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ledger

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ledger is an R package to import data from plain text accounting software like Ledger, HLedger, and Beancount into an R data frame for convenient analysis, plotting, and export.

Right now it supports reading in the register from ledger, hledger, and beancount files.

Installation

To install the last version released to CRAN use the following command in R:

To install the development version of the ledger package (and its R package dependencies) use the install_github function from the remotes package in R:

This package also has some system dependencies that need to be installed depending on which plaintext accounting files you wish to read to be able to read in:

ledger

ledger (>= 3.1)

hledger

hledger (>= 1.4)

beancount

beancount (>= 2.0)

To install hledger run the following in your shell:

To install beancount run the following in your shell:

Several pre-compiled Ledger binaries are available (often found in several open source repos).

To run the unit tests you'll also need the suggested R package testthat.

Examples

API

The main function of this package is register which reads in the register of a plaintext accounting file. This package also registers S3 methods so one can use rio::import to read in a register, a net_worth convenience function, and a prune_coa convenience function.

register

Here are some examples of very basic files stored within the package:

r

library("ledger") options(width=180) ledger_file <- system.file("extdata", "example.ledger", package = "ledger") register(ledger_file)

## # A tibble: 42 × 8
##    date       mark  payee       description                     account                    amount commodity comment
##    <date>     <chr> <chr>       <chr>                           <chr>                       <dbl> <chr>     <chr>  
##  1 2015-12-31 *     <NA>        Opening Balances                Assets:JT-Checking          5000  USD       <NA>   
##  2 2015-12-31 *     <NA>        Opening Balances                Equity:Opening             -5000  USD       <NA>   
##  3 2016-01-01 *     Landlord    Rent                            Assets:JT-Checking         -1500  USD       <NA>   
##  4 2016-01-01 *     Landlord    Rent                            Expenses:Shelter:Rent       1500  USD       <NA>   
##  5 2016-01-01 *     Brokerage   Buy Stock                       Assets:JT-Checking         -1000  USD       <NA>   
##  6 2016-01-01 *     Brokerage   Buy Stock                       Equity:Transfer             1000  USD       <NA>   
##  7 2016-01-01 *     Brokerage   Buy Stock                       Assets:JT-Brokerage            4  SP        <NA>   
##  8 2016-01-01 *     Brokerage   Buy Stock                       Equity:Transfer            -1000  USD       <NA>   
##  9 2016-01-01 *     Supermarket Grocery store ;; Link: ^grocery Expenses:Food:Grocery        501. USD       <NA>   
## 10 2016-01-01 *     Supermarket Grocery store ;; Link: ^grocery Liabilities:JT-Credit-Card  -501. USD       <NA>   
## # … with 32 more rows

r

hledger_file <- system.file("extdata", "example.hledger", package = "ledger") register(hledger_file)

## # A tibble: 42 × 11
##    date       mark  payee       description      account                    amount commodity historical_cost hc_commodity market_value mv_commodity
##    <date>     <chr> <chr>       <chr>            <chr>                       <dbl> <chr>               <dbl> <chr>               <dbl> <chr>       
##  1 2015-12-31 *     <NA>        Opening Balances Assets:JT-Checking          5000  USD                 5000  USD                 5000  USD         
##  2 2015-12-31 *     <NA>        Opening Balances Equity:Opening             -5000  USD                -5000  USD                -5000  USD         
##  3 2016-01-01 *     Landlord    Rent             Assets:JT-Checking         -1500  USD                -1500  USD                -1500  USD         
##  4 2016-01-01 *     Landlord    Rent             Expenses:Shelter:Rent       1500  USD                 1500  USD                 1500  USD         
##  5 2016-01-01 *     Brokerage   Buy Stock        Assets:JT-Checking         -1000  USD                -1000  USD                -1000  USD         
##  6 2016-01-01 *     Brokerage   Buy Stock        Equity:Transfer             1000  USD                 1000  USD                 1000  USD         
##  7 2016-01-01 *     Brokerage   Buy Stock        Assets:JT-Brokerage            4  SP                  1000  USD                 2000  USD         
##  8 2016-01-01 *     Brokerage   Buy Stock        Equity:Transfer            -1000  USD                -1000  USD                -1000  USD         
##  9 2016-01-01 *     Supermarket Grocery store    Expenses:Food:Grocery        501. USD                  501. USD                  501. USD         
## 10 2016-01-01 *     Supermarket Grocery store    Liabilities:JT-Credit-Card  -501. USD                 -501. USD                 -501. USD         
## # … with 32 more rows

r

beancount_file <- system.file("extdata", "example.beancount", package = "ledger") register(beancount_file)

