bbolker

## setup from  https://www.tjmahr.com/morgan-stanley-cursed-covid-plot/

library(tidyverse)
theme_set(theme_bw())
library(lme4)
library(ggeffects)
library(colorspace)
library(directlabels)

# a helper function to download the data from github

marcosci

library(LaCroixColoR)
library(sf)
library(fasterize)
library(rayshader)
library(raster)
library(exactextractr)
library(rayrender)

# load raster into R
elevation <- raster("Olympus_Mons_ortho-image.tif")

brshallo

library(tidyverse)

# load in source_rmd()
devtools::source_gist("https://gist.github.com/noamross/a549ee50e8a4fd68b8b1")
#> Sourcing https://gist.githubusercontent.com/noamross/a549ee50e8a4fd68b8b1/raw/40960d8280438b7e2e8d6502f3c1d4ad348caeb6/source_rmd.R
#> SHA-1 hash of file is 624ae941e51dee522994e014928448170cedf1a3
# Also could have used solution here: 
# https://stackoverflow.com/questions/10966109/how-to-source-r-markdown-file-like-sourcemyfile-r

coulmont

# ajout des informations pour l'animation
# ---------------------------------------

library(gganimate)
library(broom)

dc <- bind_rows(dc_jour,dc_insee_jour,deces_ecdc,deces_insee_provisoires)
# on démarre du fichier dc utilisé pour le graphique surmortalité

gadenbuie

App Install Plan

Critical

• iTerm2

  • https://iterm2.com/

• Command Line Tools

  • xcode-select –install

dsjohnson

library(glmmTMB)
library(mgcv)

#########################################################################
## Function to create prediction matrices from mgcv for glmmTMB
##   from ?mgcv::smooth2random
########################################################################
s2rPred <- function(sm,re,data) {
## Function to aid prediction from smooths represented as type==2
## random effects. re must be the result of smooth2random(sm,...,type=2).

d-qn

library(tidyverse)
library(sf)
library(osmdata) # to get openstreetmap geo data

# settings
bb <- getbb('Lausanne, Switzerland') # define the bbox, will be used to fetch OSM data within that box
dest_proj <- 2056

# OSM overpass query
q <- opq(bbox = bb)

gavinsimpson

## plots and animations of how basis functions are used to make
## splines and how these are fitted to data

library('ggplot2')
library('tibble')
library('tidyr')
library('dplyr')
library('mgcv')
library('mvnfast')
library('purrr')

statnmap

library(raster)
# remotes::install_github("statnmap/cartomisc")
library(cartomisc)
library(dplyr)
library(purrr)

# Create original raster
fn <- system.file("external/test.grd", package="raster")
s <- stack(fn, fn)

Valexandre

library(imager)
library(tidyverse)

URL1<-"1280px-CLaude_Monet_037.jpg"
IMAGE <- imager::load.image(URL1)
df1 <- as.data.frame(IMAGE,wide="c") %>% mutate(rgb.val=rgb(c.1,c.2,c.3))
df1<-df1%>%mutate(xNtile=ntile(x,60),
                  yNtile=ntile(y,60))%>%
  group_by(xNtile,y)%>%
  mutate(Xrang=rank(-x),