A Decade of Divergence in Press Freedom

The 10 biggest improvements and declines in RSF rank since 2015

An arrow chart tracking the 10 biggest improvements and 10 biggest declines in RSF Press Freedom rankings over a decade (2015–2025). Built in R with ggplot2 using direct RSF data. Rank movement used in place of scores to ensure comparability across methodology changes.

Figure 1: An arrow chart showing the 10 biggest improvements and 10 biggest declines in RSF Press Freedom rankings between 2015 and 2025. Blue arrows point leftward — from a higher 2015 rank to a lower 2025 rank — indicating improvement in press freedom. Burgundy arrows point to the right, indicating decline. Top improvers include Gambia (+93 places), Montenegro, and North Macedonia. The biggest decliners include Nicaragua (−98 places), El Salvador, and Hong Kong. The x-axis runs from rank 1 (most free) on the left to rank 180 (least free) on the right. A gray dot marks each country’s 2015 starting rank.

Steps to Create this Graphic

1. Load Packages & Setup

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

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
  tidyverse, ggtext, showtext,     
  janitor, scales, glue        
  )
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 6,
  height = 9,
  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

### |- RSF 2025 ----
rsf_2025 <- read_delim(
  here::here("data/30DayChartChallenge/2026/RSF_2025.csv"),
  delim          = ";",
  locale         = locale(encoding = "windows-1252", decimal_mark = ","),
  show_col_types = FALSE
)

### |- RSF 2015 ----
rsf_2015 <- read_delim(
  here::here("data/30DayChartChallenge/2026/RSF_2015.csv"),
  delim          = ";",
  locale         = locale(encoding = "windows-1252", decimal_mark = ","),
  show_col_types = FALSE
)
```

3. Examine the Data

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

glimpse(rsf_2025)
glimpse(rsf_2015)
```

4. Tidy Data

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

### |- standardise 2025 ----
r2025 <- rsf_2025 |>
  clean_names() |>
  select(
    iso = iso,
    country = country_en,
    rank_2025 = rank
  ) |>
  mutate(rank_2025 = as.integer(rank_2025))

### |- standardise 2015 ----
r2015 <- rsf_2015 |>
  clean_names() |>
  select(
    iso       = iso,
    rank_2015 = rank_n
  ) |>
  mutate(rank_2015 = as.integer(rank_2015))

### |- join on ISO ----
rsf <- r2025 |>
  inner_join(r2015, by = "iso") |>
  filter(!is.na(rank_2015), !is.na(rank_2025)) |>
  mutate(
    rank_change = rank_2015 - rank_2025,
    direction = case_when(
      rank_change > 5 ~ "improved",
      rank_change < -5 ~ "declined",
      TRUE ~ "stable"
    )
  )

### |- top 10 improvers + top 10 decliners ----
top_improved <- rsf |>
  filter(direction == "improved") |>
  slice_max(rank_change, n = 10, with_ties = FALSE)

top_declined <- rsf |>
  filter(direction == "declined") |>
  slice_min(rank_change, n = 10, with_ties = FALSE)

