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+---
+title: "The Space Race: From Cold War Duopoly to Rising Powers"
+description: |
+ A snapshot of global space launches from 1957 to 2018, highlighting the shift from Cold War superpower dominance to a space race led by rising powers and private companies.
+categories: "2025"
+author: Jueun Lee
+date: "`r Sys.Date()`"
+output:
+ distill::distill_article:
+ self_contained: false
+ toc: true
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(out.width="100%", fig.align="center", fig.showtext=TRUE)
+```
+
+## Chart Selection
+
+Once the exclusive domain of Cold War superpowers and their state-run space agencies, space launches have become increasingly driven by commercial companies and emerging nations. This shift is clearly captured in a visualization published in *The Economist* in October 2018, titled *“The space race is dominated by new contenders,”* which presents detailed space launch data from 1957 to 2018.
+
+The graph first distinguishes between state and commercial launch providers, revealing the decline of Cold War dominance alongside the rise of private companies. It then highlights major state actors and leading commercial agencies, bringing into focus the growing participation of Asian emerging powers in the space race. By also incorporating launch success and failure rates, the visualization shows how early space exploration was marked by frequent failures, in contrast to recent years where even commercial providers achieve success rates exceeding 95%.
+
+This visualization was chosen for its ability to present both the global landscape and the finer details of the modern space race through multiple layers of information, without becoming overwhelming. It serves as a compelling example of how complex geopolitical and technological shifts can be communicated clearly and effectively through thoughtful visual design.
+
+{.external width="100%"}
+
+## Getting started
+
+### Package library
+
+This project makes use of package library in RStudio to support efficient data cleaning, transformation, and visualization. Core visualization packages are combined with supplementary tools to streamline the workflow and produce clear, expressive graphics. In addition, fonts with high readability were selected to closely match the style of the original visualization, ensuring visual consistency and a polished appearance.
+
+```{r package}
+library(tidyverse)
+library(scales)
+
+# Visualization utility
+library(ggtext)
+library(patchwork)
+
+# Font rendering and management
+sysfonts::font_add_google("Source Sans 3", "source")
+showtext::showtext_auto()
+```
+
+### Data loading and reconstruction
+
+The primary data source for this project is Jonathan McDowell’s JSR Launch Vehicle Database, which provides comprehensive records of global space launch activities. In addition, *The Economist* has made downloadable data and a codebook available via GitHub, allowing further insight into the structure and intent of the original visualization.
+
+Two main datasets were used: **launches**, containing records of successful and failed space launches, and **agencies**, detailing space launch providers. Prior to analysis, unnecessary variables were removed from both datasets, after which they were imported into RStudio. The datasets were then joined to create a unified data frame that served as the foundation for all subsequent data processing and visualization steps.
+
+```{r data_load}
+launches <- readr::read_csv("launches_edited.csv")
+agencies <- readr::read_csv("agencies_edited.csv")
+
+df <- launches %>%
+ left_join(agencies, by = "agency", suffix = c("_launch", "_agency")) %>%
+ mutate(
+ state_code = coalesce(state_code_agency, state_code_launch),
+ agency_type = coalesce(agency_type_agency, agency_type_launch)
+ ) %>%
+ select(-ends_with("_launch"), -ends_with("_agency"))
+```
+
+## Data processing
+
+### Stacking levels
+
+To ensure a consistent and interpretable visual structure, the launch providers were organized into predefined stacking levels. State and commercial providers were first separated into two broad groups, reflecting the central theme of the visualization: the transition from government-led space activity to commercially driven launches.
+
+Within each group, major countries and agencies were assigned explicit factor levels. For state providers, these levels highlight historically dominant spacefaring nations alongside emerging participants, while commercial levels emphasize leading private companies as well as aggregated categories for smaller actors. By prefixing each level with its provider type (state or commercial), the stacking order remains unambiguous when the two groups are combined.
+
+This structured hierarchy allows launches to be stacked in a controlled and meaningful order, preventing arbitrary sorting and ensuring that visual comparisons across time clearly communicate shifts in dominance and participation within the global space race.
+
+```{r stack_levels}
+state_levels <- c("European","United States","India","China",
+ "USSR/Russia","Japan","Other countries")
+commercial_levels <- c("SpaceX","ULA","Other US companies",
+ "Arianespace","Russian companies","Mitsubishi","Other companies")
+
+stack_levels <- c(
+ paste0("state_", state_levels),
+ paste0("commercial_", commercial_levels)
+)
+```
+
+### Color palette
+
+A custom color palette was created to clearly differentiate space launch providers while maintaining visual consistency. Each country and agency was assigned a unique hex color, enabling easy identification across the visualization.
+
+The same base colors were applied to both state and commercial providers by prefixing category names, ensuring that identical actors retain a consistent visual identity. This approach supports clear comparisons across groups and enhances the readability of the multi-layered chart.
+
+```{r color_palette}
+base_palette <- c(
+ "United States" = "#a2b8d4",
+ "European" = "#f4c87a",
+ "USSR/Russia" = "#e89e98",
+ "China" = "#973c4c",
+ "Japan" = "#4f7e79",
+ "India" = "#2dbfd3",
+ "Other countries" = "#a9bcc3",
+ "Arianespace" = "#f3bc5d",
+ "Russian companies" = "#e9a19b",
+ "Mitsubishi" = "#527b7a",
+ "Other US companies"= "#9fb6d1",
+ "ULA" = "#81a0c4",
+ "SpaceX" = "#2073a6",
+ "Other companies" = "#a9bdca"
+)
+
+stack_palette <- c(
+ setNames(base_palette, paste0("state_", names(base_palette))),
+ setNames(base_palette, paste0("commercial_", names(base_palette)))
+)
+```
+
+### Recoding providers
+
+This step reshapes the raw launch records into a format suitable for visualization. The data is first reduced to essential variables, and launch outcomes are simplified into success and fail to allow clear comparison over time.
+
+Each launch is then classified as either state or commercial and grouped into standardized provider categories. Major spacefaring nations and leading commercial agencies are shown individually, while smaller actors are aggregated to avoid unnecessary complexity. These categories are converted into ordered factors to control the stacking structure of the chart.
+
+Finally, launches are aggregated by year and outcome. Commercial launches are assigned negative values, while state launches remain positive, creating a mirrored layout that clearly contrasts the decline of state dominance with the rise of commercial providers.
+
+```{r summary_df}
+summary_df <- df %>%
+ transmute(
+ launch_year,
+ outcome = if_else(category == "F", "fail", "success"),
+ provider_side = if_else(agency_type == "state", "state", "commercial"),
+ agency_stack = paste0(
+ provider_side, "_",
+ case_when(
+ # --- state
+ agency_type == "state" & state_code %in% c("F","UK","I","I-ESA","I-ELDO") ~ "European",
+ agency_type == "state" & state_code == "US" ~ "United States",
+ agency_type == "state" & state_code == "IN" ~ "India",
+ agency_type == "state" & state_code == "CN" ~ "China",
+ agency_type == "state" & state_code %in% c("SU","RU") ~ "USSR/Russia",
+ agency_type == "state" & state_code == "J" ~ "Japan",
+ agency_type == "state" ~ "Other countries",
+
+ # --- commercial
+ agency_type != "state" & agency == "SPX" ~ "SpaceX",
+ agency_type != "state" & str_detect(agency, "^ULA") ~ "ULA",
+ agency_type != "state" & agency == "AE" ~ "Arianespace",
+ agency_type != "state" & agency == "MHI" ~ "Mitsubishi",
+ agency_type != "state" & state_code == "RU" ~ "Russian companies",
+ agency_type != "state" & state_code == "US" & !agency %in% c("SPX","ULA") ~ "Other US companies",
+ TRUE ~ "Other companies"
+ )
+ ) %>% factor(levels = stack_levels)
+ ) %>%
+ count(launch_year, provider_side, agency_stack, outcome, name = "launches") %>%
+ mutate(
+ plot_value = if_else(provider_side == "commercial", -launches, launches),
+ outcome = factor(outcome, levels = c("success", "fail"))
+ )
+```
+
+## Chart Replication
+
+### Step 1: Base Stacked Bars
+
+This first step creates the basic structure of the chart using a stacked bar plot. Annual launch counts are displayed as vertical bars, stacked by provider category to show each group’s contribution over time.
+
+Color is used to distinguish providers, while transparency indicates launch outcomes, subtly separating successes from failures. Legends, labels, and gridlines are removed to keep the focus on the data, establishing a clean foundation for the subsequent refinement steps.
