Slave Routes

african_names <- read_csv("african_names.csv")
blackpast <- read_csv("blackpast.csv")
census <- read_csv("census.csv")
slave_routes <- read_csv("slave_routes.csv")

Mapping Slave Routes

For the major places of purchase, ports of origin, and ports of arrival, interest was in whether there had been substantial changes over time. Without more extensive knowledge of the places and ports, it is hard to connect what was happening at each. As such, the graphs show trends only for the places and ports associated with the most voyages. The most clear trend is that the rate of voyages increased quite dramatically from the seventeenth to nineteenth centuries.

slave_routes <- slave_routes %>%
  mutate(  purchase = case_when(
    str_detect(place_of_purchase, "Sierra Leone")  ~ "Sierra Leone",
    str_detect(place_of_purchase, "Windward")  ~ "Windward",
    str_detect(place_of_purchase, "Benin")  ~ "Benin",
    str_detect(place_of_purchase, "Cameroon")  ~ "Cameroon",
    str_detect(place_of_purchase, "Congo")  ~ "Congo",
    str_detect(place_of_purchase, "Senegal")  ~ "Senegal",
    str_detect(place_of_purchase, "Gold Coast")  ~ "Gold Coast",
    str_detect(place_of_purchase, "Zanzibar")  ~ "Zanzibar",
    str_detect(place_of_purchase, "Senegambia")  ~ "Senegambia",
    str_detect(place_of_purchase, "Africa.,")  ~ "Africa",
    str_detect(place_of_purchase, "West Central Africa")  ~ "West Central Africa",
    TRUE ~ place_of_purchase
  )) %>%
  filter(!is.na(n_slaves_arrived))


slave_routes %>% head()

## # A tibble: 6 x 9
##   voyage_id ship_name port_origin place_of_purcha… port_arrival
##       <dbl> <chr>     <chr>       <chr>            <chr>       
## 1     81711 Hannah    Liverpool   Calabar          St. Vincent…
## 2     81712 Hannah    Liverpool   New Calabar      Grenada, po…
## 3     81713 Hannah    Liverpool   Bight of Biafra… Kingston    
## 4     81714 Hannah    Liverpool   Bonny            St. Vincent…
## 5     81715 Hannah    Liverpool   Congo River      Grenada, po…
## 6     81716 Hannah    Liverpool   Ambriz           Kingston    
## # … with 4 more variables: year_arrival <dbl>, n_slaves_arrived <dbl>,
## #   captains_name <chr>, purchase <chr>

slave_routes %>%
  mutate(decade = 10*floor(year_arrival/10)) %>%
  add_count(purchase) %>%
  filter(n >= 500) %>%
  group_by(purchase, decade) %>%
  summarize(tot_slaves = sum(n_slaves_arrived, na.rm=TRUE)) %>%
  ggplot() +
  geom_smooth(aes(x = decade, y = tot_slaves, color = purchase), se=FALSE) +
  geom_point(aes(x = decade, y = tot_slaves, color = purchase), size=0.5, alpha = 0.75) +
  ggtitle("Number of slaves arrived over time, by place of purchase (>= 500 voyage from place)") +
  theme(plot.title = element_text(size = 10, face = "bold"))

slave_routes %>%
  mutate(decade = 10*floor(year_arrival/10)) %>%
  add_count(port_origin) %>%
  filter(n >= 500) %>%
  group_by(port_origin, decade) %>%
  summarize(tot_slaves = sum(n_slaves_arrived, na.rm=TRUE)) %>%
  ggplot() +
  geom_smooth(aes(x = decade, y = tot_slaves, color = port_origin), se=FALSE) +
  geom_point(aes(x = decade, y = tot_slaves, color = port_origin), size=0.5, alpha = 0.75)+
  ggtitle("Number of slaves arrived over time, by port of origin (>= 500 voyage from port)") +
  theme(plot.title = element_text(size = 10, face = "bold"))

slave_routes %>%
  mutate(decade = 10*floor(year_arrival/10)) %>%
  add_count(port_arrival) %>%
  filter(n >= 500) %>%
  group_by(port_arrival, decade) %>%
  summarize(tot_slaves = sum(n_slaves_arrived, na.rm=TRUE)) %>%
  ggplot() +
  geom_smooth(aes(x = decade, y = tot_slaves, color = port_arrival), se=FALSE) +
  geom_point(aes(x = decade, y = tot_slaves, color = port_arrival), size=0.5, alpha = 0.75)+
  ggtitle("Number of slaves arrived over time, by port of arrival (>= 500 voyage to port)") +
  theme(plot.title = element_text(size = 10, face = "bold"))

Census trends

Census trends over time for different regions of the country, broken down by race and status. As might be expected, in the Northeast, the number of slaves was decreasing precipitously in the second half of the nineteenth century, but in the South the number of slaves increased until the 13th amendment of the US Constitution was ratified in December of 1865.

census <- census %>%
  pivot_longer(cols = c(total, white, black, black_free, black_slaves), 
               names_to = "race", values_to = "population")

census %>% head(100)

## # A tibble: 100 x 5
##    region    division  year race         population
##    <chr>     <chr>    <dbl> <chr>             <dbl>
##  1 USA Total <NA>      1870 total          38558371
##  2 USA Total <NA>      1870 white          33589377
##  3 USA Total <NA>      1870 black           4880009
##  4 USA Total <NA>      1870 black_free      4880009
##  5 USA Total <NA>      1870 black_slaves          0
##  6 USA Total <NA>      1860 total          31443321
##  7 USA Total <NA>      1860 white          26922537
##  8 USA Total <NA>      1860 black           4441830
##  9 USA Total <NA>      1860 black_free       488070
## 10 USA Total <NA>      1860 black_slaves    3953760
## # … with 90 more rows

census %>%
  mutate(region = fct_relevel(region, c("Northeast", "Midwest", "South", "West", "USA Total"))) %>%
  group_by(region, year, race) %>%
  mutate(pop = sum(population)) %>%
ggplot() +
  geom_line(aes(x=year, y = pop, color = race)) + 
  facet_wrap(~region) +
  scale_y_continuous(trans = "log10") +
  geom_vline(xintercept = 1865) +
  ggtitle("Population over time, black vertical line at 1865")+
  ylab("Population on log10 scale") +
  labs(color = "race / status")