Tools for Romania's National Institute of Statistics Tempo Data

Find, inspect and download statistical tables from the Romanian National Institute of Statistics' TEMPO Online database. Tables can be listed and searched in Romanian or English, their structure inspected before downloading, and whole tables or filtered selections retrieved either as data frames or as CSV files that are kept up to date between sessions.

TEMPO

TEMPO gives R access to the Romanian National Institute of Statistics' TEMPO Online database: roughly 1500 statistical matrices covering population, labour, agriculture, trade and prices.

Installation

install.packages("TEMPO")

# Development version
# install.packages("remotes")
remotes::install_github("RProjectRomania/TEMPO")

Usage

library(TEMPO)

# Find a table.
tempo_search("unemployed", language = "en")

Then look before you download. A TEMPO matrix can be enormous, and tempo_meta() tells you so in one request:

meta <- tempo_meta("SOM101E", language = "en")
meta
#> <tempo_meta> SOM101E
#>   A. SOCIAL STATISTICS > LABOUR FORCE > REGISTERED UNEMPLOYED
#>   language:    en
#>   last update: 2026-10-12
#>   full table:  88,663,248 cells, over 2,000 requests
#>                too large to fetch whole; select values with tempo_get()
#>   dimensions:  5
#>     1. Sex (3 values): Total, Male, Female
#>     2. Counties (43 values): TOTAL, Alba, Arad, ...
#>     3. Localities (3182 values): TOTAL, 1017 ALBA IULIA MUNICIPALITY, ...
#>     4. Periods (216 values): Year 2010, January 2010, February 2010, ...
#>     5. MU: Persons (1 value): Number of persons

88 million cells is not a download. Fetch the slice you need instead, taking the dimension names and value labels from meta:

unemployment <- tempo_get(
  "SOM101E",
  filters = list(
    Sex        = "Total",
    Counties   = "Cluj",
    Localities = "TOTAL",
    Periods    = grep("2024", meta$values$Periods$label, value = TRUE)
  ),
  language = "en"
)

tempo_get() returns analysis-ready data: a numeric value column, the unit of measure as a column, ":" mapped to NA, and the service's footer lines removed. Time labels are parsed according to the matrix's layout - a dedicated Years dimension becomes an integer, while a combined Periods dimension like the one above keeps its labels and gains year and period columns, so an annual total stays distinguishable from the monthly figures beneath it.

For whole tables on disk, tempo_bulk() writes CSV files and keeps them up to date between sessions, recording the language, published revision and checksum of each file in a manifest beside them.

files <- tempo_bulk(
  codes = c("ACC101B", "ACC101C"),
  language = "en",
  directory = file.path(tempdir(), "tempo-data")
)

accidents <- tempo_tidy(files[["ACC101B"]], code = "ACC101B")

Function	Purpose
`tempo_toc()`	List every matrix, optionally with its classification
`tempo_search()`	Filter the table of contents by pattern
`tempo_hierarchy()`	Walk the statistical classification tree
`tempo_meta()`	Dimensions, selectable values, size and update date of one matrix
`tempo_get()`	Download a table, or a filtered part of it, into a data frame
`tempo_bulk()`	Download whole tables to CSV, skipping what is current
`tempo_tidy()`	Turn the service's CSV layout into analysis-ready data
`tempo_cache_dir()`	Inspect or change where metadata responses are cached

See vignette("using-tempo") for a full walkthrough, ?"TEMPO-options" for configuration, and ?"TEMPO-conditions" for the classed errors that let a script distinguish a bad argument from an unreachable service.

The service

TEMPO Online is a public service of the Romanian National Institute of Statistics. It is reachable only over plain HTTP and publishes no API contract or rate limit. This package identifies itself with a User-Agent, retries only what is worth retrying, caches metadata, and caps itself at four concurrent connections by default. Please keep it modest.

Every function that reaches the network needs an internet connection, and tempo_bulk() writes only to the directory you give it.