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

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)) {

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() %>%

friep

---
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)

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),

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),

hadley

# =======================================
# =     Enhancements to data tidying    =
# =          Hadley Wickham             =
# = https://rstd.io/tidyhancements-2019 =
# =======================================

# What is tidy data? ----------------------------------------------------------
# 1. Each column is a variable.
# 2. Each row is an observation.
# 3. Each cell is a value.

joshua-feldman

# Load the relevant packages
library(NCmisc)
library(stringr)

# Load the script and parse lines
file_name <- "example_script.R"
file_lines <- readLines(file_name)

# Save the function and package names
functions_list <- list.functions.in.file(file_name)

fzenoni

# Download earth data first
# https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/110m/physical/ne_110m_land.zip

library(sf)
library(lwgeom)
library(dplyr)
library(ggplot2)
library(mapview)

# Read the data

jsta

#' Download files from Zenodo record id
#' 
#' @param record_id record id
#' @param dest_folder destination folder for file downloads
#' @param token API key
#' 
#' @details https://github.com/zenodo/zenodo/issues/1629#issuecomment-435062462
#' 
#' @importFrom httr GET content
#' @importFrom jsonlite fromJSON