+
+```{r plot1, fig.width=7.5, fig.height=8}
+plot <- ggplot(summary_df) +
+ geom_col(
+ aes(x = launch_year, y = plot_value, fill = agency_stack, alpha = outcome),
+ width = 1,
+ position = position_stack(reverse = TRUE)
+ ) +
+ scale_fill_manual(values = stack_palette, guide = "none") +
+ scale_alpha_manual(values = c(success = 1, fail = 0.85), guide = "none") +
+ labs(x = NULL, y = NULL) +
+ theme_minimal() +
+ theme(panel.grid = element_blank())
+
+plot
+```
+
+### Step 2: Adding the Center Gap
+
+In this step, a thin “gap” layer is intentionally inserted between **state** and **commercial** stacks to create visual breathing room around the center axis. A new factor level (`commercial_gap`) is added to the stacking order, and it is assigned a transparent color so it doesn’t appear as a real category.
+
+To make the gap consistent across all years, one synthetic row per year is generated (`gap_rows`) with a fixed size (`axis_gap`) and a negative plot value so it sits on the commercial side of the mirrored chart. These rows are then appended to the existing summarized data.
+
+As a result, the plot gains a stable separation between the two halves, improving readability and making the mirrored structure easier to interpret.
+
+```{r plot2, fig.width=7.5, fig.height=8}
+axis_gap <- 8
+gap_level <- "commercial_gap"
+
+stack_levels_2 <- c(paste0("state_", state_levels), gap_level, paste0("commercial_", commercial_levels))
+stack_palette_2 <- c(stack_palette, commercial_gap = "transparent")
+
+gap_rows <- distinct(summary_df, launch_year) %>%
+ mutate(
+ provider_side = "commercial",
+ agency_stack = factor(gap_level, levels = stack_levels_2),
+ outcome = factor("success", levels = c("success", "fail")),
+ launches = axis_gap,
+ plot_value = -axis_gap
+ )
+
+summary_df_2 <- summary_df %>%
+ mutate(agency_stack = factor(as.character(agency_stack), levels = stack_levels_2)) %>%
+ bind_rows(gap_rows)
+
+plot_2 <- ggplot(summary_df_2) +
+ geom_col(
+ aes(launch_year, plot_value, fill = agency_stack, alpha = outcome),
+ width = 1,
+ position = position_stack(reverse = TRUE)
+ ) +
+ scale_fill_manual(values = stack_palette_2, guide = "none") +
+ scale_alpha_manual(values = c(success = 1, fail = 0.80), guide = "none") +
+ theme_void() +
+ theme(text = element_text(family = "source"))
+
+plot_2
+```
+
+### Step 3: Layer Separators
+
+This step adds thin white separator lines to make the stacked layers easier to distinguish. For each year, cumulative sums are calculated to find the **boundary height** between stacked categories: one set for the state side (positive values) and another for the commercial side (negative values).
+
+Because a gap was inserted in Step 2, the commercial boundaries are shifted downward by `axis_gap` so the lines align correctly with the mirrored layout. A small number of commercial categories are excluded from outlining to better match the visual emphasis of the original chart.
+
+These boundaries are then drawn using `geom_segment()` as horizontal lines across each year, improving legibility without changing the underlying data.
+
+```{r plot3, fig.width=7.5, fig.height=8}
+# state boundaries (positive)
+state_outline <- summary_df_2 %>%
+ filter(provider_side == "state", agency_stack != "commercial_gap") %>%
+ group_by(launch_year, agency_stack) %>%
+ summarise(plot_value = sum(plot_value), .groups = "drop") %>%
+ arrange(launch_year, agency_stack) %>%
+ group_by(launch_year) %>%
+ mutate(y = cumsum(plot_value)) %>%
+ ungroup()
+
+# commercial boundaries (negative) + gap shift
+comm_outline <- summary_df_2 %>%
+ filter(provider_side == "commercial", agency_stack != "commercial_gap") %>%
+ group_by(launch_year, agency_stack) %>%
+ summarise(plot_value = sum(plot_value), .groups = "drop") %>%
+ arrange(launch_year, agency_stack) %>%
+ group_by(launch_year) %>%
+ mutate(y = -axis_gap + cumsum(plot_value)) %>% # <- 핵심: gap만큼 아래로
+ ungroup() %>%
+ filter(!agency_stack %in% c("commercial_SpaceX", "commercial_ULA"))
+
+plot_3 <- plot_2 +
+ geom_segment(
+ data = bind_rows(state_outline, comm_outline),
+ aes(x = launch_year - 0.5, xend = launch_year + 0.5, y = y, yend = y),
+ inherit.aes = FALSE,
+ color = "white",
+ linewidth = 0.4
+ )
+
+plot_3
+```
+
+### Step 4: Axes & Guide Labels
+
+In this step, the default ggplot axes are replaced with a fully custom axis system to match the original figure’s layout and annotations. The x-range is fixed to **1957–2018**, and decade boundaries are emphasized using vertical reference lines. Custom tick marks and labels are then manually placed to show both specific years (e.g., 1957 and 2018) and decade labels (e.g., “1960s”, “1970s”).
+
+To help readers interpret magnitude without a standard y-axis, yearly totals are calculated separately for **state** and **commercial** launches. These totals are used to place guide labels at selected launch counts (e.g., 20, 40, …), with subtle underline segments that point from the label to the relevant part of the timeline. Because the commercial side is plotted below zero and includes the inserted gap, its guide positions are shifted downward by `axis_gap` so the references align correctly.
+
+Overall, this step adds the key framing elements—time markers and readable scale cues—while keeping the chart minimal and uncluttered.
+
+```{r plot4, fig.width=7.5, fig.height=8}
+# =========================================================
+# Custom axes + guide labels (for plot_3)
+# =========================================================
+
+# -----------------------
+# X-axis (figure-specific)
+# -----------------------
+x_min <- 1957
+x_max <- 2018
+
+decades <- c(1950, 1960, 1970, 1980, 1990, 2000, 2010)
+
+x_bounds <- decades - 0.5
+x_ticks <- c(1957, 1964, 1974, 1984, 1994, 2004, 2014, 2018)
+x_labels <- c("1957", "1960s", "1970s", "1980s", "1990s", "2000s", "2010s", "18")
+
+axis_y <- -2
+tick_h <- 4
+x_label_pad <- -2
+
+
+# -----------------------
+# Yearly totals (from summary_df)
+# -----------------------
+year_totals <- summary_df %>%
+ group_by(launch_year, provider_side) %>%
+ summarise(total = sum(launches), .groups = "drop")
+
+state_total <- year_totals %>% filter(provider_side == "state")
+comm_total <- year_totals %>% filter(provider_side == "commercial")
+
+
+# -----------------------
+# Guide label styling
+# -----------------------
+y_left_levels <- seq(20, 140, 20)
+y_right_levels <- c(20, 40)
+
+pad_left <- 0.40
+pad_right <- 0.40
+
+underline_gap <- 5
+
+x_left_text <- x_min - 3.5
+x_right_text <- x_max + 3.5
+
+
+# -----------------------
+# Left guides (STATE): first year reaching y
+# - if y is never reached, anchor to the peak year (for 140 launches label)
+# -----------------------
+x_peak_state <- state_total %>%
+ filter(total == max(total, na.rm = TRUE)) %>%
+ summarise(x_peak = min(launch_year), .groups = "drop") %>%
+ pull(x_peak)
+
+left_guides2 <- tibble(y = y_left_levels) %>%
+ rowwise() %>%
+ mutate(
+ x_first = suppressWarnings(min(state_total$launch_year[state_total$total >= y], na.rm = TRUE)),
+ x_anchor = if (is.finite(x_first)) x_first else x_peak_state,
+
+ x_line = x_left_text,
+ x_text = x_left_text,
+ xend = (x_anchor - 0.5) - pad_left,
+
+ y_line = y - underline_gap,
+ label = if_else(y == 140, "140 launches", as.character(y))
+ ) %>%
+ ungroup()
+
+
+# -----------------------
+# Right guides (STATE + COMM): last year reaching y_label
+# -----------------------
+right_guides2 <- tidyr::crossing(
+ y_label = y_right_levels,
+ provider_side = c("state", "commercial")
+) %>%
+ rowwise() %>%
+ mutate(
+ x_last = suppressWarnings(
+ if (provider_side == "state")
+ max(state_total$launch_year[state_total$total >= y_label], na.rm = TRUE)
+ else
+ max(comm_total$launch_year[comm_total$total >= y_label], na.rm = TRUE)
+ ),
+
+ y_pos = if (provider_side == "state") y_label else -y_label - axis_gap,
+
+ x_line = (x_last + 0.5) + pad_right,
+ x_text = x_right_text,
+ xend = x_right_text,
+
+ y_line = y_pos - underline_gap,
+ label = as.character(y_label)
+ ) %>%
+ ungroup()
+
+
+# -----------------------
+# Plot: add custom axes + guides onto plot_3
+# -----------------------
+plot_4 <- plot_3 +
+ # decade boundary lines
+ geom_vline(xintercept = x_bounds, color = "white", linewidth = 0.4) +
+
+ # x-axis ticks
+ geom_segment(
+ data = tibble(x = x_bounds, y = axis_y, yend = axis_y - tick_h),
+ aes(x = x, xend = x, y = y, yend = yend),
+ inherit.aes = FALSE,
+ color = "grey60",
+ linewidth = 0.4
+ ) +
+
+ # x-axis labels
+ geom_text(
+ data = tibble(x = x_ticks, lab = x_labels),
+ aes(x = x, y = axis_y - tick_h - x_label_pad, label = lab),
+ inherit.aes = FALSE,
+ size = 3.2,
+ color = "black"
+ ) +
+
+ # LEFT guides (underline + label)
+ geom_segment(
+ data = left_guides2,
+ aes(x = x_line, xend = xend, y = y_line, yend = y_line),
+ inherit.aes = FALSE,
+ color = "grey70",
+ linewidth = 0.4
+ ) +
+ geom_text(
+ data = left_guides2,
+ aes(x = x_text, y = y, label = label),
+ inherit.aes = FALSE,
+ hjust = 0, vjust = 1,
+ size = 3,
+ color = "grey70"
+ ) +
+
+ # RIGHT guides (underline + label)
+ geom_segment(
+ data = right_guides2,
+ aes(x = x_line, xend = xend, y = y_line, yend = y_line),
+ inherit.aes = FALSE,
+ color = "grey70",
+ linewidth = 0.4
+ ) +
+ geom_text(
+ data = right_guides2,
+ aes(x = x_text, y = y_pos, label = label),
+ inherit.aes = FALSE,
+ hjust = 1, vjust = 1,
+ size = 3,
+ color = "grey70"
+ ) +
+
+ coord_cartesian(clip = "off", xlim = c(x_min - 5, x_max + 5)) +
+ theme_void(base_family = "sans") +
+ theme(plot.margin = margin(10, 70, 25, 25))
+
+plot_4
+```
+
+### Step 5: Titles and Sources
+
+This step adds narrative context to the visualization through a title, subtitle, and caption. The title clearly states the subject of the chart, while the subtitle specifies the scope of the data, including orbital range and the reference date.
