Paulo E. Cardoso pecard

## imp_berlin.R
library(tidyverse)
library(sf)
library(terra)
library(stars)
library(ggspatial)
library(systemfonts)
library(patchwork)

register_variant(
  name = "Input Mono Light",

## SpatioTemporal-moving.md

      
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                mdsumner
                / SpatioTemporal-moving.md
            
            
              Last active
              April 26, 2021 17:41
            
          
    On-CRAN packages for "Moving objects, trajectories"

This is for an update to the the SpatioTemporal Task View.
If you have any other suggestions, please leave a comment below - or raise an issue here: https://github.com/mdsumner/ctv-mdsumner/issues (or just email/tweet me).
(This is not the final presentation format).
Moving objects, trajectories


## get_rolling_sum.R
# Generate dummy data -----------------------------------------------------
library(dplyr)
library(ggplot2)
library(lubridate)

start_day <- as.Date("2018-01-01")

# 2 Columns - Date & n
dat <- data.frame(date = start_day + days(1:90), n = round(runif(90, 1, 10)))

## efficient-raster-extract.R
## 80 polygons collectively covering 18k cells in a [681,841] grid
shp_spatial <- as(shp_tab, "Spatial")
## variable has 20k layers (modelled climate metric)
## 681, 841, 20587
filename <- "/path/to/hideous/monstrosity40Gb.nc"
## it's a regular grid, in longlat/WGS84
tbrick <- raster::brick(filename, quick = TRUE, varname = "monster1")

## build cell / polygon mapping
## devtools::install_github("hypertidy/tabularaster")

## clean_species
#I have >1000 bees to check its name, so I want to automatize taxize for
# fixing misspellings when possible
# updating synonims to accepted names
# keeping ONLY accepted species (fully resolved at species level)

# this uses taxize > 0.7.6.9157 If you are using older version (e.g. what its now on CRAN) see the history of this file.
library(taxize)
library(dplyr)

#example: good, synomin, typo, unexisting, genus only.

## license-badges.md

      
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                lukas-h
                / license-badges.md
            
            
              Last active
              July 11, 2024 07:00
            
              
                Markdown License Badges for your Project
              
          
    Markdown License badges

Collection of License badges for your Project's README file.

This list includes the most common open source and open data licenses.

Easily copy and paste the code under the badges into your Markdown files.
Notes


The badges do not fully replace the license informations for your projects, they are only emblems for the README, that the user can see the License at first glance.

Translations: (No guarantee that the translations are up-to-date)

🇫🇷 Cette liste en français


## logistic_regression.R
# Load the raw training data and replace missing values with NA
training.data.raw <- read.csv('train.csv',header=T,na.strings=c(""))

# Output the number of missing values for each column
sapply(training.data.raw,function(x) sum(is.na(x)))

# Quick check for how many different values for each feature
sapply(training.data.raw, function(x) length(unique(x)))

# A visual way to check for missing data

## poly_reg.R
# Example 1
p <- 0.5
q <- seq(0,100,1)
y <- p*q
plot(q,y,type='l',col='red',main='Linear relationship')

# Example 2
y <- 450 + p*(q-10)^3
plot(q,y,type='l',col='navy',main='Nonlinear relationship',lwd=3)

## SACRS.md

      
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                gubuntu
                / SACRS.md
            
            
              Last active
              May 12, 2021 15:30
            
              
                southern African coordinate reference systems (CRS) definitions in proj4 and WKT
              
          
    Use this file to get the CRS definitions you need in southern Africa, particularly South Africa, Lesotho, eSwatini, Zimbabwe and Namibia.

Most GIS software now implements the South African CRS (SACRS; incorrectly but popularly known as the "LO" system) which is based on south-oriented (or south-facing, 'west-south up') transverse mercator (TMSO). The codes for these are:

Hartebeesthoek94 or WGS84 datum (post 1999)

EPSG: all numbers from 2046 to 2055
ESRI (rather use EPSG): 102480 to 102489


Cape datum (for pre-1999 data)

EPSG: odd numbers from 22275 to 22293


ESRI (rather use EPSG): 102470 to 102479


## citation-analysis-sketch.R
# sources:
# http://www.jgoodwin.net/?p=1223
# http://orgtheory.wordpress.com/2012/05/16/the-fragile-network-of-econ-soc-readings/
# http://nealcaren.web.unc.edu/a-sociology-citation-network/
# http://kieranhealy.org/blog/archives/2014/11/15/top-ten-by-decade/
# http://www.jgoodwin.net/lit-cites.png


###########################################################################
# This first section scrapes content from the Web of Science webpage. It takes
	library(tidyverse)
	library(sf)
	library(terra)
	library(stars)
	library(ggspatial)
	library(systemfonts)
	library(patchwork)

	register_variant(
	name = "Input Mono Light",
	# Generate dummy data -----------------------------------------------------
	library(dplyr)
	library(ggplot2)
	library(lubridate)

	start_day <- as.Date("2018-01-01")

	# 2 Columns - Date & n
	dat <- data.frame(date = start_day + days(1:90), n = round(runif(90, 1, 10)))
	## 80 polygons collectively covering 18k cells in a [681,841] grid
	shp_spatial <- as(shp_tab, "Spatial")
	## variable has 20k layers (modelled climate metric)
	## 681, 841, 20587
	filename <- "/path/to/hideous/monstrosity40Gb.nc"
	## it's a regular grid, in longlat/WGS84
	tbrick <- raster::brick(filename, quick = TRUE, varname = "monster1")

	## build cell / polygon mapping
	## devtools::install_github("hypertidy/tabularaster")
	#I have >1000 bees to check its name, so I want to automatize taxize for
	# fixing misspellings when possible
	# updating synonims to accepted names
	# keeping ONLY accepted species (fully resolved at species level)

	# this uses taxize > 0.7.6.9157 If you are using older version (e.g. what its now on CRAN) see the history of this file.
	library(taxize)
	library(dplyr)

	#example: good, synomin, typo, unexisting, genus only.
	# Load the raw training data and replace missing values with NA
	training.data.raw <- read.csv('train.csv',header=T,na.strings=c(""))

	# Output the number of missing values for each column
	sapply(training.data.raw,function(x) sum(is.na(x)))

	# Quick check for how many different values for each feature
	sapply(training.data.raw, function(x) length(unique(x)))

	# A visual way to check for missing data
	# Example 1
	p <- 0.5
	q <- seq(0,100,1)
	y <- p*q
	plot(q,y,type='l',col='red',main='Linear relationship')

	# Example 2
	y <- 450 + p*(q-10)^3
	plot(q,y,type='l',col='navy',main='Nonlinear relationship',lwd=3)
	# sources:
	# http://www.jgoodwin.net/?p=1223
	# http://orgtheory.wordpress.com/2012/05/16/the-fragile-network-of-econ-soc-readings/
	# http://nealcaren.web.unc.edu/a-sociology-citation-network/
	# http://kieranhealy.org/blog/archives/2014/11/15/top-ten-by-decade/
	# http://www.jgoodwin.net/lit-cites.png


	###########################################################################
	# This first section scrapes content from the Web of Science webpage. It takes