3b1
Owen McAloon
2026-03-05
DATA 3210: Slavery Assignment
library(tidyverse)Defining column types
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(),
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 Data
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
)Rename Columns for Readability
trans <- trans %>%
rename(
year = voyage_dates__imp_arrival_at_port_of_dis_sparsedate__year,
slaves_disembarked = voyage_slaves_numbers__imp_total_num_slaves_disembarked,
slaves_embarked = voyage_slaves_numbers__imp_total_num_slaves_embarked,
dis_region = voyage_itinerary__imp_broad_region_slave_dis__name,
dis_port = voyage_itinerary__imp_principal_port_slave_dis__name,
country = voyage_ship__imputed_nationality__name
)
intra <- intra %>%
rename(
year = voyage_dates__imp_arrival_at_port_of_dis_sparsedate__year,
slaves_disembarked = voyage_slaves_numbers__imp_total_num_slaves_disembarked,
slaves_embarked = voyage_slaves_numbers__imp_total_num_slaves_embarked,
dis_region = voyage_itinerary__imp_broad_region_slave_dis__name,
dis_port = voyage_itinerary__imp_principal_port_slave_dis__name,
country = voyage_ship__imputed_nationality__name
)Display Column Names
names(trans)## [1] "id"
## [2] "voyage_id"
## [3] "year"
## [4] "slaves_disembarked"
## [5] "slaves_embarked"
## [6] "voyage_dates__length_middle_passage_days"
## [7] "voyage_dates__imp_length_home_to_disembark"
## [8] "voyage_crew__crew_first_landing"
## [9] "voyage_crew__crew_voyage_outset"
## [10] "voyage_ship__tonnage_mod"
## [11] "voyage_slaves_numbers__imp_jamaican_cash_price"
## [12] "voyage_slaves_numbers__imp_mortality_ratio"
## [13] "voyage_slaves_numbers__percentage_women_among_embarked_slaves"
## [14] "voyage_outcome__vessel_captured_outcome__name"
## [15] "country"
## [16] "voyage_itinerary__imp_region_voyage_begin__name"
## [17] "voyage_ship__rig_of_vessel__name"
## [18] "voyage_itinerary__place_voyage_ended__name"
## [19] "voyage_dates__slave_purchase_began_sparsedate__month"
## [20] "voyage_slaves_numbers__percentage_men"
## [21] "voyage_dates__voyage_completed_sparsedate__month"
## [22] "voyage_itinerary__region_of_return__name"
## [23] "voyage_slaves_numbers__percentage_boy"
## [24] "voyage_itinerary__imp_principal_region_slave_dis__name"
## [25] "voyage_itinerary__imp_principal_region_of_slave_purchase__name"
## [26] "voyage_dates__date_departed_africa_sparsedate__month"
## [27] "voyage_dates__voyage_began_sparsedate__month"
## [28] "voyage_itinerary__imp_port_voyage_begin__name"
## [29] "voyage_dates__first_dis_of_slaves_sparsedate__month"
## [30] "dis_region"
## [31] "voyage_slaves_numbers__percentage_girl"
## [32] "voyage_outcome__particular_outcome__name"
## [33] "dis_port"
## [34] "voyage_slaves_numbers__percentage_child"
## [35] "voyage_slaves_numbers__percentage_women"
## [36] "voyage_dates__departure_last_place_of_landing_sparsedate__month"
## [37] "voyage_outcome__outcome_owner__name"
## [38] "voyage_outcome__outcome_slaves__name"
## [39] "voyage_itinerary__imp_principal_place_of_slave_purchase__name"
## [40] "voyage_outcome__resistance__name"
## [41] "voyage_slaves_numbers__percentage_male"
## [42] "voyage_slaves_numbers__percentage_female"
## [43] "voyage_itinerary__imp_broad_region_voyage_begin__name"
## [44] "voyage_itinerary__imp_broad_region_of_slave_purchase__name"
## [45] "voyage_sources"
## [46] "enslavers"Convert Year and Slave Counts to Numeric
trans <- trans %>%
mutate(
year = as.integer(year),
slaves_embarked = as.numeric(slaves_embarked),
slaves_disembarked = as.numeric(slaves_disembarked)
)
intra <- intra %>%
mutate(
year = as.integer(year),
slaves_embarked = as.numeric(slaves_embarked),
slaves_disembarked = as.numeric(slaves_disembarked)
)Filter Out Incomplete Voyages keep only successful outcomes
trans <- trans %>%
filter(!is.na(slaves_disembarked), slaves_disembarked > 0)
intra <- intra %>%
filter(!is.na(slaves_disembarked), slaves_disembarked > 0)Add Decade, Estimated Deaths, and US Indicator Columns
trans <- trans %>%
mutate(
decade = floor(year/10)*10,
estimated_deaths = slaves_embarked - slaves_disembarked,
is_us = dis_region == "Mainland North America" | dis_port == "New Orleans"
)
intra <- intra %>%
mutate(
decade = floor(year/10)*10,
estimated_deaths = slaves_embarked - slaves_disembarked,
is_us = dis_region == "Mainland North America" | dis_port == "New Orleans"