rsf_plot <- bind_rows(top_improved, top_declined) |>
  arrange(
    direction,
    if_else(direction == "declined", rank_change, -rank_change)
  ) |>
  mutate(country = fct_inorder(country))
```

5. Visualization Parameters

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

### |- plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
    bg       = "#F5F3EE",
    text     = "#2C2C2C",
    grid     = "#E8E5E0",
    improved = "#2E6B9E",
    declined = "#8B3030",
    dot_2015 = "#AAAAAA"
  )
)

col_bg <- colors$palette$bg
col_text <- colors$palette$text
col_grid <- colors$palette$grid
col_improved <- colors$palette$improved
col_declined <- colors$palette$declined
col_dot_2015 <- colors$palette$dot_2015

### |- pre-extract arrow color as scalar vector ----
arrow_color_vec <- rsf_plot |>
  mutate(col = if_else(direction == "improved", col_improved, col_declined)) |>
  pull(col) |>
  setNames(as.character(rsf_plot$country))

### |- delta label formatting — compact ----
rsf_plot <- rsf_plot |>
  mutate(
    # compact: ↑51 or ↓48
    delta_label = if_else(
      direction == "improved",
      paste0("\u2191", rank_change),
      paste0("\u2193", abs(rank_change))
    ),
    # delta label
    label_x = rank_2025,
    label_hjust = if_else(direction == "improved", 1.15, -0.15),
    name_x = if_else(direction == "improved", rank_2015, rank_2015),
    name_hjust = if_else(direction == "improved", -0.15, 1.12)
  )

### |- section separator y position ----
n_declined <- sum(rsf_plot$direction == "declined")
sep_y <- n_declined + 0.5

### |- titles and caption ----
title_text    <- "A Decade of Divergence in Press Freedom"

subtitle_text <- glue(
  "The 10 biggest improvements and declines in RSF rank since 2015 &nbsp;<br>",
  "<span style='color:{col_dot_2015}'>&#9679;</span> 2015 rank &nbsp; ",
  "<span style='color:{col_improved}'>&#9658;</span> 2025 rank (improved) &nbsp; ",
  "<span style='color:{col_declined}'>&#9658;</span> 2025 rank (declined)"
)

caption_text  <- create_dcc_caption(
  dcc_year    = 2026,
  dcc_day     = 6,
  source_text = "Reporters Without Borders (RSF) | rsf.org/en/index — Rank comparison only;<br>scores not comparable across methodology changes"
)

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

### |- base + weekly theme ----
base_theme   <- create_base_theme(colors)

weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    plot.background = element_rect(fill = col_bg, color = NA),
    panel.background = element_rect(fill = col_bg, color = NA),
    panel.grid.major.x = element_line(color = col_grid, linewidth = 0.3),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks = element_blank(),
    axis.text.x = element_text(
      family = fonts$text, size = 7.5, color = "#777777"
    ),
    axis.title.x = element_text(
      family = fonts$text, size = 7.5, color = "#777777",
      margin = margin(t = 6)
    ),
    plot.title = element_markdown(
      family = fonts$title, size = 14, face = "bold",
      color = col_text, margin = margin(b = 4)
    ),
    plot.subtitle = element_markdown(
      family = "sans", size = 8, color = "#555555",
      lineheight = 1.4, margin = margin(b = 12)
    ),
    plot.caption = element_markdown(
      family = fonts$text, size = 6, color = "#999999",
      hjust = 0, margin = margin(t = 10)
    ),
    plot.margin = margin(t = 16, r = 16, b = 12, l = 16)
  )
)

theme_set(weekly_theme)
```

6. Plot

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

### |- main plot ----
p <- ggplot(rsf_plot) +

  # Geoms
  geom_hline(
    yintercept = sep_y,
    color      = "#DDDDDD",
    linewidth  = 0.3,
    linetype   = "dashed"
  ) +
  geom_segment(
    aes(
      x     = rank_2015, xend = rank_2025,
      y     = country,   yend = country,
      color = country
    ),
    linewidth = 1.1,
    arrow = arrow(length = unit(0.09, "inches"), type = "closed"),
    show.legend = FALSE
  ) +
  geom_point(
    aes(x = rank_2015, y = country),
    size  = 1.6,
    color = col_dot_2015
  ) +
  geom_text(
    aes(x = rank_2015, y = country, label = rank_2015),
    family = fonts$text,
    size = 2.0,
    color = "#BBBBBB",
    vjust = -0.9
  ) +
  geom_text(
    aes(
      x     = name_x,
      y     = country,
      label = country,
      hjust = name_hjust
    ),
    family = fonts$text,
    size = 2.55,
    color = col_text
  ) +
  geom_text(
    aes(
      x     = label_x,
      y     = country,
      label = delta_label,
      hjust = label_hjust,
      color = country
    ),
    family = "sans",
    size = 2.5,
    fontface = "bold",
    show.legend = FALSE
  ) +

