Assignment 4

October 10th, 2025

Chart 1 - Variable-Width Column Chart of Migrant share of population (%)

library(ggplot2)
library(scales)
library(dplyr)


# Set up properties for the chart
vw8 <- df |>
  dplyr::slice_max(order_by = migrants_total, n = 8) |>
  dplyr::mutate(
    width_scaled = scales::rescale(sp_pop_totl, to = c(0.4, 1.0))  # keep small bars visible
  ) |>
  dplyr::arrange(dplyr::desc(migrants_pct)) |>
  dplyr::rename(
    `Abbr` = iso3c,
    `Total Migrants` = migrants_total,
    `% Migrants` = migrants_pct,
    `Total Population` = sp_pop_totl
  )
vw8

  Abbr            country Total Migrants % Migrants Total Population
1  SAU       Saudi Arabia       13071258       42.2         31552510
2  AUS          Australia        7604850       29.5         25649248
3  CAN             Canada        8332892       21.8         38028638
4  DEU            Germany       15021300       18.0         83160871
5  GBR     United Kingdom       10520870       15.6         66744000
6  USA      United States       50471028       14.9        331577720
7  FRA             France        8610719       13.1         67601110
8  RUS Russian Federation        7297611        5.0        145245148
  width_scaled
1    0.4115777
2    0.4000000
3    0.4242790
4    0.5127943
5    0.4805968
6    1.0000000
7    0.4822778
8    0.6345566

# Create Variable Width Column Chart
p_vw <- ggplot(vw8, aes(x = reorder(country, `% Migrants`),
                        y = `% Migrants`)) +
  geom_col(aes(width = width_scaled), fill = "#54A24B") +
  # Create the chart
  scale_y_continuous(labels = label_number(accuracy = 0.1)) +
  labs(
    title = "Migrant % vs. Total Population (2020)",
    subtitle = "Height = Migrant share of population (%); Width = Total population",
    x = NULL,
    y = "Migrant share of population (%)"
  ) +
  theme_minimal(base_family = "serif") +
  theme(
    plot.title.position = "plot",
    panel.grid.minor = element_blank(),
    axis.text.x = element_text(angle = 45, hjust = 1, size = 8)
  )

p_vw

Variable-Width Column Chart

Chart 2 - Table of Migrant Population by Country (2020)

Chart 3 - Bar Charts of Global Migration Overview (2020)

# To create this chart I took inspiration from analyst ___. I used their use of viridis as basis for this code.
# I took the basis, used patchwork to post both charts side by side & used ChatGPT & forum.posit.co for guidance
# to solve specific syntax errors.

library(WDI)
library(tidyverse)
library(viridis)
library(janitor)
library(countrycode)
library(ggplot2)
library(scales)
library(patchwork)

#DATA
# 2020 snapshot: migrants (count + %s) for all countries
migration_raw <- WDI(
  country  = "all",
  indicator = c(
    migrants_total = "SM.POP.TOTL",
    migrants_pct   = "SM.POP.TOTL.ZS"
  ),
  start = 2020, end = 2020, extra = TRUE
)

migration <- migration_raw %>%
  clean_names() %>%
  filter(region != "Aggregates") %>% # remove World/income groups
  mutate(continent = countrycode(iso3c, "iso3c", "continent"))


# Summarize by continent (2020)

  continent_summary <- migration %>%
  group_by(continent) %>%
  summarize(
    avg_migrant_pct = mean(migrants_pct, na.rm = TRUE),
    total_migrants = sum(migrants_total, na.rm = TRUE)
  ) %>%
  arrange(desc(total_migrants))
  
  continent_summary

# A tibble: 6 × 3
  continent avg_migrant_pct total_migrants
  <chr>               <dbl>          <dbl>
1 Asia                14.9        83609413
2 Europe              17.1        82887317
3 Americas            14.6        72556607
4 Africa               3.45       24959596
5 Oceania             16.1         9253374
6 <NA>                50.6           84087

# Create dataset
# Average migrant % per continent (2020)
continent_summary_avg <- migration %>%
  group_by(continent) %>%
  summarize(avg_migrant_pct = mean(migrants_pct, na.rm = TRUE)) %>%
  arrange(desc(avg_migrant_pct))

continent_summary_avg

# A tibble: 6 × 2
  continent avg_migrant_pct
  <chr>               <dbl>
1 <NA>                50.6 
2 Europe              17.1 
3 Oceania             16.1 
4 Asia                14.9 
5 Americas            14.6 
6 Africa               3.45

# Replace NA with "N/A"
continent_summary$continent <- ifelse(
  is.na(continent_summary$continent),
  "N/A",
  continent_summary$continent
)

# Convert to factor with alphabetical levels
continent_summary$continent <- factor(
  continent_summary$continent,
  levels = sort(unique(continent_summary$continent))
)

continent_levels <- c(setdiff(sort(unique(continent_summary$continent)), "N/A"), "N/A")
continent_summary$continent <- factor(continent_summary$continent, levels = continent_levels)


# Graph 1 Average Migrant %
p1 <- ggplot(continent_summary, aes(x = continent, y = avg_migrant_pct, fill = continent)) +
  geom_col(width = 0.7) +
  geom_text(aes(label = paste0(round(avg_migrant_pct, 1), "%")),
            vjust = -0.5, size = 4) +
  scale_fill_viridis(discrete = TRUE, option = "E") +
  labs(
    title = "Average Migrant Percentage by Continent",
    x = "",
    y = "Average % of Population"
  ) +
  ylim(0, 100) +
  theme_minimal(base_size = 12) +
  theme(
        legend.position = "none",
        plot.title = element_text(size = 10, face = "bold")
        )



#Graph 2 Total Migrants
p2 <- ggplot(continent_summary, aes(x = continent, y = total_migrants / 1e6, fill = continent)) +
  geom_col(width = 0.7) +
  geom_text(aes(label = paste0(round(total_migrants / 1e6, 1), "M")),
            vjust = -0.5, size = 4) +
  scale_fill_viridis(discrete = TRUE, option = "E") +
  scale_y_continuous(limits = c(0, 90), breaks = seq(0, 90, 10)) +
  labs(
    title = "Total Migrant Population by Continent",
    x = "",
    y = "Total Migrants (millions)"
  ) +
  theme_minimal(base_size = 12) +
  theme(
        legend.position = "none",
        plot.title = element_text(size = 10, face = "bold")
        )

#Final Product
print((p1 + p2) + plot_annotation(title = "Global Migration Overview (2020)"))

Chart 4 - Column Chart of Top 8 Countries by International Migrant Stock (2020)

options(dplyr.summarise.inform = FALSE)

suppressPackageStartupMessages({
  library(WDI)
  library(tidyverse, warn.conflicts = FALSE)  # stops “masked from …” notes
  library(janitor)
  library(countrycode)
  library(ggplot2)
  library(scales)
})

migration_raw <- WDI(
  country = "all",
  indicator = c(
    migrants_total = "SM.POP.TOTL",     # international migrants (count)
    migrants_pct   = "SM.POP.TOTL.ZS"   # migrants as % of population
  ),
  start = 2020, end = 2020, extra = TRUE
)

migration <- migration_raw |>
  clean_names() |>
  filter(region != "Aggregates") |>
  mutate(continent = countrycode(iso3c, "iso3c", "continent"))

# Top 8 destinations by migrant COUNT
top8 <- migration |>
  filter(!is.na(migrants_total)) |>
  slice_max(order_by = migrants_total, n = 8) |>
  arrange(migrants_total) |>         # smallest → largest so bars rise L→R
  mutate(country = factor(country, levels = country))

light_green <- "#8BC34A"

p_top8_v <- ggplot(top8, aes(x = country, y = migrants_total)) +
  geom_col(fill = light_green) +
  scale_y_continuous(
    labels = label_number(scale_cut = cut_short_scale()),
    expand = expansion(mult = c(0, 0.03))
  ) +
  labs(
    title = "Top 8 Countries by International Migrant Stock (2020)",
    subtitle = "Source: World Bank WDI (SM.POP.TOTL)",
    x = NULL, y = "Migrant Stock (people)"
  ) +
  theme_minimal(base_family = "serif") +
  theme(plot.title.position = "plot",
        panel.grid.minor = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1))

p_top8_v

if (!dir.exists("figures")) dir.create("figures")
ggsave("figures/tanmoy_chart4_vertical_lightgreen.png",
       plot = p_top8_v, width = 10, height = 6, dpi = 300)