+
+A detailed caption is included to acknowledge data sources and provide clarifying notes on specific agencies and classifications. All text elements are aligned to the left of the plot area and styled with subtle size and color differences, ensuring they support the visualization without distracting from the data itself.
+
+```{r plot5, fig.width=7.5, fig.height=8}
+plot_5 <- plot_4 +
+ labs(
+ title = "Space launches",
+ subtitle = "To Earth orbit or higher, at October 11th 2018",
+ caption = paste(
+ "Sources: FAA; Jonathan McDowell/planet4589.org; Roscosmos; press reports",
+ "*United Launch Alliance, a partnership of Boeing and Lockheed Martin",
+ "\u2020Non-reusable version \u2021 France, Italy and European Space Agency (ESA)",
+ "The Economist",
+ sep = "\n"
+ )
+ ) +
+ theme(
+ plot.title.position = "plot",
+ plot.caption.position = "plot",
+
+ plot.title = element_text(
+ size = 12, face = "bold",
+ hjust = 0,
+ margin = margin(b = 2)
+ ),
+ plot.subtitle = element_text(
+ size = 9, color = "grey40",
+ hjust = 0,
+ margin = margin(b = 10)
+ ),
+ plot.caption = element_text(
+ size = 6, color = "grey60",
+ hjust = 0,
+ lineheight = 1.15,
+ margin = margin(t = 10)
+ )
+ )
+
+plot_5
+```
+
+### Step 6: Annotations and Key Labels
+
+This step adds the explanatory layer that guides interpretation of the chart. First, simple arrow markers and bold text labels are placed to clarify the two halves of the mirrored layout: **state launch providers** above the midline and **commercial launch providers** below it.
+
+Next, key countries and companies are labeled directly on the plot. Highlight labels (`geom_label`) are used where a colored block helps the name stand out against dense stacks (e.g., the U.S., Europe, USSR/Russia, China, and major U.S. commercial firms). Simpler text labels (`geom_text`) are used for smaller or less crowded areas to avoid unnecessary visual weight. Using `scale_colour_identity()` keeps these label colors independent from the main fill palette.
+
+Finally, two narrative callouts are added: one explaining why Soviet launch counts were high (a reliability note), and another describing how launch failures were common early on but have since dropped to very high success rates. Together, these annotations turn the chart from a purely descriptive timeline into a more interpretable story about how the space race has changed.
+
+```{r plot6, fig.width=7.5, fig.height=8}
+plot_6 <- plot_5 +
+ ## State launch providers (▲)
+ annotate("text", x = 2004, y = 59, label = "\u25B2", size = 3) +
+ annotate("text", x = 2004, y = 50, label = "State launch\nproviders",
+ fontface = "bold", size = 2.5) +
+ ## Commercial launch providers (▼)
+ annotate("text", x = 1980, y = -31, label = "\u25BC", size = 3) +
+ annotate("text", x = 1980, y = -21, label = "Commercial\nlaunch providers",
+ fontface = "bold", size = 2.5)
+
+block_labels <- tibble::tribble(
+ ~x, ~y, ~lab, ~fill, ~col,
+ 1966, 22, "United States", "#2B6CB0", "white",
+ 1966, 9, "European", "#f4c87a", "black",
+ 1988, 30, "USSR", "#E53E3E", "white",
+ 1992, 30, "Russia", "#E53E3E", "white",
+ 2015, 14, "China", "#7B1E1E", "white",
+ 2000, -13, "Other US companies", "#9fb6d1", "black",
+ 2010, -13, "ULA", "#81a0c4", "black",
+ 2016, -10, "SpaceX", "#2073a6", "white"
+
+)
+
+plot_6 <- plot_6 +
+ geom_label(
+ data = block_labels,
+ aes(
+ x = x, y = y, label = lab,
+ fill = I(fill),
+ colour = I(col)
+ ),
+ linewidth = 0,
+ label.padding = unit(0.25, "lines"),
+ size = 2,
+ fontface = "bold",
+ inherit.aes = FALSE
+ )
+
+plain_labels <- tibble::tribble(
+ ~x, ~y, ~lab, ~col,
+ 1986, 128, "Japan", "#2F6F62",
+ 2016, 3, "India", "white",
+ 1990, 118, "Other\ncountries", "#6B7C93",
+ 1988, -35, "Arianespace\nFrance/ESA", "#f3bc5d",
+ 2004, -47, "Russian\ncompanies", "#e9a19b",
+ 2013, -55, "Mitsubishi\nJapan", "#527b7a"
+)
+
+plot_6 <- plot_6 +
+ geom_text(
+ data = plain_labels,
+ aes(x, y, label = lab, colour = col),
+ size = 2,
+ fontface = "bold",
+ inherit.aes = FALSE
+ ) +
+ scale_colour_identity()
+
+soviet_text <- paste(
+ "Soviet satellites were highly\n",
+ "unreliable. Even in the 1980s,\n",
+ "they lasted only a year and a\n",
+ "half on average, compared\n",
+ "with nine years for their\n",
+ "American counterparts.\n",
+ "Thus, the USSR required far\n",
+ "more launches to keep up",
+ sep = ""
+)
+
+plot_6 <- plot_6 +
+ geom_label(
+ data = tibble::tibble(
+ x = 1978,
+ y = 74,
+ lab = soviet_text
+ ),
+ aes(x = x, y = y, label = lab),
+ fill = "#e89e98",
+ colour = "#3a1f1f",
+ label.size = 0,
+ label.padding = unit(0.6, "lines"),
+ size = 2,
+ lineheight = 1.1,
+ inherit.aes = FALSE
+ )
+
+fail_note <- paste(
+ "Launch failures, shown in a lighter colour, were common in the early years\n",
+ "of spaceflight. Today, success rates are above 95%, even for private firms",
+ sep = ""
+)
+
+plot_6 <- plot_6 +
+ annotate("segment", x = 1973, xend = 1973, y = 123, yend = 133, linewidth = 0.3) +
+ annotate("segment", x = 1971, xend = 1973, y = 123, yend = 123, linewidth = 0.3) +
+
+ annotate(
+ "text",
+ x = 1972, y = 142, label = fail_note,
+ hjust = 0, vjust = 1,
+ size = 2, colour = "grey40",
+ lineheight = 0.8
+ )
+
+plot_6
+```
+
+### Step 7: Historical Milestones and Events
+
+The final step adds historical context by annotating major milestones in spaceflight directly onto the timeline. A small event dataset is created with fixed coordinates for each callout, covering landmark achievements and failures—from **Sputnik (1957)** and **Gagarin (1961)** to **Challenger (1986)**, **Columbia (2003)**, and **Soyuz MS-10 (2018)**.
+
+Each event is drawn using a short connector line (`geom_segment`) and a small highlighted marker (`geom_rect`) to pinpoint the exact year on the chart. The accompanying descriptions are added with `geom_richtext`, allowing simple formatting (bold text and line breaks) while keeping the annotations compact and readable.
+
+With these callouts, the chart becomes more than a record of launch volume: it links changes in launch activity to recognizable moments in space history, helping viewers interpret the data within its broader narrative.
