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allan <- readxl::read_excel("~/../Downloads/Dados 13F ARCE.xlsx") %>% 
  janitor::clean_names() %>% 
  mutate(no_periodo = no_periodo %>% 
           str_remove_all(",(?=\\d{3})") %>% 
           str_replace(",", ".") %>% 
           as.numeric()) 

allan2 <-  allan %>% 
  select(1, 2, 6) %>% 
  mutate(ano = str_extract(periodo, "\\d{4}"),

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#' Inclui dummies
#'
#' @param str input com formula
#'
#' @return string da formula
#' @export
#'
#' @examples
#' incluir_dummies("ac ~ factor(mes)")
incluir_dummies <- function(str) {

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buscar_placa <- function(placa) {
  dormir <- sample(c(0, 1), 1, prob = c(0.2, 0.8))
  
  if (dormir) {
    Sys.sleep(rnorm(1, mean = 5, sd = 0.3))
  }
  
  resp <- httr::GET(
    glue::glue("https://apicarros.com/v1/consulta/{placa}/json")

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---
title: Mapa Pietro
output: pdf_document
---

# Mapa

```{r, include=FALSE}
library(openxlsx)
# library(descr)

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library(tidyverse)

pibs <- RCEPAL::CEPAL_stat(2203)
pibs %>% 
  filter(str_detect(País_desc, "México|Brasil|Venezuela|Chile|Argentina|Colombia|Uruguay")) %>% 
  ggplot(aes(Años_desc, valor, col = País_desc)) + 
  geom_line() + 
  scale_y_log10()

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library(tidyverse)
pib <- ipeadatar::ipeadata("SCN10_PIBG10")
pib_periodos <- pib %>% 
  mutate(ano = lubridate::year(date),
         periodo = cut(ano, c(0, 1929, 1944, 1963, 1988, 2005, 2013, Inf), 
                       labels = c("Café com leite", "Vargas", 
                                 "1945-1964",
                                 "Ditadura", "Nova república", 
                                 "Auge PT", "Nova república")))
media_geom <- function(value) {

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library(pnadc)
library(tidyverse)

motos <- tibble::tibble(
  ano = 2012:2020,
  trimestre = rep(1, seq.along = 2012:2020)
) %>%
  mutate(dado = purrr::map2(ano, trimestre, ler_pnad)) %>% 
  pull(dado) %>% 
  map_df(I) 

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library(pnadc)
library(tidyverse)

motos <- tibble::tibble(
  ano = 2012:2020,
  trimestre = rep(1, seq.along = 2012:2020)
) %>%
  mutate(dado = purrr::map2(ano, trimestre, ler_pnad)) %>% 
  pull(dado) %>% 
  map_df(I) 

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wiod <- readxl::read_excel("WIOT2014_Nov16_ROW.xlsx", skip = 5)
 
# wiod <- wiod %>% 
#   rename(cod_setor = 1, setor_input = 2, pais_input = 3, id_linha = 4) #%>% 
# set_names(str_remove(names(wiod), "(?<=\\d)\\.{3}\\d+")) 

wiod_long <- wiod %>% 
  rename(cod_setor_input = 1, setor_input = 2, pais_input = 3, id_linha = 4) %>% 
  gather(id_col, valor, -(cod_setor_input:id_linha))

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res <- RCEPAL::series_CEPAL()
res %>% glimpse()

res %>% 
  filter(str_detect(nome_serie, "informal")) %>% 
  as_tibble() %>% 
  unique()

informal1 <- RCEPAL::CEPAL_stat(252)
informal1 %>%