bjulius

# This code creates an example Nested Column Chart of the type developed by Brittany Rosenau
# this code will produce the graphic below if you paste it into R Studio and run it
# to run it in a Power BI R visual, just import the csv into Power Query
# and then paste in the code below, changing df <- dataset
# note that "dataset" is the variable name used by convention in Power BI to call the table 
# resulting from the previous step

# Load necessary libraries
library(ggplot2)
library(dplyr)

ivabrunec

library(sf)
library(elevatr)
library(raster)
library(rayshader)
library(dplyr)
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))

# read in andes shapefile from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5466557/
# idk exactly what i'm doing yet
# get extent we're interested in

jkr216

---
title: "Untitled"
output: html_document
---

```{r include = FALSE}
library(tidyverse)
library(tidyquant)
library(timetk)
library(readxl)

djnavarro

if(interactive()) {

  cat("\014") # clear screen
  cli::cli_text("")
  cli::cli_text(R.version$version.string)
  cli::cli_text("")

  cli::cli_alert_success(
    paste0(

ledell

### Kaggle Days SF: Hackathon submission (8th place) 

# I used the latest version of H2O (3.24.0.1)
# Latest stable always here: http://h2o-release.s3.amazonaws.com/h2o/latest_stable.html
# H2O 3.24.0.1: http://h2o-release.s3.amazonaws.com/h2o/rel-yates/1/index.html
# If you are a Python user, you can use the demo Python code available on the H2O AutoML User Guide 
# instead: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html

# Unfortunately it was a private competition, so the data is not publicly available! 

timwaterhouse

library(ggplot2)
library(tidyr)
library(dplyr)
library(maps)
library(gganimate)

WorldData <- map_data('world')
#WorldData %>% filter(region != "Antarctica") -> WorldData
WorldData <- fortify(WorldData)

johnburnmurdoch

# Load the packages we’re going to be using:
# Alongside the usual stuff like tidyverse and magrittr, we’ll be using rvest for some web-scraping, jsonline to parse some JSON, and extrafont to load some nice custom fonts
needs(tidyverse, magrittr, rvest, jsonlite, extrafont)

# Before we go on, two things to note:

# First, on web scraping:
# You should always check the terms of the site you are extracting data from, to make sure scraping (often referred to as `crawling`) is not prohibited. One way to do this is to visit the website’s `robots.txt` page, and ensure that a) there is nothing explicitly stating that crawlers are not permitted, and b) ideally, the site simply states that all user agents are permitted (indicated by a line saying `User-Agect: *`). Both of those are the case for our use-case today (see https://www.ultimatetennisstatistics.com/robots.txt).

# And second, about those custom fonts:

djnavarro

# devtools::install_github("yixuan/fontr")
library(fontr)
library(transformr)
library(showtext)
library(tidyverse)
library(gganimate)

# use showtext to loading Google fonts (http://www.google.com/fonts)
font_add_google("Allura")

gadenbuie

---
title: "Presentation Ninja"
subtitle: "⚔<br/>with xaringan"
author: "Yihui Xie"
date: "2016/12/12 (updated: `r Sys.Date()`)"
output:
  xaringan::moon_reader:
    lib_dir: libs
    css:
      - default

walkerke

library(tidycensus)
library(mapdeck)
library(tidyverse)

token <- "your mapbox token"

hv <- get_acs(geography = "tract", 
              variables = "B25077_001", 
              state = "CA", 
              geometry = TRUE) %>%