Analysis of Richard Dafforne’s Journal and
Ledger
Account books contain various sorts of historically significant data,
but the use of non-decimal monetary systems complicates computational
analysis of the early development of accounting practices such as
double-entry bookkeeping. How should the three separate monetary units
be recorded in a data base, and how can they be brought together in ways
that facilitate the analysis of account books? So long as these issues
exist, the time consuming task of transcribing the transactions in
account books into a spreadsheet or data base hardly seems a valuable
use of time, thereby precluding the use of digital tools to analyze or
visualize the contents.
The debkeepr package seeks to facilitate digital
analysis of account books through the implementation of the
deb_lsd and deb_decimal classes or types that
integrate pounds, shillings, and pence values into R. At a very practical
level, the goal of debkeepr is to make the transcription of
historical account books into a data base a more valuable research
activity. Once the contents of a set of account books are transcribed
into a data base — such as the example journal and ledger from Richard
Dafforne’s The Merchant’s Mirrour, Or Directions for the Perfect
Ordering and Keeping of His Accounts (London, 1660) used here —
debkeepr assists the researcher in both the analysis and
presentation of the data. By creating an environment to analyze the
contents of account books in R, debkeepr also seeks to make
the analysis of accounts more transparent and amendable to the practices
of reproducible research.
This vignette uses the practice journal and ledger from the 1660
edition of Richard Dafforne’s Merchant’s Mirrour to show some
of the ways that debkeepr facilitates the examination and
visualization of a set of account books. An introduction to Richard
Dafforne and his manual on double-entry bookkeeping, along with analysis
of individual transactions in the journal, can be found in the Transactions
in Richard Dafforne’s Journal vignette. The practice journal and
ledger in Merchant’s Mirrour provide a good starting point
because they are printed — and therefore more accessible — and have a
reasonable length. A PDF copy of the journal is available
for download.
The data
Dafforne’s example journal and ledger record the mercantile
activities of a fictional merchant in London over a seven month period
in 1634.
Following the practices of double-entry bookkeeping, the journal
documents each transaction and clearly lists one account as creditor and
one as debtor. The accompanying ledger derives entirely from the journal
and does not add any new information. Whereas the journal is organized
by transaction, the ledger is organized by account. Each transaction is
placed in the ledger twice, once under the credit account and once under
the debit account — hence double-entry bookkeeping. Dafforne’s journal
contains 191 transactions between 47 accounts, and thus the ledger
possesses 282 entries divided among the 47 accounts.
The data from these account books are included in the
debkeepr package. dafforne_transactions is a
data frame that includes all of the transactions from the journal except
those dealing with the balance account used to close the books. These
transactions are excluded because they can be recreated with the help of
debkeepr, as shown in more detail below.
dafforne_transactions has 177 transactions or rows and 8
variables. Each transaction has a creditor and debtor account, the date
of the transaction, and the value in pounds sterling contained in a
deb_lsd column. Extra details include the pages on which
the transaction can be found in Dafforne’s journal and ledger and a
short description of the transaction. Addition information about the
accounts that make up the books are contained in the
dafforne_accounts data frame. The account id matches those
used in the credit and debit variables in
dafforne_transactions. This second data frame also has a
name for each account and a short description of the account. The
investor column presents an extra organizing variable, listing the
investor or the person’s whose capital is involved in the account.
Overview of the account books
At a basic level, placing the contents of a set of account books into
a data base can expedite the exploration of the transactions and
relationships between the accounts. Even a modestly sized account book
such as Dafforne’s journal has too many accounts and transactions to
easily keep in one’s mind. To get a sense of the data, it helps to
create a visual overview of the transactions and a summary of the
accounts. This vignette uses dplyr to manipulate the data and
to prepare it for plotting with ggplot2.
