russia",
"United States", "China", "Russia", "United States"),
Partner = c("Afghanistan", "Belarus", "Bangladesh", "India", "Iran", "Bangladesh", "India", "Iran",
"Iraq", "Israel", "Japan", "Myanmar", "Pakistan", "Qatar", "Saudi Arabia", "South Korea"),
Latitude = c(37.0902, 56.130366, 23.634501, -14.235004, 51.165691, 35.8617, 20.5937,
-1.8312, 23.6850, 30.3753,9.0820, 52.1326, -1.2864, 15.552727, 55.378051,23.885942),
Longitude = c(-95.7129, -106.346771, -102.552784, -51.92528, 10.451526, 104.1954, 78.96288,
-78.1834, 90.3563, 69.3451, 8.6753, 5.2913,37.9062, 48.5164,-3.4359, 45.0792))
trade_data_coords <- trade_data %>%
left_join(region_mapping, by = c("Supplier" = "Country")) %>%
rename(Export_Lat = Latitude, Export_Lon = Longitude, Region_Export = Supplier) %>%
left_join(region_mapping, by = c("Recipient" = "Partner")) %>%
rename(Import_Lat = Latitude, Import_Lon = Longitude, Region_Import = Recipient)
trade_data_clean = trade_data_coords %>%
filter(!is.na(Region_Export), !is.na(Region_Import)) %>%
mutate(Route = paste(Region_Export, "to", Region_Import)) %>%
filter(!is.na(Export_Lon), !is.na(Export_Lat), !is.na(Import_Lon), !is.na(Import_Lat)) %>%
filter(!(Export_Lon == Import_Lon & Export_Lat == Import_Lat))
# create map
world <- map_data("world")
world_regions <- world %>%
mutate(Region = case_when(
region %in% c("USA", "Canada", "Mexico") ~ "North America",
region %in% c("Brazil", "Argentina", "Ecuador") ~ "South and Central America",
region %in% c("Germany", "Russia", "France", "Netherlands", "UK") ~ "Europe & Eurasia",
region %in% c("China", "India", "Bangladesh", "Pakistan") ~ "Asia",
region %in% c("Japan", "Australia") ~ "Asia Pacific",
region %in% c("Nigeria", "South Africa", "Kenya") ~ "Africa",
region %in% c("Saudi Arabia", "Yemen") ~ "Middle East",
TRUE ~ "Other"
))
# Color palette for regions
region_colors <- c(
"North America" = "mistyrose1",
"South and Central America" = "tomato",
"Europe & Eurasia" = "lightskyblue3",
"Asia" = "antiquewhite",
"Asia Pacific" = "sandybrown",
"Middle East" = "pink",
"Africa" = "aquamarine",
"Other" = "gray90")
# Create base map
ggplot() +
geom_polygon(data = world_regions, aes(x = long, y = lat, group = group, fill = Region), color = "black") +
geom_curve(data = trade_data_clean, aes(x = Export_Lon, y = Export_Lat,
xend = Import_Lon, yend = Import_Lat, color = Route, size = Amount),
size = 0.3 ,curvature = 0.1, arrow = arrow(length = unit(0.1, "cm"))) +
scale_fill_manual(values = region_colors, name = "Region") +
coord_fixed(1.3) +
labs(title = "Routes of Arms Partners",
x = "Longitude",
y = "Latitude",
subtitle = "A Global 'Network' View",
caption = "Sources: WITS and SIPRI") +
theme_few() +
theme(text = element_text(family = "Optima")) +
theme(legend.position = "right") +
theme(legend.key.size = unit(0.5, 'cm'))
```
### Flows by 'Supplier'
```{r}
library(hrbrthemes)
fult22 <- fult2 %>% filter(Armament != 'NA' )
fult222 <- fult22 %>% filter(Armament != 'Other')
f2 <- fult222 %>%
arrange(desc(SIPRI.estimate)) %>%
#mutate(country = factor(country, country)) %>%
ggplot(aes(x=Amount, y=TII.Score, size=TIV.delivery.values, color=Armament)) +
geom_point(alpha=0.5) +
scale_colour_ft() +
scale_x_log10(labels=scales::dollar) +
scale_size(range = c(.1, 24), name="") +
dark_theme_light() +
theme(text = element_text(family = "Optima")) +
labs(x = "Overall Trade Amount per Year (Logged, Thousand $)", y = "TII Rank",
title = "Trade Flows and Weapons by 'Supplier'",
subtitle = "How Trade Levels Relate to\nWeapons Transfered (sans 'Other')\nImplicit TIV Value Range",
caption = "Sources: SIPRI and WITS") +
geom_hline(aes(yintercept=1.67),
color="red", linetype="dashed", linewidth=0.5) +