+
+```{r plot7, fig.width=7.5, fig.height=8}
+events <- tibble::tribble(
+ ~x0, ~y0, ~x1, ~y1, ~label,
+ 1957, 2, 1957, 5, "Sputnik First man-made satellite",
+ 1961, 41, 1961, 46, "Yuri Gagarin First person in space",
+ 1965, 105, 1965,105, "Alexei Leonov First spacewalk",
+ 1969, 15, 1969, 15, "Apollo 11 First crewed Moon landing",
+ 1986, 8, 1986, 15, "Challenger explosion",
+ 2003, 1, 2003, 13, "Columbia accident",
+ 2018, 38, 2018, 62, "Soyuz MS-10 Failed to carry crew to International Space Station on October 11th"
+) %>%
+ dplyr::filter(label != "")
+
+plot_7 <- plot_6 +
+
+ geom_segment(
+ data = events,
+ aes(x = x0, y = y0, xend = x1, yend = y1),
+ linewidth = 0.5,
+ colour = "grey30",
+ inherit.aes = FALSE
+ ) +
+
+ geom_rect(
+ data = events,
+ aes(
+ xmin = x0 - 0.6, xmax = x0 + 0.6,
+ ymin = y0 - 0.7, ymax = y0 + 0.7
+ ),
+ fill = "white",
+ colour = "grey30",
+ linewidth = 0.6,
+ inherit.aes = FALSE
+ ) +
+
+ ggtext::geom_richtext(
+ data = events,
+ aes(x = x1 - 2, y = y1 + 5, label = label),
+ hjust = 0, vjust = 0.5,
+ fill = NA, label.color = NA,
+ size = 2,
+ lineheight = 1,
+ inherit.aes = FALSE
+ )
+
+plot_7
+```
+
+## Chart Improvement
+
+This final section re-examines the same launch dataset through a different lens: **composition over time**, rather than absolute launch counts alone. The data is reshaped to calculate, for each year, the share (%) of launches contributed by each provider group, shown separately for state and commercial actors. To complement this view, a rescaled line is overlaid to indicate changes in the total number of launches, allowing composition and scale to be interpreted together.
+
+The resulting stacked area charts reveal how the balance among major spacefaring nations evolves over time in the state panel, while the commercial panel highlights the growing dominance of leading private firms alongside shifts among smaller players. A shaded band marks the Cold War period (1957–1991) in the state chart, providing historical context for changes in both market structure and launch intensity.
+
+Compared with the original mirrored stacked bars—which emphasize absolute launch volumes and mission outcomes—this alternative visualization makes it easier to identify **relative dominance within each year**, while the overlaid line preserves information about overall growth or decline in launch activity. Together, these elements offer a clearer view of how the space race has transitioned from a geopolitically driven competition to a market-driven expansion.
+
+```{r improvement}
+# 1) Data processing: yearly shares within each side
+share_state <- summary_df %>%
+ filter(provider_side == "state") %>%
+ mutate(agency_group = str_remove(agency_stack, "^state_")) %>%
+ count(launch_year, agency_group, name = "n") %>%
+ group_by(launch_year) %>%
+ mutate(share = n / sum(n)) %>%
+ ungroup() %>%
+ mutate(agency_group = factor(agency_group, levels = state_levels))
+
+share_comm <- summary_df %>%
+ filter(provider_side == "commercial") %>%
+ mutate(agency_group = str_remove(agency_stack, "^commercial_")) %>%
+ count(launch_year, agency_group, name = "n") %>%
+ group_by(launch_year) %>%
+ mutate(share = n / sum(n)) %>%
+ ungroup() %>%
+ mutate(agency_group = factor(agency_group, levels = commercial_levels))
+
+x_min <- min(summary_df$launch_year, na.rm = TRUE)
+x_max <- max(summary_df$launch_year, na.rm = TRUE)
+
+# 1-2) Yearly totals (for overlay line)
+year_totals <- summary_df %>%
+ group_by(launch_year, provider_side) %>%
+ summarise(total = sum(launches), .groups = "drop")
+
+state_total <- year_totals %>% filter(provider_side == "state")
+comm_total <- year_totals %>% filter(provider_side == "commercial")
+
+# rescale totals to 0~1 so they can be overlaid on the share axis
+state_line <- state_total %>%
+ mutate(total01 = scales::rescale(total, to = c(0.02, 0.98)))
+
+comm_line <- comm_total %>%
+ mutate(total01 = scales::rescale(total, to = c(0.02, 0.98)))
+
+
+# 2) State area + total launches (rescaled) line
+p_state <- ggplot(share_state, aes(x = launch_year, y = share, fill = agency_group)) +
+ geom_area(linewidth = 0) +
+ geom_line(
+ data = state_line,
+ aes(x = launch_year, y = total01),
+ inherit.aes = FALSE,
+ linewidth = 0.6,
+ alpha = 0.9
+ ) +
+ annotate(
+ "text",
+ x = x_max - 2, y = 0.93,
+ label = "Total launches (rescaled)",
+ hjust = 1, vjust = 1,
+ size = 3, color = "grey35"
+ ) +
+ scale_y_continuous(labels = percent_format(accuracy = 1)) +
+ scale_x_continuous(limits = c(x_min, x_max), expand = c(0, 0)) +
+ scale_fill_manual(values = base_palette[state_levels], drop = FALSE) +
+ labs(title = "State launch providers", x = NULL, y = NULL, fill = NULL) +
+ theme_minimal(base_size = 11) +
+ theme(
+ panel.grid.minor = element_blank(),
+ plot.title = element_text(face = "bold"),
+ legend.position = "right"
+ )
+
+# 3) Cold War shading (state only)
+cold_war <- tibble(xmin = 1957, xmax = 1991, ymin = -Inf, ymax = Inf)
+
+p_state <- p_state +
+ geom_rect(
+ data = cold_war,
+ aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
+ inherit.aes = FALSE,
+ fill = "grey70",
+ alpha = 0.18
+ ) +
+ annotate(
+ "text",
+ x = 1974, y = 0.96,
+ label = "Cold War",
+ hjust = 0.5, vjust = 1,
+ size = 4, color = "grey30",
+ fontface = "bold"
+ )
+
+
+# 4) Commercial area + total launches (rescaled) line
+p_comm <- ggplot(share_comm, aes(x = launch_year, y = share, fill = agency_group)) +
+ geom_area(linewidth = 0) +
+ geom_line(
+ data = comm_line,
+ aes(x = launch_year, y = total01),
+ inherit.aes = FALSE,
+ linewidth = 0.6,
+ alpha = 0.9
+ ) +
+ annotate(
+ "text",
+ x = x_max - 2, y = 0.93,
+ label = "Total launches (rescaled)",
+ hjust = 1, vjust = 1,
+ size = 3, color = "grey35"
+ ) +
+ scale_y_continuous(labels = percent_format(accuracy = 1)) +
+ scale_x_continuous(limits = c(x_min, x_max), expand = c(0, 0)) +
+ scale_fill_manual(values = base_palette[commercial_levels], drop = FALSE) +
+ labs(title = "Commercial launch providers", x = NULL, y = NULL, fill = NULL) +
+ theme_minimal(base_size = 11) +
+ theme(
+ panel.grid.minor = element_blank(),
+ plot.title = element_text(face = "bold"),
+ legend.position = "right"
+ )
+
+
+# 5) Final plot
+final_plot <- p_state / p_comm + plot_layout(heights = c(1, 1))
+final_plot
+
+```
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+ The Space Race: From Cold War Duopoly to Rising Powers
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The Space Race: From Cold War Duopoly to Rising Powers
+
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+
2025
+
+
+
A snapshot of global space launches from 1957 to 2018, highlighting the shift from Cold War superpower dominance to a space race led by rising powers and private companies.
+
+
+
+
+ Jueun Lee
+
+ 2025-12-14
+
+
+
+
+
+
+
Chart Selection
+
Once the exclusive domain of Cold War superpowers and their state-run space agencies, space launches have become increasingly driven by commercial companies and emerging nations. This shift is clearly captured in a visualization published in The Economist in October 2018, titled “The space race is dominated by new contenders,” which presents detailed space launch data from 1957 to 2018.
+
The graph first distinguishes between state and commercial launch providers, revealing the decline of Cold War dominance alongside the rise of private companies. It then highlights major state actors and leading commercial agencies, bringing into focus the growing participation of Asian emerging powers in the space race. By also incorporating launch success and failure rates, the visualization shows how early space exploration was marked by frequent failures, in contrast to recent years where even commercial providers achieve success rates exceeding 95%.
+
This visualization was chosen for its ability to present both the global landscape and the finer details of the modern space race through multiple layers of information, without becoming overwhelming. It serves as a compelling example of how complex geopolitical and technological shifts can be communicated clearly and effectively through thoughtful visual design.
