TITLE: Analysing Ledger Personal Accounting Data Using R
DATE: 2017-09-01
AUTHOR: John L. Godlee
====================================================================


Ledger

-   You can read the file without any fancy programs, just a text
editor
-   Even if Ledger stops being maintained, you can still use the
journal file
-   The file can be interpreted using many other programming
languages, like R!

To properly visualise how my finances are changing over time
however, I find the text based reports provided by ledger-cli a bit
dense and fiddly.

This post might be an exercise in reinventing the wheel. Ledger
already has a decent web-based reporting system that can provide
pretty graphs and lots of other ledger-like apps that can do
similar. But my language of choice for making pretty graphs and
manipulating data is 'R', so I'm going to use that.

 [web-based reporting system]: https://github.com/ledger/ledger-web

ost of the code below is actually data manipulation, which I've
chosen to do with the dplyr package, creating the plots in ggplot2
isn't too taxing. I've created an example script here and you can
find the example .ledger.journal file I used here


[here](https://johngodlee.xyz/files/ledger/ledger_journal_analysis.R
)
 [1](https://johngodlee.xyz/files/ledger/example_ledger.journal)

Firstly, export your ledger journal file (.ledger.journal) as a
csv in the terminal, the name and filepath of your journal file
might be different:

   touch ledger.csv

   ledger csv -f ~/.ledger.journal > ledger.csv

And that's the last thing we'll be doing in the shell, everything
else will be in R. So fire up an R session to start analysing the
data.

Firstly set the working directory, import the csv file and load
some packages:

   # set working directory to `.ledger.journal`
   setwd()

   # Create vector of column names
   journal_names <- c("Date", "NA_1", "Description", "Source",
"Currency", "Amount", "NA_2", "NA_3")

   # Import csv
   ledger <- read.csv("ledger.csv", col.names = journal_names)

   # Load packages
   library(dplyr)
   library(ggplot2)

Now to make the ledger dataframe easier to use:

   # Convert "Date" column to date class
   ledger$Good_date <- as.Date(ledger$Date, format = "%Y/%m/%d")
   class(ledger$Good_date)  # To check the above worked

   # Sort by "Good_date"
   ledger_sort <- ledger[order(ledger$Good_date),]

   # Add cumulative column for each source
   ledger_cumsum <- ledger_sort %>%
       group_by(Source) %>%
       mutate(Cumulative = cumsum(Amount))

The rest involves creating a few graphs that I find useful. For all
these plots to work in their current form however, your Source or
"Account" structure must be the similar to the recommended
structure found in the ledger-cli example journal, e.g.:

 [ledger-cli example journal]:
http://ledger-cli.org/3.0/doc/ledger3.html#Example-Journal-File

   ┃
   ┣Assets
   ┃┣Checking
   ┃┣Savings
   ┃┗Cash
   ┣Income
   ┃┣Work
   ┃┗Ebay_sales
   ┗Expenses
    ┣Socialising
    ┣Bills
    ┗Mortgage

For instance if I had to pay a bill in ledger, the journal entry
might look like this:

   2017/12/06 Electricity bill
       Assets:Checking         $-65.51
       Expenses:Bills          $ 65.51

But it should be trivial to change the code to match your journal
structure.

Assets over time

 ![ggplot of assets over
time](https://johngodlee.xyz/img_full/ledger/assets_time_plot.png)

Create a data frame only containing assets:

   assets <- ledger_cumsum %>%
       filter(grepl("Assets", Source))

Then make the plot:

   ggplot(assets, aes(x = Good_date, y = Cumulative, group =
Source)) +
       geom_hline(aes(yintercept = 0), colour = "red") +
       geom_line(aes(colour = Source), size = 1.2) +
       geom_point(aes(colour = Source), size = 2) +
       scale_x_date(date_breaks = "1 week", date_labels = "%W/%y")

Viewing a particular asset in detail over time

 ![Detailed view of asset with
transactions](https://johngodlee.xyz/img_full/ledger/single_asset_pl
ot.png)

