# Load required library
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)
#Rename long column names for readability
clean_data <- function(df, source_type){
df %>%
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,
dis_broad = voyage_itinerary__imp_broad_region_slave_dis__name,
dis_region = voyage_itinerary__imp_principal_region_slave_dis__name,
dis_port = voyage_itinerary__imp_principal_port_slave_dis__name,
outcome = voyage_outcome__outcome_slaves__name,
nationality = voyage_ship__imputed_nationality__name
) %>%
filter(!is.na(slaves_disembarked), slaves_disembarked > 0) %>%
mutate(
year = as.integer(year),
decade = floor(year/10)*10,
estimated_deaths = slaves_embarked - slaves_disembarked,
is_us = dis_broad == "Mainland North America" |
str_detect(tolower(dis_port), "new orleans|charleston|savannah|norfolk|new york|philadelphia|boston"),
source = source_type
)
}
trans_clean <- clean_data(trans, "Trans-Atlantic")
intra_clean <- clean_data(intra, "Intra-American")
combined <- bind_rows(trans_clean, intra_clean)
us_total <- combined %>%
filter(is_us) %>%
summarise(total = sum(slaves_disembarked, na.rm=TRUE))
us_total
## # A tibble: 1 × 1
## total
## <dbl>
## 1 439667
total_africa <- sum(trans_clean$slaves_embarked, na.rm=TRUE)
us_from_africa <- sum(trans_clean$slaves_disembarked[trans_clean$is_us], na.rm=TRUE)
us_from_africa / total_africa
## [1] 0.02896453
us_by_decade <- combined %>%
filter(is_us) %>%
group_by(decade) %>%
summarise(total = sum(slaves_disembarked, na.rm=TRUE))
ggplot(us_by_decade, aes(x=factor(decade), y=total)) +
geom_col(fill="steelblue") +
labs(title="US Slave Imports by Decade", x="Decade", y="Slaves Disembarked")
countries <- trans_clean %>%
group_by(decade, nationality) %>%
summarise(total_embarked = sum(slaves_embarked, na.rm=TRUE), .groups="drop") %>%
arrange(decade, desc(total_embarked))
head(countries, 20)
## # A tibble: 20 × 3
## decade nationality total_embarked
## <dbl> <chr> <dbl>
## 1 1510 Portugal / Brazil 624
## 2 1510 0 223
## 3 1510 Spain / Uruguay 144
## 4 1520 Spain / Uruguay 1043
## 5 1520 0 597
## 6 1530 0 1777
## 7 1530 Portugal / Brazil 919
## 8 1530 Spain / Uruguay 224
## 9 1540 0 19385
## 10 1540 Portugal / Brazil 160
## 11 1550 0 16949
## 12 1550 Portugal / Brazil 718
## 13 1560 0 11791
## 14 1560 Great Britain 1749
## 15 1560 Spain 400
## 16 1560 Spain / Uruguay 295
## 17 1560 Portugal / Brazil 176
## 18 1570 0 29608
## 19 1570 Portugal / Brazil 856
## 20 1570 France 104