The goal of mmex is to make it easy to read information from Money Manager EX (MMEX) database. MMEX is an excellent open source personal finance manager and you can find and download from here
For instance, MMEX has the following benefits
- It is freeware and open source
- It is available in multiple platform (e.g. PC and Android).
- The database (SQLite format) syncs across platform using Google Drive
This package is built for my personal use to create reports of my personal finance. As a result, it does not support all features of MMEX. For instance, I do not use investment accounts so this package does not support it. Even though I have some investments, I just keep track of initial investment amount as Bank accounts.
However, this package includes utility functions to
- list all tables
- get one table by name
So, you can pull tables of interest and join them properly by using the table relationship described here
- Only supports up to version 1.6.1
- After version 1.6.2, the category structure is more flexible and I don’t personally use them.
Taekyun (TK) Kim (taekyunk@gmail.com)
You can install the github version of mmex with:
library(devtools)
install_github("taekyunk/mmex") Find tables in MMEX db
library(mmex)
test_db <- system.file("extdata", "mmex_test_database_2014-01-26.mmb", package = "mmex")
list_mmex_table(test_db)
#> [1] "ACCOUNTLIST_V1" "ASSETS_V1"
#> [3] "BILLSDEPOSITS_V1" "BUDGETSPLITTRANSACTIONS_V1"
#> [5] "BUDGETTABLE_V1" "BUDGETYEAR_V1"
#> [7] "CATEGORY_V1" "CHECKINGACCOUNT_V1"
#> [9] "CURRENCYFORMATS_V1" "INFOTABLE_V1"
#> [11] "PAYEE_V1" "REPORT_V1"
#> [13] "SETTING_V1" "SPLITTRANSACTIONS_V1"
#> [15] "STOCK_V1" "SUBCATEGORY_V1"Read one table from MMEX db.
- To find the list of tables, use
list_mmex_table() - To find the relationship between tables, refer to this https://github.com/moneymanagerex/database
library(mmex)
test_db <- system.file("extdata", "mmex_test_database_2014-01-26.mmb", package = "mmex")
df_account <- read_mmex_table('accountlist_v1', test_db)Read some selected tables as one data.frame/tibble. Note that this function joins mostly useful for me, but not all tables.
library(mmex)
test_db <- system.file("extdata", "mmex_test_database_2014-01-26.mmb", package = "mmex")
df <- read_mmex_db(test_db)
df
#> # A tibble: 8,069 × 22
#> transid accoun…¹ toacc…² payeeid trans…³ trans…⁴ status trans…⁵ notes categid
#> <int> <int> <int> <int> <chr> <dbl> <chr> <chr> <chr> <int>
#> 1 1 3 4 -1 Transf… -10000 R "" "Ini… 16
#> 2 2 1 -1 5 Deposit 1200 R "" "" 13
#> 3 3 1 -1 5 Withdr… -300 R "" "" 13
#> 4 4 1 -1 5 Deposit 1500 R "" "" 13
#> 5 5 1 -1 5 Withdr… -375 R "" "" 13
#> 6 6 1 -1 5 Deposit 1200 R "" "" 13
#> 7 7 1 -1 5 Withdr… -300 R "" "" 13
#> 8 8 1 -1 5 Deposit 1200 R "" "" 13
#> 9 9 1 -1 5 Withdr… -300 R "" "" 13
#> 10 10 1 -1 6 Deposit 1600 R "" "" 13
#> # … with 8,059 more rows, 12 more variables: subcategid <int>,
#> # transdate <date>, followupid <int>, totransamount <dbl>, accountname <chr>,
#> # accounttype <chr>, initialbal <int>, toaccountname <chr>, payeename <chr>,
#> # categname <chr>, subcategname <chr>, cat_name <chr>, and abbreviated
#> # variable names ¹accountid, ²toaccountid, ³transcode, ⁴transamount,
#> # ⁵transactionnumberNote that the test database is from this link