Horrors of the Alantic Ocean
When I saw this data set, I was intrigued about the data set. When I
opened it, I was shocked. How many lives have been snatched away, taken
away, uprooted from their homes, and crossed the Atlantic into the
Americas? All men and women with vast histories on this 32.5 MB files.
How much documentation is there on the enslaved people from the vessel
names, captains, crew members, number of enslaved people per ship, to
deaths during their journeys to the flags of their respective countries?
Unbelievable amounts of details! It reminds me of how the Nazis
documented everything in their wrath of destruction in World War 2.
Since I am very spiritual, I am fascinated by reincarnation. The concept
of pain and suffering and wondered if the enslaved people ever got into
heaven. Countless lives, countless eyes, and countless stories all ended
with shattered dreams. As I write this, I can hear the ocean and the
waves crashing onto the boats as they tilt. Captains and crew members
whip the enslaved people as they paddle the boats. A generation from now
a new set of enslaved people will be born soon after they will forget
their history.
On October 12th, 1492, Christopher Columbus discovered the Americas.
Twenty-Two years later, the first enslaved people set foot in the
Americas. The first chart documents the beginning of American history.
The Native Americans didn’t know what was coming on the horizon. Total
destruction of Native Americans was given to them on Thanksgiving. The
first plot tells you about the first that set foot in the Americas.
Portugal/Brazil brought the most enslaved people. As America gained its
independence, you started to see the increase of slaves traveling from
American Vessels. I am really shocked about how many countries were
involved in the slave trade and how Portugal/Brazil brought so many
enslaved people across the Atlantic from the very beginning till the
end. The ### ### Portuguese and Spain were the first to enslave and
transport them to the Americas.
The second graph describes the countries that sponsored the ship and
the number of enslaved people embarked. Each vessel had several enslaved
people that were tightly packed into each other. The box plot shows how
many enslaved people were transported per country. You can see the
outliers of each country and then the density of how much they brought
to the Americas.The third graph compares the year of arrival from each
country by the number of enslaved people they brought to the Americas.
Portugal/Brazilian vessels brought the most enslaved people into the
Americas. Each color represents the number of enslaved people that
arrived in the Americas. They are color-coded by the population size.
For example, the USA brought in a far lower number of slaves compared to
Great Britain and Portugal, and Brazil.
Overall, this project opened my eyes to the fact that humans are
very flawed. From the first enslaved people in 1514 till the 1960s, the
African diaspora from the Americas has suffered generational amounts of
emotional suffering. With the help of data, we can only imagine what
they have been going through. What we can do as world citizens is learn
from our past mistakes. This project had an impact on my life. And I am
glad I did it. It makes me realize how I am so fortunate to be blessed
with what I have been given.
Loading Data
library(tidyverse)
library(dplyr)
library(tidyr)
