Jordi jrosell

## check_installed_universe_packages.R
# remove.packages(c("universe", "sfdep"))
rlang::check_installed("universe", action = \(pkg, ...) {
    install.packages('universe', repos = c('https://ropensci.r-universe.dev', 'https://cloud.r-project.org'))
})
universe::add("ropensci")
universe::add("josiahparry")
rlang::check_installed("sfdep")

## loops-are-not-that-bad.R
library(tidyverse)

n <- 1000
v <- 1:n

vector_c <- function(n) {
    out <- c()
    for (i in 1:n) {
        out <- c(out, i)
    }

## anomalies.qmd
---
title: "anomalies"
format: html
jupyter: python3
editor_options:
  chunk_output_type: console
---

Install requisites:

## anomalies.R
# GAM model with a local smoother
library(tidyverse)
set.seed(2)
elapsed <- arima.sim(model = list(order = c(0, 1, 0)), n=200) + 20
elapsed <- pmax(elapsed, 1)
data <- tibble(
    x = 1:201,
    elapsed = elapsed
)
plot(data)

## base-r-summarise.R
# Single grouping in base R: Multiple functions

library(dplyr, warn.conflicts = FALSE)
expected_output <- mtcars %>%
    group_by(cyl) %>%
    summarise(mean = mean(disp), n = n())  %>%
    as.data.frame()
expected_output

by <- c("cyl")

## stacked_rasters.vrt
<VRTDataset rasterXSize="10" rasterYSize="10">
  <SRS dataAxisToSRSAxisMapping="2,1">GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]</SRS>
  <GeoTransform>  0.0000000000000000e+00,  1.0000000000000000e+00,  0.0000000000000000e+00,  9.0000000000000000e+01,  0.0000000000000000e+00, -1.8000000000000000e+01</GeoTransform>
  <VRTRasterBand dataType="Float32" band="1">
    <NoDataValue>nan</NoDataValue>
    <ColorInterp>Gray</ColorInterp>
    <ComplexSource>
      <SourceFilename relativeToVRT="0">rasters/raster.tif</SourceFilename>
      <SourceBand>1</SourceBand>
      <SourceProperties RasterXSize="10" RasterYSize="10" DataType="Float32" BlockXSize="10" BlockYSize="10" />

## combine_multylayered_raster_vtr.R
# combine_multylayered_raster_vtr.R: Combine multilayered_raster vtr using terra package
library(terra)

test <- TRUE
test_create_vrt_from_multilayers <- function() {
    raster_objects <- create_multiplayered_rasters()
    print(class(raster_objects[[1]]) == "SpatRaster")
    print(class(raster_objects[[2]]) == "SpatRaster")
    out <- create_vrt_from_multilayers(raster_objects)
    print(out)

## tasa_paro.R
library(tidyverse)

economics_raw <- read_csv("https://gist.githubusercontent.com/jrosell/cf35e54a70914a3b23946c0a0d3a2cee/raw/47e52b910ecb23c3c7a8c549c9fd277797bd9970/economics_long.csv")
print(economics_raw, n = 5)

glimpse(economics_raw)

economics_raw %>%
    glimpse(x = .)

## economics_long.csv

          
            date
            variable
            value
            value01

            
              1967-07-01
              pce
              506.7
              0

            
              1967-08-01
              pce
              509.8
              2.652497197765077e-4

            
              1967-09-01
              pce
              515.6
              7.615233890357775e-4

            
              1967-10-01
              pce
              512.2
              4.7060434153896666e-4

            
              1967-11-01
              pce
              517.4
              9.155393553576157e-4

            
              1967-12-01
              pce
              525.1
              0.001574385433512166

            
              1968-01-01
              pce
              530.9
              0.002070659102771431

            
              1968-02-01
              pce
              533.6
              0.002301683052254198

            
              1968-03-01
              pce
              544.3
              0.003217222407611809

