Overview
This vignette demonstrates how to query IBGE aggregate
tables that serve as short-term tracking indicators for
state-level GDP components — particularly in services,
retail, manufacturing, and construction.
The workflow is always the same:
- Inspect metadata with
ibge_metadata()
to discover available variables, classifications, and categories.
- Fetch data with
ibge_variables(),
specifying aggregate, variable, classification, localities, and
periods.
- Post-process the
value column with
parse_ibge_value() and convert period codes to proper
dates.
Note on value: the IBGE API may return
special symbols ("-", "..",
"...", "X") instead of numbers. Always use
parse_ibge_value() to convert reliably.
Helper: convert period codes to dates
IBGE returns periods as character codes: "202501" for
monthly data (January 2025) and "202501" for quarterly data
(Q1 2025). We need format-specific converters:
# Monthly periods: "202501" -> 2025-01-01
period_to_monthly <- function(x) ym(x)
# Quarterly periods: "202501" -> 2025-01-01
# lubridate::yq() expects "2025.1", so we reformat first
period_to_quarterly <- function(x) {
yr <- substr(x, 1, 4)
qt <- as.integer(substr(x, 5, 6))
as.Date(paste0(yr, "-", qt * 3 - 2, "-01"))
}
2) IPCA (7060) — Vehicle insurance
Same logic — only the category changes in classification
"315".
# Find category ID for "Seguro" (insurance)
unnest(meta_7060$classifications, categories) |>
filter(str_detect(category_name, "Seguro|Índice")) |>
select(id, category_id, category_name)
#> # A tibble: 2 × 3
#> id category_id category_name
#> <chr> <chr> <chr>
#> 1 315 7169 Índice geral
#> 2 315 7643 5102005.Seguro voluntário de veículo
ipca_vehicle_ins <- ibge_variables(
aggregate = 7060,
variable = 63,
periods = -12,
classification = list("315" = c("7169", "7643")), # General + Vehicle insurance
localities = "N7[2601]"
) |>
mutate(
value = parse_ibge_value(value),
period = period_to_monthly(period)
) |>
select(period, classification_315, locality_name, value)
ipca_vehicle_ins |>
ggplot(aes(period, value, color = classification_315)) +
geom_line() +
geom_point() +
labs(
title = "IPCA — Vehicle insurance vs General index",
subtitle = "Recife Metropolitan Area, monthly variation (%)",
x = NULL, y = "Monthly variation (%)", color = NULL
) +
theme_minimal() +
theme(legend.position = "bottom")
plot of chunk unnamed-chunk-9
3) PMS (8693) — Transportation and postal services
The Monthly Survey of Services (PMS) aggregate 8693 is a proxy for
service-sector activity. Here we filter by:
- Index type (classification
11046):
revenue vs volume indices
- Activity group (classification
12355):
transportation, storage and postal services
meta_8693 <- ibge_metadata(8693)
# Browse classifications and categories
unnest(meta_8693$classifications, categories)
#> # A tibble: 8 × 6
#> id name category_id category_name category_unit category_level
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 11046 Tipos de índice 56725 Índice de re… <NA> 0
#> 2 11046 Tipos de índice 56726 Índice de vo… <NA> 0
#> 3 12355 Atividades de se… 107071 Total <NA> 0
#> 4 12355 Atividades de se… 106869 1. Serviços … <NA> 1
#> 5 12355 Atividades de se… 106874 2. Serviços … <NA> 1
#> 6 12355 Atividades de se… 31399 3. Serviços … <NA> 1
#> 7 12355 Atividades de se… 106876 4. Transport… <NA> 1
#> 8 12355 Atividades de se… 31426 5. Outros se… <NA> 1
meta_8693$variables
#> # A tibble: 6 × 3
#> id name unit
#> <chr> <chr> <chr>
#> 1 7167 PMS - Número-índice (2022=100) Núme…
