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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE, message=FALSE, warning=FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
dpi = 300
)
library(tidyverse)
```
# ibdp
<!-- badges: start -->
<!-- badges: end -->
Materiais da apresentação no Congresso do IBDP.
```{r}
# base será colocada nos releases.
pgfn <- readr::read_rds("data-raw/pgfn.rds")
```
## Visualizações
### Tabela
Tipo de crédito
```{r}
# tabela
pgfn |>
count(tipo_credito, sort = TRUE) |>
mutate(prop = scales::percent(n / sum(n))) |>
knitr::kable()
```
Situação da inscrição
```{r}
pgfn |>
count(tipo_situacao_inscricao, sort = TRUE) |>
mutate(prop = scales::percent(n / sum(n))) |>
knitr::kable()
```
### Mapa
```{r}
# mapa
estados <- geobr::read_state(showProgress = FALSE)
codigos <- estados |>
as_tibble() |>
select(code_state, abbrev_state)
populacao <- abjData::pnud_uf |>
filter(ano == 2010) |>
select(code_state = uf, popt) |>
left_join(codigos, "code_state")
pgfn_uf <- pgfn |>
group_by(abbrev_state = uf_unidade_responsavel) |>
summarise(
n = n(),
valor = sum(valor_consolidado)
) |>
left_join(populacao, "abbrev_state") |>
mutate(
n_pop = n / popt,
vl_pop = valor / popt
)
estados |>
left_join(pgfn_uf, c("abbrev_state")) |>
ggplot() +
geom_sf(aes(fill = vl_pop), color = "black", size = .1) +
scale_fill_viridis_c(
begin = .2, end = .8,
option = "A", trans = "log10"
) +
theme_void() +
labs(
title = "Dívida total / população"
)
```
### No tempo
```{r}
pgfn_mes <- pgfn |>
mutate(data = lubridate::floor_date(data_inscricao, "month")) |>
filter(data >= "2008-01-01") |>
count(data)
pgfn_mes |>
ggplot(aes(x = data, y = n/1e3)) +
geom_line(size = 1) +
theme_minimal() +
scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
labs(
x = "Mês", y = "Quantidade (milhares)",
title = "Quantidade de inscrições ao longo dos anos"
)
```
## Modelo
```{r}
library(tsibble)
library(fable)
pgfn_tsibble <- pgfn_mes |>
mutate(data = yearmonth(data), n = sqrt(n/1e3)) |>
as_tsibble(index = data)
fit <- pgfn_tsibble |>
model(
model = ARIMA(n ~ pdq(2,1,2) + PDQ(1,1,1))
)
fit |>
forecast(h = 12) |>
mutate(.mean = (.mean^2), n = (n^2)) |>
autoplot(pgfn_tsibble |> mutate(n = (n^2))) +
theme_minimal() +
labs(
x = "Mês",
y = "Quantidade (milhares)",
title = "Quantidade de inscrições ao longo dos anos"
)
```
## Apresentação
Link da apresentação [aqui](https://docs.google.com/presentation/d/1sFtU7FqHGEV7OOjBLaGT7dh5WQNL3EhuMOAf_p6y3zY/edit?usp=sharing).