## pak.R
# install.packages("pak")
stopifnot(
    '{pak} no está instalado. Por favor, ejecuta install.packages("pak") en la consola y vuelve a intentarlo.' =
        sum("pak" == row.names(installed.packages())) == 1
)

instalar <- setdiff(
    c("ggplot2", "dplyr", "tidyr", "readr", "purrr", "tibble", "stringr", "forcats", "lubridate", "magrittr", "tidyverse"),
    rownames(installed.packages())
)
	# remove.packages(c("universe", "sfdep"))
	rlang::check_installed("universe", action = \(pkg, ...) {
	install.packages('universe', repos = c('https://ropensci.r-universe.dev', 'https://cloud.r-project.org'))
	})
	universe::add("ropensci")
	universe::add("josiahparry")
	rlang::check_installed("sfdep")
	library(tidyverse)

	n <- 1000
	v <- 1:n

	vector_c <- function(n) {
	out <- c()
	for (i in 1:n) {
	out <- c(out, i)
	}
	---
	title: "anomalies"
	format: html
	jupyter: python3
	editor_options:
	chunk_output_type: console
	---

	Install requisites:
	# GAM model with a local smoother
	library(tidyverse)
	set.seed(2)
	elapsed <- arima.sim(model = list(order = c(0, 1, 0)), n=200) + 20
	elapsed <- pmax(elapsed, 1)
	data <- tibble(
	x = 1:201,
	elapsed = elapsed
	)
	plot(data)
	# Single grouping in base R: Multiple functions

	library(dplyr, warn.conflicts = FALSE)
	expected_output <- mtcars %>%
	group_by(cyl) %>%
	summarise(mean = mean(disp), n = n()) %>%
	as.data.frame()
	expected_output

	by <- c("cyl")
	<VRTDataset rasterXSize="10" rasterYSize="10">
	<SRS dataAxisToSRSAxisMapping="2,1">GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]</SRS>
	<GeoTransform> 0.0000000000000000e+00, 1.0000000000000000e+00, 0.0000000000000000e+00, 9.0000000000000000e+01, 0.0000000000000000e+00, -1.8000000000000000e+01</GeoTransform>
	<VRTRasterBand dataType="Float32" band="1">
	<NoDataValue>nan</NoDataValue>
	<ColorInterp>Gray</ColorInterp>
	<ComplexSource>
	<SourceFilename relativeToVRT="0">rasters/raster.tif</SourceFilename>
	<SourceBand>1</SourceBand>
	<SourceProperties RasterXSize="10" RasterYSize="10" DataType="Float32" BlockXSize="10" BlockYSize="10" />
	# combine_multylayered_raster_vtr.R: Combine multilayered_raster vtr using terra package
	library(terra)

	test <- TRUE
	test_create_vrt_from_multilayers <- function() {
	raster_objects <- create_multiplayered_rasters()
	print(class(raster_objects[[1]]) == "SpatRaster")
	print(class(raster_objects[[2]]) == "SpatRaster")
	out <- create_vrt_from_multilayers(raster_objects)
	print(out)
	library(tidyverse)

	economics_raw <- read_csv("https://gist.githubusercontent.com/jrosell/cf35e54a70914a3b23946c0a0d3a2cee/raw/47e52b910ecb23c3c7a8c549c9fd277797bd9970/economics_long.csv")
	print(economics_raw, n = 5)

	glimpse(economics_raw)

	economics_raw %>%
	glimpse(x = .)
date	variable	value	value01
1967-07-01	pce	506.7	0
1967-08-01	pce	509.8	2.652497197765077e-4
1967-09-01	pce	515.6	7.615233890357775e-4
1967-10-01	pce	512.2	4.7060434153896666e-4
1967-11-01	pce	517.4	9.155393553576157e-4
1967-12-01	pce	525.1	0.001574385433512166
1968-01-01	pce	530.9	0.002070659102771431
1968-02-01	pce	533.6	0.002301683052254198
1968-03-01	pce	544.3	0.003217222407611809
	# install.packages("pak")
	stopifnot(
	'{pak} no está instalado. Por favor, ejecuta install.packages("pak") en la consola y vuelve a intentarlo.' =
	sum("pak" == row.names(installed.packages())) == 1
	)

	instalar <- setdiff(
	c("ggplot2", "dplyr", "tidyr", "readr", "purrr", "tibble", "stringr", "forcats", "lubridate", "magrittr", "tidyverse"),
	rownames(installed.packages())
	)