#> 2 7168 PMS - Número-índice com ajuste sazonal (2022=100) Núme…
#> 3 11623 PMS - Variação mês/mês imediatamente anterior, com ajuste sazonal… %
#> 4 11624 PMS - Variação mês/mesmo mês do ano anterior (M/M-12) %
#> 5 11625 PMS - Variação acumulada no ano (em relação ao mesmo período do a… %
#> 6 11626 PMS - Variação acumulada em 12 meses (em relação ao período anter… %
pms_transport <- ibge_variables(
aggregate = 8693,
variable = 7167, # Index number (2022 = 100)
periods = -12,
classification = list(
"11046" = "all", # All index types (revenue + volume)
"12355" = "106876" # Transportation/postal services
),
localities = "N3[26]" # Pernambuco
) |>
mutate(
value = parse_ibge_value(value),
period = period_to_monthly(period)
) |>
select(period, classification_11046, locality_name, value)
pms_transport |>
ggplot(aes(period, value, color = classification_11046)) +
geom_line() +
geom_point() +
labs(
title = "PMS — Index numbers (2022 = 100)",
subtitle = "Transportation, storage and postal services (Pernambuco)",
x = NULL, y = "Index (2022 = 100)", color = NULL
) +
theme_minimal() +
theme(legend.position = "bottom")
plot of chunk unnamed-chunk-12
4) PNAD Contínua (5434) — Accommodation and food services
The Continuous PNAD aggregate 5434 provides quarterly employment data
(persons aged 14+ employed) by activity group.
meta_5434 <- ibge_metadata(5434)
unnest(meta_5434$classifications, categories)
#> # A tibble: 13 × 6
#> id name category_id category_name category_unit category_level
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 888 Grupamento de a… 47946 Total <NA> 0
#> 2 888 Grupamento de a… 47947 Agricultura,… <NA> 1
#> 3 888 Grupamento de a… 47948 Indústria ge… <NA> 1
#> 4 888 Grupamento de a… 60031 Indústria de… <NA> 2
#> 5 888 Grupamento de a… 47949 Construção <NA> 1
#> 6 888 Grupamento de a… 47950 Comércio, re… <NA> 1
#> 7 888 Grupamento de a… 56622 Transporte, … <NA> 1
#> 8 888 Grupamento de a… 56623 Alojamento e… <NA> 1
#> 9 888 Grupamento de a… 56624 Informação, … <NA> 1
#> 10 888 Grupamento de a… 60032 Administraçã… <NA> 1
#> 11 888 Grupamento de a… 56627 Outros servi… <NA> 1
#> 12 888 Grupamento de a… 56628 Serviços dom… <NA> 1
#> 13 888 Grupamento de a… 60033 Atividades m… <NA> 1
meta_5434$variables
#> # A tibble: 4 × 3
#> id name unit
#> <chr> <chr> <chr>
#> 1 4090 Pessoas de 14 anos ou mais de idade ocupadas na semana de referên… Mil …
#> 2 4091 Coeficiente de variação - Pessoas de 14 anos ou mais de idade ocu… %
#> 3 4108 Distribuição percentual das pessoas de 14 anos ou mais de idade o… %
#> 4 4109 Coeficiente de variação - Distribuição percentual das pessoas de … %
pnad_accommodation <- ibge_variables(
aggregate = 5434,
variable = 4090, # Employed persons (thousands)
periods = -12, # Last 12 quarters
classification = list("888" = "56623"), # Accommodation and food services
localities = "N3[26]" # Pernambuco
) |>
mutate(
value = parse_ibge_value(value),
period = period_to_quarterly(period)
) |>
select(period, classification_888, locality_name, value)
pnad_accommodation |>
ggplot(aes(period, value)) +
geom_line() +
geom_point() +
labs(
title = "PNAD Contínua — Employed persons (14+)",
subtitle = "Accommodation and food services (Pernambuco, thousands)",
x = NULL, y = "Employed (thousands)"
) +
theme_minimal()
plot of chunk unnamed-chunk-15
5) PMS (8693) — Professional and administrative services
Same aggregate as section 3, switching only the activity category in
classification 12355:
pms_professional <- ibge_variables(
aggregate = 8693,