   # Create data frame
   assets_bank_current <- ledger_cumsum %>%
       filter(Source == "Assets:Checking")

   # Line plot of student account over time with description of
expenditure
   ggplot(assets_bank_current, aes(x = Good_date, y = Cumulative,
group = Source, label = Description)) +
       geom_line() +
       geom_text() +
       scale_x_date(date_breaks = "2 days", date_labels = "%W/%y")
+
       xlab("Date WW-YY") +
       ylab("Balance ($)")

Bar plots with breakdown of expenses

 ![Bar plot of
expenses](https://johngodlee.xyz/img_full/ledger/bar_expenses_plot.p
ng)

 ![Stacked bar plot of
expenses](https://johngodlee.xyz/img_full/ledger/stack_expenses_plot
png)

   # Create summary dataframe of expenses
   expenses_sum <- ledger_cumsum %>%
       filter(grepl("Expenses", Source)) %>%
       group_by(Source) %>%
       summarise(Amount = sum(Amount)) %>%
       mutate(Percentage = Amount / sum(Amount) * 100) %>%
       mutate(Source = factor(Source, levels =
Source[order(Amount, decreasing = TRUE)]))  # Create ordered factor
for x axis

   # Bar plot
   ggplot(expenses_sum, aes(x = Source, y = Amount)) +
       geom_bar(stat = "identity", aes(fill = Source)) +
       theme(legend.position = "none") +
       ylab("Amount ($)")

   # Stacked percentage bar chart
   ggplot(expenses_sum, aes(x = NA, y = Percentage, fill =
Source)) +
       geom_bar(stat = "identity") +
       geom_text(aes(label = paste(round(Percentage, digits = 2),
"% - ", Source, sep="")), position=position_stack(vjust=0.5))

Last 30 days income/expenses summary

 ![30 days income vs.
expenses](https://johngodlee.xyz/img_full/ledger/30d_summ_plot.png)

Creating this plot was fun, I had a go at using ifelse() arguments
inside the ggplot() call in order to change the position of an
error bar and text (which I've used to show deficit) depending on
whether I've made a net gain or loss that month.

   # Create summary dataframe
   ledger_30d_summ <- ledger_cumsum %>%
       filter(Good_date > as.Date(Sys.Date(), format = "%Y-%m-%d")
- 30) %>%
       filter(grepl("Assets", Source)) %>%
       mutate(expense_income = if_else(Amount > 0, "Income",
"Expense")) %>%
       group_by(expense_income) %>%
       summarise(Total = sum(Amount)) %>%
       mutate(Total = abs(Total))


   # Create colour palette
   expense_income_palette <- c("#D43131", "#1CB5DB")

   # Create plot
   ggplot(ledger_30d_summ, aes(x = expense_income, y = Total),
environment = environment()) +
       geom_bar(stat = "identity", fill = expense_income_palette)
+
       geom_errorbar(aes(x = ifelse(ledger_30d_summ$Total[1] >
ledger_30d_summ$Total[2], "Income", "Expense"),
               ymax = max(ledger_30d_summ$Total),
               ymin = min(ledger_30d_summ$Total))) +
       geom_text(aes(x = ifelse(ledger_30d_summ$Total[1] >
ledger_30d_summ$Total[2], "Income", "Expense"),
           y = min(ledger_30d_summ$Total) +
0.5*(max(ledger_30d_summ$Total) - min(ledger_30d_summ$Total)),
           label = ifelse(ledger_30d_summ$Total[1] >
ledger_30d_summ$Total[2],
           paste("$ -", max(ledger_30d_summ$Total) -
min(ledger_30d_summ$Total), sep = ""),
           paste("$ ", max(ledger_30d_summ$Total) -
min(ledger_30d_summ$Total), sep = "")),
           hjust = -0.5)) +
       xlab("Expense/Income") +
       ylab("Amount ($)")

Now that I've defined all these plots, it shouldn't take too much
effort to turn them into a basic Shiny app that I can load up in my
web browser, or run a script that saves the plots as images on my
computer so I can look at them later.

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