walkerke

library(tidycensus)
library(ggiraph)
library(tidyverse)
library(patchwork)

vt_income <- get_acs(
  geography = "county",
  variables = "B19013_001",
  state = "VT",
  year = 2019,

chelseaparlett

library(tidyverse)
library(rstan)
library(bayesplot)

#code
model_code <- "
data {
  int<lower = 2> K; // # of categories
  vector[K] priorprobs; //prior
  int<lower=0> n_total_1; //total in group 1

dantonnoriega

# cool bash codes

# search a directory for all lines that match a pattern (not perfect but useful) ------
## e.g. grep is searching for all lines matching "::" in `R/` to determine package calls
## -h hides the file names; -i ignores case
## sed -E uses regular expressions to search and match groups;
## we then sort and use -u
grep -hi :: -R R/* | sed -E 's/(.*)([ ]+[a-z]+::)(.*)/\2/g' | sort -u

# COUNT COLUMNS -----------------

javierluraschi

This script uses an Amazon EMR Spark cluster with 10 m3.xlarge instances.

Installing

install.packages(devtools)
install.packages(sparklyr)
devtools::install_github("tylermorganwall/rayrender")

system2("hadoop", args = c("fs", "-mkdir", "/rendering"))

malcolmbarrett

# install.packages(c("here", "fs", "stringr", "purrr", "git2r"))

# to add invisibly in your R profile, open with usethis::edit_r_profile()
# then define it in an environment, e.g.
# .env <- new.env()
# .env$move_slides_to_web <- {function definition}

move_slides_to_web <- function(folder = NULL, index = NULL) {
  
  if (is.null(folder)) {

vankesteren

library(tidyverse)

lights_dat <- read_csv("https://ckan.dataplatform.nl/dataset/83402c68-1c05-4aa5-ab28-2e99d2bc2261/resource/dc10e0ac-351a-49b6-b3db-d0152c29dc02/download/paal-20180906.csv")

pp <- 
  lights_dat %>% 
  filter(latitude > 50) %>% 
  ggplot(aes(x = longitude, y = latitude)) + 
  geom_point(alpha = 0.03, fill = "#FAFAAB", stroke = 0, pch = 21, size = 1.6) +
  geom_point(alpha = 0.8,  fill = "#FAFAAB", stroke = 0, pch = 21, size = 0.2) +

seanjtaylor

mrecos

### ------- Load Packages ---------- ###
library("purrr")
library("future")
library("dplyr")
library("randomForest")
library("rsample")
library("ggplot2")
library("viridis")
### ------- Helper Functions for map() ---------- ###
# breaks CV splits into train (analysis) and test (assessmnet) sets

timelyportfolio

As an extension to the blog post React in R, I thought it would be helpful to demonstrate how we can combine a React component with R Shiny.

Code

library(htmltools)
library(shiny)
library(reactR)

kanishkamisra

library(tidyverse)
library(lubridate)
library(extrafont)
library(kani)
library(directlabels)
library(gganimate)
library(scales)
library(tweenr)

sea_ice <- read.csv("https://raw.githubusercontent.com/kanishkamisra/sea-ice-extent/master/seaice.csv") %>% select(-Source.Data)