jebyrnes

library(tidyverse)
library(ggmap)
library(sf)
library(tmap)

#whereami?
here::here()

#get my apis flowin'
register_google(readLines("~/goog.api"))

jebyrnes

library(tidyverse)
library(tidytext)
library(rvest)
library(ggplot)

#get the initial info from the site
site <- "http://www.guscantieni.com/bindarrr/"

index <- read_html(paste0(site, "index.html"))

jebyrnes

#Rayshader reprex to try and put a flat raster map under a 
#set of 3d points
library(rayshader)
library(ggplot2)
library(dplyr)
library(maps)
library(ggmap)

data(world.cities)

jebyrnes

#see also https://davidsmale.netlify.com/portfolio/wes-anderson-actor-network/
# https://www.data-imaginist.com/2017/introducing-tidygraph/
# https://www.jessesadler.com/post/network-analysis-with-r/

#load libraries
library(rglobi)
library(tidygraph)
library(ggraph)
library(NetIndices)

jebyrnes

library(spdep)
library(spatialreg)
library(SpatialTools)
library(dplyr)
library(nlme)
library(mgcv)
library(purrr)
library(stringr)
library(ggplot2)
library(broom)

jebyrnes

library(MASS)
library(tidyverse)
library(ggplot2)
library(mgcv)

gaussprocess <- function(from = 0, to = 1, K = function(s, t) {min(s, t)},
                         start = NULL, m = 1000) {
  # Simulates a Gaussian process with a given kernel
  #
  # args:

jebyrnes

library(heatwaveR)

library(tsibble)
library(lubridate)
library(dplyr)

# Detect the events in a time series
ts <- ts2clm(sst_WA, climatologyPeriod = c("1982-01-01", "2011-12-31"))
mhw <- detect_event(ts, minDuration = 1, maxGap = 0)

jebyrnes

library(ggplot2)
library(gganimate)

mygridstart <- tidyr::crossing(x = 1:10, y = 1:10)

mygrid <- rbind(mygridstart, mygridstart, mygridstart)
mygrid$rep <- sort(rep(1:3, nrow(mygrid)/3))
mygrid$pointgroup <- rep(1:100, 3)
mygrid$f <- factor(sample(1:3, replace=TRUE, size = nrow(mygrid)))

jebyrnes

library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
library(gganimate)

#make those samples!
set.seed(3003)
samps <- replicate(9, rbinom(1,1,prob = 0.3))
running_samps <- cumsum(samps)

jebyrnes

#some libraries
library(dplyr)
library(tidyr)
library(broom)
library(purrr)

#let's use the npk data
head(npk)

#but make up a few extra columns