Use of verbose
When the argument verbose = TRUE is passed to a
function, a number of information is displayed in the console, including
the URL used to call the API.
library(ineapir)
# Variables used in the operation IPC and verbose = TRUE
variables <- get_metadata_variables(operation = "IPC", verbose = TRUE)
#> - Check lang: OK
#> - API URL: https://servicios.ine.es/wstempus/js/ES/OPERACIONES_DISPONIBLES?page=1&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/OPERACIONES_DISPONIBLES?page=2&ver=3
#> - Check operation: OK
#> - Check page: OK
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VARIABLES_OPERACION/IPC?page=1&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VARIABLES_OPERACION/IPC?page=2&ver=3Use of validate
If it is not necessary to validate the values of the arguments passed
to a function, this validation can be turned off with
validate = FALSE.
# Variables used in the operation IPC with verbose = TRUE and validate = FALSE
variables <- get_metadata_variables(operation = "IPC", verbose = TRUE,
validate = FALSE)
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VARIABLES_OPERACION/IPC?page=1&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VARIABLES_OPERACION/IPC?page=2&ver=3This is useful when we are filtering data and the filter we are using is the one we want. Turning off validation means making fewer API calls. For better performance, it is recommended to use codes in the filter instead of shortcuts and disable validation if we are sure that the filter is correct.
# The filter
filter <- list("115" = "2", # variable id = 115, value id = 2
"3" = "74" , # variable id = 3, value id = 74
"762" = "304092" # variable id = 762, value id = 304092
)
# Request data using the filter with verbose = TRUE and validate = TRUE
ipc <- get_data_series_filter(operation = "IPC", filter = filter, periodicity = 1,
verbose = TRUE)
#> - Check lang: OK
#> - API URL: https://servicios.ine.es/wstempus/js/ES/OPERACIONES_DISPONIBLES?page=1&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/OPERACIONES_DISPONIBLES?page=2&ver=3
#> - Check operation: OK
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VARIABLES_OPERACION/IPC?page=1&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VARIABLES_OPERACION/IPC?page=2&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/3/IPC?det=0&page=1&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/3/IPC?det=0&page=2&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/70/IPC?det=0&page=1&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/70/IPC?det=0&page=2&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/115/IPC?det=0&page=1&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/115/IPC?det=0&page=2&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/269/IPC?det=0&page=1&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/269/IPC?det=0&page=2&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/270/IPC?det=0&page=1&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/270/IPC?det=0&page=2&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/349/IPC?det=0&page=1&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/349/IPC?det=0&page=2&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/544/IPC?det=0&page=1&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/544/IPC?det=0&page=2&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/762/IPC?det=0&page=1&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/762/IPC?det=0&page=2&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/763/IPC?det=0&page=1&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/763/IPC?det=0&page=2&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/764/IPC?det=0&page=1&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/764/IPC?det=0&page=2&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/765/IPC?det=0&page=1&ver=3
#> - API URL: https://servicios.ine.es/wstempus/js/ES/VALORES_VARIABLEOPERACION/765/IPC?det=0&page=2&ver=3
#> - Check filter: OK
#> - API URL: https://servicios.ine.es/wstempus/js/ES/PERIODICIDAD_OPERACION/IPC?ver=3
#> - Check periodicity: OK
#> - Check nlast: OK
#> - Check det: OK
#> - Check page: OK
#> - Processing filter: 0% - Processing filter: 67% - Processing filter: 83% - Processing filter: 100% - Processing filter: 100%
#> - API URL: https://servicios.ine.es/wstempus/js/ES/DATOS_METADATAOPERACION/IPC?p=1&nult=1&det=0&page=1&ver=3&g1=115%3A2&g2=3%3A74&g3=762%3A304092
# Request data using the filter with verbose = TRUE and validate = FALSE
ipc <- get_data_series_filter(operation = "IPC", filter = filter, periodicity = 1,
verbose = TRUE, validate = FALSE)
#> - Processing filter: 0% - Processing filter: 67% - Processing filter: 83% - Processing filter: 100% - Processing filter: 100%
#> - API URL: https://servicios.ine.es/wstempus/js/ES/DATOS_METADATAOPERACION/IPC?p=1&nult=1&det=0&page=1&ver=3&g1=115%3A2&g2=3%3A74&g3=762%3A304092Use of lang
It is possible to retrieve information in English passing the
argument lang = "EN" to certain functions.
# Variables used in the operation IPC and lang = "EN"
variables <- get_metadata_variables(operation = "IPC", lang = "EN")
variables
#> Id Nombre Codigo
#> 1 3 Type of data
#> 2 70 Autonomous Communities and Cities CCAA
#> 3 115 Provinces PROV
#> 4 269 Special groups 2001
#> 5 270 Headings 2001
#> 6 349 Regional totals NAC
#> 7 544 Correction of effects
#> 8 762 Grupos ECOICOP
#> 9 763 Subgrupos ECOICOP
#> 10 764 Clases ECOICOP
#> 11 765 Subclases ECOICOPWe can use the English language as well filtering data using shortcuts.
# Filter with the values wrapper (CPI)
filter <- list(values = c("annual change" , "overall index"))
# Request data using the filter with shortcut = TRUE and lang = "EN"
cpi <- get_data_table(idTable = 50902, filter = filter, unnest = TRUE,
tip = "A", nlast = 5, lang = "EN")
cpi[,c("Nombre", "T3_Periodo", "Anyo", "Valor")]
#> Nombre
#> 1 National Total. Overall index. Annual variation (for monthly series).
#> 1.1 National Total. Overall index. Annual variation (for monthly series).
#> 1.2 National Total. Overall index. Annual variation (for monthly series).
#> 1.3 National Total. Overall index. Annual variation (for monthly series).
