presentation.qmd

---
title: "The Last Giants"
subtitle: "A data-driven safari into the savannah"
author: "Visual Virtuosos"
title-slide-attributes:
  data-background-image: images/bg.jpeg
  data-background-size: stretch
  data-background-opacity: "0.7"
  data-slide-number: none
format:
  revealjs:
    embed-resources: true
    theme: simple
    transition: slide
    background-transition: fade
    slide-number: true
  
editor: visual
execute:
  echo: false
---

```{r}

if (!require("pacman")) 
  install.packages("pacman")


pacman::p_load(knitr,
               tidyverse,
               lubridate,
               here,
               dplyr,
               ggplot2,
               ggmap,
               maps,
               rnaturalearth,
               rnaturalearthdata,
               sf,
               plotly,
               caret,
               glmnet,
               viridis,
               rcartocolor,
               forcats,
               tidymodels)

```

```{r}

elephant <- readxl::read_xlsx(here("data","elephant_data.xlsx"))
```

# The Data

## The Data source

-   Chase, M. J., Schlossberg, S., Griffin, C. R., Bouché, P. J., Djene, S. W., Elkan, P. W., Ferreira, S., Grossman, F., Kohi, E. M., Landen, K., Omondi, P., Peltier, A., Selier, S. A., & Sutcliffe, R. (2016). Continent-wide survey reveals massive decline in African savannah elephants. PeerJ, 4, e2354. [doi:10.7717/peerj.2354](https://doi.org/10.7717/peerj.2354)

-   [Convention on International Trade in Endangered Species of Wild Fauna and Flora](https://trade.cites.org)

# The 3D perspective

## The 3D plot {background-video="images/filename_movie3.mp4" background-video-loop="true" background-video-muted="true"}

# How has elephant population varied over the years in Africa?

## The Trend

```{r}


total_estimate_per_year <- elephant |>
  group_by(Year) |>
  summarise(Population = sum(Estimate))

total_estimate_per_year <- total_estimate_per_year |>
  filter(Year != 2015)

# Fitting a linear regression model
lm_model <- lm(Estimate ~ Year, data = elephant)

# Defining a recipe
ols_recipe <- recipe(Population ~ Year, data = total_estimate_per_year) |>
  step_center(Year, all_predictors()) |>
  step_scale(Year, all_predictors())

# Defining the linear regression model
ols_model <- linear_reg() |>
  set_engine("lm")

# Combining recipe and model into a workflow
ols_workflow <- workflow() |>
  add_recipe(ols_recipe) |>
  add_model(ols_model)

# Training the OLS model
ols_fit <- ols_workflow |>
  fit(data = total_estimate_per_year)

# Creating a new data frame for prediction (between years 2015-2023)
new_years <- data.frame(Year = 2015:2023)

# Making predictions using the trained OLS model
ols_predictions <- predict(ols_fit, new_data = new_years)

# Combining the new years and predictions
ols_predictions_data <- data.frame(Year = new_years$Year, Population = ols_predictions$.pred)

# Specify the training control
train_control <- trainControl(method = "cv", number = 5)

# Train the OLS model using cross-validation
ols_model <- train(Population ~ Year, data = total_estimate_per_year, method = "lm", trControl = train_control)

# Plotting the estimated of elephant population between 1995 to 2014
p <- ggplot(total_estimate_per_year, aes(x = Year, y = Population, group = 1)) +
  geom_line() +
  stat_smooth(method = "loess", se = FALSE) +
  ggtitle("Combined Data: Estimate Over Time") +
  xlab("Year") +
  ylab("Estimate") +
  scale_y_continuous(limits = c(0, 500000), expand = c(0, 0), labels = comma_format(scale = 1e-3)) +
  scale_color_manual(values = c("black", "red3"), guide = FALSE) +
  labs(
    x = "Year",
    y = "Population (in Thousands)",
    title = "Trend in Elephant Population from 1995-2014",
    caption = "Source: PeerJ PeerJ Article
doi: 10.7717/peerj.2354 "
  ) +
  theme_minimal()

# Convert ggplot to plotly
population_trend <- ggplotly(p)

population_trend

```
This matches with results in the reasearch article!!

## The Trend (Continued...)

Results of the prediction models 


| Model           | RMSE   |
|-----------------|--------|
| OLS             | 312.46 |
| Poly Regression | 326.09 |
| Random Forest   | 333.23 |

## The Trend (Continued...)


```{r}

# Combining both the actual and predicted data using rowbind
combined_data <- rbind(total_estimate_per_year, ols_predictions_data)

# Plotting all the estimates of elephant population between 1995-2023
combined_prediction <- ggplot(combined_data, aes(x = Year, y = Population, group = 1)) +
  geom_line(aes(color = ifelse(Year <= 2014, "Population Trend", "Prediction"))) +
  stat_smooth(method = "loess", se = FALSE) +
  scale_y_continuous(limits = c(0, 500000), expand = c(0, 0), labels = scales::comma_format(scale = 1e-3)) +
  scale_color_manual(values = c("Population Trend" = "black", "Prediction" = "red3"), guide = FALSE) +
  labs(
    x = "Year",
    y = "Population (in Thousands)",
    title = "Trend in Elephant Population from 1995-2023",
    caption = "Source: PeerJ PeerJ Article doi: 10.7717/peerj.2354 ",
  ) +
  theme_minimal()

# Convert ggplot to plotly
combined_prediction_plot <- ggplotly(combined_prediction, tooltip = c("Year", "y"))

combined_prediction_plot