  # Annotate
  annotate(
    "text",
    x = 10, y = sep_y + 1.0,
    label = "Improved",
    family = fonts$text,
    size = 2.5, fontface = "bold",
    color = col_improved, hjust = 0
  ) +
  annotate(
    "text",
    x = 10, y = sep_y - 0.4,
    label = "Declined",
    family = fonts$text,
    size = 2.5, fontface = "bold",
    color = col_declined, hjust = 0
  ) +

  # Scales
  scale_color_manual(values = arrow_color_vec) +
  scale_x_continuous(
    breaks = c(1, 50, 100, 150, 180),
    labels = c("1\nMore free", "50", "100", "150", "180\nLess free"),
    expand = expansion(mult = c(0.22, 0.22))
  ) +
  coord_cartesian(clip = "off") +

  # Labs
  labs(
    title    = title_text,
    subtitle = subtitle_text,
    caption  = caption_text,
    x        = "RSF Rank",
    y        = NULL
  )
```

7. Save

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

### |-  plot image ----  
save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2026, 
  day = 06, 
  width = 6, 
  height = 9
  )
```

8. Session Info

TipExpand for Session Info

R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 26100)

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 utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.2      glue_1.8.0      scales_1.4.0    janitor_2.2.1  
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.5 forcats_1.0.1   stringr_1.6.0   dplyr_1.2.0    
[13] purrr_1.2.1     readr_2.2.0     tidyr_1.3.2     tibble_3.2.1   
[17] ggplot2_4.0.2   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.56          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] vctrs_0.7.1        tools_4.3.1        generics_0.1.4     curl_7.0.0        
 [9] parallel_4.3.1     gifski_1.32.0-2    pacman_0.5.1       pkgconfig_2.0.3   
[13] RColorBrewer_1.1-3 S7_0.2.0           lifecycle_1.0.5    compiler_4.3.1    
[17] farver_2.1.2       textshaping_1.0.4  codetools_0.2-19   snakecase_0.11.1  
[21] litedown_0.9       htmltools_0.5.9    yaml_2.3.12        pillar_1.11.1     
[25] crayon_1.5.3       camcorder_0.1.0    magick_2.8.6       commonmark_2.0.0  
[29] tidyselect_1.2.1   digest_0.6.39      stringi_1.8.7      rsvg_2.6.2        
[33] rprojroot_2.1.1    fastmap_1.2.0      grid_4.3.1         cli_3.6.5         
[37] magrittr_2.0.3     withr_3.0.2        bit64_4.6.0-1      timechange_0.4.0  
[41] rmarkdown_2.30     bit_4.6.0          otel_0.2.0         ragg_1.5.0        
[45] hms_1.1.4          evaluate_1.0.5     knitr_1.51         markdown_2.0      
[49] rlang_1.1.7        gridtext_0.1.6     Rcpp_1.1.1         xml2_1.5.2        
[53] svglite_2.1.3      rstudioapi_0.18.0  vroom_1.7.0        jsonlite_2.0.0    
[57] R6_2.6.1           systemfonts_1.3.2 

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in 30dcc_2026_06.qmd.

For the full repository, click here.

10. References

TipExpand for References

1. Data Sources:
   • Reporters Without Borders. (2025). World Press Freedom Index 2025. Retrieved April 2026 from https://rsf.org/en/index?year=2025
   • Reporters Without Borders. (2015). World Press Freedom Index 2015. Retrieved April 2026 from https://rsf.org/en/index?year=2015
2. Methodology Note:
   • RSF revised its scoring methodology in 2022. Scores are not directly comparable across editions. Rank position is used instead to enable valid 2015–2025 comparisons.

11. Custom Functions Documentation

Note📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils