Rafael H M Pereira rafapereirabr

Trying different methods to clean polygons to remove slivers

Load libraries

library(sf)
library(dplyr)
library(lwgeom)

# using planar geometry
sf::sf_use_s2(FALSE)

library(gtfstools)

# read gtfs
data_path <- system.file("extdata/spo_gtfs.zip", package = "gtfstools")
gtfs <- read_gtfs(data_path)

# merge trips and freq
trip_df <- merge(gtfs$trips, gtfs$frequencies)

análise rápida dos dados de evolução diária do N. de bagagens pagas e gratuidas X Km

Download data

#' data downloaded from ANAC using flightsbr, https://github.com/ipeaGIT/flightsbr
#' data dictionary at https://www.gov.br/anac/pt-br/assuntos/regulados/empresas-aereas/envio-de-informacoes/descricao-de-variaveis

library(flightsbr)
library(data.table)

Creating a map of cycling, walking and driving routes in R

Quick reproducible example showing how to use the r5r package to estimate cycling, walking and driving routes in R.

Prepare R environment

options(java.parameters = "-Xmx4G")

library(r5r)
library(geobr)

	library(r5r)
	library(mapview)

	# build transport network
	data_path <- system.file("extdata/spo", package = "r5r")
	r5r_core <- setup_r5(data_path)

	# load origin/destination points
	points <- read.csv(file.path(data_path, "spo_hexgrid.csv"))
	origin_1 <- subset(points, id =='89a8100c393ffff')

	library(flightsbr)
	library(ggplot2)
	library(data.table)

	# download data
	df <- flightsbr::read_flights(date = 2019:2023)

	# filters
	df <- df[ nr_ano_chegada_real >= 2019,]
	df_rj <- df[ sg_iata_origem %in% c('SDU', 'GIG') \|


	library(sfdep)
	library(data.table)
	library(cppRouting)

	# get distance between neighbors
	geo <- sf::st_geometry(guerry)
	nb <- sfdep::st_contiguity(geo)
	dists <- sfdep::st_nb_dists(geo, nb)

	# Library
	library(ggplot2)
	library(viridis)

	# Dummy data
	set.seed(42)
	x <- LETTERS[1:20]
	y <- paste0("var", seq(1,20))
	data <- expand.grid(X=x, Y=y)
	data$Z <- runif(400, 0, 10000)

	library(sf)
	library(terra)
	library(gdalio)
	library(geobr)

	### Choose either a small or large area

	i = 49 # small area

	i = 1066 # large area

	#' Comparing air passenger demand over time in Brazil
	#' Reproducible code to create a figure visualizing how daily air passenger demand in Brazil has changed over time.


	### Load libraries
	library(ggplot2)
	library(flightsbr)
	library(lubridate)
	library(data.table)