ineapir provides a set of functions to obtain open data and metadata published by the National Statistics Institute of Spain (INE). The data is obtained thanks to calls to the INE API service which allows access via URL requests to the available statistical information published by INE.
Install the released version of ineapir from CRAN:
install.packages("ineapir")To install the development version of ineapir from GitHub:
remotes::install_github("es-ine/ineapir")Alternatively, you can download the source code as a zip file and then install it as follows:
remotes::install_local(path = "path/to/file.zip")
The data is only associated with the series object and these can be grouped together into statistical tables. The field named ‘Valor’ is the only one that contains data. The rest of the fields are necessary for the data to be well defined.
To get all the data of a table it is necessary to pass the idTable
argument, which is the identification code of the table, to the function
get_data_table().
library(ineapir)
# We use the function get_data_table with the argument idTable
# and the argument tip = 'A' for a more friendly output
table <- get_data_table(idTable = 76125, tip = "A")
# Each row represents a series
table[1,c("COD", "Nombre")]
#> COD Nombre
#> 1 IPC290751 Nacional. Índice general. Índice.
# The Data column contains a data frame for each row with the values
# of the different periods of each series
head(table$Data[[1]])
#> Fecha T3_TipoDato T3_Periodo Anyo Valor
#> 1 2026-05-01T00:00:00.000+02:00 Definitivo M05 2026 102.951
#> 2 2026-04-01T00:00:00.000+02:00 Definitivo M04 2026 102.883
#> 3 2026-03-01T00:00:00.000+01:00 Definitivo M03 2026 102.440
#> 4 2026-02-01T00:00:00.000+01:00 Definitivo M02 2026 101.261
#> 5 2026-01-01T00:00:00.000+01:00 Definitivo M01 2026 100.836
#> 6 2025-12-01T00:00:00.000+01:00 Definitivo M12 2025 101.289
# We can concatenate all data frames into one using unnest = TRUE
table <- get_data_table(idTable = 76125, tip = "A", unnest = TRUE)
head(table[,c("COD", "Nombre", "Fecha", "Valor")])
#> COD Nombre Fecha
#> 1 IPC290751 Nacional. Índice general. Índice. 2026-05-01T00:00:00.000+02:00
#> 1.1 IPC290751 Nacional. Índice general. Índice. 2026-04-01T00:00:00.000+02:00
#> 1.2 IPC290751 Nacional. Índice general. Índice. 2026-03-01T00:00:00.000+01:00
#> 1.3 IPC290751 Nacional. Índice general. Índice. 2026-02-01T00:00:00.000+01:00
#> 1.4 IPC290751 Nacional. Índice general. Índice. 2026-01-01T00:00:00.000+01:00
#> 1.5 IPC290751 Nacional. Índice general. Índice. 2025-12-01T00:00:00.000+01:00
#> Valor
#> 1 102.951
#> 1.1 102.883
#> 1.2 102.440
#> 1.3 101.261
#> 1.4 100.836
#> 1.5 101.289To get the last n data from a table it is necessary to pass the nlast
argument as well.
# We use the function get_data_table with arguments idTable and nlast
table <- get_data_table(idTable = 76125, nlast = 2)
table[1,c("COD", "Nombre")]
#> COD Nombre
#> 1 IPC290751 Nacional. Índice general. Índice.
head(table$Data[[1]])
#> Fecha FK_TipoDato FK_Periodo Anyo Valor Secreto
#> 1 1.777586e+12 1 5 2026 102.951 FALSETo get the last data of a series it is necessary to pass the codSeries
argument, which is the identification code of the series, to the
function get_data_series().
# We use the function get_data_series with the argument codSeries
series <- get_data_series(codSeries = "IPC290750", tip = "A")
series$Data
#> Fecha T3_TipoDato T3_Periodo Anyo Valor
#> 1 2026-06-01T00:00:00.000+02:00 Avance M06 2026 3.2To get the last n data from a series it is necessary to pass the nlast
argument as well.
# We use the function get_data_series with arguments codSeries and nlast
series <- get_data_series(codSeries = "IPC290750", tip = "A", nlast = 5)
series$Data
#> Fecha T3_TipoDato T3_Periodo Anyo Valor
#> 1 2026-02-01T00:00:00.000+01:00 Definitivo M02 2026 2.3
#> 2 2026-03-01T00:00:00.000+01:00 Definitivo M03 2026 3.4
#> 3 2026-04-01T00:00:00.000+02:00 Definitivo M04 2026 3.2
#> 4 2026-05-01T00:00:00.000+02:00 Definitivo M05 2026 3.2
#> 5 2026-06-01T00:00:00.000+02:00 Avance M06 2026 3.2
# Using unnest = TRUE
series <- get_data_series(codSeries = "IPC290750", tip = "A", nlast = 5,
unnest = TRUE)
head(series[,c("COD", "Nombre", "Fecha", "Valor")])
#> COD Nombre
#> 1 IPC290750 Nacional. Índice general. Variación anual.
