COVID-19 in Oregon

Oregon COVID data I wanted to create a self-updating visualization of the data on COVID-19 in the state of Oregon provided by OHA. I still have yet to do that but decided to build this one to visualize the New York Times data. There is a separate page of daily maps. Oregon reports a set of daily snapshots while progression requires ingesting new data each day so I began tracking it March 20; the process of scraping it is detailed in a separate file.

New York Times Data on COVID

New York Times data for the US The New York Times has a wonderful compilation of United States on the novel coronavirus. The data update automatically so the following graphics were generated with data retrieved at 2020-11-30 16:51:46. The Basic State of Things options(scipen=9) library(tidyverse); library(hrbrthemes); library(patchwork); library(plotly); library(ggdark); library(ggrepel); library(lubridate) CTP <- read.csv("") <- read_csv(url("")) Rect.NYT <- complete(, state,date) # Create new cases and new deaths Rect.

A Quick tidyTuesday on Beer, Breweries, and Ingredients

Beer Distribution The #tidyTuesday for March 31, 2020 is on beer. The essential elements and a method for pulling the data are shown: Imgur A Comment on Scraping .pdf The Tweet The details on how the data were obtained are a nice overview of scraping .pdf files. The code for doing it is at the bottom of the page. @thomasmock has done a great job commenting his way through it.

Tracking COVID-19 2020-03-24

R to Import COVID Data library(tidyverse) library(gganimate) COVID.states <- read.csv(url("")) COVID.states <- COVID.states %>% mutate(Date = as.Date(as.character(date), format = "%Y%m%d")) The Raw Testing Incidence I want to use patchwork to show the testing rate by state in the United States. Then I want to show where things currently stand. In both cases, a base-10 log is used on the number of tests.

tidyTuesday Measles

tidyTuesday: December 10, 2019 Replicating plots from simplystatistics. One nice twist is the development of a tidytuesdayR package to grab the necessary data in an easy way. You can install the package via github. I will also use fiftystater and ggflags. devtools::install_github("thebioengineer/tidytuesdayR") devtools::install_github("ellisp/ggflags") devtools::install_github("wmurphyrd/fiftystater") tuesdata <- tidytuesdayR::tt_load(2019, week = 50) ## --- Downloading #TidyTuesday Information for 2019-12-10 ---- ## --- Identified 4 files available for download ---- ## --- Downloading files --- ## Warning in identify_delim(temp_file): Not able to detect delimiter for the file.

Fariss Human Rights Data with Animation

Fariss Data Is neat and complete. load("FarissHRData.RData") skimr::skim(HR.Data) Table 1: Data summary Name HR.Data Number of rows 11717 Number of columns 27 _______________________ Column type frequency: factor 1 numeric 26 ________________________ Group variables None Variable type: factor skim_variable n_missing complete_rate ordered n_unique top_counts COW_YEAR 0 1 FALSE 11717 100: 1, 100: 1, 100: 1, 100: 1 Variable type: numeric

Visualisation with Archigos: Leaders of the World

Archigos Is an amazing collaboration that produced a comprehensive dataset of world leaders going pretty far back; see Archigos on the web. For thinking about leadership, it is quite natural. In this post, I want to do some reshaping into country year and leader year datasets and explore the basic confines of Archigos. I also want to use gganimate for a few things. So what do we know?