## # A tibble: 42 × 12
##    date       mark  payee         description      account                    amount commodity historical_cost hc_commodity market_value mv_commodity tags 
##    <chr>      <chr> <chr>         <chr>            <chr>                       <dbl> <chr>               <dbl> <chr>               <dbl> <chr>        <chr>
##  1 2015-12-31 *     ""            Opening Balances Assets:JT-Checking          5000  USD                 5000  USD                 5000  USD          ""   
##  2 2015-12-31 *     ""            Opening Balances Equity:Opening             -5000  USD                -5000  USD                -5000  USD          ""   
##  3 2016-01-01 *     "Landlord"    Rent             Assets:JT-Checking         -1500  USD                -1500  USD                -1500  USD          ""   
##  4 2016-01-01 *     "Landlord"    Rent             Expenses:Shelter:Rent       1500  USD                 1500  USD                 1500  USD          ""   
##  5 2016-01-01 *     "Brokerage"   Buy Stock        Assets:JT-Checking         -1000  USD                -1000  USD                -1000  USD          ""   
##  6 2016-01-01 *     "Brokerage"   Buy Stock        Equity:Transfer             1000  USD                 1000  USD                 1000  USD          ""   
##  7 2016-01-01 *     "Brokerage"   Buy Stock        Assets:JT-Brokerage            4  SP                  1000  USD                 2000  USD          ""   
##  8 2016-01-01 *     "Brokerage"   Buy Stock        Equity:Transfer            -1000  USD                -1000  USD                -1000  USD          ""   
##  9 2016-01-01 *     "Supermarket" Grocery store    Expenses:Food:Grocery        501. USD                  501. USD                  501. USD          ""   
## 10 2016-01-01 *     "Supermarket" Grocery store    Liabilities:JT-Credit-Card  -501. USD                 -501. USD                 -501. USD          ""   
## # … with 32 more rows

Here is an example reading in a beancount file generated by bean-example:

r

bean_example_file <- tempfile(fileext = ".beancount") system(paste("bean-example -o", bean_example_file), ignore.stderr=TRUE) df <- register(bean_example_file) options(width=240) print(df)

## # A tibble: 3,330 × 12
##    date       mark  payee   description                          account                              amount commodity historical_cost hc_commodity market_value mv_commodity tags 
##    <chr>      <chr> <chr>   <chr>                                <chr>                                 <dbl> <chr>               <dbl> <chr>               <dbl> <chr>        <chr>
##  1 2019-01-01 *     ""      Opening Balance for checking account Assets:US:BofA:Checking              4119.  USD                4119.  USD                4119.  USD          ""   
##  2 2019-01-01 *     ""      Opening Balance for checking account Equity:Opening-Balances             -4119.  USD               -4119.  USD               -4119.  USD          ""   
##  3 2019-01-01 *     ""      Allowed contributions for one year   Income:US:Federal:PreTax401k       -18500   IRAUSD           -18500   IRAUSD           -18500   IRAUSD       ""   
##  4 2019-01-01 *     ""      Allowed contributions for one year   Assets:US:Federal:PreTax401k        18500   IRAUSD            18500   IRAUSD            18500   IRAUSD       ""   
##  5 2019-01-03 *     "Hooli" Payroll                              Assets:US:BofA:Checking              1351.  USD                1351.  USD                1351.  USD          ""   
##  6 2019-01-03 *     "Hooli" Payroll                              Assets:US:Vanguard:Cash              1200   USD                1200   USD                1200   USD          ""   
##  7 2019-01-03 *     "Hooli" Payroll                              Income:US:Hooli:Salary              -4615.  USD               -4615.  USD               -4615.  USD          ""   
##  8 2019-01-03 *     "Hooli" Payroll                              Income:US:Hooli:GroupTermLife         -24.3 USD                 -24.3 USD                 -24.3 USD          ""   
##  9 2019-01-03 *     "Hooli" Payroll                              Expenses:Health:Life:GroupTermLife     24.3 USD                  24.3 USD                  24.3 USD          ""   
## 10 2019-01-03 *     "Hooli" Payroll                              Expenses:Health:Dental:Insurance        2.9 USD                   2.9 USD                   2.9 USD          ""   
## # … with 3,320 more rows

r

suppressPackageStartupMessages(library("dplyr")) dplyr::filter(df, grepl("Expenses", account), grepl("trip", tags)) %>% group_by(trip = tags, account) %>% summarise(trip_total = sum(amount))

