jlmelville

# License: Apache 2.0

# A straightforward translation of the Python code:

turbo_colormap_data <- matrix(
  c(
    c(0.18995, 0.07176, 0.23217),
    c(0.19483, 0.08339, 0.26149),
    c(0.19956, 0.09498, 0.29024),
    c(0.20415, 0.10652, 0.31844),

mschnetzer

library(tidyverse)
library(msthemes)
library(sf)


# GeoJSON: http://opendatalab.de/projects/geojson-utilities/
geodat <- st_read("landkreise_simplify200.geojson", quiet=TRUE, stringsAsFactors=FALSE) %>%
  mutate(
    center = map(geometry, st_centroid),
    centercoord = map(center, st_coordinates),

pr130

---
title: "DKB Export Cleanup"
author: "Frie"
date: "10/10/2020"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(purrr)

davidsjoberg

library(tidyverse)
library(ggsankey)

## data via https://ourworldindata.org/grapher/total-agricultural-area-over-the-long-term
hablar::set_wd_to_script_path()


df <-
  read_csv("total-agricultural-area-over-the-long-term.csv") %>%
  janitor::clean_names() %>%

tylermorganwall

library(geojsonsf)
library(sf)
library(rayrender)

#Data source: https://github.com/telegeography/www.submarinecablemap.com
cables = geojson_sf("cable-geo.json")

cablescene = list()
counter = 1
for(i in 1:length(cables$geometry)) {

steveharoz

library(tidyverse)
library(ggdist)

# make some data
expand_grid(
  condition = c("A", "B"),
  stimulus = seq(-3,3,0.3),
  repetitions = 1:20
) %>%
  # make some noisy response data