--- title: "Putting the tables back together" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Putting the tables back together} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- ```{r, include = FALSE} knitr::opts_chunk$set(collapse = TRUE, comment = "#>", eval = FALSE) ``` Each module is a normalized database served one table at a time. Almost nothing useful is answerable from a single table: the money is in one, who received it in another, and what it was spent on in a third. This vignette maps how they fit together. ```{r} library(transferegovr) library(dplyr) ``` ## The APIs do not declare their keys The OpenAPI documents these services publish describe columns and query parameters, and nothing else — no primary keys, no foreign keys. `tg_fields()` therefore cannot tell you what joins to what. The relationships below come from the data models the government publishes alongside the APIs, at . The convention is regular enough to follow without them: a column named `id_x` in table B refers to the row of table X whose own `id_x` matches. ## especiais Everything hangs off the action plan, `planos_acao_especiais`. ``` programas_especiais ──< planos_acao_especiais >── beneficiarios_especiais │ ├──< planos_trabalho_especiais │ ├──< planos_trabalho_analises_especiais │ │ └──< plano_trabalho_analise_historico_especiais │ ├──< planos_trabalho_historico │ └──< orgaos_analises_pendentes_especiais ├──< executores_especiais │ ├──< meta_especiais │ └──< finalidade_especiais ├──< empenhos_especiais │ └──< documentos_habeis_especiais │ └──< ordens_pagamentos_ordens_bancarias_especiais ├──< planos_acao_historico_especiais ├──< relatorios_gestao_especiais ├──< devolucao_especiais └──< relatorios_gestao_novos_especiais ├──< relatorios_gestao_analise_especiais └──< relatorios_gestao_documento_liquidacao_especiais saldo_conta_gestao_financeira_especiais ──< gestao_financeira_lancamentos_especiais └──< gestao_financeira_subtransacoes_especiais ``` The join columns are the obvious ones: `id_plano_acao`, `id_plano_trabalho`, `id_executor`, `id_empenho`, `id_dh`, `id_beneficiario`, `id_programa`, `id_agencia_conta`, `id_lancamento_gestao_financeira`, `id_relatorio_gestao_novo`. The three tables published in September 2026 — returned funds and the two report tables under `relatorios_gestao_novos_especiais` — are not in the government's data model yet. Their links above were checked against the data instead: every sampled identifier found its parent. The goals and the public-policy area hang off the executor rather than off the plan: `meta_especiais` and `finalidade_especiais` both key on `id_executor`. Note where the beneficiary lives. The action plan carries only `id_beneficiario`; the name, CNPJ and state are in `beneficiarios_especiais`. There is no way to filter action plans by state directly — you filter the beneficiaries and join: ```{r} beneficiarios <- tg_get("especiais", "beneficiarios_especiais", .limit = Inf) pernambuco <- beneficiarios |> filter(uf_beneficiario == "PE") planos <- tg_get("especiais", "planos_acao_especiais", .limit = Inf) |> semi_join(pernambuco, by = "id_beneficiario") ``` `beneficiarios_especiais` has five columns and is small enough to take whole, which makes this cheaper than it looks. ## fundoafundo Same shape, with the program at the top. ``` programas ──< planos_acao ├──< programas_beneficiarios │ └──< programas_gestao_agil ├──< planos_acao_metas │ └──< planos_acao_metas_acoes ├──< planos_acao_dados_bancarios ├──< planos_acao_destinacao_recursos ├──< planos_acao_historico │ └──< planos_acao_analises │ └──< planos_acao_analises_responsaveis ├──< termos_adesao │ └──< termos_adesao_historico ├──< empenhos └──< relatorios_gestao ├──< relatorios_gestao_acoes └──< relatorios_gestao_analises └──< relatorios_gestao_analises_responsaveis gestao_financeira_lancamentos ──< gestao_financeira_subtransacoes ``` Here the action plan does carry the state, so a filter does the work the join would: ```{r} planos <- tg_get( "fundoafundo", "planos_acao", uf_ente_recebedor_plano_acao = "PE", .limit = Inf ) ``` `relatorios_gestao_acoes` joins two ways — to its report through `id_relatorio_gestao` and to the plan's action through `id_acao_meta_plano_acao` — which is what lets you tie what was reported to what was planned. ## parcerias The chain here is the longest, and it is the one worth following end to end: it runs from the program that announces money to the bank statement of the account it leaves from. ``` programa ──< proposta ──< parceria ──< parceria_conta ──< extrato_bancario │ │ │ │ │ └──< documento_habil ──< ordem_pagamento │ │ └──< empenho_parceria │ ├──< meta_proposta │ ├──< item_proposta │ ├──< cronograma_desembolso │ ├──< distribuicao_recurso_proposta │ ├──< proposta_resultado_indicador │ └──< analise_proposta └──< beneficiario_emenda_parlamentar └──< indicacao_beneficiario_emenda_parlamentar ``` One link changes name on the way: `indicacao_beneficiario_emenda_parlamentar` refers to its parent through `id_beneficiario_emenda_parlamentar`, which the parent calls `id_beneficiario_emenda_parlamentar_programa`. The same nominations also arrive nested in the parent, as `indicacoes_beneficiario`. `opp` holds payment orders issued from a partnership's bank account — Pix transfers, tax payments, bills — with the payee, the amount and whether it went through. It is new and small: 137 rows between May and September 2026, 21 of them described as tests (`"teste boleto"`, `"teste Pix cpf"`), so filter those out before adding anything up. It hangs off the bank account, but not by the account's own key: it carries `id_conta_gf`, which matches the column of the same name in `parceria_conta`, not `id_parceria_conta`. The difference matters because the wrong join half-works: checked against the whole of `parceria_conta`, all of `opp`'s accounts match on `id_conta_gf`, one row each, while two of the three also coincide numerically with some unrelated `id_parceria_conta`. `opp` is in no published data model yet, so the data is the only evidence. ``` parceria_conta ──< opp (on id_conta_gf) ``` Following it: ```{r} propostas <- tg_get( "parcerias", "proposta", sg_uf_recebedor = "PE", situacao_proposta = "Aprovada", .limit = Inf ) parcerias <- tg_get("parcerias", "parceria", .limit = Inf) |> semi_join(propostas, by = "id_proposta") contas <- tg_get("parcerias", "parceria_conta", .limit = Inf) |> semi_join(parcerias, by = "id_parceria") ``` `extrato_bancario` holds 1.4 million rows, so join into it rather than collecting it whole — filter by the accounts you care about. `id_parceria_conta` takes up to 200 of them per request, so send them in groups: ```{r} library(purrr) grupos <- split(contas$id_parceria_conta, ceiling(seq_along(contas$id_parceria_conta) / 200)) extratos <- list_rbind(map(grupos, function(ids) { tg_get("parcerias", "extrato_bancario", id_parceria_conta = ids, .limit = Inf) })) ``` `tg_params()` says which identifiers take a list, and how many values each accepts, in its `multiple` and `max_values` columns. ## ted Decentralized credit hangs off the action plan too, and the action plan off the program. Every link below is declared in the government's data model, and each was also checked against the data: of 200 identifiers sampled per link, every one found exactly one parent row. ``` programas ──< planos_acao ──< termos_execucao │ │ │ ├──< notas_credito ──< eventos │ ├──< programacoes_financeiras ──< programacoes_financeiras_trf │ ├──< planos_acao_metas ──< planos_acao_metas_etapas │ ├──< planos_trabalho_cronogramas │ ├──< planos_acao_analises │ └──< planos_acao_pareceres ├──< programas_acoes_orcamentarias └──< programas_beneficiarios ``` The join columns are `id_programa`, `id_plano_acao`, `id_nota`, `id_programacao` and `id_meta`, each under the same name on both sides. The identifiers here all take lists, so following the money from a set of plans to the budget events of their credit notes is two requests per 200 plans rather than one per plan: ```{r} planos <- tg_get("ted", "planos_acao", .limit = 200) notas <- tg_get("ted", "notas_credito", id_plano_acao = planos$id_plano_acao, .limit = Inf) eventos <- tg_get("ted", "eventos", id_nota = head(unique(notas$id_nota), 200), .limit = Inf) ``` ## Children that arrive already joined Several child tables have no endpoint. The API folds them into the parent as an array, which means the join is already done and you only have to unnest. In `parcerias`: | Nested column | Parent | What it holds | |---|---|---| | `ufs_habilitadas` | `programa` | States the program is open to | | `programa_atende_a` | `programa` | Who the program serves | | `categorias_despesa` | `programa` | Permitted expense categories | | `resultados_esperados` | `programa` | Expected results | | `indicadores_programa` | `programa` | Indicators | | `intervenientes_proposta` | `proposta` | Intervening parties | | `categorias_despesa_proposta` | `proposta` | Expense categories used | | `etapas_proposta` | `meta_proposta` | Stages of a goal | | `publicacoes_parceria` | `parceria` | Official gazette publications | | `classificacoes_ingresso` | `parceria_conta` | Classified receipts | | `tipos_analise` | `analise_proposta` | Kinds of review | | `indicacoes_beneficiario` | `beneficiario_emenda_parlamentar` | Nominations | | `classificacao_despesa` | `item_proposta` | Expense classification | In `fundoafundo`: `programa_acao_orcamentaria` and `programa_natureza_despesa` on `programas`, `categorias_despesa_lancamento` on `gestao_financeira_lancamentos`, `categorias_despesa_subtransacao` on `gestao_financeira_subtransacoes`, and `saldo_final_dado_bancario` on `planos_acao_dados_bancarios`. In `ted`: `esfera_orcamentaria_evento` and `natureza_despesa_evento` on `eventos`, and `formalizacao_termo_execucao` and `link_termo_execucao` on `termos_execucao`. `especiais` has none: all twenty-three of its tables have endpoints. To flatten one: ```{r} library(tidyr) programas <- tg_get("parcerias", "programa", .limit = Inf) programas |> select(id_programa, ufs_habilitadas) |> unnest_longer(ufs_habilitadas) |> unnest_wider(ufs_habilitadas) #> # A tibble: … × 4 #> id_programa nm_uf sg_uf cd_ibge #> #> 1 7 MINAS GERAIS MG 31 #> … ``` `tg_fields(nested = )` tells you the shape before you unnest: ```{r} tg_fields("parcerias", "programa", nested = "ufs_habilitadas") ``` Note that an empty array is an empty list, not `NA`, so `unnest_longer()` drops those rows unless you ask it to keep them with `keep_empty = TRUE`. ## Joins that do not fully resolve Not every identifier finds its parent. Government systems have rows that predate a constraint, and rows whose parent has since been removed. Check rather than assume: ```{r} planos <- tg_get("especiais", "planos_acao_especiais", .limit = 500) beneficiarios <- tg_get("especiais", "beneficiarios_especiais", .limit = Inf) sum(!planos$id_beneficiario %in% beneficiarios$id_beneficiario) ``` An `inner_join()` would drop those rows silently. Use `left_join()` and count the `NA`s, or `anti_join()` to see what did not match, so a gap upstream shows up as a number rather than as a quietly smaller answer. ## Repeated identifiers are not always a bug Some endpoints are views with a join already baked in, so an identifier can appear on more than one row. That is real data, not a pagination fault — it can be told apart by fetching a single page and checking whether the repeat is already there: ```{r} one_page <- tg_get("fundoafundo", "programas", .limit = 200) nrow(one_page) length(unique(one_page$id_programa)) ``` If the two differ within one request, no amount of pagination caused it.