facet_wrap(~Country, ncol = 2)
ggplotly(f2)
```
## Row {.tabset .tabset-fade}
-------------------------------------------------
### Sankley Graph: Trade
```{r}
library(tidyverse)
library(viridis)
library(patchwork)
library(hrbrthemes)
library(circlize)
library(networkD3)
nodes <- data.frame(name=c(as.character(fult2$Country), as.character(fult2$Partner)) %>% unique())
fult2$IDsource=match(fult2$Country, nodes$name)-1
fult2$IDtarget=match(fult2$Partner, nodes$name)-1
ColourScal ='d3.scaleOrdinal() .range(["#FDE725FF","#B4DE2CFF","#6DCD59FF","#35B779FF","#1F9E89FF","#26828EFF","#31688EFF","#3E4A89FF","#482878FF","#440154FF"])'
# Trade
links = data.frame(data.frame(
source = fult2$Country,
target = fult2$Partner,
value = fult2$Amount))
nodes <- data.frame(
name=c(as.character(links$source),
as.character(links$target)) %>% unique()
)
links$IDsource <- match(links$source, nodes$name)-1
links$IDtarget <- match(links$target, nodes$name)-1
p8 <- sankeyNetwork(Links = links, Nodes = nodes,
Source = "IDsource", Target = "IDtarget",
Value = "value", NodeID = "name",
nodeWidth=40, fontSize=15,
nodePadding=5,
height = 300,
width = 500,
iterations = 32,
sinksRight=FALSE)
p8
```
### Sankley Graph: Arms
```{r}
# TIVs
links = data.frame(data.frame(
source = fult2$Country,
target = fult2$Partner,
value = fult2$TIV.delivery.values))
nodes <- data.frame(
name=c(as.character(links$source),
as.character(links$target)) %>% unique()
)
links$IDsource <- match(links$source, nodes$name)-1
links$IDtarget <- match(links$target, nodes$name)-1
p9 <- sankeyNetwork(Links = links, Nodes = nodes,
Source = "IDsource", Target = "IDtarget",
Value = "value", NodeID = "name",
nodeWidth=40, fontSize=23,
nodePadding=30,
height = 800,
width = 800,
sinksRight=FALSE)
p9
```
### Trade Trends
```{r}
Paired2 <- fult2 %>% filter(Year != "2022")
Paired22 <- Paired2 %>% filter(Year >= "1996")
set.seed(123)
sampled_trade <- Paired22 %>%
sample_n(500) # Sample 500 observations for a clearer plot
aggregated_trade <- Paired22 %>%
group_by(Year, Trade.Flow) %>%
summarise(Mean_Value = mean(Amount, na.rm = TRUE), .groups = 'drop')
p7 <- ggplot(aggregated_trade, aes(x = Year, y = Mean_Value, color = Trade.Flow)) +
geom_line(size = 1) +
scale_y_continuous(labels=scales::dollar) +
geom_point() +
labs(
title = "Trade Trends Over Time",
x = "Year",
y = "Mean Trade Value (Thousand)",
subtitle = "An Overall View",
caption = "Sources: WITS",
color = "Trade Flow"
) +
theme_few()
ggplotly(p7)
```
### TII Distro
```{r}
wpns3 <- c("Aircraft", "Sensors", "Armoured vehicles",
"Air defense systems", "Artillery",
"Engines", "Missles",
"Naval weapons", "Ships",
"Other")
p5 <- ggplot(data = subset(fult2, subset = Armament %in% wpns3),
mapping = aes(x = TIV.delivery.values, fill = Armament, color = Armament)) +
theme(text = element_text(family = "Optima")) +
labs(x = "TIV Values", y = "Density Estimate",
title = "Distro of TIVs",
subtitle = "Getting an Sense of Composition (Upto ~ 300)",
caption = "Sources: SIPRI") +
geom_vline(aes(xintercept=mean(TIV.delivery.values)),
color="blue", linetype="dashed", linewidth=0.5) +
theme_few() +
xlim(0, 300) +
geom_density(alpha = 0.3)
ggplotly(p5)
```
### TIV Distro
```{r}
p4 <- ggplot(data = (fult2),
mapping = aes(x = TII.Score, fill = Country, color = Country)) +
theme(text = element_text(family = "Optima")) +
labs(x = "TII Rank", y = "Proportional Density Estimate",
title = "Distro of TIIs",
subtitle = "Getting an Sense of Composition (Upto ~ 10)",
caption = "Sources: WITS") +
geom_vline(aes(xintercept=1.67),
color="red", linetype="dashed", linewidth=0.5) +
theme_few() +
xlim(0, 10) +
geom_density(alpha = 0.3, mapping = aes(y = ..scaled..))
ggplotly(p4)
```