# load packages
library(debkeepr)
library(dplyr)
library(ggplot2)
# load data
transactions <- dafforne_transactions
accounts <- dafforne_accounts
Note that deb_lsd vectors cannot currently be plotted
with ggplot(). However, the ability to cast
deb_lsd() to deb_decimal() makes this
limitation relatively easy to overcome. This casting can be done at any
point in the data analysis pipeline. The following code simplifies the
dafforne_transactions data frame and adds a
deb_decimal column.
(transactions <- transactions %>%
mutate(dec = deb_as_decimal(lsd)) %>%
select(id, credit, debit, date, lsd, dec))
#> # A tibble: 177 × 6
#> id credit debit date lsd dec
#> <dbl> <dbl> <dbl> <date> <lsd[20s:12d]> <l[20s:12d]>
#> 1 1 2 1 1633-01-01 1000:15s:7d 1000.779167
#> 2 2 2 3 1633-01-01 477:10s:0d 477.500000
#> 3 3 2 4 1633-01-01 55:0s:6d 55.025000
#> 4 4 2 5 1633-01-01 240:0s:0d 240.000000
#> 5 5 2 6 1633-01-01 229:0s:0d 229.000000
#> 6 6 2 8 1633-01-01 3:17s:8d 3.883333
#> 7 7 7 2 1633-01-01 150:0s:0d 150.000000
#> 8 8 9 11 1633-01-04 360:0s:0d 360.000000
#> 9 9 1 9 1633-01-04 144:0s:0d 144.000000
#> 10 10 5 10 1633-01-04 120:0s:0d 120.000000
#> # … with 167 more rows
To get a visual overview of the transactions, the value of each
transaction can be plotted. Because each transaction is associated with
two accounts — a debtor account and a creditor account — the journal
could be visualized from the perspective of either the debtor or
creditor accounts.
ggplot(data = transactions) +
geom_point(aes(x = credit, y = dec), alpha = 0.7) +
scale_y_continuous(labels = scales::label_dollar(prefix = "\u00a3")) +
labs(x = "Creditor accounts",
y = "Transaction values",
title = "Value of Transactions by Creditor Account") +
theme_light()
Alternatively, both the credit and debit account can be shown,
essentially reproducing a double-entry-bookkeeping ledger, as each
transaction is shown twice.
ggplot(data = transactions) +
geom_point(aes(x = credit, y = dec, color = "Credit"), alpha = 0.7) +
geom_point(aes(x = debit, y = dec, color = "Debit"), alpha = 0.7) +
scale_color_manual(values = c(Credit = "black", Debit = "red")) +
scale_y_continuous(labels = scales::label_dollar(prefix = "\u00a3")) +
labs(x = "Accounts",
y = "Transaction values",
color = "Relationship",
title = "Value of Transactions by Accounts") +
theme_light()
The exact values of the transactions are not necessarily easy to
follow in these plots with 177 and 354 points respectively, but they
give a good sense of the basic distribution of transactions. Only a
handful of transactions involved values over £1,000 sterling, and the
majority of transactions were for less that £200. The smaller number of
transactions over £1,000 make it possible to match up the credit and
debit accounts in the plot with both sets of accounts.
The above plots provide a view of the raw data from Dafforne’s
journal, but they do little to show an overview of the accounts. What
was the total credit or value sent by each account, what was the total
debit or value received by each account, and how much value, if any,
remained on the account at the closing of the book? These questions can
be answered separately with deb_credit(),
deb_debit(), and deb_current() or together
with deb_account_summary().
# Summary of accounts
deb_account_summary(transactions)
#> # A tibble: 46 × 4
#> account_id credit debit current
#> <dbl> <lsd[20s:12d]> <lsd[20s:12d]> <lsd[20s:12d]>
#> 1 1 1956:10s:11d 2903:13s:0d -947:-2s:-1d
#> 2 2 2006:3s:9d 150:0s:0d 1856:3s:9d
#> 3 3 570:0s:0d 570:0s:0d 0:0s:0d
#> 4 4 75:0s:8d 75:0s:8d 0:0s:0d
#> 5 5 813:3s:0d 813:3s:0d 0:0s:0d
#> 6 6 568:1s:11d 869:2s:7d -301:0s:-8d
#> 7 7 2958:18s:10d 2958:18s:10d 0:0s:0d
#> 8 8 1580:10s:0d 1580:10s:0d 0:0s:0d
#> 9 9 1744:1s:4d 1744:1s:4d 0:0s:0d
#> 10 10 606:2s:6d 606:2s:6d 0:0s:0d
#> # … with 36 more rows
From this information, a line-range plot can be constructed with the
upper limit represented by the total credit, the lower limit the total
debit, and the current value by a point. Though conceptually it is not
always helpful to think of debits as negative values — debits represent
the values received by an account — it makes sense in this instance to
distinguish credit values from debit values by making debits
negative.