#> 1.4 National Total. Overall index. Annual variation (for monthly series).
#> T3_Periodo Anyo Valor
#> 1 M05 2024 3.6
#> 1.1 M04 2024 3.3
#> 1.2 M03 2024 3.2
#> 1.3 M02 2024 2.8
#> 1.4 M01 2024 3.4Use of unnest
When we request data from tables or series using the functions
get_data_table(), get_data_series() and
get_data_series_filter, the $Data column
containing the data values is a list of data frames, one for each
entry.
# Filter with the values wrapper (CPI)
filter <- list(values = c("variación anual" , "variación mensual", "índice general"))
# Request data using the filter and unnest = FALSE
ipc <- get_data_table(idTable = 50902, filter = filter, tip = "A", nlast = 3)
# We have two entries
ipc[,c("COD","Nombre")]
#> COD Nombre
#> 1 IPC251855 Total Nacional. Índice general. Variación mensual.
#> 2 IPC251856 Total Nacional. Índice general. Variación anual.
# Data column is a list of two data frames. One for each entry.
ipc$Data
#> [[1]]
#> Fecha T3_TipoDato T3_Periodo Anyo Valor
#> 1 2024-05-01T00:00:00.000+02:00 Definitivo M05 2024 0.3
#> 2 2024-04-01T00:00:00.000+02:00 Definitivo M04 2024 0.7
#> 3 2024-03-01T00:00:00.000+01:00 Definitivo M03 2024 0.8
#>
#> [[2]]
#> Fecha T3_TipoDato T3_Periodo Anyo Valor
#> 1 2024-05-01T00:00:00.000+02:00 Definitivo M05 2024 3.6
#> 2 2024-04-01T00:00:00.000+02:00 Definitivo M04 2024 3.3
#> 3 2024-03-01T00:00:00.000+01:00 Definitivo M03 2024 3.2If we want to get only one data frame for all data values, we can
pass the argument unnest = TRUE.
# Filter with the values wrapper (CPI)
filter <- list(values = c("variación anual" , "variación mensual", "índice general"))
# Request data using the filter and unnest = TRUE
ipc <- get_data_table(idTable = 50902, filter = filter, tip = "A",
nlast = 3, unnest = TRUE)
# Unique data frame with 6 rows
ipc[,c("Nombre", "T3_Periodo", "Anyo", "Valor")]
#> Nombre T3_Periodo Anyo Valor
#> 1 Total Nacional. Índice general. Variación mensual. M05 2024 0.3
#> 1.1 Total Nacional. Índice general. Variación mensual. M04 2024 0.7
#> 1.2 Total Nacional. Índice general. Variación mensual. M03 2024 0.8
#> 2 Total Nacional. Índice general. Variación anual. M05 2024 3.6
#> 2.1 Total Nacional. Índice general. Variación anual. M04 2024 3.3
#> 2.2 Total Nacional. Índice general. Variación anual. M03 2024 3.2Use of metanames and metacodes
When we pass the argument metanames = TRUE we get
additional columns that contain the names of the values extracted from
the metadata information. To get additional columns containing the codes
and ids of the values extracted from the metadata information we pass
the argument metacodes = TRUE.
# Filter for total population of men and women
filter <- list(nacionalidad = "total", sexo = c("hombres", "mujeres"))
# Table: Resident population by date, sex, age group and nationality
poblacion <- get_data_table(idTable = 9674, filter = filter, validate = FALSE,
tip = "AM", unnest = TRUE, nlast = 1)
# Names of columns
names(poblacion)
#> [1] "COD" "Nombre" "T3_Unidad" "T3_Escala" "MetaData"
#> [6] "Fecha" "T3_TipoDato" "T3_Periodo" "Anyo" "Valor"
# Request data with metanames = TRUE
poblacion <- get_data_table(idTable = 9674, filter = filter, validate = FALSE,
tip = "AM", unnest = TRUE, nlast = 1,
metanames = TRUE, metacodes = TRUE)
# Additional columns of Nacionalidad, Edad and Sexo
names(poblacion)
#> [1] "COD" "Nombre"
#> [3] "T3_Unidad" "T3_Escala"
#> [5] "MetaData" "Nacionalidad"
#> [7] "Nacionalidad.Codigo" "Nacionalidad.Id"
#> [9] "Grupo.quinquenal.de.edad" "Grupo.quinquenal.de.edad.Codigo"
#> [11] "Grupo.quinquenal.de.edad.Id" "Sexo"
#> [13] "Sexo.Codigo" "Sexo.Id"
#> [15] "Fecha" "T3_TipoDato"
#> [17] "T3_Periodo" "Anyo"
#> [19] "Valor"Filtering with regular expresions
When we use shortcuts names in the filter, we can also include as values regular expressions if this helps us filter the data better.
# Filter with a regular expression for ages between 20 and 25 years old
filter <- list(sexo = "total", edad = "2[0-5]+")
# Request data using the filter with a regular expression
pob <- get_data_table(idTable = 56934, filter = filter, tip = "A", nlast = 1,
unnest = TRUE)
# Only ages between 20 and 25 years old
pob[,c("Nombre", "Anyo", "Valor")]
#> Nombre Anyo Valor
#> 1 Total Nacional. 20 años. Total. Población. Número. 2023 499051
#> 2 Total Nacional. 21 años. Total. Población. Número. 2023 500702
#> 3 Total Nacional. 22 años. Total. Población. Número. 2023 508918
#> 4 Total Nacional. 23 años. Total. Población. Número. 2023 501369
#> 5 Total Nacional. 24 años. Total. Población. Número. 2023 492950
#> 6 Total Nacional. 25 años. Total. Población. Número. 2023 504965