## `summarise()` has grouped output by 'trip'. You can override using the `.groups` argument.
## # A tibble: 7 × 3
## # Groups:   trip [3]
##   trip                    account                  trip_total
##   <chr>                   <chr>                         <dbl>
## 1 trip-boston-2020        Expenses:Food:Coffee           6.39
## 2 trip-boston-2020        Expenses:Food:Restaurant     234.  
## 3 trip-los-angeles-2021   Expenses:Food:Alcohol         52.6 
## 4 trip-los-angeles-2021   Expenses:Food:Coffee          24.3 
## 5 trip-los-angeles-2021   Expenses:Food:Restaurant     458.  
## 6 trip-san-francisco-2019 Expenses:Food:Coffee          30.0 
## 7 trip-san-francisco-2019 Expenses:Food:Restaurant     624.

Using rio::import and rio::convert

If one has loaded in the ledger package one can also use rio::import to read in the register:

r

df2 <- rio::import(bean_example_file) all.equal(df, tibble::as_tibble(df2))

## [1] TRUE

The main advantage of this is that it allows one to use rio::convert to easily convert plaintext accounting files to several other file formats such as a csv file. Here is a shell example:

net_worth

Some examples of using the net_worth function using the example files from the register examples:

r

dates <- seq(as.Date("2016-01-01"), as.Date("2018-01-01"), by="years") net_worth(ledger_file, dates)

## # A tibble: 3 × 6
##   date       commodity net_worth assets liabilities revalued
##   <date>     <chr>         <dbl>  <dbl>       <dbl>    <dbl>
## 1 2016-01-01 USD           5000    5000          0         0
## 2 2017-01-01 USD           4361.   4882       -521.        0
## 3 2018-01-01 USD           6743.   6264       -521.     1000

r

net_worth(hledger_file, dates)

## # A tibble: 3 × 5
##   date       commodity net_worth assets liabilities
##   <date>     <chr>         <dbl>  <dbl>       <dbl>
## 1 2016-01-01 USD           5000    5000          0 
## 2 2017-01-01 USD           4361.   4882       -521.
## 3 2018-01-01 USD           6743.   7264       -521.

r

net_worth(beancount_file, dates)

## # A tibble: 3 × 5
##   date       commodity net_worth assets liabilities
##   <date>     <chr>         <dbl>  <dbl>       <dbl>
## 1 2016-01-01 USD           5000    5000          0 
## 2 2017-01-01 USD           4361.   4882       -521.
## 3 2018-01-01 USD           6743.   7264       -521.

r

net_worth(bean_example_file, dates)

## # A tibble: 0 × 3
## # … with 3 variables: date <date>, commodity <chr>, net_worth <lgl>

prune_coa

Some examples using the prune_coa function to simplify the "Chart of Account" names to a given maximum depth:

r

suppressPackageStartupMessages(library("dplyr")) df <- register(bean_example_file) %>% dplyr::filter(!is.na(commodity)) df %>% prune_coa() %>% group_by(account, mv_commodity) %>% summarize(market_value = sum(market_value), .groups = "drop")

## # A tibble: 11 × 3
##    account     mv_commodity market_value
##    <chr>       <chr>               <dbl>
##  1 Assets      IRAUSD                 0 
##  2 Assets      USD               119287.
##  3 Assets      VACHR                 87 
##  4 Equity      USD                -4119.
##  5 Expenses    IRAUSD             55500 
##  6 Expenses    USD               269081.
##  7 Expenses    VACHR                288 
##  8 Income      IRAUSD            -55500 
##  9 Income      USD              -377046.
## 10 Income      VACHR               -375 
## 11 Liabilities USD                -2248.

r

df %>% prune_coa(2) %>%

group_by(account, mv_commodity) %>% summarize(market_value = sum(market_value), .groups = "drop")

## # A tibble: 17 × 3
##    account                     mv_commodity market_value
##    <chr>                       <chr>               <dbl>
##  1 Assets:US                   IRAUSD           0       
##  2 Assets:US                   USD              1.19e+ 5
##  3 Assets:US                   VACHR            8.7 e+ 1
##  4 Equity:Opening-Balances     USD             -4.12e+ 3
##  5 Expenses:Financial          USD              4.44e+ 2
##  6 Expenses:Food               USD              1.87e+ 4
##  7 Expenses:Health             USD              7.27e+ 3
##  8 Expenses:Home               USD              8.86e+ 4
##  9 Expenses:Taxes              IRAUSD           5.55e+ 4
## 10 Expenses:Taxes              USD              1.50e+ 5
## 11 Expenses:Transport          USD              4.08e+ 3
## 12 Expenses:Vacation           VACHR            2.88e+ 2
## 13 Income:US                   IRAUSD          -5.55e+ 4
## 14 Income:US                   USD             -3.77e+ 5
## 15 Income:US                   VACHR           -3.75e+ 2
## 16 Liabilities:AccountsPayable USD              5.68e-14
## 17 Liabilities:US              USD             -2.25e+ 3

Basic personal accounting reports

Here is some examples using the functions in the package to help generate various personal accounting reports of the beancount example generated by bean-example.