+
+
+Original chart. Source: The Economist (2018)
+
+
Getting started
+
Package library
+
This project makes use of package library in RStudio to support efficient data cleaning, transformation, and visualization. Core visualization packages are combined with supplementary tools to streamline the workflow and produce clear, expressive graphics. In addition, fonts with high readability were selected to closely match the style of the original visualization, ensuring visual consistency and a polished appearance.
The primary data source for this project is Jonathan McDowell’s JSR Launch Vehicle Database, which provides comprehensive records of global space launch activities. In addition, The Economist has made downloadable data and a codebook available via GitHub, allowing further insight into the structure and intent of the original visualization.
+
Two main datasets were used: launches, containing records of successful and failed space launches, and agencies, detailing space launch providers. Prior to analysis, unnecessary variables were removed from both datasets, after which they were imported into RStudio. The datasets were then joined to create a unified data frame that served as the foundation for all subsequent data processing and visualization steps.
To ensure a consistent and interpretable visual structure, the launch providers were organized into predefined stacking levels. State and commercial providers were first separated into two broad groups, reflecting the central theme of the visualization: the transition from government-led space activity to commercially driven launches.
+
Within each group, major countries and agencies were assigned explicit factor levels. For state providers, these levels highlight historically dominant spacefaring nations alongside emerging participants, while commercial levels emphasize leading private companies as well as aggregated categories for smaller actors. By prefixing each level with its provider type (state or commercial), the stacking order remains unambiguous when the two groups are combined.
+
This structured hierarchy allows launches to be stacked in a controlled and meaningful order, preventing arbitrary sorting and ensuring that visual comparisons across time clearly communicate shifts in dominance and participation within the global space race.
A custom color palette was created to clearly differentiate space launch providers while maintaining visual consistency. Each country and agency was assigned a unique hex color, enabling easy identification across the visualization.
+
The same base colors were applied to both state and commercial providers by prefixing category names, ensuring that identical actors retain a consistent visual identity. This approach supports clear comparisons across groups and enhances the readability of the multi-layered chart.
This step reshapes the raw launch records into a format suitable for visualization. The data is first reduced to essential variables, and launch outcomes are simplified into success and fail to allow clear comparison over time.
+
Each launch is then classified as either state or commercial and grouped into standardized provider categories. Major spacefaring nations and leading commercial agencies are shown individually, while smaller actors are aggregated to avoid unnecessary complexity. These categories are converted into ordered factors to control the stacking structure of the chart.
+
Finally, launches are aggregated by year and outcome. Commercial launches are assigned negative values, while state launches remain positive, creating a mirrored layout that clearly contrasts the decline of state dominance with the rise of commercial providers.
This first step creates the basic structure of the chart using a stacked bar plot. Annual launch counts are displayed as vertical bars, stacked by provider category to show each group’s contribution over time.
+
Color is used to distinguish providers, while transparency indicates launch outcomes, subtly separating successes from failures. Legends, labels, and gridlines are removed to keep the focus on the data, establishing a clean foundation for the subsequent refinement steps.
In this step, a thin “gap” layer is intentionally inserted between state and commercial stacks to create visual breathing room around the center axis. A new factor level (commercial_gap) is added to the stacking order, and it is assigned a transparent color so it doesn’t appear as a real category.
+
To make the gap consistent across all years, one synthetic row per year is generated (gap_rows) with a fixed size (axis_gap) and a negative plot value so it sits on the commercial side of the mirrored chart. These rows are then appended to the existing summarized data.
+
As a result, the plot gains a stable separation between the two halves, improving readability and making the mirrored structure easier to interpret.
This step adds thin white separator lines to make the stacked layers easier to distinguish. For each year, cumulative sums are calculated to find the boundary height between stacked categories: one set for the state side (positive values) and another for the commercial side (negative values).
+
Because a gap was inserted in Step 2, the commercial boundaries are shifted downward by axis_gap so the lines align correctly with the mirrored layout. A small number of commercial categories are excluded from outlining to better match the visual emphasis of the original chart.
+
These boundaries are then drawn using geom_segment() as horizontal lines across each year, improving legibility without changing the underlying data.
In this step, the default ggplot axes are replaced with a fully custom axis system to match the original figure’s layout and annotations. The x-range is fixed to 1957–2018, and decade boundaries are emphasized using vertical reference lines. Custom tick marks and labels are then manually placed to show both specific years (e.g., 1957 and 2018) and decade labels (e.g., “1960s”, “1970s”).
+
To help readers interpret magnitude without a standard y-axis, yearly totals are calculated separately for state and commercial launches. These totals are used to place guide labels at selected launch counts (e.g., 20, 40, …), with subtle underline segments that point from the label to the relevant part of the timeline. Because the commercial side is plotted below zero and includes the inserted gap, its guide positions are shifted downward by axis_gap so the references align correctly.
+
Overall, this step adds the key framing elements—time markers and readable scale cues—while keeping the chart minimal and uncluttered.
This step adds narrative context to the visualization through a title, subtitle, and caption. The title clearly states the subject of the chart, while the subtitle specifies the scope of the data, including orbital range and the reference date.
+
A detailed caption is included to acknowledge data sources and provide clarifying notes on specific agencies and classifications. All text elements are aligned to the left of the plot area and styled with subtle size and color differences, ensuring they support the visualization without distracting from the data itself.
+
+
+
plot_5<-plot_4+
+labs(
+ title ="Space launches",
+ subtitle ="To Earth orbit or higher, at October 11th 2018",
+ caption =paste(
+"Sources: FAA; Jonathan McDowell/planet4589.org; Roscosmos; press reports",
+"*United Launch Alliance, a partnership of Boeing and Lockheed Martin",
+"\u2020Non-reusable version \u2021 France, Italy and European Space Agency (ESA)",
+"The Economist",
+ sep ="\n"
+)
+)+
+theme(
+ plot.title.position ="plot",
+ plot.caption.position ="plot",
+
+ plot.title =element_text(
+ size =12, face ="bold",
+ hjust =0,
+ margin =margin(b =2)
+),
+ plot.subtitle =element_text(
+ size =9, color ="grey40",
+ hjust =0,
+ margin =margin(b =10)
+),
+ plot.caption =element_text(
+ size =6, color ="grey60",
+ hjust =0,
+ lineheight =1.15,
+ margin =margin(t =10)
+)
+)
+
+plot_5
+
+
+
+
Step 6: Annotations and Key Labels
+
This step adds the explanatory layer that guides interpretation of the chart. First, simple arrow markers and bold text labels are placed to clarify the two halves of the mirrored layout: state launch providers above the midline and commercial launch providers below it.
+
Next, key countries and companies are labeled directly on the plot. Highlight labels (geom_label) are used where a colored block helps the name stand out against dense stacks (e.g., the U.S., Europe, USSR/Russia, China, and major U.S. commercial firms). Simpler text labels (geom_text) are used for smaller or less crowded areas to avoid unnecessary visual weight. Using scale_colour_identity() keeps these label colors independent from the main fill palette.
+
Finally, two narrative callouts are added: one explaining why Soviet launch counts were high (a reliability note), and another describing how launch failures were common early on but have since dropped to very high success rates. Together, these annotations turn the chart from a purely descriptive timeline into a more interpretable story about how the space race has changed.