# Plot summary of accounts
deb_account_summary(transactions, lsd = dec) %>%
mutate(debit = -debit) %>%
ggplot() +
geom_linerange(aes(x = account_id, ymin = debit, ymax = credit)) +
geom_point(aes(x = account_id, y = current,
color = if_else(current == 0, "Closed", "Open"))) +
scale_color_manual(values = c(Open = "black", Closed = "blue")) +
scale_y_continuous(labels = scales::label_dollar(prefix = "\u00a3")) +
labs(x = "Accounts",
y = "Pounds sterling",
color = "Status",
title = "Summary of the accounts") +
theme_light()
This summary plot shows the accounts through which the largest
amounts of capital passed and highlights the accounts that remain open
at the end of the books compared to those that were closed by having
their balance zeroed out. The plot provides a good basis to identify
and further investigate the most valuable accounts. For instance, we can
find the accounts that had a total credit exceeding £1,900 with the help
of deb_credit().
deb_credit(transactions) %>%
filter(lsd > 1900) %>%
left_join(select(accounts, id, account), by = c("account_id" = "id")) %>%
arrange(desc(lsd))
#> # A tibble: 6 × 3
#> account_id lsd account
#> <dbl> <lsd[20s:12d]> <chr>
#> 1 13 3430:7s:9d James Wilkinson
#> 2 7 2958:18s:10d Jacob Symonson - account current
#> 3 18 2447:3s:5d Randoll Rice - account current
#> 4 38 2215:13s:9d Amsterdam exchange - company with Jacob Symonson
#> 5 2 2006:3s:9d Stock
#> 6 1 1956:10s:11d Cash
Relationship between accounts
The above plots treat either the transactions or accounts as discrete
entities. However, an account book is naturally relational. The
structure of dafforne_transactions and
dafforne_accounts purposely mimics that used to create network
graphs. dafforne_transactions has the form of a
directed edge list: each transaction is a link between two accounts with
the values going from the credit account to the debit account.
dafforne_accounts is a node list or a data frame in which
each account id found in either the credit or debit column of
dafforne_transactions is listed along with additional
information about the accounts. There are a variety of ways to create
and plot network graphs in R, but this vignette uses igraph to create the graph object and ggraph to plot the
graph.
A network graph can show various types of information beyond the
linking of the accounts by associating the edges or links and the nodes
or accounts with variables from the data. The links between the accounts
can be related to the total value of the transactions in each direction,
and the nodes can be associated with either the total value they
received (debit) or sent (credit). Extra information can be added to the
nodes by joining the total values for each account with columns from the
dafforne_accounts data. In this case, the “investor”
variable is attached to the debit values for each account.
ggraph currently works best with regular numeric vectors,
and so the deb_lsd column is transformed into numeric for
both nodes and edges and these columns are labeled as val.
library(igraph)
library(ggraph)
# Nodes: Total debit for each account
debits <- deb_debit(transactions, lsd = lsd) %>%
mutate(val = as.numeric(lsd)) %>%
left_join(select(accounts, id, investor), by = c("account_id" = "id"))
# Edges: Sum of transactions by link
transactions_sum <- transactions %>%
group_by(credit, debit) %>%
summarise(lsd = sum(lsd), .groups = "drop") %>%
mutate(val = as.numeric(lsd)) %>%
arrange(val)
# Create igraph object
ledger_graph <- graph_from_data_frame(d = transactions_sum,
vertices = debits,
directed = TRUE)
The creation of an igraph object opens a whole set of
tools for analyzing the account books, but here the network analysis is
primarily restricted to its visualization. The following graph shows the
total value of the transactions between each set of accounts using the
transparency of the link, and arrows are present on the edges to show
the direction of the transaction from the creditor account to the debtor
account. The size of the node is used to represent the total accumulated
value for each account. The nodes are labeled with their account id and
colored by the “investor” variable.
