| library(tidyverse) | |
| library(sf) | |
| library(terra) | |
| library(stars) | |
| library(ggspatial) | |
| library(systemfonts) | |
| library(patchwork) | |
| register_variant( | |
| name = "Input Mono Light", |
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).
| # 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. |
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.
Translations: (No guarantee that the translations are up-to-date)
| # 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) |
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:
| # 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 |