+
+
+
plot_6<-plot_5+
+## State launch providers (▲)
+annotate("text", x =2004, y =59, label ="\u25B2", size =3)+
+annotate("text", x =2004, y =50, label ="State launch\nproviders",
+ fontface ="bold", size =2.5)+
+## Commercial launch providers (▼)
+annotate("text", x =1980, y =-31, label ="\u25BC", size =3)+
+annotate("text", x =1980, y =-21, label ="Commercial\nlaunch providers",
+ fontface ="bold", size =2.5)
+
+block_labels<-tibble::tribble(
+~x, ~y, ~lab, ~fill, ~col,
+1966, 22, "United States", "#2B6CB0", "white",
+1966, 9, "European", "#f4c87a", "black",
+1988, 30, "USSR", "#E53E3E", "white",
+1992, 30, "Russia", "#E53E3E", "white",
+2015, 14, "China", "#7B1E1E", "white",
+2000, -13, "Other US companies", "#9fb6d1", "black",
+2010, -13, "ULA", "#81a0c4", "black",
+2016, -10, "SpaceX", "#2073a6", "white"
+
+)
+
+plot_6<-plot_6+
+geom_label(
+ data =block_labels,
+aes(
+ x =x, y =y, label =lab,
+ fill =I(fill),
+ colour =I(col)
+),
+ linewidth =0,
+ label.padding =unit(0.25, "lines"),
+ size =2,
+ fontface ="bold",
+ inherit.aes =FALSE
+)
+
+plain_labels<-tibble::tribble(
+~x, ~y, ~lab, ~col,
+1986, 128, "Japan", "#2F6F62",
+2016, 3, "India", "white",
+1990, 118, "Other\ncountries", "#6B7C93",
+1988, -35, "Arianespace\nFrance/ESA", "#f3bc5d",
+2004, -47, "Russian\ncompanies", "#e9a19b",
+2013, -55, "Mitsubishi\nJapan", "#527b7a"
+)
+
+plot_6<-plot_6+
+geom_text(
+ data =plain_labels,
+aes(x, y, label =lab, colour =col),
+ size =2,
+ fontface ="bold",
+ inherit.aes =FALSE
+)+
+scale_colour_identity()
+
+soviet_text<-paste(
+"Soviet satellites were highly\n",
+"unreliable. Even in the 1980s,\n",
+"they lasted only a year and a\n",
+"half on average, compared\n",
+"with nine years for their\n",
+"American counterparts.\n",
+"Thus, the USSR required far\n",
+"more launches to keep up",
+ sep =""
+)
+
+plot_6<-plot_6+
+geom_label(
+ data =tibble::tibble(
+ x =1978,
+ y =74,
+ lab =soviet_text
+),
+aes(x =x, y =y, label =lab),
+ fill ="#e89e98",
+ colour ="#3a1f1f",
+ label.size =0,
+ label.padding =unit(0.6, "lines"),
+ size =2,
+ lineheight =1.1,
+ inherit.aes =FALSE
+)
+
+fail_note<-paste(
+"Launch failures, shown in a lighter colour, were common in the early years\n",
+"of spaceflight. Today, success rates are above 95%, even for private firms",
+ sep =""
+)
+
+plot_6<-plot_6+
+annotate("segment", x =1973, xend =1973, y =123, yend =133, linewidth =0.3)+
+annotate("segment", x =1971, xend =1973, y =123, yend =123, linewidth =0.3)+
+
+annotate(
+"text",
+ x =1972, y =142, label =fail_note,
+ hjust =0, vjust =1,
+ size =2, colour ="grey40",
+ lineheight =0.8
+)
+
+plot_6
+
+
+
+
Step 7: Historical Milestones and Events
+
The final step adds historical context by annotating major milestones in spaceflight directly onto the timeline. A small event dataset is created with fixed coordinates for each callout, covering landmark achievements and failures—from Sputnik (1957) and Gagarin (1961) to Challenger (1986), Columbia (2003), and Soyuz MS-10 (2018).
+
Each event is drawn using a short connector line (geom_segment) and a small highlighted marker (geom_rect) to pinpoint the exact year on the chart. The accompanying descriptions are added with geom_richtext, allowing simple formatting (bold text and line breaks) while keeping the annotations compact and readable.
+
With these callouts, the chart becomes more than a record of launch volume: it links changes in launch activity to recognizable moments in space history, helping viewers interpret the data within its broader narrative.
This final section re-examines the same launch dataset through a different lens: composition over time, rather than absolute launch counts alone. The data is reshaped to calculate, for each year, the share (%) of launches contributed by each provider group, shown separately for state and commercial actors. To complement this view, a rescaled line is overlaid to indicate changes in the total number of launches, allowing composition and scale to be interpreted together.
+
The resulting stacked area charts reveal how the balance among major spacefaring nations evolves over time in the state panel, while the commercial panel highlights the growing dominance of leading private firms alongside shifts among smaller players. A shaded band marks the Cold War period (1957–1991) in the state chart, providing historical context for changes in both market structure and launch intensity.
+
Compared with the original mirrored stacked bars—which emphasize absolute launch volumes and mission outcomes—this alternative visualization makes it easier to identify relative dominance within each year, while the overlaid line preserves information about overall growth or decline in launch activity. Together, these elements offer a clearer view of how the space race has transitioned from a geopolitically driven competition to a market-driven expansion.
+
+
+
# 1) Data processing: yearly shares within each side
+share_state<-summary_df%>%
+filter(provider_side=="state")%>%
+mutate(agency_group =str_remove(agency_stack, "^state_"))%>%
+count(launch_year, agency_group, name ="n")%>%
+group_by(launch_year)%>%
+mutate(share =n/sum(n))%>%
+ungroup()%>%
+mutate(agency_group =factor(agency_group, levels =state_levels))
+
+share_comm<-summary_df%>%
+filter(provider_side=="commercial")%>%
+mutate(agency_group =str_remove(agency_stack, "^commercial_"))%>%
+count(launch_year, agency_group, name ="n")%>%
+group_by(launch_year)%>%
+mutate(share =n/sum(n))%>%
+ungroup()%>%
+mutate(agency_group =factor(agency_group, levels =commercial_levels))
+
+x_min<-min(summary_df$launch_year, na.rm =TRUE)
+x_max<-max(summary_df$launch_year, na.rm =TRUE)
+
+# 1-2) Yearly totals (for overlay line)
+year_totals<-summary_df%>%
+group_by(launch_year, provider_side)%>%
+summarise(total =sum(launches), .groups ="drop")
+
+state_total<-year_totals%>%filter(provider_side=="state")
+comm_total<-year_totals%>%filter(provider_side=="commercial")
+
+# rescale totals to 0~1 so they can be overlaid on the share axis
+state_line<-state_total%>%
+mutate(total01 =scales::rescale(total, to =c(0.02, 0.98)))
+
+comm_line<-comm_total%>%
+mutate(total01 =scales::rescale(total, to =c(0.02, 0.98)))
+
+
+# 2) State area + total launches (rescaled) line
+p_state<-ggplot(share_state, aes(x =launch_year, y =share, fill =agency_group))+
+geom_area(linewidth =0)+
+geom_line(
+ data =state_line,
+aes(x =launch_year, y =total01),
+ inherit.aes =FALSE,
+ linewidth =0.6,
+ alpha =0.9
+)+
+annotate(
+"text",
+ x =x_max-2, y =0.93,
+ label ="Total launches (rescaled)",
+ hjust =1, vjust =1,
