Religious Representation Gap: Migrants vs. General Population

Christians are significantly overrepresented among global migrants, while Hindus and the religiously unaffiliated are underrepresented

A visualization remake of Pew Research Center data exploring how religious groups are represented differently among migrants versus the general global population. This diverging bar chart highlights that Christians, Muslims, and Jews make up higher shares of migrants than of the general population, while Hindus and the religiously unaffiliated are notably underrepresented among migrants.

Original

The original visualization Globally, Christians are the lartgest migran group comes from The Religious Composition of the World’s Migrants:

Original visualization

Makeover

Figure 1: A diverging bar chart showing religious representation gaps between migrants and general population. Christians (+16.6), Muslims (+3.7), and Jewish (+0.9) are overrepresented among migrants (purple bars extending right), while Hindus (-10.2) and religiously unaffiliated (-10) are underrepresented (pink bars extending left). Buddhist representation is nearly equal (-0.1). Horizontal arrows indicate the direction of over- and underrepresentation.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
    tidyverse,      # Easily Install and Load the 'Tidyverse'
    ggtext,         # Improved Text Rendering Support for 'ggplot2'
    showtext,       # Using Fonts More Easily in R Graphs
    scales,         # Scale Functions for Visualization
    glue,           # Interpreted String Literals
    here,           # A Simpler Way to Find Your Files
    lubridate,      # Make Dealing with Dates a Little Easier
    paletteer       # Comprehensive Collection of Color Palettes
  )
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  =  10,
    height =  8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

## Original Chart
# The Religious Composition of the World’s Migrants
# https://data.world/makeovermonday/2025w20-the-religious-composition-of-the-worlds-migrants

## Article
# Pew Research Center "The Religious Composition of the World’s Migrants"
# https://www.pewresearch.org/religion/2024/08/19/the-religious-composition-of-the-worlds-migrants/

migrants_raw  <- readxl::read_excel(
  here::here('data/MakeoverMonday/2025/Incoming and Outgoing Migrant Counts.xlsx'),
                                   sheet = 1) |> 
  janitor::clean_names()
```

3. Examine the Data

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(migrants_raw)
skimr::skim(migrants_raw)
```

4. Tidy Data

```{r}
#| label: tidy
#| warning: false

### |-  tidy data ----
# Extract the global data for 2020 (most recent)
migrants_global <- migrants_raw |>
  filter(direction == "Incoming", year == "2020", country == "Global Total") |>
  select(religion, migrant_percent = percent)

# Derive general population data from on the chart
general_pop <- tibble(
  religion = c("Christian", "Muslim", "Jewish", "Buddhist", "Unaffiliated", "Hindu", "Other religions"),
  general_percent = c(30, 25, 0.2, 4, 23, 15, 2.8)
)

# Join the datasets
comparison_data <- migrants_global |>
  filter(religion != "All") |>
  mutate(religion = case_when(
    religion == "Jew" ~ "Jewish",
    religion == "Religiously unaffiliated" ~ "Unaffiliated",
    TRUE ~ religion
  )) |>
  left_join(general_pop, by = "religion") |>
  filter(!is.na(general_percent)) |>
  mutate(
    difference = migrant_percent - general_percent,
    migrant_percent_display = round(migrant_percent, 1),
    general_percent_display = round(general_percent, 1)
  ) |>
  mutate(
    religion = fct_reorder(religion, difference),
    abs_diff = abs(difference),
    direction = ifelse(difference >= 0, "overrepresented", "underrepresented"),
    percent_label = paste0(general_percent_display, "% → ", migrant_percent_display, "%")
  )
```

5. Visualization Parameters

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = c(
  "overrepresented" =  "#6761A8",
  "underrepresented" = "#CC3363"
))
  
### |-  titles and caption ----
title_text <- str_glue("Religious Representation Gap: Migrants vs. General Population")
subtitle_text <- str_wrap("Christians are significantly overrepresented among global migrants,\nwhile Hindus and the religiously unaffiliated are underrepresented",
                          width = 100)

# Create caption
caption_text <- create_mm_caption(
    mm_year = 2025,
    mm_week = 20,
    source_text = "Pew Research Center"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(

    # Legend formatting
    legend.position = "plot",
    legend.title = element_text(face = "bold"),

    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5), 
    axis.ticks.length = unit(0.2, "cm"), 

    # Axis formatting
    axis.title.x = element_text(face = "bold", size = rel(0.85)),
    axis.title.y = element_text(face = "bold", size = rel(0.85)),
    axis.text.y = element_text(face = "bold", size = rel(0.85)),
    