variable = 7167,
periods = -12,
classification = list(
"11046" = "all",
"12355" = "31399" # Professional/administrative services
),
localities = "N3[26]"
) |>
mutate(
value = parse_ibge_value(value),
period = period_to_monthly(period)
) |>
select(period, classification_11046, locality_name, value)
pms_professional |>
ggplot(aes(period, value, color = classification_11046)) +
geom_line() +
geom_point() +
labs(
title = "PMS — Index numbers (2022 = 100)",
subtitle = "Professional and administrative services (Pernambuco)",
x = NULL, y = "Index (2022 = 100)", color = NULL
) +
theme_minimal() +
theme(legend.position = "bottom")
plot of chunk unnamed-chunk-17
6) PNAD Contínua (5434) — Domestic services
pnad_domestic <- ibge_variables(
aggregate = 5434,
variable = 4090,
periods = -12,
classification = list("888" = "56628"), # Domestic services
localities = "N3[26]"
) |>
mutate(
value = parse_ibge_value(value),
period = period_to_quarterly(period)
) |>
select(period, classification_888, locality_name, value)
pnad_domestic |>
ggplot(aes(period, value)) +
geom_line() +
geom_point() +
labs(
title = "PNAD Contínua — Employed persons (14+)",
subtitle = "Domestic services (Pernambuco, thousands)",
x = NULL, y = "Employed (thousands)"
) +
theme_minimal()
plot of chunk unnamed-chunk-19
7) PIM-PF (8888) — Industrial production (selected CNAE
sectors)
The PIM-PF (Monthly Industrial Survey — Physical Production)
aggregate 8888 covers manufacturing output. Classification
544 filters by industrial activity (CNAE sections).
meta_8888 <- ibge_metadata(8888)
unnest(meta_8888$classifications, categories)
#> # A tibble: 27 × 6
#> id name category_id category_name category_unit category_level
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 544 Seções e ativid… 129314 1 Indústria … <NA> 0
#> 2 544 Seções e ativid… 129315 2 Indústrias… <NA> 0
#> 3 544 Seções e ativid… 129316 3 Indústrias… <NA> 0
#> 4 544 Seções e ativid… 129317 3.10 Fabrica… <NA> 0
#> 5 544 Seções e ativid… 129318 3.11 Fabrica… <NA> 0
#> 6 544 Seções e ativid… 129319 3.12 Fabrica… <NA> 0
#> 7 544 Seções e ativid… 129320 3.13 Fabrica… <NA> 0
#> 8 544 Seções e ativid… 129321 3.14 Confecç… <NA> 0
#> 9 544 Seções e ativid… 129322 3.15 Prepara… <NA> 0
#> 10 544 Seções e ativid… 129323 3.16 Fabrica… <NA> 0
#> # ℹ 17 more rows
meta_8888$variables
#> # A tibble: 6 × 3
#> id name unit
#> <chr> <chr> <chr>
#> 1 12606 PIMPF - Número-índice (2022=100) Núme…
#> 2 12607 PIMPF - Número-índice com ajuste sazonal (2022=100) Núme…
#> 3 11601 PIMPF - Variação mês/mês imediatamente anterior, com ajuste sazon… %
#> 4 11602 PIMPF - Variação mês/mesmo mês do ano anterior (M/M-12) %
#> 5 11603 PIMPF - Variação acumulada no ano (em relação ao mesmo período do… %
#> 6 11604 PIMPF - Variação acumulada em 12 meses (em relação ao período ant… %
pim_selected <- ibge_variables(
aggregate = 8888,
variable = 12606, # Index number (2022 = 100)
periods = -12,
classification = list(
"544" = c(129318, 129338) # Beverages; Motor vehicles
),
localities = "N3[26]"
) |>
mutate(
value = parse_ibge_value(value),
period = period_to_monthly(period)
) |>
select(period, classification_544, locality_name, value)
pim_selected |>
ggplot(aes(period, value, color = classification_544)) +
geom_line() +
geom_point() +
labs(
title = "PIM-PF — Index numbers (2022 = 100)",
subtitle = "Beverages and Motor vehicles (Pernambuco)",
x = NULL, y = "Index (2022 = 100)", color = NULL
) +
theme_minimal() +
theme(legend.position = "bottom")
plot of chunk unnamed-chunk-22