```



# What is the trend of ivory trade in Africa?

## The ivory trade

```{r}

# Loading ivory data
ivory_exports_data <- read_csv(here("data","cites_ivory_export.csv"))
```

```{r}

# Counting the number of instances for each year
export_count_per_year <- ivory_exports_data |>
  group_by(Year) |>
  summarise(export_count = n())

# Assuming `count_per_year` is your dataset
export_count_per_year <- export_count_per_year |>
  filter(Year > 1994)

#glimpse(export_count_per_year)
```

```{r}
# Plotting the Ivory exports between 1990 to 2022
exports_barplot <- ggplot(export_count_per_year, aes(x = as.factor(Year), y = export_count, fill = as.factor(Year), text = paste("Export count: ", export_count))) +
  geom_bar(stat = "identity", color = "black") +
  scale_fill_viridis_d(option = "C") +
  labs(
    x = "Year",
    y = "Number of exports",
    title = "Ivory exports in Africa from 1995 to 2022",
    caption = "Source: CITES (Convention of International Trade in Endangered Species of Wild Fauna and Flora)"
  ) +
  guides(fill = FALSE) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

# Convert ggplot to plotly
exports_plotly <- ggplotly(exports_barplot, tooltip = "text")

# Display the interactive plot
exports_plotly
```


## The ivory trade (continued...)

```{r}

# Extracing data from ivory_exports dataset
country_export_count <- ivory_exports_data %>%
  filter(Exporter %in% c("ZW", "ZM", "ZA", "BW", "MZ")) %>%
  group_by(Exporter) %>%
  summarise(count = n()) %>%
  mutate(Country = case_when(
    Exporter == "ZW" ~ "Zimbabwe",
    Exporter == "ZM" ~ "Zambia",
    Exporter == "ZA" ~ "South Africa",
    Exporter == "BW" ~ "Botswana",
    Exporter == "MZ" ~ "Mozambique",
    TRUE ~ as.character(Exporter)
  ))

africa <- ne_countries(scale = "medium", continent = "Africa", returnclass = "sf")

export_country_map <- merge(africa, country_export_count, by.x = "name", by.y = "Country")

# Create a ggplot map
gg_export_countries <- ggplot() +
  geom_sf(data = africa, fill = "white") +
  geom_sf(data = export_country_map, aes(fill = count, text = paste("Country: ", country_export_count$Country,"<br>",  
    "Export count: ",count )), color = "#5D2606", size = 0.2) +
  scale_fill_gradient(low = "#F2A64F", high = "#B61210", name = "Export count") + 
  theme_void() +
  labs(title = "Ivory exports from 1995 to 2022 (Top 5 largest ecosystems)", caption = "Source: CITES (Convention of International Trade 
    in Endangered Species of Wild Fauna and Flora)")

# Convert ggplot to plotly
gg_export_countries_plotly <- ggplotly(gg_export_countries, tooltip = "text")

# Show the Plotly map
gg_export_countries_plotly
```

## The ivory trade (continued...)

```{r warning = FALSE,message=FALSE}
#| fig-alt: Visualizing the Type of Ivory exports in Africa

ivory_term_count <- ivory_exports_data |>
  group_by(Year,Term) |>
  summarise(instance_count = n()) |>
  ungroup()

ivory_term_count <- ivory_term_count |>
  mutate(Term = fct_reorder(Term, instance_count, .desc = TRUE))

carving_ggplot <- ivory_term_count |>
  ggplot(aes( y = fct_rev(Term), size = instance_count, fill = Term)) +
  geom_bar(alpha = 0.7, color = "black") +
  scale_size(range = c(1.4, 10), name = "Population (M)") +
  scale_fill_carto_d(palette = "Safe") +
  theme_minimal() +
  theme(legend.position = "none") +
  labs(
    x = "Count",
    y = "Export Type",
    title = "Type of ivory export",
    caption = "Source: CITES (Convention of International Trade 
    in Endangered Species of Wild Fauna and Flora)"
  )

# converting ggplot to plotly
carvings_plotly <- ggplotly(carving_ggplot, tooltip = "text")

# display the interactive plot
carvings_plotly
```


## The ivory trade (continued...)

::: {#fig-elephants layout-ncol="3"}
![](images/i_Jwellery.jpeg){width="150%"}

![](images/i_Pieces.jpeg){width="150%"}

![](images/Carving.jpeg){width="150%"}

Ivory Products
:::

# Wrapping Up

## ![](images/last.png){width="150%"}