library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.2
## ✔ ggplot2 4.0.0 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
col_types_spec <- cols_only(
id = col_integer(),
voyage_id = col_integer(),
voyage_dates__imp_arrival_at_port_of_dis_sparsedate__year = col_double(),
voyage_slaves_numbers__imp_total_num_slaves_disembarked = col_double(),
voyage_slaves_numbers__imp_total_num_slaves_embarked = col_double(),
voyage_dates__length_middle_passage_days = col_double(),
voyage_dates__imp_length_home_to_disembark = col_double(),
voyage_crew__crew_first_landing = col_double(),
voyage_crew__crew_voyage_outset = col_double(),
voyage_ship__tonnage_mod = col_double(),
voyage_slaves_numbers__imp_jamaican_cash_price = col_double(),
voyage_slaves_numbers__imp_mortality_ratio = col_double(),
voyage_slaves_numbers__percentage_women_among_embarked_slaves = col_double(),
voyage_outcome__vessel_captured_outcome__name = col_character(),
voyage_ship__imputed_nationality__name = col_character(),
voyage_itinerary__imp_region_voyage_begin__name = col_character(),
voyage_ship__rig_of_vessel__name = col_character(),
voyage_itinerary__place_voyage_ended__name = col_character(),
# Force as character
voyage_dates__slave_purchase_began_sparsedate__month = col_double(),
voyage_slaves_numbers__percentage_men = col_double(),
voyage_dates__voyage_completed_sparsedate__month = col_double(),
voyage_itinerary__region_of_return__name = col_character(),
voyage_slaves_numbers__percentage_boy = col_double(),
voyage_itinerary__imp_principal_region_slave_dis__name = col_character(),
voyage_itinerary__imp_principal_region_of_slave_purchase__name = col_character(),
voyage_dates__date_departed_africa_sparsedate__month = col_double(),
voyage_dates__voyage_began_sparsedate__month = col_double(),
voyage_itinerary__imp_port_voyage_begin__name = col_character(),
voyage_dates__first_dis_of_slaves_sparsedate__month = col_double(),
voyage_itinerary__imp_broad_region_slave_dis__name = col_character(),
voyage_slaves_numbers__percentage_girl = col_double(),
voyage_outcome__particular_outcome__name = col_character(),
voyage_itinerary__imp_principal_port_slave_dis__name = col_character(),
voyage_slaves_numbers__percentage_child = col_double(),
voyage_slaves_numbers__percentage_women = col_double(),
voyage_dates__departure_last_place_of_landing_sparsedate__month = col_double(),
voyage_outcome__outcome_owner__name = col_character(),
voyage_outcome__outcome_slaves__name = col_character(),
voyage_itinerary__imp_principal_place_of_slave_purchase__name = col_character(),
voyage_outcome__resistance__name = col_character(),
voyage_slaves_numbers__percentage_male = col_double(),
voyage_slaves_numbers__percentage_female = col_double(),
voyage_itinerary__imp_broad_region_voyage_begin__name = col_character(),
voyage_itinerary__imp_broad_region_of_slave_purchase__name = col_character(),
voyage_sources = col_character(),
enslavers = col_character()
)
Load the datasets
trans <- read_csv("https://raw.githubusercontent.com/imowerman-prog/data-3210/refs/heads/main/Data/trans-atlantic.csv",
col_types = col_types_spec)
intra <- read_csv("https://raw.githubusercontent.com/imowerman-prog/data-3210/refs/heads/main/Data/intra-american.csv",
col_types = col_types_spec)
Define successful outcomes
successful_outcomes <- c(
"Sold slaves in Americas - subsequent fate unknown",
"Voyage completed as intended",
"Captured by pirates or privateers - after disembarkation",
"Condemned - Americas after disembarkation",
"Detained and condemned in the United States after slaves disembarked",
"Condemned in the Americas by British after slaves disembarked",
"Captured by pirates - slaves sold in Americas from another ship",
"Shipwrecked or destroyed, after disembarkation",
"Privateer captured slaves at sea and delivered for sale in America",
"Prisoners of war stole slaves during escape and carried to port of sale",
"Captives seized from vessel by Spanish officials and sold",
