rafapereirabr

#' Code by Rafael H. M. Pereira and Marcus Saraiva
#'
#' Radiation model (Simini et al 2012)
#' Tij = Ti * ((Pi * Pj) / ((Pi + Pij) * (Pi + Pj + Pij)))
#' 
#' Where:
#'
#' Tij: Avarage flux between origin i and destination j predicted by the radiation model
#' Ti:  Proportion of population in i relative to total population
#' Pi:  Absolute population in i

rafapereirabr

library(gtfs2gps)
library(gtfsrouter)


# original process
sp <- system.file("extdata/saopaulo.zip", package ="gtfs2gps")

time_gps <- system.time(gps_gps <- gtfs2gps(sp, parallel = T, spatial_resolution = 200))

rafapereirabr

library(geobr)
library(ggplot2)
library(sf)
library(sfheaders)

df <- read_municipality(year=2018)
centrd <- st_centroid(df)

centrd <- sfheaders::sf_to_df(centrd, fill=T)
df$position <- centrd$y + centrd$x

rafapereirabr

library(googleAnalyticsR)
library(data.table)
library(ggplot2)
library(dplyr)
library(rworldmap)
library(sf)
options(scipen=999)

## setup
ga_auth()

rafapereirabr

library(ggmap)
library(data.table)

# registrar Google API Key
register_google(key = 'xxxxx-xxxxxx-xxxxx-xxxxx')

rafapereirabr

library(geobr)
library(sf)
library(mapview)
library(stringr)
mapviewOptions(platform = 'leafgl')

# read original census tracts
urban_cts <- read_census_tract(year = 2000, code_tract = 'RJ', zone ='urban', simplified = F)
rural_cts <- read_census_tract(year = 2000, code_tract = 'RJ', zone ='rural', simplified = F)

rafapereirabr

A simple reproducible Hexsticker for r5r, a package for Rapid Realistic Routing with R5 in R.

### Libraries ------------------------
remotes::install_github("GuangchuangYu/hexSticker")

library(hexSticker) # https://github.com/GuangchuangYu/hexSticker
library(ggplot2)
library(sf)

rafapereirabr

Calculating distance between spatial object and the nearest border

Quick example showing how to calculate the distance from each census tract to the nearest muncipality border

library(sf)
library(furrr)
library(geobr)
library(mapview)
library(magrittr)
library(data.table)

rafapereirabr

quick code snippet to create (weighted) quantiles in R using data.table.

Create reproducible example

library(data.table)
library(Hmisc)

# create data.table
n <- 100000

rafapereirabr

```
library(sf)
library(geobr)


# sample spatial data
df <- read_state()

# create random pop and cases
df$pop <- sample(1:27, 27, replace = T)