The goal of statnipokladna is to provide programmatic access to open data from the Státní pokladna system. Státní pokladna is a comprehensive budgeting, reporting and accounting system for Czech public organisations. This package provides user-friendly ways to access the open data from that system available at https://monitor.statnipokladna.cz. The vignettes in the package also provide an introduction to the underlying data.
You can install the released version from CRAN:
install.packages("statnipokladna")You can install the current development release of statnipokladna from GitHub with:
remotes::install_github("petrbouchal/statnipokladna",
build_vignettes = TRUE,
ref = github_release())or the latest in-development version with
remotes::install_github("petrbouchal/statnipokladna",
build_vignettes = TRUE)I also keep binaries in a drat repo, which you can access by
install.packages("statnipokladna", repos = "https://petrbouchal.xyz/drat")Please report bugs at https://github.com/petrbouchal/statnipokladna/issues.
- get cleaned-up, ready to analyse data frames based on open data dumps
from the public finance database
- the package draws on the online data and returns a clean data frame
- the resulting data is ready to merge into time series
- time series is built based on user input
- do this through a consistent API which supplements some of the documentation that is missing from the official endpoints (still subject to some change)
- access registers published alongside the data (e.g. lists of public organisations with their identifiers and metadata), some of which can be useful in other contexts
- augment the core data with the desired type of register
See the Get started vignette for background on the underlying data.
See also Background information below.
How does this compare to the official analytical interface?
- no limit on the number of data points
- no limits on the number of organisations, unlike the official interface which forces you to use a filter on some tables
- different reports (local gov, central gov…) in one place in consistent form
- much faster for analysis (the current version of the online interface takes long to render)
- reproducible!!! While the online interface provides a permanent link to your analysis, this must be copied manually and does not necessarily provide an easily legible overview of how the analysis was produced (filters, columns etc.)
- no need for the web => excel => R dance
- drawback: for some reports, the data is published in different forms for different time periods (pre- and post-2015)
- drawback: consolidation must be done manually
the official system has been partially overhauled in February 2020 and I am trying to find out about which parts of its new API will remain stable and can be used externally. Depending on the result, some functionality in this package can be streamlined and some can be added - e.g.
- listing of available releases
- checking against existing releases and data sets
- retrieving some previously unpublished data e.g. calculated indicators and budget responsibility monitoring
library(statnipokladna)Get data from a particular part (file) of a dataset (“výkaz”):
local_budgets <- sp_get_table(table_id = "budget-local", # table ID, see `sp_tables`
year = 2019,
month = 9)
#> ✔ Storing downloaded archive in '/var/folders/fr/6f85xds52pq7g55fpmk4z7f80000gn/T//RtmpoP6RVe/finm/2019/09'
#> • Set `dest_dir` for more control over downloaded files.The data is automatically downloaded to a temp directory, so it will be
reused by future calls to sp_get_table() made in the same session,
unless you set force_redownload = TRUE. You set the dest_dir
parameter e.g. to ".", a directory will be created in your current
working directory and the data will be downloaded into it so that it can
persist across sessions.
It is a rather raw-looking data frame…
head(local_budgets)
#> # A tibble: 6 × 15
#> vykaz vtab vykaz_year vykaz_month ucjed ico kraj nuts polozka_typ
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 051 000200 2019 09 1000003163 750869… CZ03 CZ03 3
#> 2 051 000200 2019 09 1000003163 750869… CZ03 CZ03 3
#> 3 051 000100 2019 09 1000017768 000645… CZ010 CZ01… 2
#> 4 051 000100 2019 09 1000017768 000645… CZ010 CZ01… 2
#> 5 051 000100 2019 09 1000017768 000645… CZ010 CZ01… 2
#> 6 051 000100 2019 09 1000017768 000645… CZ010 CZ01… 2
#> # ℹ 6 more variables: paragraf <chr>, polozka <chr>, budget_adopted <dbl>,
#> # budget_amended <dbl>, budget_spending <dbl>, vykaz_date <date>but it has been cleaned up, and can be enriched with any of the metadata codelists:
functional_categories <- sp_get_codelist("paragraf")
#> ℹ Storing codelist in '/var/folders/fr/6f85xds52pq7g55fpmk4z7f80000gn/T//RtmpoP6RVe'
#> ℹ Set `dest_dir` for more control over downloaded files.functional_categories
#> # A tibble: 841 × 8
#> paragraf start_date end_date nazev skupina oddil pododdil poznamka
#> <chr> <date> <date> <chr> <chr> <chr> <chr> <chr>
#> 1 0000 2010-01-01 9999-12-31 Pro příjmy (t… Příjmy Příj… Příjmy Pro pří…
#> 2 1011 2010-01-01 9999-12-31 Udržování výr… Zemědě… Země… Zeměděl… Zeměděl…
#> 3 1012 2010-01-01 9999-12-31 Podnikání a r… Zemědě… Země… Zeměděl… Podniká…
#> 4 1013 2010-01-01 9999-12-31 Genetický pot… Zemědě… Země… Zeměděl… Genetic…
#> 5 1014 2010-01-01 9999-12-31 Ozdravování h… Zemědě… Země… Zeměděl… Ozdrav.…
#> 6 1019 2010-01-01 9999-12-31 Ostatní zeměd… Zemědě… Země… Zeměděl… Ostatní…
#> 7 1021 2010-01-01 9999-12-31 Organizace tr… Zemědě… Země… Regulac… Regulac…
#> 8 1022 2010-01-01 9999-12-31 Organizace tr… Zemědě… Země… Regulac… Org. tr…
#> 9 1023 2010-01-01 9999-12-31 Organizace tr… Zemědě… Země… Regulac… Organiz…
#> 10 1024 2010-01-01 9999-12-31 Organizace tr… Zemědě… Země… Regulac… Reg.trh…
#> # ℹ 831 more rowsThis contains all codes for this codelist, some of which are not valid
for the time period of our core data. The function add_codelist()
resolves this automatically.
