The goal of ineptR is to facilitate and automate data extraction from
Statistics Portugal (Instituto Nacional de Estatistica - INE, PT) with
R.
It consists mainly of wrapper functions around the INE API, and some
helper functions to inspect metadata.
One limitation of the INE API is that data extraction is limited to 40k
data points in each API call. For convenience, this package deals with
that limitation internally, and the user can get all the records in a
single get_ine_data() function call.
For a basic usage example, keep reading.
For more in-depth examples, check vignette("use_cases").
You can install the development version of ineptR from GitHub with:
install.packages("ineptR") #Release version, from CRAN
# install.packages("devtools")
devtools::install_github("c-matos/ineptR") #Development version, from GitHubThis is a basic workflow to inspect and get data from INE.
#Load package
library(ineptR)- Navigate to the INE website and identify the desired indicador ID.
# Example:
# Proportion of domestic budget funded by domestic taxes (%); Annual
# Available at:
# https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_indicadores&indOcorrCod=0010003&contexto=bd&selTab=tab2&xlang=pt.- Get the indicator ID from the indOcorrCod query parameter in the URL. In this case the ID is 001003
- Confirm that the indicator ID is valid:
#Check if indicator is valid
is_indicator_valid("0010003")
#> [1] TRUE- Inspect metadata:
get_metadata("0010003")
#> $IndicadorCod
#> [1] "0010003"
#>
#> $IndicadorNome
#> [1] "Percentagem do orçamento de Estado financiado por impostos cobrados internamente (%); Anual - Direção Geral do Orçamento (Ministério das Finanças)"
#>
#> $Periodic
#> [1] "Anual"
#>
#> $PrimeiroPeriodo
#> [1] "2010"
#>
#> $UltimoPeriodo
#> [1] "2023"
#>
#> $UnidadeMedida
#> [1] "Percentagem (%)"
#>
#> $Potencia10
#> [1] "0"
#>
#> $PrecisaoDecimal
#> [1] "2"
#>
#> $Lingua
#> [1] "PT"
#>
#> $DataUltimaAtualizacao
#> [1] "2022-12-28"
#>
#> $DataExtracao
#> [1] "2023-08-17T21:01:49.507+01:00"- Get information about the dimensions:
- How many dimensions are in this indicator and what are they?
get_dim_info("0010003")
#> # A tibble: 2 × 3
#> dim_num abrv versao
#> <chr> <chr> <chr>
#> 1 1 Período de referência dos dados XXXXX
#> 2 2 Localização geográfica 03505- What are the unique values for each dimension?
get_dim_values("0010003")
#> # A tibble: 15 × 7
#> dim_num cat_id categ_cod categ_dsg categ_ord categ_nivel value_id
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 1 S7A2010 S7A2010 2010 20100101 1 Dim_Num1_S7A2010
#> 2 1 S7A2011 S7A2011 2011 20110101 1 Dim_Num1_S7A2011
#> 3 1 S7A2012 S7A2012 2012 20120101 1 Dim_Num1_S7A2012
#> 4 1 S7A2013 S7A2013 2013 20130101 1 Dim_Num1_S7A2013
#> 5 1 S7A2014 S7A2014 2014 20140101 1 Dim_Num1_S7A2014
#> 6 1 S7A2015 S7A2015 2015 20150101 1 Dim_Num1_S7A2015
#> 7 1 S7A2016 S7A2016 2016 20160101 1 Dim_Num1_S7A2016
#> 8 1 S7A2017 S7A2017 2017 20170101 1 Dim_Num1_S7A2017
#> 9 1 S7A2018 S7A2018 2018 20180101 1 Dim_Num1_S7A2018
#> 10 1 S7A2019 S7A2019 2019 20190101 1 Dim_Num1_S7A2019
#> 11 1 S7A2020 S7A2020 2020 20200101 1 Dim_Num1_S7A2020
#> 12 1 S7A2021 S7A2021 2021 20210101 1 Dim_Num1_S7A2021
#> 13 1 S7A2022 S7A2022 2022 20220101 1 Dim_Num1_S7A2022
#> 14 1 S7A2023 S7A2023 2023 20230101 1 Dim_Num1_S7A2023
#> 15 2 PT PT Portugal 1 1 Dim_Num2_PT- Get the data:
get_ine_data("0010003")
#> dim_1 geocod geodsg valor
#> 1 2010 PT Portugal 58.31
#> 2 2011 PT Portugal 60.04
#> 3 2012 PT Portugal 60.53
#> 4 2013 PT Portugal 58.92
#> 5 2014 PT Portugal 62.1
#> 6 2015 PT Portugal 61.28
#> 7 2016 PT Portugal 64.61
#> 8 2017 PT Portugal 64.55
#> 9 2018 PT Portugal 67.41
#> 10 2019 PT Portugal 69.16
#> 11 2020 PT Portugal 58.01
#> 12 2021 PT Portugal 60.72
#> 13 2022 PT Portugal 64.18
#> 14 2023 PT Portugal 66.08