#> 1.1 IPC290750 Nacional. Índice general. Variación anual.
#> 1.2 IPC290750 Nacional. Índice general. Variación anual.
#> 1.3 IPC290750 Nacional. Índice general. Variación anual.
#> 1.4 IPC290750 Nacional. Índice general. Variación anual.
#> Fecha Valor
#> 1 2026-02-01T00:00:00.000+01:00 2.3
#> 1.1 2026-03-01T00:00:00.000+01:00 3.4
#> 1.2 2026-04-01T00:00:00.000+02:00 3.2
#> 1.3 2026-05-01T00:00:00.000+02:00 3.2
#> 1.4 2026-06-01T00:00:00.000+02:00 3.2Additionally, it is possible to obtain data from a series between two dates. The date must have and specific format (yyyy/mm/dd). If the end date is not specified we obtain all the data from the start date.
# We use the function get_data_series with arguments codSeries, dateStart and dataEnd
series <- get_data_series(codSeries = "IPC290750", dateStart = "2023/01/01",
dateEnd = "2023/04/01")
series$Data
#> Fecha FK_TipoDato FK_Periodo Anyo Valor Secreto
#> 1 1.672528e+12 1 1 2023 5.9 FALSE
#> 2 1.675206e+12 1 2 2023 6.0 FALSE
#> 3 1.677625e+12 1 3 2023 3.3 FALSE
#> 4 1.680300e+12 1 4 2023 4.1 FALSEStructural metadata are objects that describe both time series and statistical tables and allow their definition. All these database objects have an associated identifier that is essential for the correct use of the service.
The database contains information about all short-term statistical
operations, those with a periodicity for disseminating results of less
than a year, as well as some structural statistical operations. We can
get all the operations using the function get_metadata_operations().
# We use the function get_metadata_operations
operations <- get_metadata_operations()
head(operations)
#> Id Cod_IOE Nombre Codigo
#> 1 4 30147 Estadística de Efectos de Comercio Impagados EI
#> 2 6 30211 Índice de Coste Laboral Armonizado ICLA
#> 3 7 30168 Estadística de Transmisión de Derechos de la Propiedad ETDP
#> 4 10 30256 Indicadores Urbanos UA
#> 5 13 30219 Estadística del Procedimiento Concursal EPC
#> 6 14 30182 Índices de Precios del Sector Servicios IPS
#> Url
#> 1 <NA>
#> 2 /dyngs/INEbase/operacion.htm?c=Estadistica_C&cid=1254736053992&idp=1254735976596
#> 3 /dyngs/INEbase/operacion.htm?c=Estadistica_C&cid=1254736171438&idp=1254735576606
#> 4 https://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176957&idp=1254735976608
#> 5 /dyngs/INEbase/operacion.htm?c=Estadistica_C&cid=1254736177018&idp=1254735576606
#> 6 /dyngs/INEbase/operacion.htm?c=Estadistica_C&cid=1254736176864&idp=1254735576778An operation can be identify by a numerical code (‘Id’), an alphabetic
code (‘Codigo’) or by the code of the statistical operation in the
Inventory of Statistical Operations (IOE + ‘Cod_IOE’). To obtain
information about only one operation we have to pass the operation
argument with one of these codes.
# We use the function get_metadata_operations with argument operation
operation <- get_metadata_operations(operation = "IPC")
as.data.frame(operation)
#> Id Cod_IOE Nombre Codigo
#> 1 25 30138 Índice de Precios de Consumo (IPC) IPC
#> Url
#> 1 /dyngs/INEbase/operacion.htm?c=Estadistica_C&cid=1254736176802&idp=1254735976607We can get all the variables of the system using the function
get_metadata_variables().
# We use the function get_metadata_variables
variables <- get_metadata_variables()
head(variables)
#> Id Nombre Codigo
#> 1 349 Total Nacional NAC
#> 2 954 Total
#> 3 70 Comunidades y Ciudades Autónomas CCAA
#> 4 516 Nacionalidad 1
#> 5 955 Cultivos, pastos y huertos
#> 6 956 SAU y Otras tierrasA variable can be identify by a numerical code (‘Id’). In addition, if
we pass the operation argument we obtain the variables used in an
operation.
# We use the function get_metadata_variables with argument operation,
# e.g., operation code = 'IPC'
variables <- get_metadata_variables(operation = "IPC")
head(variables)
#> Id Nombre Codigo
#> 1 3 Tipo de dato
#> 2 70 Comunidades y Ciudades Autónomas CCAA
#> 3 115 Provincias PROV
#> 4 269 Grupos especiales 2001
#> 5 270 Rúbricas 2001
#> 6 349 Total Nacional NACTo get all the values that a variable can take it is necessary to pass
the variable argument, which is the identifier of the variable, to the
function get_metadata_values().
