Following the money: contracts and procurement • alepe

Public procurement data is where open legislative data meets social accountability. This vignette explores the Assembly’s contracts and bidding processes.

library(alepe)
library(dplyr)
library(ggplot2)

Contract values by modality

contracts <- alepe_contracts()

contracts |>
  summarise(
    n = n(),
    total_brl = sum(valor, na.rm = TRUE),
    .by = modalidade
  ) |>
  arrange(desc(total_brl))
#> # A tibble: 10 × 3
#>    modalidade                    n  total_brl
#>    <chr>                     <int>      <dbl>
#>  1 Pregão Eletrônico           325 397636268.
#>  2 Pregão Presencial            86 107029186.
#>  3 Concorrência - Presencial    11  96633877.
#>  4 NA                           46  59623440.
#>  5 Dispensa                     67  57009695.
#>  6 Inexigibilidade              70  20914208.
#>  7 Convite                      38   3071596.
#>  8 Concorrência - Eletrônica     1   1865008.
#>  9 Tomada de Preços              1    777140 
#> 10 Dispensa Eletrônica           1      9980

Largest contractors

contracts |>
  summarise(total_brl = sum(valor, na.rm = TRUE), .by = contratada) |>
  slice_max(total_brl, n = 10) |>
  ggplot(aes(x = reorder(contratada, total_brl), y = total_brl / 1e6)) +
  geom_col(fill = "#e6550d") +
  coord_flip() +
  labs(
    x = NULL, y = "Total contracted (BRL, millions)",
    title = "Ten largest ALEPE contractors"
  ) +
  theme_minimal()

Contracts active today

Validity dates are parsed to Date, so filtering active contracts is a one-liner:

contracts |>
  filter(vigencia_inicio <= Sys.Date(), vigencia_fim >= Sys.Date()) |>
  select(contratada, objeto, valor, vigencia_fim) |>
  arrange(vigencia_fim)
#> # A tibble: 53 × 4
#>    contratada                                objeto  valor vigencia_fim
#>    <chr>                                     <chr>   <dbl> <date>      
#>  1 FRANCIELE ELETRO LTDA                     5.000… 8.12e4 2026-08-18  
#>  2 V. S. COSTA & CIA LTDA                    5.000… 1.7 e4 2026-08-24  
#>  3 NORT MED PRODUTOS HOSPITALARES LTDA       5.000… 1.49e5 2026-08-28  
#>  4 R G DISTRIBUIDORA DE ALIMENTOS LTDA       5.000… 2.59e4 2026-09-03  
#>  5 54.024.431 JEFFERSON PEREIRA MELO DO NAS… 5.000… 2.44e4 2026-09-03  
#>  6 MAPROS LTDA                               5.000… 1.87e6 2026-09-08  
#>  7 GPB SERVICOS LTDA                         5.000… 5.48e5 2026-09-17  
#>  8 IURY HERLEN DE SOUZA SANTOS LTDA          5.000… 5.33e6 2026-10-13  
#>  9 MCR SISTEMAS E CONSULTORIA LTDA           1.204… 5.71e5 2026-10-18  
#> 10 INSTITUTO SAUDE EXPRESS                   5.000… 3.28e6 2026-10-23  
#> # ℹ 43 more rows

Procurement outcomes

/licitacoes is the slowest endpoint of the API — allow it half a minute, and remember that a failed request yields a zero-row tibble rather than an error:

procurements <- alepe_procurements()
nrow(procurements)
#> [1] 618

procurements |>
  count(ano, status) |>
  ggplot(aes(x = ano, y = n, fill = status)) +
  geom_col() +
  labs(
    x = NULL, y = "Processes",
    fill = NULL,
    title = "ALEPE procurement processes by year and status"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom") +
  guides(fill = guide_legend(ncol = 1))