+ size =3, color ="grey35"
+)+
+scale_y_continuous(labels =percent_format(accuracy =1))+
+scale_x_continuous(limits =c(x_min, x_max), expand =c(0, 0))+
+scale_fill_manual(values =base_palette[state_levels], drop =FALSE)+
+labs(title ="State launch providers", x =NULL, y =NULL, fill =NULL)+
+theme_minimal(base_size =11)+
+theme(
+ panel.grid.minor =element_blank(),
+ plot.title =element_text(face ="bold"),
+ legend.position ="right"
+)
+
+# 3) Cold War shading (state only)
+cold_war<-tibble(xmin =1957, xmax =1991, ymin =-Inf, ymax =Inf)
+
+p_state<-p_state+
+geom_rect(
+ data =cold_war,
+aes(xmin =xmin, xmax =xmax, ymin =ymin, ymax =ymax),
+ inherit.aes =FALSE,
+ fill ="grey70",
+ alpha =0.18
+)+
+annotate(
+"text",
+ x =1974, y =0.96,
+ label ="Cold War",
+ hjust =0.5, vjust =1,
+ size =4, color ="grey30",
+ fontface ="bold"
+)
+
+
+# 4) Commercial area + total launches (rescaled) line
+p_comm<-ggplot(share_comm, aes(x =launch_year, y =share, fill =agency_group))+
+geom_area(linewidth =0)+
+geom_line(
+ data =comm_line,
+aes(x =launch_year, y =total01),
+ inherit.aes =FALSE,
+ linewidth =0.6,
+ alpha =0.9
+)+
+annotate(
+"text",
+ x =x_max-2, y =0.93,
+ label ="Total launches (rescaled)",
+ hjust =1, vjust =1,
+ size =3, color ="grey35"
+)+
+scale_y_continuous(labels =percent_format(accuracy =1))+
+scale_x_continuous(limits =c(x_min, x_max), expand =c(0, 0))+
+scale_fill_manual(values =base_palette[commercial_levels], drop =FALSE)+
+labs(title ="Commercial launch providers", x =NULL, y =NULL, fill =NULL)+
+theme_minimal(base_size =11)+
+theme(
+ panel.grid.minor =element_blank(),
+ plot.title =element_text(face ="bold"),
+ legend.position ="right"
+)
+
+
+# 5) Final plot
+final_plot<-p_state/p_comm+plot_layout(heights =c(1, 1))
+final_plot
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/_projects/2025/100564676/100564676_files/anchor-4.2.2/anchor.min.js b/_projects/2025/100564676/100564676_files/anchor-4.2.2/anchor.min.js
new file mode 100644
index 00000000..1342f5f6
--- /dev/null
+++ b/_projects/2025/100564676/100564676_files/anchor-4.2.2/anchor.min.js
@@ -0,0 +1,9 @@
+// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
+//
+// AnchorJS - v4.2.2 - 2019-11-14
+// https://www.bryanbraun.com/anchorjs/
+// Copyright (c) 2019 Bryan Braun; Licensed MIT
+//
+// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
+!function(A,e){"use strict";"function"==typeof define&&define.amd?define([],e):"object"==typeof module&&module.exports?module.exports=e():(A.AnchorJS=e(),A.anchors=new A.AnchorJS)}(this,function(){"use strict";return function(A){function f(A){A.icon=A.hasOwnProperty("icon")?A.icon:"",A.visible=A.hasOwnProperty("visible")?A.visible:"hover",A.placement=A.hasOwnProperty("placement")?A.placement:"right",A.ariaLabel=A.hasOwnProperty("ariaLabel")?A.ariaLabel:"Anchor",A.class=A.hasOwnProperty("class")?A.class:"",A.base=A.hasOwnProperty("base")?A.base:"",A.truncate=A.hasOwnProperty("truncate")?Math.floor(A.truncate):64,A.titleText=A.hasOwnProperty("titleText")?A.titleText:""}function p(A){var e;if("string"==typeof A||A instanceof String)e=[].slice.call(document.querySelectorAll(A));else{if(!(Array.isArray(A)||A instanceof NodeList))throw new Error("The selector provided to AnchorJS was invalid.");e=[].slice.call(A)}return e}this.options=A||{},this.elements=[],f(this.options),this.isTouchDevice=function(){return!!("ontouchstart"in window||window.DocumentTouch&&document instanceof DocumentTouch)},this.add=function(A){var e,t,i,n,o,s,a,r,c,h,l,u,d=[];if(f(this.options),"touch"===(l=this.options.visible)&&(l=this.isTouchDevice()?"always":"hover"),0===(e=p(A=A||"h2, h3, h4, h5, h6")).length)return this;for(!function(){if(null!==document.head.querySelector("style.anchorjs"))return;var A,e=document.createElement("style");e.className="anchorjs",e.appendChild(document.createTextNode("")),void 0===(A=document.head.querySelector('[rel="stylesheet"], style'))?document.head.appendChild(e):document.head.insertBefore(e,A);e.sheet.insertRule(" .anchorjs-link { opacity: 0; text-decoration: none; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; }",e.sheet.cssRules.length),e.sheet.insertRule(" *:hover > .anchorjs-link, .anchorjs-link:focus { opacity: 1; }",e.sheet.cssRules.length),e.sheet.insertRule(" [data-anchorjs-icon]::after { content: attr(data-anchorjs-icon); }",e.sheet.cssRules.length),e.sheet.insertRule(' @font-face { font-family: "anchorjs-icons"; src: url(data:n/a;base64,AAEAAAALAIAAAwAwT1MvMg8yG2cAAAE4AAAAYGNtYXDp3gC3AAABpAAAAExnYXNwAAAAEAAAA9wAAAAIZ2x5ZlQCcfwAAAH4AAABCGhlYWQHFvHyAAAAvAAAADZoaGVhBnACFwAAAPQAAAAkaG10eASAADEAAAGYAAAADGxvY2EACACEAAAB8AAAAAhtYXhwAAYAVwAAARgAAAAgbmFtZQGOH9cAAAMAAAAAunBvc3QAAwAAAAADvAAAACAAAQAAAAEAAHzE2p9fDzz1AAkEAAAAAADRecUWAAAAANQA6R8AAAAAAoACwAAAAAgAAgAAAAAAAAABAAADwP/AAAACgAAA/9MCrQABAAAAAAAAAAAAAAAAAAAAAwABAAAAAwBVAAIAAAAAAAIAAAAAAAAAAAAAAAAAAAAAAAMCQAGQAAUAAAKZAswAAACPApkCzAAAAesAMwEJAAAAAAAAAAAAAAAAAAAAARAAAAAAAAAAAAAAAAAAAAAAQAAg//0DwP/AAEADwABAAAAAAQAAAAAAAAAAAAAAIAAAAAAAAAIAAAACgAAxAAAAAwAAAAMAAAAcAAEAAwAAABwAAwABAAAAHAAEADAAAAAIAAgAAgAAACDpy//9//8AAAAg6cv//f///+EWNwADAAEAAAAAAAAAAAAAAAAACACEAAEAAAAAAAAAAAAAAAAxAAACAAQARAKAAsAAKwBUAAABIiYnJjQ3NzY2MzIWFxYUBwcGIicmNDc3NjQnJiYjIgYHBwYUFxYUBwYGIwciJicmNDc3NjIXFhQHBwYUFxYWMzI2Nzc2NCcmNDc2MhcWFAcHBgYjARQGDAUtLXoWOR8fORYtLTgKGwoKCjgaGg0gEhIgDXoaGgkJBQwHdR85Fi0tOAobCgoKOBoaDSASEiANehoaCQkKGwotLXoWOR8BMwUFLYEuehYXFxYugC44CQkKGwo4GkoaDQ0NDXoaShoKGwoFBe8XFi6ALjgJCQobCjgaShoNDQ0NehpKGgobCgoKLYEuehYXAAAADACWAAEAAAAAAAEACAAAAAEAAAAAAAIAAwAIAAEAAAAAAAMACAAAAAEAAAAAAAQACAAAAAEAAAAAAAUAAQALAAEAAAAAAAYACAAAAAMAAQQJAAEAEAAMAAMAAQQJAAIABgAcAAMAAQQJAAMAEAAMAAMAAQQJAAQAEAAMAAMAAQQJAAUAAgAiAAMAAQQJAAYAEAAMYW5jaG9yanM0MDBAAGEAbgBjAGgAbwByAGoAcwA0ADAAMABAAAAAAwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABAAH//wAP) format("truetype"); }',e.sheet.cssRules.length)}(),t=document.querySelectorAll("[id]"),i=[].map.call(t,function(A){return A.id}),o=0;o\]\.\/\(\)\*\\\n\t\b\v]/g,"-").replace(/-{2,}/g,"-").substring(0,this.options.truncate).replace(/^-+|-+$/gm,"").toLowerCase()},this.hasAnchorJSLink=function(A){var e=A.firstChild&&-1<(" "+A.firstChild.className+" ").indexOf(" anchorjs-link "),t=A.lastChild&&-1<(" "+A.lastChild.className+" ").indexOf(" anchorjs-link ");return e||t||!1}}});
+// @license-end
\ No newline at end of file
diff --git a/_projects/2025/100564676/100564676_files/bowser-1.9.3/bowser.min.js b/_projects/2025/100564676/100564676_files/bowser-1.9.3/bowser.min.js
new file mode 100644
index 00000000..3da61049
--- /dev/null
+++ b/_projects/2025/100564676/100564676_files/bowser-1.9.3/bowser.min.js
@@ -0,0 +1,6 @@
+/*!