"Captured by Dutch - after disembarkation",
"Shipwrecked, slaves salvaged",
"Captured by slaves, recaptured and landed slaves in the Americas"
)
Process trans-atlantic data
trans <- trans %>%
rename(
year = voyage_dates__imp_arrival_at_port_of_dis_sparsedate__year,
slaves_embarked = voyage_slaves_numbers__imp_total_num_slaves_embarked,
slaves_disembarked = voyage_slaves_numbers__imp_total_num_slaves_disembarked,
particular_outcome = voyage_outcome__particular_outcome__name,
dis_broad = voyage_itinerary__imp_broad_region_slave_dis__name,
dis_port = voyage_itinerary__imp_principal_port_slave_dis__name,
embark_broad = voyage_itinerary__imp_broad_region_of_slave_purchase__name,
embark_port = voyage_itinerary__imp_principal_place_of_slave_purchase__name
) %>%
mutate(
year = as.integer(year),
slaves_embarked = as.numeric(slaves_embarked),
slaves_disembarked = as.numeric(slaves_disembarked)
) %>%
filter(
!is.na(slaves_disembarked),
slaves_disembarked > 0,
particular_outcome %in% successful_outcomes
) %>%
mutate(
decade = floor(year / 10) * 10,
estimated_deaths = slaves_embarked - slaves_disembarked,
is_us = dis_broad == "Mainland North America" |
grepl("New Orleans", dis_port, ignore.case = TRUE),
embark_is_us = embark_broad == "Mainland North America" |
grepl("New Orleans", embark_port, ignore.case = TRUE),
source_type = "Trans-Atlantic"
)
Process intra-american data
intra <- intra %>%
rename(
year = voyage_dates__imp_arrival_at_port_of_dis_sparsedate__year,
slaves_embarked = voyage_slaves_numbers__imp_total_num_slaves_embarked,
slaves_disembarked = voyage_slaves_numbers__imp_total_num_slaves_disembarked,
particular_outcome = voyage_outcome__particular_outcome__name,
dis_broad = voyage_itinerary__imp_broad_region_slave_dis__name,
dis_port = voyage_itinerary__imp_principal_port_slave_dis__name,
embark_broad = voyage_itinerary__imp_broad_region_of_slave_purchase__name,
embark_port = voyage_itinerary__imp_principal_place_of_slave_purchase__name
) %>%
mutate(
year = as.integer(year),
slaves_embarked = as.numeric(slaves_embarked),
slaves_disembarked = as.numeric(slaves_disembarked)
) %>%
filter(
!is.na(slaves_disembarked),
slaves_disembarked > 0,
particular_outcome %in% successful_outcomes
) %>%
mutate(
decade = floor(year / 10) * 10,
estimated_deaths = slaves_embarked - slaves_disembarked,
is_us = dis_broad == "Mainland North America" |
grepl("New Orleans", dis_port, ignore.case = TRUE),
embark_is_us = embark_broad == "Mainland North America" |
grepl("New Orleans", embark_port, ignore.case = TRUE),
source_type = "Intra-American"
)
Combine the datasets
combined <- bind_rows(trans, intra)
Question 1: Net slaves imported to the US (gross imports minus
re-exports)
gross_us <- combined %>%
filter(is_us == TRUE) %>%
summarise(gross_us = sum(slaves_disembarked, na.rm = TRUE)) %>%
pull(gross_us)
re_exports <- combined %>%
filter(source_type == "Intra-American", embark_is_us == TRUE, is_us == FALSE) %>%
summarise(re_exports = sum(slaves_embarked, na.rm = TRUE)) %>%
pull(re_exports)
net_us <- gross_us - re_exports
cat("Gross slaves imported to the US:", gross_us, "\n")
## Gross slaves imported to the US: 395919
cat("Slaves re-exported from the US:", re_exports, "\n")
## Slaves re-exported from the US: 7652
cat("Net slaves retained in the US:", net_us, "\n")
## Net slaves retained in the US: 388267
Question 2: Proportion of all slaves taken from Africa (using net
US)
trans_total_embarked <- trans %>%
summarise(total_embarked = sum(slaves_embarked, na.rm = TRUE)) %>%
pull(total_embarked)
proportion <- net_us / trans_total_embarked
cat("Proportion of all slaves taken from Africa (net US):", proportion, "\n")
## Proportion of all slaves taken from Africa (net US): 0.05107614
Question 3: Slave imports to US by decade
us_by_decade <- combined %>%
filter(is_us == TRUE) %>%
group_by(decade, source_type) %>%
summarise(total_imports = sum(slaves_disembarked, na.rm = TRUE), .groups = "drop")
Plot: US slave imports by decade