# Ledger graph
set.seed(240)
ggraph(ledger_graph, layout = "kk") +
geom_edge_fan(aes(alpha = val),
width = 1,
arrow = arrow(length = unit(2, 'mm'),
type = "closed"),
end_cap = circle(3, 'mm')) +
scale_edge_alpha(labels = scales::dollar_format(prefix = "£")) +
geom_node_point(aes(size = val, color = investor), alpha = 0.9) +
scale_color_brewer(palette = "Paired", direction = -1) +
geom_node_text(aes(label = name)) +
scale_size_continuous(range = c(1, 10),
labels = scales::dollar_format(prefix = "£")) +
labs(color = "Investor",
size = "Accumulated Value",
edge_alpha = "Accumulated \n Transactions",
title = "Network of Dafforne's Journal") +
theme_graph()
#> Warning: Using the `size` aesthetic in this geom was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` in the `default_aes` field and elsewhere instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
The graph helps to identify the accounts that had the largest amount
of capital pass through them, and the level of transparency of the edges
shows the movement of capital. The transparency of the edges makes clear
the significant movement of capital among communities of accounts such
as the triad of accounts 7, 8, and 13 deriving from the sale of
cochineal — an insect used to make a red dye — for Jacob Symonson to
James Wilkinson. The Kamada-Kawai layout algorithm places
the nodes with the highest centrality, or the most connections, towards
the center. Those that have fewer connections are placed on the outside.
It is unsurprising to see the central position of accounts such as
account 1, the cash account, since any payment or reception of cash went
through this account. This arrangement of the nodes can be confirmed by
showing the accounts that have the most connections or links to other
accounts.
# 10 accounts with the most connections
sort(degree(ledger_graph), decreasing = TRUE)[1:10]
Connections per account
| 24 |
18 |
15 |
15 |
14 |
12 |
11 |
9 |
8 |
8 |
The use of the “investor” variable for the color of the nodes is one
of a variety of ways in which the accounts could be grouped. The graph
helps to demonstrate the meaningfulness of the grouping, as well as
highlighting something about Dafforne’s bookkeeping practices. This can
be seen through a comparison of the involvement of four different
merchants in the account book: Jacob Symonson, Randoll Rice, George
Pinchback, and Diego del Varino. Both Jacob Symonson and Randoll Rice
invested in a number of companies with the bookkeeper, but the accounts
that involved the capital of Symonson (pink) interacted with more of the
bookkeeper’s accounts (light orange) and a greater variety of accounts
that involved other merchants than did those of Randoll Rice. Whereas
the Kamada-Kawai algorithm places Symonson’s account close to the
bookkeeper’s most significant accounts, Rice’s many accounts (light
blue) mostly interacted with each other and so are shown towards the top
of the graph, away from the center.
Compare the placement of the accounts of Rice to the single account
for the merchant George Pinchback (red). Pinchback was involved in both
the purchase and sale of goods, and this one account has 15 links to and
from other accounts, making it quite central to the network. On the
other end of the spectrum are the three accounts containing Diego del
Varino’s capital (orange). Del Varino served as a factor in Lisbon for
ventures made by the company that the bookkeeper had with Randoll Rice
(accounts 30 and 36), but the orange accounts (40, 41, and 42) represent
the bookkeeper’s work as Del Varino’s factor. These accounts have
transactions between each other but with relatively few other accounts,
leading to their placement on the periphery of the graph. The comparison
of the visual representation of the accounts of these four merchants is
further demonstrated by creating a table relating the total links and
accounts per the investor grouping.
