Rafael H M Pereira rafapereirabr

## Flow Map in R
## This gist shows how to create Flow Maps in R using ggplot2.
## source: This is based on different bits of code from other with amazing R skills:

@ceng_l          : http://web.stanford.edu/~cengel/cgi-bin/anthrospace/great-circles-on-a-recentered-worldmap-in-ggplot
@3wen            : http://egallic.fr/maps-with-r/
@spatialanalysis : http://spatialanalysis.co.uk/2012/06/mapping-worlds-biggest-airlines/
@freakonometrics : http://freakonometrics.hypotheses.org/48184


# Libraries

## ortho_map.md

      
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                rafapereirabr
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                how to create a world map using full hemispheric orthographic projection in `R`
              
          
    Simple example of how to create a world map using full hemispheric orthographic projection in R

library(sf)
library(ggplot2)
library(mapview)
library(lwgeom)
library(rnaturalearth)


## clean_polygons_remove_slivers.md

      
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                rafapereirabr
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                Clean polygons to remove slivers
              
          
    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)


## walking_isochrone_sao_paulo_r5r.R
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')

## flight_passengers_sdu_gig.R
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') |

## get_spatial_link_dist_matrix.R

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)

## stacked_map_R_ggplot2.md

      
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                Creating a stacked map in R using ggplot2
              
          
    This gist shows in two steps how to tilt and stack maps using ggplot2 in order to create an image like this one:
[![enter image description here][1]][1]
Let's load the necessary libraries and data to use a reproducible example:
# load libraries
  library(rgeos)
  library(UScensus2000tract)
  library(ggplot2)


## heatmap_pseudo_log.R
# 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)

## bivariate_lisa.R
library(stringr)
library(spdep)
library(rgdal)
library(magrittr)
library(ggplot2)
library(sf)


#======================================================

## test_gdalio.R
library(sf)
library(terra)
library(gdalio)
library(geobr)

### Choose either a small or large area

i = 49 # small area

i = 1066 # large area
	## This gist shows how to create Flow Maps in R using ggplot2.
	## source: This is based on different bits of code from other with amazing R skills:

	@ceng_l : http://web.stanford.edu/~cengel/cgi-bin/anthrospace/great-circles-on-a-recentered-worldmap-in-ggplot
	@3wen : http://egallic.fr/maps-with-r/
	@spatialanalysis : http://spatialanalysis.co.uk/2012/06/mapping-worlds-biggest-airlines/
	@freakonometrics : http://freakonometrics.hypotheses.org/48184


	# Libraries
	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(stringr)
	library(spdep)
	library(rgdal)
	library(magrittr)
	library(ggplot2)
	library(sf)



	#======================================================
	library(sf)
	library(terra)
	library(gdalio)
	library(geobr)

	### Choose either a small or large area

	i = 49 # small area

	i = 1066 # large area