baptiste

library(ggplot2)
# dput(ls(pattern = "theme",package:ggplot2))
defaults <- c("theme_bw", "theme_classic", "theme_dark", 
              "theme_grey", "theme_light", "theme_linedraw", 
              "theme_minimal", "theme_void")
library(ggthemes)
# dput(ls(pattern = "theme",package:ggthemes))
addons <- c("theme_base", "theme_calc", "theme_economist", 
            "theme_economist_white", "theme_excel", "theme_few", "theme_fivethirtyeight", 
            "theme_foundation", "theme_gdocs", "theme_hc", "theme_igray", 

chichacha

library(tidyverse)
library(lubridate)
library(scales)

df <- tibble(
  date = seq.Date(from=ymd("2019-01-01"),to=ymd("2019-12-31"),by="day"),
  wkdy = wday(date,label=T),
  mo = month(date,label=T),
  mo.num = month(date,label=F),
  day = day(date)

mertedali

#####################################################
#####################################################
### R Script for Metamodeling and Rule Extraction ###
#####################################################
#####################################################

#############################
### Step I: Load Packages ###
#############################

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),

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.

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)

maartenzam

Porting Jon Schwabish's World Tile Grid Map to ggplot2.

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() %>%

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