# Total links per investor
total_links <- tibble(id = as.numeric(names(degree(ledger_graph))),
links = degree(ledger_graph)) %>%
left_join(select(accounts, id, investor), by = "id") %>%
group_by(investor) %>%
summarise(links = sum(links), .groups = "drop") %>%
arrange(desc(links))
# Accounts per investor
accounts_investor <- accounts %>%
group_by(investor) %>%
summarise(accounts = n(), .groups = "drop")
# Create table
left_join(total_links, accounts_investor, by = "investor") %>%
knitr::kable(caption = "Links and accounts per investor")
Links and accounts per investor
| Ego |
72 |
8 |
| Jacob Symonson |
71 |
12 |
| Randoll Rice |
60 |
13 |
| George Pinchback |
15 |
1 |
| James Wilkinson |
15 |
1 |
| Andrew Hitchcock |
11 |
1 |
| Diego del Varino |
11 |
3 |
| Jean du Boys |
10 |
2 |
| Arthur Mumperson |
9 |
2 |
| Linden, Does, Reinst |
8 |
3 |
Moving beyond the comparison of accounts bearing the capital of
merchants, the visual structure of the graph and the grouping by
investor demonstrates a more general bookkeeping practice recommended by
Dafforne, namely the division of types of capital or ventures into
separate accounts. This practice is seen most clearly in the multiple
accounts involving Jacob Symonson and Randoll Rice, but the simplest
example may be the three accounts of Diego del Varino. Instead of
encompassing the work the bookkeeper performed as Del Varino’s factor
within a single account, the bookkeeper spread the capital among three
separate accounts. Accounts 40, 41, and 42 record the sale of fruit for
Del Varino to Randoll Rice (account 40), Rice’s partial payment in cash
resulting in the creation of an account of ready money for Del Varino
(account 41), and a separate current account for the remainder of the
sale (account 42).
The practice of creating multiple accounts facilitated the
calculation of profits or losses associated with individual ventures,
but it also means that the total transactional value of an account book
is a less meaningful measurement of capital investment than it might
first appear. To properly keep track of various types of capital,
Dafforne often transferred capital from one account to another, leading
the same or similar values to be recorded multiple times. Thus, the
total debit and sum of the transactions relating to the sale of fruit
for Del Varino in his three accounts of £1446 18s. was much greater than
the proceeds of the sale of £541 4s. 9d.
(varino_debits <- filter(debits, account_id %in% 40:42))
#> # A tibble: 3 × 4
#> account_id lsd val investor
#> <dbl> <lsd[20s:12d]> <dbl> <chr>
#> 1 40 541:4s:9d 541. Diego del Varino
#> 2 41 405:10s:7d 406. Diego del Varino
#> 3 42 500:2s:8d 500. Diego del Varino
# Total debit
sum(varino_debits$lsd)
#> <deb_lsd[1]>
#> [1] 1446:18s:0d
#> # Bases: 20s 12d
Analyzing individual accounts
The summary of accounts in Dafforne’s practice journal and ledger and
the visualization of their relationships provide a good basis for
further analysis into the workings of individual accounts. The accounts
chosen for further inquiry and the nature of the analysis will differ
according to the account book and the research questions being asked.
This example concentrates on the development of the bookkeeper’s capital
during the period covered by the journal and ledger by investigating the
opening of the books through the stock account (account 2), the
calculation of profits and losses during this period (account 23), and
the state of the books at the time of their closing.
According to Dafforne, at the opening of a new set of account books
the merchant must create an inventory in which all cash, unsold wares,
debtors, and creditors are listed in relation to the stock account. Of
this account Dafforne explains that it
“containeth in it, all what a man possesseth; whether Money,
Wares, Debts due to us, or the like: and (marke this well)
Cash, yea, each particular thing that I possesse, is but a
member of that whole body stocke; therefore by the joynt
meeting of all those members, the body (Stock) is made
compleat.”