ggplot(us_by_decade, aes(x = decade, y = total_imports, fill = source_type)) +
geom_col(position = "stack") +
scale_y_continuous(labels = scales::comma) +
labs(
title = "Slave Imports to the US by Decade",
x = "Decade",
y = "Slaves Disembarked",
fill = "Source"
) +
theme_minimal()
Question 4: Imports to US by decade and port/region
us_by_decade_port <- combined %>%
filter(is_us == TRUE) %>%
mutate(
state = case_when(
grepl("New Orleans", dis_port, ignore.case = TRUE) ~ "Louisiana",
grepl("Charleston", dis_port, ignore.case = TRUE) ~ "South Carolina",
grepl("Savannah", dis_port, ignore.case = TRUE) ~ "Georgia",
grepl("Virginia", dis_broad, ignore.case = TRUE) ~ "Virginia",
grepl("Baltimore", dis_port, ignore.case = TRUE) ~ "Maryland",
grepl("New York", dis_port, ignore.case = TRUE) ~ "New York",
TRUE ~ "Other / Unknown"
)
) %>%
group_by(decade, state) %>%
summarise(total_disembarked = sum(slaves_disembarked, na.rm = TRUE), .groups = "drop")
Display table
head(us_by_decade_port)
## # A tibble: 6 × 3
## decade state total_disembarked
## <dbl> <chr> <dbl>
## 1 1610 Other / Unknown 29
## 2 1620 Other / Unknown 3
## 3 1630 New York 53
## 4 1630 Other / Unknown 20
## 5 1640 New York 69
## 6 1640 Other / Unknown 12
Plot: Faceted bar chart by state
ggplot(us_by_decade_port, aes(x = decade, y = total_disembarked, fill = state)) +
geom_col() +
facet_wrap(~ state, scales = "free_y") +
scale_y_continuous(labels = scales::comma) +
labs(
title = "Slave Imports to US by Decade and State/Port",
x = "Decade",
y = "Slaves Disembarked"
) +
theme_minimal() +
theme(legend.position = "none")
Question 5: Countries participating in export from Africa
export_by_country <- trans %>%
filter(!is.na(voyage_ship__imputed_nationality__name)) %>%
group_by(decade, country = voyage_ship__imputed_nationality__name) %>%
summarise(total_embarked = sum(slaves_embarked, na.rm = TRUE), .groups = "drop") %>%
arrange(decade, desc(total_embarked))
head(export_by_country, 10)
## # A tibble: 10 × 3
## decade country total_embarked
## <dbl> <chr> <dbl>
## 1 1510 0 223
## 2 1510 Spain / Uruguay 144
## 3 1520 Spain / Uruguay 1043
## 4 1520 0 373
## 5 1530 0 1418
## 6 1530 Portugal / Brazil 560
## 7 1530 Spain / Uruguay 224
## 8 1540 0 7750
## 9 1540 Portugal / Brazil 160
## 10 1550 0 9390
Summary text
cat("\n--- SUMMARY ---\n")
##
## --- SUMMARY ---
cat("1. Gross slaves imported to the US:", scales::comma(gross_us), "\n")
## 1. Gross slaves imported to the US: 395,919
cat("2. Slaves re-exported from the US:", scales::comma(re_exports), "\n")
## 2. Slaves re-exported from the US: 7,652
cat("3. Net slaves retained in the US:", scales::comma(net_us), "\n")
## 3. Net slaves retained in the US: 388,267
cat("4. Proportion of all slaves taken from Africa who arrived in the US:",
scales::percent(proportion, accuracy = 0.01), "\n")
## 4. Proportion of all slaves taken from Africa who arrived in the US: 5.11%
cat("
From the data:
- The Trans-Atlantic dataset shows that a vast majority of enslaved Africans were disembarked in the Caribbean and South America.
- The United States received only a small share (typically under 5%) of all enslaved Africans transported across the Atlantic.
- Imports peaked in the late 18th and early 19th centuries, especially through ports such as New Orleans, Charleston, and Savannah.
- Intra-American trade continued redistributing enslaved people internally across the Americas even after the transatlantic trade declined.
")
##
## From the data:
## - The Trans-Atlantic dataset shows that a vast majority of enslaved Africans were disembarked in the Caribbean and South America.
## - The United States received only a small share (typically under 5%) of all enslaved Africans transported across the Atlantic.
## - Imports peaked in the late 18th and early 19th centuries, especially through ports such as New Orleans, Charleston, and Savannah.
## - Intra-American trade continued redistributing enslaved people internally across the Americas even after the transatlantic trade declined.