# We use the function get_metadata_values with argument variable,
# e.g., id = 3 (variable 'Tipo de dato')
values <- get_metadata_values(variable = 3)
head(values)
#> Id Fk_Variable Nombre
#> 1 70 3 Datos brutos
#> 2 71 3 Datos corregidos de efectos estacionales y de calendario
#> 3 72 3 Dato base
#> 4 73 3 Variación trimestral
#> 5 74 3 Variación anual
#> 6 75 3 Euros
#> Codigo
#> 1
#> 2
#> 3
#> 4
#> 5
#> 6A value can be identify by a numerical code (‘Id’). In addition, if we
pass the operation argument as well we obtain the values that the
variable takes in that particular operation.
# We use the function get_metadata_values with arguments operation and variable,
# e.g., operation code = 'IPC'
values <- get_metadata_values(operation = "IPC", variable = 3)
head(values)
#> Id Fk_Variable Nombre Codigo
#> 1 72 3 Dato base
#> 2 74 3 Variación anual
#> 3 83 3 Índice
#> 4 84 3 Variación mensual
#> 5 85 3 Media anual M
#> 6 86 3 Variación anualWe can get the tables associated with an statistical operation using the
function get_metadata_tables_operation().
# We use the function get_metadata_tables with argument operation
tables <- get_metadata_tables_operation(operation = "IPC")
head(tables[,c("Id","Nombre")])
#> Id
#> 1 24077
#> 2 24080
#> 3 35083
#> 4 53458
#> 5 76125
#> 6 76131
#> Nombre
#> 1 Índice general nacional. Series desde enero de 1961
#> 2 Índice nacionales de grupos ECOICOP. Series desde enero de 1993
#> 3 Índices nacionales: Componentes para el análisis de la COVID-19
#> 4 Índices por comunidades autónomas de subgrupos
#> 5 Índices nacionales: general y de grupos ECOICOP ver.2
#> 6 Índices nacionales a impuestos constantes: general y de grupos ECOICOP ver.2A table is defined by different groups or selection combo boxes and each
of them by the values that one or several variables take. To obtain the
variables and values present in a table first we have to query the
groups that define the table using the function
get_metadata_table_groups().
# We use the function get_metadata_table_groups with argument idTable
groups <- get_metadata_table_groups(idTable = 76125)
head(groups)
#> Id Nombre
#> 1 155577 Grupos ECOICOP ver.2
#> 2 155578 Tipo de datoOnce we have the identification codes of the groups, we can query the
values for an specific group using the function
get_metadata_table_values().
# We use the function get_metadata_table_values with arguments idTable and idGroup
values <- get_metadata_table_values(idTable = 76125, idGroup = 155577)
head(values, 4)
#> Id Fk_Variable Nombre Codigo
#> 1 304092 762 Índice general 00
#> 2 304093 762 Alimentos y bebidas no alcohólicas 01
#> 3 418050 762 Bebidas alcohólicas y tabaco 02
#> 4 304095 762 Vestido y calzado 03
#> FK_JerarquiaPadres
#> 1 NULL
#> 2 304092
#> 3 304092
#> 4 304092Alternatively, we can use the get_metadata_table_varval() function to
get the variables and values present in a table.
# Using the function get_metadata_table_varval
values <- get_metadata_table_varval(idTable = 76125)
head(values, 4)
#> Id Fk_Variable Nombre Codigo
#> 1 304092 762 Índice general 00
#> 2 304093 762 Alimentos y bebidas no alcohólicas 01
#> 3 418050 762 Bebidas alcohólicas y tabaco 02
#> 4 304095 762 Vestido y calzado 03The data is only associated with the series object. To obtain
information about a particular series it is necessary to pass the
codSeries argument, which is the identification code of the series, to
the function get_metadata_series().
# We use the function get_metadata_series with argument codSeries
series <- get_metadata_series(codSeries = "IPC290750")
as.data.frame(series)
#> Id COD FK_Operacion Nombre
#> 1 290750 IPC290750 25 Nacional. Índice general. Variación anual.
#> Decimales FK_Periodicidad FK_Publicacion FK_Clasificacion FK_Escala FK_Unidad
#> 1 1 1 8 120 1 135To get the values and variables that define a series it is necessary to
pass the codSeries argument as well.
# We use the function get_metadata_series_values with argument codSeries
values <- get_metadata_series_values(codSeries = "IPC290750")
head(values)
#> Id Fk_Variable Nombre Codigo
#> 1 16473 349 Nacional 00
#> 2 304092 762 Índice general 00
#> 3 74 3 Variación anual 2To get all the series that define a table it is necessary to pass the
idTable argument, which is the identification code of the table, to
the function get_metadata_series_table().
# We use the function get_metadata_series_table with argument idTable
series <- get_metadata_series_table(idTable = 76125)
head(series[,c("COD", "Nombre")], 4)
#> COD Nombre
#> 1 IPC290751 Nacional. Índice general. Índice.
#> 2 IPC290752 Nacional. Índice general. Variación mensual.
#> 3 IPC290750 Nacional. Índice general. Variación anual.
#> 4 IPC290753 Nacional. Índice general. Variación en lo que va de año.