We can get a better understanding of the inventory or opening of the
books by inspecting the stock account. One way to do this is to recreate
the ledger entries for the account. The
dafforne_transactions data is presented in the form of the
journal: each transaction is listed once and tied to two accounts. The
ledger can be replicated by finding all debit transactions for an
account — the left side of the page in the ledger — and all credit
transactions for the account — the right side of the ledger.
# Stock is debtor
filter(transactions, debit == 2)
#> # A tibble: 1 × 6
#> id credit debit date lsd dec
#> <dbl> <dbl> <dbl> <date> <lsd[20s:12d]> <l[20s:12d]>
#> 1 7 7 2 1633-01-01 150:0s:0d 150
# Stock is creditor
filter(transactions, credit == 2)
#> # A tibble: 6 × 6
#> id credit debit date lsd dec
#> <dbl> <dbl> <dbl> <date> <lsd[20s:12d]> <l[20s:12d]>
#> 1 1 2 1 1633-01-01 1000:15s:7d 1000.779167
#> 2 2 2 3 1633-01-01 477:10s:0d 477.500000
#> 3 3 2 4 1633-01-01 55:0s:6d 55.025000
#> 4 4 2 5 1633-01-01 240:0s:0d 240.000000
#> 5 5 2 6 1633-01-01 229:0s:0d 229.000000
#> 6 6 2 8 1633-01-01 3:17s:8d 3.883333
The ledger also includes the sum of the debits and credits, which can
be presented using the deb_account() function.
# Summary of stock account
deb_account(transactions, account_id = 2)
#> # A tibble: 3 × 2
#> relation lsd
#> <chr> <lsd[20s:12d]>
#> 1 credit 2006:3s:9d
#> 2 debit 150:0s:0d
#> 3 current 1856:3s:9d
The inventory consisted of seven items: six credits or items that
served to increase the bookkeeper’s stock and one debit of money he owed
to another. When taken together, the inventory demonstrated that the
bookkeeper’s credits exceeded his liabilities by £1,856 3s. 9d., which
represents the sum of the bookkeeper’s estate on 1 January 1633 English
style. This means that over 92% of the capital in the inventory belonged
to the bookkeeper.
# Percentage of bookkeeper's stock to total capital
deb_lsd(1856, 3, 9) / deb_lsd(2006, 3, 9)
#> [1] 0.9252313
The most significant account for the development of the bookkeeper’s
capital or stock over the course of the journal and ledger was that of
profit and loss (account 23). Completed ventures or accounts were
balanced by bringing the difference between the total credit and debit
to the profit and loss account. Ventures that result in more credit than
debit — for instance if the sale of goods (credit) was more than the
purchase of the goods plus any expenses (debit) — create a profit. In
such as case profit and loss is made creditor to the account for the
difference. The opposite occurs with losses. Thus, a merchant hopes to
have more credit than debit in the account of profit and loss, which is
what occurred in Dafforne’s practice books.
# Summary of profit and loss
deb_account(transactions, account_id = 23)
#> # A tibble: 3 × 2
#> relation lsd
#> <chr> <lsd[20s:12d]>
#> 1 credit 1075:8s:11d
#> 2 debit 29:0s:1d
#> 3 current 1046:8s:10d
The account books only showed three small losses or debits during the
course of the books, so it is more interesting to concentrate on the
much more substantial profits. A useful measurement is to see not only
the raw value that each account created in profits but also the relation
of that value to the total profits, shown here as a percentage.
