# ============================================================
# GRAPH 1: Total timeline (linegraph)
# ============================================================
# Counting number per year
per_Ã¥r <- steri %>%
count(SterÃ…r, name = "Antal")
ggplot(per_Ã¥r, aes(x = SterÃ…r, y = Antal)) +
geom_line(color = "#2c3e6b", linewidth = 1.2) +
geom_point(aes(color = SterÃ…r == 1934), size = 3) +
scale_color_manual(values = c("FALSE" = "#2c3e6b", "TRUE" = "#c0392b"), guide = "none") +
# Mark law change in 1934
geom_vline(xintercept = 1934, linetype = "dashed",
color = "#c0392b", linewidth = 0.9) +
annotate("text", x = 1934.2, y = 30,
label = "1934-law\n(compulsory sterilization)",
hjust = 0, vjust = -0.7,
color = "#c0392b", size = 3.5) +
scale_x_continuous(breaks = 1930:1943) +
labs(
title = "Sterilizations at the Keller institutions 1930–1943",
subtitle = "Annual total of procedures",
x = "Year",
y = "Total procedures",
caption = "Source: Rigsarkivet.dk"
) +
theme_minimal(base_size = 13) +
theme(
plot.title = element_text(face = "bold"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank()
)
ggsave("tidsserie_samlet.png", width = 10, height = 6, dpi = 300)
# ============================================================
# GRAPH 3: Waffle chart – Social class distribution among sterilized individuals 1930–1943
# ============================================================
# Tjek værdier i Hovedgrp
unique(steri$Hovedgrp)
## [1] "B" "A" "D" "C"
# Tæl antal per hovedgruppe
hovedgrp_count <- steri %>%
filter(!is.na(Hovedgrp), Hovedgrp != "") %>%
count(Hovedgrp, name = "Antal") %>%
arrange(Hovedgrp)
# Byg waffle-grid manuelt
n_cols <- 10
waffle_df <- hovedgrp_count %>%
uncount(Antal) %>%
mutate(
id = row_number(),
x = (id - 1) %/% n_cols,
y = (id - 1) %% n_cols
)
ggplot(waffle_df, aes(x = x, y = y, fill = Hovedgrp)) +
geom_tile(color = "white", linewidth = 0.5) +
scale_fill_manual(
values = c(
"A" = "#1a3a6b",
"B" = "#4F7942",
"C" = "#FFC000",
"D" = "#8b0000"
),
labels = c(
"A" = "A – highest social class",
"B" = "B – working class",
"C" = "C – Benefit recipients",
"D" = "D – Unspecified/
living on private means"
)
) +
coord_equal() +
labs(
title = "Social class distribution among sterilized individuals 1930–1943",
subtitle = "Each square represents one individual",
fill = "Social class",
caption = "Source: Rigsarkivet.dk"
) +
theme_void(base_size = 13) +
theme(
plot.title = element_text(face = "bold", margin = margin(b = 5)),
plot.subtitle = element_text(margin = margin(b = 10)),
legend.position = "bottom"
)
ggsave("waffle_socialklasse.png", width = 10, height = 6, dpi = 300)
# ============================================================
# GRAPH 4: Age at sterilization × law × gender (boxplot)
# ============================================================
steri_alder <- steri %>%
mutate(
SterÃ…r = as.numeric(SterÃ…r),
Fødselsår = as.numeric(Fødselsår),
Alder = SterÅr - Fødselsår,
Lov_label = case_when(
Lov == 1929 ~ "Act 1929\n(voluntary)",
Lov == 1934 ~ "Act 1934\n(compulsory)",
Lov == 1935 ~ "Act 1935\n(extended)"
),
Lov_label = factor(Lov_label, levels = c(
"Act 1929\n(voluntary)",
"Act 1934\n(compulsory)",
"Act 1935\n(extended)"
))
) %>%
filter(!is.na(Alder), !is.na(Lov_label))
ggplot(steri_alder, aes(x = Lov_label, y = Alder, fill = Køn, color = Køn)) +
geom_boxplot(
alpha = 0.2,
width = 0.5,
outlier.shape = 1,
outlier.size = 2,
position = position_dodge(width = 0.6)
) +
scale_fill_manual(values = c("Kvinde" = "#c0392b", "Mand" = "#2c5fa8")) +
scale_color_manual(values = c("Kvinde" = "#c0392b", "Mand" = "#2c5fa8")) +
scale_y_continuous(breaks = seq(15, 65, by = 5)) +
labs(
