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)

rafapereirabr

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')

rafapereirabr

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') |

rafapereirabr

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)

rafapereirabr

# 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)

rafapereirabr

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

### Choose either a small or large area

i = 49 # small area

i = 1066 # large area

rafapereirabr

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)

rafapereirabr

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)

rafapereirabr

#' 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)

rafapereirabr

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)