# Percentage of profits by account
transactions %>%
filter(credit == 23) %>%
group_by(debit) %>%
summarise(lsd = sum(lsd), .groups = "drop") %>%
mutate(pct = lsd / deb_lsd(1046, 8, 10) * 100) %>%
left_join(accounts, by = c("debit" = "id")) %>%
select(id = debit, account, lsd, pct) %>%
arrange(desc(pct))
#> # A tibble: 16 × 4
#> id account lsd pct
#> <dbl> <chr> <lsd[20s:12d]> <dbl>
#> 1 21 Profit and loss - company with Randoll Rice 298:18s:4d 28.6
#> 2 12 Kerseys in company with Jacob Symonson 128:5s:0d 12.3
#> 3 29 Figs - company with Randoll Rice 114:15s:5d 11.0
#> 4 11 Voyage to Amsterdam - Jacob Symonson 111:17s:0d 10.7
#> 5 3 Wares 92:10s:0d 8.84
#> 6 24 Voyage to Lisbon - company with Jacob Symonson 63:17s:9d 6.11
#> 7 5 Jean du Boys - account current 56:5s:6d 5.38
#> 8 6 Jacob Symonson - account by him in company 50:0s:0d 4.78
#> 9 9 George Pinchback 36:5s:0d 3.46
#> 10 8 Jacob Symonson - Cochineal 31:12s:2d 3.02
#> 11 38 Amsterdam exchange - company with Jacob Symonson 22:0s:8d 2.11
#> 12 4 Kettles 20:0s:2d 1.91
#> 13 16 Interest reckoning 16:6s:2d 1.56
#> 14 1 Cash 13:4s:0d 1.26
#> 15 26 Danzig exchange - company with Arthur Mumperson 10:19s:9d 1.05
#> 16 27 Jacob Symonson - Cambric cloth 8:12s:0d 0.822
Closing the books
The process of closing a set of books as described by Dafforne
involved identifying open accounts — those that still had a positive or
negative balance — and transferring the remaining balance to an account
Dafforne called “Balance.” This account and the transactions that
serve to balance the accounts in the books are absent from
dafforne_transactions and dafforne_accounts,
but they can be recreated here. The first step is to identify all
accounts that remained open on July 20th, 1634 when Dafforne closed the
books.
# Open accounts: Arranged from credits to debits
(balance <- deb_open(transactions) %>%
left_join(select(accounts, id, account), by = c("account_id" = "id")) %>%
arrange(desc(lsd)))
#> # A tibble: 14 × 3
#> account_id lsd account
#> <dbl> <lsd[20s:12d]> <chr>
#> 1 2 1856:3s:9d Stock
#> 2 23 1046:8s:10d Profit and loss
#> 3 19 991:7s:6d Randoll Rice - account by me in company
#> 4 14 512:3s:8d Jacob Symonson - account by me in company
#> 5 33 99:7s:7d Hendrick vander Linden, John van Does, Jaques Rei…
#> 6 46 93:19s:8d Hendrick vander Linden, John van Does, Jaques Rei…
#> 7 35 -189:-12s:0d Voyage to Antwerp - company with Randoll Rice
#> 8 6 -301:0s:-8d Jacob Symonson - account by him in company
#> 9 39 -402:-12s:-1d Arthur Mumperson
#> 10 43 -413:-6s:-8d Thomas Trust - company with Randoll Rice - accoun…
#> 11 37 -446:-12s:-9d Andrew Hitchcock
#> 12 45 -806:-6s:-11d Figs - company with Jacob Symonson
#> 13 1 -947:-2s:-1d Cash
#> 14 20 -1092:-17s:-10d Jean du Boys - company with Randoll Rice
This operation essentially recreates the the Balance account as used
by Dafforne, though one alteration must be made before they are truly
equivalent. According to Dafforne, the penultimate step in creating a
balance is to close the account for profits and loss by “carrying the
difference to your Stock account.” Adding the value remaining in the profit
and loss account (account 23) to the stock account (account 2)
calculates the total capital held by the bookkeeper, while also
balancing the profit and loss account so that it can again calculate the
profits and losses experienced during the duration of the next set of
books. Thus, a true balance as described by Dafforne would show that the
stock account concluded with a total credit of £2,902 12s. 7d.
# Stock at the close of the books
stock_balance <- filter(balance, account_id == 2 | account_id == 23)
sum(stock_balance$lsd)
#> <deb_lsd[1]>
#> [1] 2902:12s:7d
#> # Bases: 20s 12d
This information enabled merchants to understand the development of
their capital. In this instance, the bookkeeper’s capital grew by over
56% in less than eight months, as his capital or stock — the amount that
the credits on his book surpassed the debits — increased from £1,856 3s.