+ * Bowser - a browser detector
+ * https://github.com/ded/bowser
+ * MIT License | (c) Dustin Diaz 2015
+ */
+!function(e,t,n){typeof module!="undefined"&&module.exports?module.exports=n():typeof define=="function"&&define.amd?define(t,n):e[t]=n()}(this,"bowser",function(){function t(t){function n(e){var n=t.match(e);return n&&n.length>1&&n[1]||""}function r(e){var n=t.match(e);return n&&n.length>1&&n[2]||""}function N(e){switch(e){case"NT":return"NT";case"XP":return"XP";case"NT 5.0":return"2000";case"NT 5.1":return"XP";case"NT 5.2":return"2003";case"NT 6.0":return"Vista";case"NT 6.1":return"7";case"NT 6.2":return"8";case"NT 6.3":return"8.1";case"NT 10.0":return"10";default:return undefined}}var i=n(/(ipod|iphone|ipad)/i).toLowerCase(),s=/like android/i.test(t),o=!s&&/android/i.test(t),u=/nexus\s*[0-6]\s*/i.test(t),a=!u&&/nexus\s*[0-9]+/i.test(t),f=/CrOS/.test(t),l=/silk/i.test(t),c=/sailfish/i.test(t),h=/tizen/i.test(t),p=/(web|hpw)os/i.test(t),d=/windows 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+
+
+`);class Hi extends Pi(HTMLElement){constructor(){super()}connectedCallback(){}listen(e){this.bindDivEvents(this),this.bindTriggerEvents(e)}bindDivEvents(e){e.addEventListener('mouseover',()=>{this.visible||this.showAtNode(e),this.stopTimeout()}),e.addEventListener('mouseout',()=>{this.extendTimeout(500)}),e.addEventListener('touchstart',(e)=>{e.stopPropagation()},{passive:!0}),document.body.addEventListener('touchstart',()=>{this.hide()},{passive:!0})}bindTriggerEvents(e){e.addEventListener('mouseover',()=>{this.visible||this.showAtNode(e),this.stopTimeout()}),e.addEventListener('mouseout',()=>{this.extendTimeout(300)}),e.addEventListener('touchstart',(t)=>{this.visible?this.hide():this.showAtNode(e),t.stopPropagation()},{passive:!0})}show(e){this.visible=!0,this.style.display='block',this.style.top=Pn(e[1]+10)+'px'}showAtNode(e){const t=e.getBoundingClientRect();this.show([e.offsetLeft+t.width,e.offsetTop+t.height])}hide(){this.visible=!1,this.style.display='none',this.stopTimeout()}stopTimeout(){this.timeout&&clearTimeout(this.timeout)}extendTimeout(e){this.stopTimeout(),this.timeout=setTimeout(()=>{this.hide()},e)}}class zi extends HTMLElement{static get is(){return'd-title'}}const Yi=ti('d-references',`
+
+`,!1);class Bi extends Yi(HTMLElement){}class Wi extends HTMLElement{static get is(){return'd-toc'}connectedCallback(){this.getAttribute('prerendered')||(window.onload=()=>{const e=document.querySelector('d-article'),t=e.querySelectorAll('h2, h3');k(this,t)})}}class Vi extends HTMLElement{static get is(){return'd-figure'}static get readyQueue(){return Vi._readyQueue||(Vi._readyQueue=[]),Vi._readyQueue}static addToReadyQueue(e){-1===Vi.readyQueue.indexOf(e)&&(Vi.readyQueue.push(e),Vi.runReadyQueue())}static runReadyQueue(){const e=Vi.readyQueue.sort((e,t)=>e._seenOnScreen-t._seenOnScreen).filter((e)=>!e._ready).pop();e&&(e.ready(),requestAnimationFrame(Vi.runReadyQueue))}constructor(){super(),this._ready=!1,this._onscreen=!1,this._offscreen=!0}connectedCallback(){this.loadsWhileScrolling=this.hasAttribute('loadsWhileScrolling'),Vi.marginObserver.observe(this),Vi.directObserver.observe(this)}disconnectedCallback(){Vi.marginObserver.unobserve(this),Vi.directObserver.unobserve(this)}static get marginObserver(){if(!Vi._marginObserver){const e=window.innerHeight,t=Fn(2*e),n=Vi.didObserveMarginIntersection,i=new IntersectionObserver(n,{rootMargin:t+'px 0px '+t+'px 0px',threshold:0.01});Vi._marginObserver=i}return Vi._marginObserver}static didObserveMarginIntersection(e){for(const t of e){const e=t.target;t.isIntersecting&&!e._ready&&Vi.addToReadyQueue(e)}}static get directObserver(){return Vi._directObserver||(Vi._directObserver=new IntersectionObserver(Vi.didObserveDirectIntersection,{rootMargin:'0px',threshold:[0,1]})),Vi._directObserver}static didObserveDirectIntersection(e){for(const t of e){const e=t.target;t.isIntersecting?(e._seenOnScreen=new Date,e._offscreen&&e.onscreen()):e._onscreen&&e.offscreen()}}addEventListener(e,t){super.addEventListener(e,t),'ready'===e&&-1!==Vi.readyQueue.indexOf(this)&&(this._ready=!1,Vi.runReadyQueue()),'onscreen'===e&&this.onscreen()}ready(){this._ready=!0,Vi.marginObserver.unobserve(this);const e=new CustomEvent('ready');this.dispatchEvent(e)}onscreen(){this._onscreen=!0,this._offscreen=!1;const e=new CustomEvent('onscreen');this.dispatchEvent(e)}offscreen(){this._onscreen=!1,this._offscreen=!0;const e=new CustomEvent('offscreen');this.dispatchEvent(e)}}if('undefined'!=typeof window){Vi.isScrolling=!1;let e;window.addEventListener('scroll',()=>{Vi.isScrolling=!0,clearTimeout(e),e=setTimeout(()=>{Vi.isScrolling=!1,Vi.runReadyQueue()},500)},!0)}const Ki=ti('d-interstitial',`
+
+
+
+
+
This article is in review.
+
Do not share this URL or the contents of this article. Thank you!
+
+
Enter the password we shared with you as part of the review process to view the article.
+
+`);class qr extends Rr(HTMLElement){}const Fr=function(){if(1>window.distillRunlevel)throw new Error('Insufficient Runlevel for Distill Template!');if('distillTemplateIsLoading'in window&&window.distillTemplateIsLoading)throw new Error('Runlevel 1: Distill Template is getting loaded more than once, aborting!');else window.distillTemplateIsLoading=!0,console.info('Runlevel 1: Distill Template has started loading.');p(document),console.info('Runlevel 1: Static Distill styles have been added.'),console.info('Runlevel 1->2.'),window.distillRunlevel+=1;for(const[e,t]of Object.entries(hi.listeners))'function'==typeof t?document.addEventListener(e,t):console.error('Runlevel 2: Controller listeners need to be functions!');console.info('Runlevel 2: We can now listen to controller events.'),console.info('Runlevel 2->3.'),window.distillRunlevel+=1;if(2>window.distillRunlevel)throw new Error('Insufficient Runlevel for adding custom elements!');const e=[ki,wi,Ci,Li,Ai,Di,Oi,Ni,Ri,Fi,pi,Hi,zi,T,Bi,Wi,Vi,Mr,$i].concat([Ir,jr,qr]);for(const t of e)console.info('Runlevel 2: Registering custom element: '+t.is),customElements.define(t.is,t);console.info('Runlevel 3: Distill Template finished registering custom elements.'),console.info('Runlevel 3->4.'),window.distillRunlevel+=1,hi.listeners.DOMContentLoaded(),console.info('Runlevel 4: Distill Template initialisation complete.')};window.distillRunlevel=0,yi.browserSupportsAllFeatures()?(console.info('Runlevel 0: No need for polyfills.'),console.info('Runlevel 0->1.'),window.distillRunlevel+=1,Fr()):(console.info('Runlevel 0: Distill Template is loading polyfills.'),yi.load(Fr))});
+//# sourceMappingURL=template.v2.js.map
+}
diff --git a/_projects/2025/100564676/100564676_files/figure-html5/improvement-1.png b/_projects/2025/100564676/100564676_files/figure-html5/improvement-1.png
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diff --git a/_projects/2025/100564676/100564676_files/figure-html5/plot2-1.png b/_projects/2025/100564676/100564676_files/figure-html5/plot2-1.png
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diff --git a/_projects/2025/100564676/100564676_files/figure-html5/plot3-1.png b/_projects/2025/100564676/100564676_files/figure-html5/plot3-1.png
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diff --git a/_projects/2025/100564676/100564676_files/figure-html5/plot4-1.png b/_projects/2025/100564676/100564676_files/figure-html5/plot4-1.png
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diff --git a/_projects/2025/100564676/100564676_files/figure-html5/plot5-1.png b/_projects/2025/100564676/100564676_files/figure-html5/plot5-1.png
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diff --git a/_projects/2025/100564676/100564676_files/figure-html5/plot6-1.png b/_projects/2025/100564676/100564676_files/figure-html5/plot6-1.png
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diff --git a/_projects/2025/100564676/100564676_files/figure-html5/plot7-1.png b/_projects/2025/100564676/100564676_files/figure-html5/plot7-1.png
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diff --git a/_projects/2025/100564676/100564676_files/figure-html5/unnamed-chunk-6-1.png b/_projects/2025/100564676/100564676_files/figure-html5/unnamed-chunk-6-1.png
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diff --git a/_projects/2025/100564676/100564676_files/header-attrs-2.29/header-attrs.js b/_projects/2025/100564676/100564676_files/header-attrs-2.29/header-attrs.js
new file mode 100644
index 00000000..dd57d92e
--- /dev/null
+++ b/_projects/2025/100564676/100564676_files/header-attrs-2.29/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+ var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+ var i, h, a;
+ for (i = 0; i < hs.length; i++) {
+ h = hs[i];
+ if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
+ a = h.attributes;
+ while (a.length > 0) h.removeAttribute(a[0].name);
+ }
+});
diff --git a/_projects/2025/100564676/100564676_files/jquery-3.6.0/jquery-3.6.0.js b/_projects/2025/100564676/100564676_files/jquery-3.6.0/jquery-3.6.0.js
new file mode 100644
index 00000000..fc6c299b
--- /dev/null
+++ b/_projects/2025/100564676/100564676_files/jquery-3.6.0/jquery-3.6.0.js
@@ -0,0 +1,10881 @@
+/*!
+ * jQuery JavaScript Library v3.6.0
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright OpenJS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2021-03-02T17:08Z
+ */
+( function( global, factory ) {
+
+ "use strict";
+
+ if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+ // For CommonJS and CommonJS-like environments where a proper `window`
+ // is present, execute the factory and get jQuery.
+ // For environments that do not have a `window` with a `document`
+ // (such as Node.js), expose a factory as module.exports.
+ // This accentuates the need for the creation of a real `window`.
+ // e.g. var jQuery = require("jquery")(window);
+ // See ticket #14549 for more info.
+ module.exports = global.document ?
+ factory( global, true ) :
+ function( w ) {
+ if ( !w.document ) {
+ throw new Error( "jQuery requires a window with a document" );
+ }
+ return factory( w );
+ };
+ } else {
+ factory( global );
+ }
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+ return arr.flat.call( array );
+} : function( array ) {
+ return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+ // Support: Chrome <=57, Firefox <=52
+ // In some browsers, typeof returns "function" for HTML
+
+