    # Grid lines
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

```{r}
#| label: plot
#| warning: false

### |-  Initial Plot  ----
p <- comparison_data |>
  ggplot(aes(x = religion, y = difference, fill = direction)) +
  # Geoms
  geom_col(width = 0.7) +
  geom_hline(yintercept = 0, color = "gray40", size = 0.5) +
  geom_text( # primary labels: difference in percentage points
    aes(
      label = paste0(ifelse(difference >= 0, "+", ""), round(difference, 1), " pts"),
      y = ifelse(difference >= 0,
        difference + 0.8,
        difference - 0.8
      )
    ),
    hjust = ifelse(comparison_data$difference >= 0, 0, 1),
    size = 4,
    fontface = "bold"
  ) +
  geom_text( # secondary labels: comparison of percentages
    aes(
      label = percent_label,
      y = ifelse(difference >= 0,
        difference + 5.5,
        difference - 5.5
      )
    ),
    hjust = ifelse(comparison_data$difference >= 0, 0, 1),
    size = 3.5,
    color = "gray30"
  ) +
  # Scale
  scale_fill_manual(
    values = colors$palette
  ) +
  scale_y_continuous(
    breaks = seq(-25, 30, 10),
    limits = c(-25, 30),
    expand = c(0, 0)
  ) +
  coord_flip(clip = "off") +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = NULL,
    y = "Percentage Point Difference",
    fill = NULL
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.8),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.1,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_text(
      size = rel(0.9),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 30)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$caption,
      color = colors$caption,
      hjust = 0.5,
      margin = margin(t = 10)
    )
  )

### |-  Annotated Plot  ----
p <- p +
  annotate(
    "label",
    x = 5.2,
    y = -24.5,
    label = paste(
      "Christians make up 30% of the general population but",
      "46.6% of migrants, a difference of +16.6 percentage points.",
      "This pattern may reflect historical migration flows and",
      "religious persecution.",
      sep = "\n"
    ),
    hjust = 0,
    vjust = 0,
    fill = NA,
    color = "gray20",
    size = 3.5,
    fontface = "italic",
    alpha = 0.9,
    label.size = NA
  ) +
  annotate("segment",
    x = 3.5, xend = 3.5,
    y = 5, yend = 8,
    arrow = arrow(length = unit(0.3, "cm"), type = "closed"),
    color = colors$palette["overrepresented"],
    size = 1
  ) +
  annotate("text",
    x = 3.5, y = 15,
    label = "Overrepresented",
    color = colors$palette["overrepresented"],
    fontface = "bold",
    size = 4,
    hjust = 0.5
  ) +
  annotate("segment",
    x = 3.5, xend = 3.5,
    y = -5, yend = -8,
    arrow = arrow(length = unit(0.3, "cm"), type = "closed"),
    color = colors$palette["underrepresented"],
    size = 1
  ) +
  annotate("text",
    x = 3.5, y = -15,
    label = "Underrepresented",
    color = colors$palette["underrepresented"],
    fontface = "bold",
    size = 4,
    hjust = 0.5
  )
```

7. Save

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  p, 
  type = "makeovermonday", 
  year = 2025,
  week = 20,
  width = 10, 
  height = 8
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] paletteer_1.6.0 here_1.0.1      glue_1.8.0      scales_1.3.0   
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[13] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[17] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.5.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] gifski_1.32.0-1   fansi_1.0.6       pkgconfig_2.0.3   skimr_2.1.5      
[13] readxl_1.4.3      lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0   
[17] textshaping_0.4.0 munsell_0.5.1     janitor_2.2.0     repr_1.1.7       
[21] codetools_0.2-20  snakecase_0.11.1  htmltools_0.5.8.1 yaml_2.3.10      
[25] pillar_1.9.0      camcorder_0.1.0   magick_2.8.5      commonmark_1.9.2 
[29] tidyselect_1.2.1  digest_0.6.37     stringi_1.8.4     rematch2_2.1.2   
[33] rsvg_2.6.1        rprojroot_2.0.4   fastmap_1.2.0     grid_4.4.0       
[37] colorspace_2.1-1  cli_3.6.4         magrittr_2.0.3    base64enc_0.1-3  
[41] utf8_1.2.4        withr_3.0.2       timechange_0.3.0  rmarkdown_2.29   
[45] cellranger_1.1.0  ragg_1.3.3        hms_1.1.3         evaluate_1.0.1   
[49] knitr_1.49        markdown_1.13     rlang_1.1.6       gridtext_0.1.5   
[53] Rcpp_1.0.13-1     xml2_1.3.6        renv_1.0.3        svglite_2.1.3    
[57] rstudioapi_0.17.1 jsonlite_1.8.9    R6_2.5.1          systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in mm_2025_20.qmd.

For the full repository, click here.

10. References

Expand for References

1. Data:

• Makeover Monday 2025 Week 20: The Religious Composition of the World’s Migrants

2. Article

• Pew Research Center: The Religious Composition of the World’s Migrants