As you can see below, you can
- add multiple codelists in one pipe
- add a codelist without downloading it first - just pass its ID to the function as a character instead of an object.
Codelists are also cached, but you have one in your namespace, you can pass it as an object, provided that it has the right columns.
local_budgets %>%
sp_add_codelist(functional_categories) %>%
sp_add_codelist("polozka")
#> ℹ Storing codelist in '/var/folders/fr/6f85xds52pq7g55fpmk4z7f80000gn/T//RtmpoP6RVe'
#> ℹ Set `dest_dir` for more control over downloaded files.
#> ℹ Joining on 2 columns: polozka, poznamka.This may indicate a problem with the data.Set `by` if needed.
#> # A tibble: 1,189,627 × 34
#> vykaz vtab vykaz_year vykaz_month ucjed ico kraj nuts polozka_typ
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 051 000200 2019 09 1000003163 75086… CZ03 CZ03 3
#> 2 051 000200 2019 09 1000003163 75086… CZ03 CZ03 3
#> 3 051 000100 2019 09 1000017768 00064… CZ010 CZ01… 2
#> 4 051 000100 2019 09 1000017768 00064… CZ010 CZ01… 2
#> 5 051 000100 2019 09 1000017768 00064… CZ010 CZ01… 2
#> 6 051 000100 2019 09 1000017768 00064… CZ010 CZ01… 2
#> 7 051 000100 2019 09 1000017768 00064… CZ010 CZ01… 2
#> 8 051 000100 2019 09 1000017768 00064… CZ010 CZ01… 2
#> 9 051 000100 2019 09 1000017768 00064… CZ010 CZ01… 2
#> 10 051 000100 2019 09 1000017768 00064… CZ010 CZ01… 2
#> # ℹ 1,189,617 more rows
#> # ℹ 25 more variables: paragraf <chr>, polozka <chr>, budget_adopted <dbl>,
#> # budget_amended <dbl>, budget_spending <dbl>, vykaz_date <date>,
#> # functional_categories_start_date <date>,
#> # functional_categories_end_date <date>, functional_categories_nazev <chr>,
#> # skupina <chr>, oddil <chr>, pododdil <chr>, poznamka <chr>,
#> # polozka_id <chr>, polozka_start_date <date>, polozka_end_date <date>, …Download a whole “výkaz” (dataset/data dump):
sp_get_dataset("finm", year = 2019) # dataset ID, see `sp_datasets`
#> ! `month` not set. Using default of 12.
#> ✔ Storing downloaded archive in '/var/folders/fr/6f85xds52pq7g55fpmk4z7f80000gn/T//RtmpoP6RVe/finm/2019/12'
#> • Set `dest_dir` for more control over downloaded files.This will put the files in a temp directory.
Then look at its documentation:
statnipokladna::sp_get_dataset_doc("finm")
#> ℹ Getting dataset documentation from <https://monitor.statnipokladna.cz/data/struktura/finm.xlsx>
#> ℹ File downloaded to '/var/folders/fr/6f85xds52pq7g55fpmk4z7f80000gn/T//RtmpoP6RVe/finm.xlsx'.You can get details of all the available tables in the sp_tables data
frame; for datasets, see sp_datasets.
The above examples present a simple all-in-one workflow, which is
concise but can be too opaque when transparency and reproducibility
matter. It is primarity aimed at workflows which prioritise updating
data: every time the script is run, data is redownloaded, unless cached
via the dest_dir parameter.
In other situations, the priority might be to keep track of individual
source files as they are downloaded from the data provider, or checking
for changes at the data provider and keeping track of individual URLs
from which the data was downloaded. For these situations, a workflow
composed of lower-level functions is available, offering finer control
of the steps. See the
workflow
vignette (vignette("workflow", package = "statnipokladna")).
Note that while the package provides a bridge from complicated data dumps to a clean data structure, you still need quite a bit of domain knowledge to be able analyse the data safely.
See the “How the data works” vignette (in Czech only, the terminology is impossible to translate) for an overview of the structure of the data on which this package draws. This also contains some notes useful for interpreting the data.
A subset of this information is in the Get started vignette.
There is also a log of various data gotchas I discovered, also in Czech only, stored in the data issues vignette.
A basic glossary of some of the terms used in the data sets is at https://monitor.statnipokladna.cz/metodika/.
Not created or endorsed by the Czech Ministry of Finance, who produce the data - but they definitely deserve credit for releasing the data and maintaining the application.
- CzechData by @JanCaha for (mainly) geospatial data about the Czech Republic (both admin. boundaries and topology and geography)
- RCzechia for another approach to Czech geospatial data and access to the official public geocoder and reverse geocoder
- czso for access to Czech statistical open data
- eurostat for access to Eurostat data
- OECD for access to OECD data, incl. a large amount of financial and economic data
- National Open Data Catalogue
- KNOD by Ondřej Kokeš for an overview of public data
- Hlídač státu by @michalblaha for easy (web and API) access to a large suite of transparency-focused datasets and their integration (public disclosures of contracts, tenders, political contributions…)
- CEDR (Centrální registr dotací) for a database of public subsidies, incl. to public bodies
Thanks to @smallhillcz and the Státní pokladna/Monitor developers and maintainers for responding to queries and generally keeping the thing running.
See CONTRIBUTING.md for a guide on how to contribute to the project.
Please note that the ‘statnipokladna’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.