Oregon COVID data In this post, I want to visualize the data on COVID-19 in the state of Oregon. I now have a few days of data that I want to scrape, separate, clean up, and plot. The process of scraping it is detailed in a separate file. These data are current as of March 26, 2020.
load(url(paste0("https://github.com/robertwwalker/rww-science/raw/master/content/R/COVID/data/OregonCOVID",Sys.Date(),".RData"))) These data are current as of 2020-03-28.
Building a base map To build a map to work from, I need a map library.

NB: This was last updated on March 25, 2020.
Building Oregon COVID data I have a few days of data now. To rebuild it, I will have to use the waybackmachine. The files that I need to locate and follow updates to this page from Oregon’s OHA.
A Scraper Let me explain the logic for the scraper. NB: I had to rewrite it; the original versions of the website had three tables without data on hospitalizations.

As is often the case with \(R\), there are many ways to do things that are equivalent or nearly equivalent. It is the nearly equivalent part that is frustrating; one of the first encounters with this can come with attempts to predict a regression. The ultimate source of troubles is scoping and environments; the use of the $ syntax sometimes has unintended side effects.
lm() Syntax is Important I will refer to an example from a recent homework on regression.

The Package: finreport The key tool to facilitate the financial analysis of companies that file regular SEC reports of certain forms is finreportr. To make use of it, we must first have R install it and dependencies. To install it, install.packages("finreportr", dependencies=TRUE).
The Commands The first command is CompanyInfo().
library(finreportr) CompanyInfo("JPM") ## company CIK SIC state state.inc FY.end street.address ## 1 JPMORGAN CHASE & CO 0000019617 6021 NY DE 1231 383 MADISON AVENUE ## city.

Some Data I will start with some inline data.
library(tidyverse); library(skimr); Support.Times <- structure(list(Screened = c(26.9, 28.4, 23.9, 21.8, 22.4, 25.9, 26.5, 20, 23.7, 23.7, 22.6, 19.4, 27.3, 25.3, 27.7, 25.3, 28.4, 24.2, 20.4, 29.6, 27, 23.6, 18.3, 28.1, 20.5, 24.1, 27.2, 26.4, 24.5, 25.6, 17.9, 23.5, 25.3, 20.2, 26.3, 27.9), Not.Screened = c(24.7, 19.1, 21, 17.8, 22.8, 24.4, 17.9, 20.5, 20, 26.2, 14.5, 22.4, 21.1, 24.3, 22, 24.3, 23.

A Proportions Example We started with an equation:
\[ z = \frac{\hat{\pi} - \pi}{\sqrt{\frac{\pi(1-\pi)}{n}}} \]
In language, the difference between the sample proportion (recall that with only two outcomes the sample proportion \(\hat{\pi}\) is between 0 [all No’s] and 1 [all Yes’s]) and the true probability \(\pi\) divided by the standard error of the proportion \(\sqrt{\frac{\pi(1-\pi)}{n}}\) has a \(z\) [Normal(0,1)] distribution under the condition that \(n\pi > 10\) and \(n(1-\pi) > 10\).

cars data I will work with R’s internal dataset on cars: cars. There are two variables in the dataset, this is what they look like.
plot(cars) An Hypothesis Test I will work with the speed variable. The hypothesis to advance is that 17 or greater is the true average speed. The alternative must then be that the average speed is less than 17. Knowing only the sample size, I can figure out what \(t\) must be to reject 17 or greater and conclude that the true average must be less with 90% probability.

Alluvial and Sankey Diagrams The aforementioned plots are methods for visualising the flow of data through a stream of markers. I was motivated to show this because enough of you deal in orders, tickets, and the like the flow visualisation of a system might prove of use. I will work with a familiar dataset. These are data on Admissions at the University of California Berkeley. The data exist as an internal R file in tabular form.

A Citation I found a starting point on local maps in Seattle.
library(ggmap) library(osmdata) library(tidyverse) # SLE <- get_map(getbb("Salem, OR"), source="osm") # SLE %>% ggmap() An Oregon Map of Liquor Stores The setup for a Google Cloud account is kind of a pain and it requires a billing option. That was annoying but eventually fixed. It is required for geocoding addresses as OSM doesn’t do that anymore.

R Markdown There is detailed help for all that Markdown can do under Help in the RStudio. The key to it is knitting documents with the Knit button in the RStudio. If we use helpers like the R Commander, Radiant, or esquisse, we will need the R code implanted in the Markdown document in particular ways. I will use Markdown for everything. I even use a close relation of Markdown in my scholarly pursuits.

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