title = "Age at sterilization by legal basis 1930–1943",
subtitle = "Age = Sterilization year − birthday, split by gender (Kvinde=Woman) (Mand=Man)",
x = NULL,
y = "Age at sterilization",
fill = NULL,
color = NULL,
caption = "Source: Rigsarkivet.dk"
) +
theme_minimal(base_size = 13) +
theme(
plot.title = element_text(face = "bold"),
legend.position = "top",
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank()
)
ggsave("age_by_law_gender.png", width = 9, height = 6, dpi = 300)
# ============================================================
# GRAPH 5: Geographical origin × gender
# ============================================================
geo_køn <- steri %>%
filter(!is.na(Geo_1911), !is.na(Køn)) %>%
count(Geo_1911, Køn, name = "Antal") %>%
mutate(Geo_1911 = factor(Geo_1911, levels = c("Land", "By", "Hovedstaden")))
ggplot(geo_køn, aes(x = Geo_1911, y = Antal, fill = Køn)) +
geom_col(position = "dodge", width = 0.6) +
geom_text(aes(label = Antal),
position = position_dodge(width = 0.6),
vjust = -0.5, size = 3.5, fontface = "bold") +
scale_fill_manual(values = c("Kvinde" = "#c0392b", "Mand" = "#2c5fa8")) +
scale_y_continuous(expand = expansion(mult = c(0, 0.1))) +
labs(
title = "Geographical origin of sterilized individuals 1930–1943",
subtitle = "By place of residence recorded in the 1911 census",
x = NULL,
y = "Number of procedures",
fill = NULL,
caption = "Source: Rigsarkivet.dk"
) +
theme_minimal(base_size = 13) +
theme(
plot.title = element_text(face = "bold"),
legend.position = "top",
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank()
)
ggsave("geo_origin_by_gender.png", width = 8, height = 6, dpi = 300)
# ============================================================
# GRAPH 6: Illegitimacy
# ============================================================
# Plot A: % uægtefødte pr. køn og lov
p_lov <- steri %>%
filter(!is.na(Ægte), !is.na(Lov), !is.na(Køn)) %>%
group_by(Lov, Køn) %>%
summarise(
pct_uægte = mean(Ægte == "Nej") * 100,
n = n(), .groups = "drop"
) %>%
mutate(Lov_label = factor(paste0("Act ", Lov),
levels = c("Act 1929", "Act 1934", "Act 1935"))) %>%
ggplot(aes(x = Lov_label, y = pct_uægte, fill = Køn)) +
geom_col(position = "dodge", width = 0.6) +
scale_fill_manual(values = c("Kvinde" = "#c0392b", "Mand" = "#2c5fa8")) +
scale_y_continuous(labels = function(x) paste0(x, "%"), limits = c(0, 45)) +
labs(title = "% illegitimate births by gender and law",
x = NULL, y = NULL, fill = NULL) +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", size = 11),
legend.position = "top",
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank())
# Plot B: % uægtefødte pr. social klasse
p_klasse <- steri %>%
filter(!is.na(Ægte), !is.na(Hovedgrp)) %>%
group_by(Hovedgrp) %>%
summarise(
pct_uægte = mean(Ægte == "Nej") * 100,
n = n(), .groups = "drop"
) %>%
mutate(highlight = Hovedgrp == "D") %>%
ggplot(aes(x = Hovedgrp, y = pct_uægte, fill = highlight)) +
geom_col(width = 0.6) +
scale_fill_manual(values = c("FALSE" = "#73726c", "TRUE" = "#c0392b"), guide = "none") +
scale_y_continuous(labels = function(x) paste0(x, "%"), limits = c(0, 105)) +
scale_x_discrete(labels = c("A\n(highest)", "B\n(working)", "C\n(benefits)", "D\n(unspecified)")) +
labs(title = "% illegitimate births by social class",
x = NULL, y = NULL) +
theme_minimal(base_size = 12) +
theme(plot.title = element_text(face = "bold", size = 11),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank())
p_lov + p_klasse +
plot_annotation(
title = "Illegitimate birth and sterilization 1930–1943",
caption = "Source: Rigsarkivet.dk"
) &
theme(plot.title = element_text(face = "bold"))
ggsave("aegtefodt_analyse.png", width = 11, height = 6, dpi = 300)