library(plyr)
library(ggplot2)
library(treemap)
library(alluvial)
library(ggalluvial)
setwd ("C:\\Users\\asing\\Desktop\\data_science\\data_110\\week_3")
voyage <- read_csv("acrosstheatlantic.csv")
Checking the data
head(voyage)
## # A tibble: 6 × 72
## `Captain's name` `Crew deaths during voyage` Crew at first landing…¹
## <chr> <dbl> <dbl>
## 1 Renault, Jacques-Joseph-Fr 4 NA
## 2 Mouchel, J-Fr 1 NA
## 3 Vieillard, Grégoire NA NA
## 4 Donat, Thomas 8 NA
## 5 Auvray, Luc-David 18 NA
## 6 Guinel, Jean 1 NA
## # ℹ abbreviated name: ¹`Crew at first landing of captives`
## # ℹ 69 more variables: `Crew at voyage outset` <dbl>,
## # `Date vessel departed with captives` <dttm>,
## # `Date vessel departed for homeport` <dttm>,
## # `Display in compact mode` <lgl>,
## # `Date vessel arrived with captives` <dttm>,
## # `First place where captives were landed` <lgl>, …
glimpse(voyage)
## Rows: 36,080
## Columns: 72
## $ `Captain's name` <chr> …
## $ `Crew deaths during voyage` <dbl> …
## $ `Crew at first landing of captives` <dbl> …
## $ `Crew at voyage outset` <dbl> …
## $ `Date vessel departed with captives` <dttm> …
## $ `Date vessel departed for homeport` <dttm> …
## $ `Display in compact mode` <lgl> …
## $ `Date vessel arrived with captives` <dttm> …
## $ `First place where captives were landed` <lgl> …
## $ `First place where captives were purchased` <lgl> …
## $ `Guns mounted` <dbl> …
## $ Cargo <lgl> …
## $ `Year of arrival at port of disembarkation` <dbl> …
## $ `Voyage duration, homeport to disembarkation (in days)` <dbl> …
## $ `Place where vessel's voyage began` <lgl> …
## $ `Principal place where captives were purchased` <lgl> …
## $ `Principal place where captives were landed` <lgl> …
## $ `Total embarked...18` <dbl> …
## $ `Total disembarked` <dbl> …
## $ `Captive deaths during crossing` <dbl> …
## $ `Percentage of captives who died during crossing` <dbl> …
## $ `Captive Background` <lgl> …
## $ `Flag of vessel...23` <chr> …
## $ `Percent boys` <dbl> …
## $ `Percent children` <dbl> …
## $ `Percent girls` <dbl> …
## $ `Percent males` <dbl> …
## $ `Percent men` <dbl> …
## $ `Percent women` <dbl> …
## $ `Sterling cash price in Jamaica` <dbl> …
## $ `Duration of captives' crossing (in days)` <dbl> …
## $ `Flag of vessel...32` <chr> …
## $ `Captives carried from 1st port` <dbl> …
## $ `Captives carried from 2nd port` <dbl> …
## $ `Captives carried from 3rd port` <dbl> …
## $ `Captives landed at 1st port` <dbl> …
## $ `Captives landed at 2nd port` <dbl> …
## $ `Captives landed at 3rd port` <dbl> …
## $ `Captives intended to be purchased at 1st place` <dbl> …
## $ `Outcome of voyage for owner` <chr> …
## $ `Outcome of voyage if ship captured` <chr> …
## $ `Outcome of voyage for captives` <chr> …
## $ `Particular outcome of voyage` <chr> …
## $ `Vessel owner` <chr> …
## $ `Place where vessel's voyage ended` <lgl> …
## $ `Places of call before Atlantic crossing` <lgl> …
## $ `Place registered` <dbl> …
## $ `Year registered` <dbl> …
## $ Resistance <chr> …
## $ `Rig or type of vessel` <chr> …
## $ `Show advanced variables in search filters` <lgl> …
## $ `Second place where captives were landed` <lgl> …
## $ `Second place where captives were purchased` <lgl> …
## $ `Vessel name` <chr> …
## $ `Date captive embarkation began` <dttm> …
## $ `Source of data` <chr> …
## $ `Third place where captives were landed` <lgl> …
## $ `Third place where captives were purchased` <lgl> …
## $ Tonnage <dbl> …
## $ `Standardized tonnage` <dbl> …
## $ `Captives arrived at 1st port` <dbl> …
## $ `Total embarked...62` <dbl> …
## $ `Place constructed` <dbl> …
## $ `Date vessel's voyage began` <dttm> …
## $ `Date vessel arrived at homeport` <dttm> …
## $ `Voyage ID` <dbl> …
## $ `Year constructed` <dbl> …
## $ VOYAGEID2 <chr> …
## $ `Voyage itinerary imputed port where began (ptdepimp) place` <chr> …
## $ `Voyage itinerary imputed principal place of slave purchase (mjbyptimp)` <chr> …
## $ `Voyage itinerary imputed principal port of slave disembarkation (mjslptimp) place` <chr> …
## $ `Voyage ship place where vessel registered` <chr> …
unique(voyage$`Captive deaths during crossing`)
## [1] NA 25 9 11 16 3 27 18 0 10 20 4 60 29 6 26 19 70
## [19] 22 8 66 41 17 2 249 5 64 65 130 43 38 68 53 50 132 75
## [37] 49 13 7 15 570 51 61 217 62 54 14 79 33 72 1 23 24 37
## [55] 52 21 44 87 74 34 12 32 94 408 45 373 47 77 55 46 28 190
## [73] 136 104 218 159 101 39 36 40 260 30 320 88 56 78 192 95 91 80
## [91] 42 48 81 100 73 185 35 31 244 393 275 137 107 300 110 250 59 686
## [109] 560 123 67 227 200 280 97 199 58 151 57 76 150 140 99 155 180 120
## [127] 114 143 69 145 90 430 119 149 157 116 105 71 141 142 161 106 135 253
## [145] 210 84 109 166 139 63 86 113 82 238 131 225 197 162 98 93 85 214
## [163] 118 288 377 455 121 270 174 134 208 308 89 83 202 261 325 169 243 111
## [181] 237 316 171 178 96 196 92 209 283 103 112 204 183 216 266 274 258 164
## [199] 184 293 148 193 144 127 205 108 102 167 236 198 138 339 229 194 124 213
## [217] 168 228 215 153 360 165 158 128 201 263 122 186 301 700 363 189 328 680
## [235] 368 340 188 179 125 117 310 115 242 154 160 129 223 175 230 126 156 330
## [253] 176 212 232 234 177 367 342 262 146 235 147 133 350 191 203 206 380 492
## [271] 527 222 305 239 152 182 259 195 304 163 219 240 981 285 998 231 425 265
## [289] 383 246 226 299 220 816 447 207 382 616 326 241 500 297 530 332 278 351
## [307] 181 443 312 273 245 520 420 362 247 302
Year of Arrival and Voyages per year
colnames(voyage)[13] = "Arrival_Year"
colnames(voyage)[23] = "Flag_Of_Vessel"
colnames(voyage)[19] = "Total_Disembarked"
colnames(voyage)[18] = "Total_Embarked"
voyage1<- voyage %>%
mutate(Arrival_Year = as.factor(Arrival_Year)) %>%
filter(!is.na(Arrival_Year)) %>%
group_by(Arrival_Year) %>%
tally()
voyage1
## # A tibble: 337 × 2
## Arrival_Year n
## <fct> <int>
## 1 1514 1
## 2 1516 1
## 3 1519 1
## 4 1520 1
## 5 1526 5
## 6 1527 1
## 7 1532 5
## 8 1533 1
## 9 1534 2
## 10 1535 2
## # ℹ 327 more rows
Visualization of Country of Origin
p1 <- voyage %>%
drop_na(Flag_Of_Vessel) %>%
ggplot(aes(Arrival_Year,Flag_Of_Vessel))+
geom_point()
p1
## Warning: Removed 1 rows containing missing values (`geom_point()`).
THe Amount of Slaves Per Country
p2 <- voyage %>%
drop_na(Flag_Of_Vessel) %>%
ggplot(aes(x = Flag_Of_Vessel, y = Total_Embarked, fill = Flag_Of_Vessel)) +
geom_boxplot(alpha = 0.3) +
theme(legend.position = "none",
axis.text.x = element_text(face = "bold",
size = 10, angle = 50, hjust= 1),
axis.text.y = element_text(face="bold",
size =10, angle=50))
p2
## Warning: Removed 1231 rows containing non-finite values (`stat_boxplot()`).
Treemap Of The Arrival Year, The Total Arrival Of The Slaves, and
Which Country Brought Them To The America’s First
treemap(voyage,index = "Flag_Of_Vessel", vSize = "Arrival_Year",
vColor = "Total_Disembarked", type = "manual", palette = "RdYlBu")
