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library(tidyverse)

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)

1. Combine datasets and add source_type

trans <- trans %>% mutate(source_type = “Trans-Atlantic”) intra <- intra %>% mutate(source_type = “Intra-American”)

combined_data <- bind_rows(trans, intra)

2. Rename and Clean Data

cleaned_data <- combined_data %>% # Rename for readability 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, outcome_slaves = voyage_outcome__outcome_slaves__name ) %>% # Convert types mutate( year = as.integer(year), slaves_embarked = as.numeric(slaves_embarked), slaves_disembarked = as.numeric(slaves_disembarked) ) %>% # Filter for incomplete voyages (No zeros or NAs in disembarked) filter(!is.na(slaves_disembarked), slaves_disembarked > 0) %>% # Filter for successful outcomes filter(outcome_slaves %in% c(“Slaves disembarked”, “Voyage completed”, “Sold slaves”)) %>% # Add new columns mutate( decade = floor(year / 10) * 10, estimated_deaths = slaves_embarked - slaves_disembarked, # Logic for US-based disembarkation is_us = if_else( dis_broad == “Mainland North America” | voyage_itinerary__imp_principal_port_slave_dis__name == “New Orleans”, TRUE, FALSE, missing = FALSE ) )

Preview the results

head(cleaned_data)