9d. to £2,902 12s. 7d.
# Percentage of growth: profits over opening capital
balance$lsd[[2]] / balance$lsd[[1]]
#> [1] 0.5637586
Not only did the bookkeeper’s own capital grow rapidly, but the
capital contained in the account books increased even more quickly.
Whereas there had only been a total of £2,006 3s. 9d. in the books at
the time of the inventory, by the closing of the books, the assets and
liabilities remaining on the books had more than doubled to £4,599 11s.,
a growth of 129%. This of course meant that bookkeeper’s stock, or the
amount that his assets exceeded his liabilities, now constituted a much
smaller percentage of the capital as a whole, having decreased from the
original 92% to a much more reasonable 63%. This ratio of the
bookkeeper’s estate to total capital in his account books indicates a
more mature and diversified trading portfolio than is present at the
time of the inventory.
# Total balance remaining on the books
deb_balance(transactions)
#> # A tibble: 2 × 2
#> relation lsd
#> <chr> <lsd[20s:12d]>
#> 1 credit 4599:11s:0d
#> 2 debit -4599:-11s:0d
# Growth in the capital on the books
(deb_lsd(4599, 11, 0) - deb_lsd(2006, 3, 9)) / deb_lsd(2006, 3, 9)
#> [1] 1.292682
# Percentage of bookkeeper's stock to total capital
sum(balance$lsd[1:2]) / deb_lsd(4599, 11, 0)
#> [1] 0.6310681
Conclusion
The analysis of Dafforne’s practice journal and ledger presented here
provides a glimpse into the types of investigations, calculations, and
visualizations that debleepr makes possible. Most of the
calculations and even the visuals presented in this vignette could be
made without debkeepr. Of course, in the seventeenth
century merchants had to make all of their accounting calculations by
hand. Yet, this was and is a cumbersome activity. It is not a
particularly mathematically difficult task to add together the credits
or debits of the accounts remaining open at the closing of the book, but
it is certainly easier to use deb_balance(transactions).
The task is more difficult when the question is the total transactional
value that passed through the account book. Here,
sum(transactions$lsd) is much preferable to manually adding
every single value in the book. Moreover, while it is possible to
reproduce the plots showing the values of the transactions or the
summary of the accounts, this would involve long periods of data entry
that might not be of much use beyond the single implementation.
At the same time, debkeepr introduces tools of analysis
that are not possible without the ability to manipulate pounds,
shillings, and pence within a data base. The network graph of the
transactions in Dafforne’s journal would be difficult to recreate
without debkeepr, especially if you wanted to maintain
total accuracy and be able to see the values in pounds, shillings, and
pence form. The transformation of data from an account book into a
network graph opens up all kinds of new forms of analysis. This vignette
has only scratched the surface of the possibilities.
The most valuable tool that debkeepr brings to the
analysis of historical economic data that takes the form of pounds,
shillings, and pence values is the possibility of quickly iterating over
problems and providing solutions that are reproducible.
debkeepr enables a workflow for entering data from account
books into a data base, tidying the data
by transforming separate pounds, shillings, and pence variables into
deb_lsd or deb_decimal columns, and then
exploring, analyzing, and visualizing the data in ways that integrate
with the tidyverse and more
general practices of data analysis in R. Take for instance the last set
of calculations concerning the balance to close the books and its
relation to the bookkeeper’s stock. The calculations could be done by
hand, but the steps become more transparent when done through code.
Another example of this is the calculation of percentage of profits by
account for the profit and loss account. Each step in the analysis can
be followed and individually critiqued to ensure that the calculations
are properly made. In addition, the process is easily replicated for all
other accounts in the account book. In other words,
debkeepr provides a powerful and transparent set of tools
for the investigation and analysis of the social interactions found in
historical account books.