First we load the (mainly tidyverse) libraries we'll be using and adjusting terminal output:

r

options(width=240) # tibble output looks better in wide terminal output library("ledger") library("dplyr") filter <- dplyr::filter library("ggplot2") library("scales") library("tidyr") library("zoo") filename <- tempfile(fileext = ".beancount") system(paste("bean-example -o", filename), ignore.stderr=TRUE) df <- register(filename) %>% mutate(yearmon = zoo::as.yearmon(date)) %>% filter(commodity=="USD") nw <- net_worth(filename)

Then we'll write some convenience functions we'll use over and over again:

r

print_tibble_rows <- function(df) {

print(df, n=nrow(df))

} count_beans <- function(df, filter_str = "", ..., amount = "amount", commodity="commodity", cutoff=1e-3) { commodity <- sym(commodity) amount_var <- sym(amount) filter(df, grepl(filter_str, account)) %>% group_by(account, !!commodity, ...) %>% summarize(!!amount := sum(!!amount_var), .groups = "drop") %>% filter(abs(!!amount_var) > cutoff & !is.na(!!amount_var)) %>% arrange(desc(abs(!!amount_var))) }

Basic balance sheets

Here is some basic balance sheets (using the market value of our assets):

r

print_balance_sheet <- function(df) {
assets <- count_beans(df, "^Assets",

amount="market_value", commodity="mv_commodity")

print_tibble_rows(assets) liabilities <- count_beans(df, "^Liabilities", amount="market_value", commodity="mv_commodity") print_tibble_rows(liabilities)

} print(nw)

## # A tibble: 3 × 5
##   date       commodity net_worth  assets liabilities
##   <date>     <chr>         <dbl>   <dbl>       <dbl>
## 1 2021-11-12 IRAUSD           0       0           0 
## 2 2021-11-12 USD         123655. 126076.      -2421.
## 3 2021-11-12 VACHR           15      15           0

r

print_balance_sheet(prune_coa(df, 2))

## # A tibble: 1 × 3
##   account   mv_commodity market_value
##   <chr>     <chr>               <dbl>
## 1 Assets:US USD                 6785.
## # A tibble: 1 × 3
##   account        mv_commodity market_value
##   <chr>          <chr>               <dbl>
## 1 Liabilities:US USD                -2421.

r

print_balance_sheet(df)

## # A tibble: 3 × 3
##   account                 mv_commodity market_value
##   <chr>                   <chr>               <dbl>
## 1 Assets:US:ETrade:Cash   USD             6297.    
## 2 Assets:US:BofA:Checking USD              489.    
## 3 Assets:US:Vanguard:Cash USD               -0.0200
## # A tibble: 1 × 3
##   account                    mv_commodity market_value
##   <chr>                      <chr>               <dbl>
## 1 Liabilities:US:Chase:Slate USD                -2421.

Basic net worth chart

Here is a basic chart of one's net worth from the beginning of the plaintext accounting file to today by month:

r

next_month <- function(date) {

zoo::as.Date(zoo::as.yearmon(date) + 1/12)

} nw_dates <- seq(next_month(min(df$date)), next_month(Sys.Date()), by="months") df_nw <- net_worth(filename, nw_dates) %>% filter(commodity=="USD") ggplot(df_nw, aes(x=date, y=net_worth, colour=commodity, group=commodity)) + geom_line() + scale_y_continuous(labels=scales::dollar)

Basic net worth chart

Basic net worth chart

Basic income sheets

r

month_cutoff <- zoo::as.yearmon(Sys.Date()) - 2/12 compute_income <- function(df) { count_beans(df, "^Income", yearmon) %>% mutate(income = -amount) %>% select(-amount) %>% ungroup() } print_income <- function(df) { compute_income(df) %>% filter(yearmon >= month_cutoff) %>% spread(yearmon, income, fill=0) %>% print_tibble_rows() } compute_expenses <- function(df) { count_beans(df, "^Expenses", yearmon) %>% mutate(expenses = amount) %>% select(-amount) %>% ungroup() } print_expenses <- function(df) { compute_expenses(df) %>% filter(yearmon >= month_cutoff) %>% spread(yearmon, expenses, fill=0) %>% print_tibble_rows() } compute_total <- function(df) { full_join(compute_income(prune_coa(df)) %>% select(-account), compute_expenses(prune_coa(df)) %>% select(-account), by=c("yearmon", "commodity")) %>% mutate(income = ifelse(is.na(income), 0, income), expenses = ifelse(is.na(expenses), 0, expenses), net = income - expenses) %>% gather(type, amount, -yearmon, -commodity) } print_total <- function(df) { compute_total(df) %>% filter(yearmon >= month_cutoff) %>% spread(yearmon, amount, fill=0) %>% print_tibble_rows() } print_total(df)

