Maps

GDPR Violations

R Markdown I love this intro photo from the tidyTuesday page. This week’s tidyTuesday data cover violations of the GDPR. gdpr_violations <- readr::read_tsv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-04-21/gdpr_violations.tsv') ## Parsed with column specification: ## cols( ## id = col_double(), ## picture = col_character(), ## name = col_character(), ## price = col_double(), ## authority = col_character(), ## date = col_character(), ## controller = col_character(), ## article_violated = col_character(), ## type = col_character(), ## source = col_character(), ## summary = col_character() ## ) gdpr_text <- readr::read_tsv('https://raw.

R for Driving Directions?

Driving Directions from R There is no reason that maps with driving directions cannot be produced in R. Given the directions api from Google, it should be doable. As it happens, I was surprised how easy it was. Let me try to map a simple A to B location. First, to the locations; I will specify two. It is possible to geolocate addresses for this also, I happened to have the GPS coordinates in hand.

The Carbon Footprint of Food Produced for Consumption

tidyTuesday on the Carbon Footprint of Feeding the Planet The tidyTuesday for this week relies on data scraped from the Food and Agricultural Organization of the United Nations. The blog post for obtaining the data can be found on r-tastic. The scraping exercise is nice and easy to follow and explored a case of cleaning up a very messy data structure. I took this exercise as practice for using pivot_wider and pivot_longer.

Simple Point Maps in R

Mapping Points in R My goal is a streamlined and self-contained freeware map maker with points denoting addresses. It is a three step process that involves: Get a map. Geocode the addresses into latitude and longitude. Combine the the two with a first map layer and a second layer on top that contains the points. From there, it is pretty easy to get fancy using ggplotly to put relevant text hovers into place.

Simple Oregon County Mapping

Some Data for the Map I want to get some data to place on the map. I found a website with population and population change data for Oregon in .csv format. I cannot direct download it from R, instead I have to button download it and import it. library(tidyverse) ## ── Attaching packages ────────────────────────── tidyverse 1.3.0 ── ## ✓ ggplot2 3.2.1 ✓ purrr 0.3.3 ## ✓ tibble 2.1.3 ✓ dplyr 0.

tidyTuesday does Pizza

Pizza Ratings The #tidyTuesday for this week involves pizza shop ratings data. Let’s see what we have. pizza_jared <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-01/pizza_jared.csv") ## Parsed with column specification: ## cols( ## polla_qid = col_double(), ## answer = col_character(), ## votes = col_double(), ## pollq_id = col_double(), ## question = col_character(), ## place = col_character(), ## time = col_double(), ## total_votes = col_double(), ## percent = col_double() ## ) pizza_barstool <- readr::read_csv("https://raw.

fredr is very neat

FRED via fredr The Federal Reserve Economic Database [FRED] is a wonderful public resource for data and the r api that connects to it is very easy to use for the things that I have previously needed. For example, one of my students was interested in commercial credit default data. I used the FRED search instructions from the following vignette to find that data. My first step was the vignette for using fredr.

tidyTuesday: coffee chains

The tidyTuesday for this week is coffee chain locations For this week: 1. The basic link to the #tidyTuesday shows an original article for Week 6. First, let’s import the data; it is a single Excel spreadsheet. The page notes that starbucks, Tim Horton, and Dunkin Donuts have raw data available. library(readxl) library(tidyverse) ## ── Attaching packages ────────────────────────── tidyverse 1.3.0 ── ## ✓ ggplot2 3.2.1 ✓ purrr 0.3.3 ## ✓ tibble 2.

Mapping with the Government Finance Database

The Government Finance Database Some of my colleagues (Kawika Pierson, Mike Hand, and Fred Thompson) have put together a convenient access point for the Government Finance data available from the Census. They published an article in PLoS One with the rationale; I want to build some maps from their project with extensible code and functions. The overall dataset is enormous. I have downloaded the whole thing and filtered out the states.