)Combine
trans$source_type <- "Trans-Atlantic"
intra$source_type <- "Intra-American"
combined <- bind_rows(trans, intra)Calculate Total Slaves Imported to the United States
us_total <- combined %>%
filter(is_us == TRUE) %>%
summarise(`Total Slaves in the US` = sum(slaves_disembarked, na.rm = TRUE))
us_total## # A tibble: 1 × 1
## `Total Slaves in the US`
## <dbl>
## 1 439667Calculate Total Slaves Embarked from Africa
total_africa <- sum(trans$slaves_embarked, na.rm = TRUE)
total_africa## [1] 10575764Calculate Proportion of Africans Imported into the US
proportion_percent <- (us_total$`Total Slaves in the US` / total_africa) * 100
proportion_percent## [1] 4.157307Group US Imports by Decade
us_decade <- combined %>%
filter(is_us == TRUE) %>%
group_by(decade) %>%
summarise(total_slaves = sum(slaves_disembarked, na.rm = TRUE))Plot: Slave Imports to the US by Decade
ggplot(us_decade, aes(x = decade, y = total_slaves)) +
geom_col() +
labs(
title = "Slave Imports to the United States by Decade",
x = "Decade",
y = "Number of Enslaved Individuals"
)
Create table
us_region_port <- combined %>%
filter(is_us == TRUE) %>%
group_by(decade, dis_region, dis_port) %>%
summarise(total_slaves = sum(slaves_disembarked, na.rm = TRUE), .groups = "drop")
us_region_port## # A tibble: 377 × 4
## decade dis_region dis_port total_slaves
## <dbl> <chr> <chr> <dbl>
## 1 1610 Mainland North America Hampton 29
## 2 1620 Mainland North America Virginia, port unspecified 3
## 3 1630 Mainland North America Boston 7
## 4 1630 Mainland North America New York 53
## 5 1630 Mainland North America Virginia, port unspecified 13
## 6 1640 Mainland North America New York 69
## 7 1640 Mainland North America Virginia, port unspecified 435
## 8 1650 Mainland North America Maryland, port unspecified 5
## 9 1650 Mainland North America New York 477
## 10 1650 Mainland North America Virginia, port unspecified 469
## # ℹ 367 more rowsCreate State Variable from Port Names
us_region_port <- combined %>%
filter(is_us == TRUE) %>%
mutate(state = case_when(
dis_port %in% c("New Orleans") ~ "Louisiana",
dis_port %in% c("Charleston") ~ "South Carolina",
dis_port %in% c("Savannah") ~ "Georgia",
dis_port %in% c("Baltimore", "Annapolis") ~ "Maryland",
dis_port %in% c("New York") ~ "New York",
dis_port %in% c("Boston") ~ "Massachusetts",
TRUE ~ "Other"
))Summarize US Imports by Decade and State
us_state <- us_region_port %>%
group_by(decade, state) %>%
summarise(total_slaves = sum(slaves_disembarked, na.rm = TRUE), .groups = "drop")Plot: Slave Imports to the US by State and Decade
ggplot(us_state, aes(x = decade, y = total_slaves)) +
geom_col() +
facet_wrap(~ state) +
labs(
title = "Slave Imports to the US by State and Decade",
x = "Decade",
y = "Number of Enslaved Individuals"
)
Create Table of Exporting Countries by Decade
country_table <- trans %>%
group_by(decade, country) %>%
summarise(total_embarked = sum(slaves_embarked, na.rm = TRUE), .groups = "drop")
country_table## # A tibble: 229 × 3
## decade country total_embarked
## <dbl> <chr> <dbl>
## 1 1510 0 223
## 2 1510 Portugal / Brazil 624
## 3 1510 Spain / Uruguay 144
## 4 1520 0 597
## 5 1520 Spain / Uruguay 1043
## 6 1530 0 1777
## 7 1530 Portugal / Brazil 919
## 8 1530 Spain / Uruguay 224
## 9 1540 0 19385
## 10 1540 Portugal / Brazil 160
## # ℹ 219 more rowsSummary
After cleaning and combining the data sets, I calculated total slave imports into the United States, the share of all Africans taken from Africa who ended up in the US, and how US imports changed over time. The combined data shows 439,667 enslaved people were imported into the US, which is about 4.16% of the 10,575,764 individuals embarked from Africa in the Trans‑Atlantic trade. US imports increased from the 1600s through the early 1800s. When broken down by state (using ports to approximate states), South Carolina and Louisiana received the largest volumes, while states like Maryland, New York, Massachusetts, and Georgia received smaller amounts. This shows that US arrivals were concentrated in a few major locations. For exports from Africa, grouping by decade and ship nationality shows that countries such as Portugal/Brazil, Spain/Uruguay, Great Britain, France, and the Netherlands dominated the trade, with some voyages having unknown nationality recorded as “0.”