This week, I used the dataset that lists every slave that lived and worked at Mount Vernon. It essentially combines every slave census into one spreadsheet.

slaves <- read.csv("~/Desktop/MountVernon/Spreadsheets/slaves.csv", stringsAsFactors = FALSE)
library(dplyr)
## 
## Attaching package: 'dplyr'
## 
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(ggplot2)
library(tidyr)
library(lubridate)

This function creates a table that only lists the children.

slave_children <- slaves %>% 
  select(Name, Gender, Birth.Year, Skill, Farm, Census) %>%
  filter(Skill == "Child")
head(slave_children)
##          Name Gender Birth.Year Skill       Farm Census
## 1     Abbay A Female       1789 Child  Dogue Run   1799
## 2      Adam A   Male       1792 Child Muddy Hole   1799
## 3      Alce A Female       1791 Child Muddy Hole   1799
## 4 Alexander A   Male       1796 Child Muddy Hole   1799
## 5   Ambrose A   Male       1798 Child Union Farm   1799
## 6   Ambrose B   Male       1786 Child River Farm   1786

This chart shows the number of children on each farm for 1786 and 1799. Ferry Farm evolved into Union Farm, which the chart illustrates. Every farm increased in children, especially Muddy Hole and Mansion House.

ggplot(data = slave_children, aes(x = Farm, stat = "identity")) + geom_bar() + facet_wrap(~Census) + theme(axis.text.x=element_text(angle = 90, hjust = 0))

plot of chunk unnamed-chunk-3

This chart shows the number of children born on each farm during each birth year listed on the census. It is misleading, because it also shows the counts of unknown birth years. The dates are also not standardized in the original spreadsheet.

ggplot(data = slave_children, aes(x = Birth.Year, na.rm = TRUE, stat = "identity")) + geom_bar() + facet_wrap(~ Farm) + theme(axis.text.x=element_text(angle = 90, hjust = 0))

plot of chunk unnamed-chunk-4

That chart led me to consider the number of children born on the entire estate over time. Of couse, this also is misleading because a number of children do not have birth years on the census.

slave_br <- slaves %>%
  filter(Skill == "Child") %>%
  group_by(Birth.Year) %>%
  summarize(Name = n())
head(slave_br)
## Source: local data frame [6 x 2]
## 
##   Birth.Year Name
## 1              13
## 2       1774    2
## 3       1775    3
## 4       1777    1
## 5       1778    1
## 6       1779    1

This chart attempts to show the slave population on the estate per census, but the spreadsheet lists overlapping census dates and they’re not standardized.

slave_pop <- slaves%>%
  group_by(Census)%>%
  summarize(Name = n())

This table shows the number of children born to each mother.

slave_mothers <- slaves %>%
  group_by(Mother) %>%
  summarize(Name = n())
head(slave_mothers)
## Source: local data frame [6 x 2]
## 
##    Mother Name
## 1          232
## 2 Agnes A    3
## 3 Agnes B    1
## 4  Alce B    4
## 5  Alce C    9
## 6  Alce D    2

ggplot(data = slave_mothers, aes(x = Mother, y = n)) + geom_bar() + theme(axis.text.x=element_text(angle = 90, hjust = 0))

It would be interesting to compare the distribution of spouses across the estate. Slaves could not legally marry, so it’s interesting anyway that Washington listed slave spouses. With this new table, I wanted to see how many slave couples were on the same farm or on different farms. I then wanted to compare that finding to their children.

slave_spouses <- slaves %>%
  select(Name, Spouse, Farm) 
head(slave_spouses)
##      Name  Spouse       Farm
## 1 Aaron A                   
## 2 Abbay A          Dogue Run
## 3 Abram A Nancy F Union Farm
## 4  Acco A                   
## 5  Adam A         Muddy Hole
## 6  Adam B         River Farm

With this table, you can see that not every mother had a spouse. This chart makes me question the significance of slave spouses, because women who had no husband on the census still had children.

slave_fam <- slaves %>%
  select(Name, Spouse, Mother, Siblings, Farm)
head(slave_fam)
##      Name  Spouse     Mother
## 1 Aaron A                   
## 2 Abbay A         Sall Twine
## 3 Abram A Nancy F           
## 4  Acco A                   
## 5  Adam A             Alce B
## 6  Adam B                   
##                                                     Siblings       Farm
## 1                                                                      
## 2 Barbary A, George C, Hannah A, Jesse B, Kate B, Lawrence B  Dogue Run
## 3                                                            Union Farm
## 4                                                                      
## 5                                George D, Cecelia B, Kate A Muddy Hole
## 6                                                            River Farm