## # A tibble: 3 × 5
##   commodity type     `Sep 2021` `Oct 2021` `Nov 2021`
##   <chr>     <chr>         <dbl>      <dbl>      <dbl>
## 1 USD       expenses      7408.      7453.      2227.
## 2 USD       income        9437.      9279.      4640.
## 3 USD       net           2028.      1826.      2413.

r

print_income(prune_coa(df, 2))

## # A tibble: 1 × 5
##   account   commodity `Sep 2021` `Oct 2021` `Nov 2021`
##   <chr>     <chr>          <dbl>      <dbl>      <dbl>
## 1 Income:US USD            9437.      9279.      4640.

r

print_expenses(prune_coa(df, 2))

## # A tibble: 6 × 5
##   account            commodity `Sep 2021` `Oct 2021` `Nov 2021`
##   <chr>              <chr>          <dbl>      <dbl>      <dbl>
## 1 Expenses:Financial USD               4        39.8        4  
## 2 Expenses:Food      USD             504.      502.       134. 
## 3 Expenses:Health    USD             194.      194.        96.9
## 4 Expenses:Home      USD            2602.     2614.         0  
## 5 Expenses:Taxes     USD            3984.     3984.      1992. 
## 6 Expenses:Transport USD             120       120          0

r

print_income(df)

## # A tibble: 3 × 5
##   account                        commodity `Sep 2021` `Oct 2021` `Nov 2021`
##   <chr>                          <chr>          <dbl>      <dbl>      <dbl>
## 1 Income:US:ETrade:GLD:Dividend  USD            157.         0          0  
## 2 Income:US:Hoogle:GroupTermLife USD             48.6       48.6       24.3
## 3 Income:US:Hoogle:Salary        USD           9231.      9231.      4615.

r

print_expenses(df)

## # A tibble: 19 × 5
##    account                            commodity `Sep 2021` `Oct 2021` `Nov 2021`
##    <chr>                              <chr>          <dbl>      <dbl>      <dbl>
##  1 Expenses:Financial:Commissions     USD             0         35.8        0   
##  2 Expenses:Financial:Fees            USD             4          4          4   
##  3 Expenses:Food:Groceries            USD           249.       230.        68.5 
##  4 Expenses:Food:Restaurant           USD           256.       272.        65.2 
##  5 Expenses:Health:Dental:Insurance   USD             5.8        5.8        2.9 
##  6 Expenses:Health:Life:GroupTermLife USD            48.6       48.6       24.3 
##  7 Expenses:Health:Medical:Insurance  USD            54.8       54.8       27.4 
##  8 Expenses:Health:Vision:Insurance   USD            84.6       84.6       42.3 
##  9 Expenses:Home:Electricity          USD            65         65          0   
## 10 Expenses:Home:Internet             USD            80.0       79.9        0   
## 11 Expenses:Home:Phone                USD            56.8       68.6        0   
## 12 Expenses:Home:Rent                 USD          2400       2400          0   
## 13 Expenses:Taxes:Y2021:US:CityNYC    USD           350.       350.       175.  
## 14 Expenses:Taxes:Y2021:US:Federal    USD          2126.      2126.      1063.  
## 15 Expenses:Taxes:Y2021:US:Medicare   USD           213.       213.       107.  
## 16 Expenses:Taxes:Y2021:US:SDI        USD             2.24       2.24       1.12
## 17 Expenses:Taxes:Y2021:US:SocSec     USD           563.       563.       282.  
## 18 Expenses:Taxes:Y2021:US:State      USD           730.       730.       365.  
## 19 Expenses:Transport:Tram            USD           120        120          0

And here is a plot of income, expenses, and net income over time:

r

ggplot(compute_total(df), aes(x=yearmon, y=amount, group=commodity, colour=commodity)) +

facet_grid(type ~ .) + geom_line() + geom_hline(yintercept=0, linetype="dashed") + scale_x_continuous() + scale_y_continuous(labels=scales::comma)

Monthly income chart

Monthly income chart