eliocamp

library(targets)

meta_pattern <- tar_meta(son_files) 
meta_branches <- tar_meta(all_of(meta_pattern$children[[1]]))

Total number of branches:

eliocamp

library(Rvision)


X <- 1920
Y <- 1080
corners_screen <- matrix(c(0, Y,
                           X, Y,
                           X, 0,
                           0, 0), 
                         ncol = 2, byrow = TRUE)

eliocamp

library(ggplot2)
library(dplyr)
library(extrafont)

# 
# extrafont::font_import("~/Downloads/")
CHANEWEI <- "Chalet-NewYorkNineteenEighty"

# Download data
url <- "https://apitransporte.buenosaires.gob.ar/ecobici/gbfs/stationInformation"

eliocamp

library(metR)
library(data.table)
library(ggplot2)


proj <-  "+proj=lcc +lon_0=-60 +lat_1=-0 +lat_2=-40"

topo <- GetTopography(ConvertLongitude(-85),
                      330,  lat.north = 14, lat.south = -58, 
                      resolution = 2/60) |> 

eliocamp

library(ggplot2)
library(units)
library(sf)

world <- rnaturalearth::ne_countries(returnclass = "sf", scale = 10) |> 
  st_transform(crs = "+proj=moll")


# Make a hexagonal grid
size <- 1000000

eliocamp

total <- 116653684*1024   # change your total dir size here

folder_size <- function() {
  system('du -s "[folder]"', intern = TRUE) %>%    # change your dest folder here
    strsplit(split = "\t", fixed = TRUE) %>% 
    .[[1]] %>% 
    .[1] %>% 
    as.numeric()
}

eliocamp

# This is now available into ggpercentogram. 
# https://github.com/eliocamp/ggpercentogram/
StatQuantileBin <- ggplot2::ggproto("StatQuantileBin", ggplot2::StatBin, 
   default_aes = ggplot2::aes(x = ggplot2::after_stat(density), y = ggplot2::after_stat(density), weight = 1),                                   
   compute_group = function(data, scales, 
                            binwidth = NULL, bins = 30, breaks = NULL, trim = 0,
                            closed = c("right", "left"), pad = FALSE,
                            flipped_aes = FALSE,
                            # The following arguments are not used, but must
                            # be listed so parameters are computed correctly

eliocamp

#' Smooths a 2D field
#' 
#' @param x,y Vector of x and y coordinates
#' @param value Vector of values
#' @param kx,ky Proportion of components to keep in the x and 
#' y direction respectively. Lower values increased the smoothness.
#' 
#' @examples 
#' library(ggplot2)
#' # Creates a noisy version of the volcano dataset and applies the smooth

eliocamp

# Define the directory where to save the plot data
knitr::opts_chunk$set(plot_data_dir = "plot-data2")

save_plot_data <- function(plot, label = "plot", dir = NULL) {
  # If the user didn't set up a dir explicitly, don't do anything
  if (is.null(dir)) {
    return(invisible(NULL))
  }
  
  if (!dir.exists(dir)) {

eliocamp

library(magrittr)
library(ggplot2)
library(rgl)
library(plotly)

set.seed(42)

# Cantidad de puntos por grupos
N <- 20 
# Localización de los grupos