Last update: November 15. 2022 In preparation for the dumpster fire that is Oregon election reporting, I previously posed on importing a directory of .csv files. At present, that is what I can find to build this. What does the interface look like? library(magick) Img <- image_read("./img/SShot.png") image_ggplot(Img) This is terrible, there is a javascript button to download each separately. Nevertheless, here we go.
The New York Times has a wonderful compilation of United States on the novel coronavirus. The data are organized as a panel for US counties and have been continuously collected and updated since March of 2020. For US data, it is as authoritative a source as I am aware of and it provides a nice basis for visualizing various aspects of the pandemic. This commentary was originally provided in late March of 2020.
The Johns Hopkins dashboard This is what Johns Hopkins has provided as a dashboard using ARCGIS. They have essentially layered out the data into national and subnational data and then used the arcgis dashboard to cycle through them. The data There are a few different types of data available. I am relying on the same sources that Johns Hopkins is using for the county level incident data.
Oregon COVID data I now have a few days of data. These data are current as of March 24, 2020. I will present the first version of these visualizations here and then move the auto-update to a different location. A messy first version of the scraping exercise is at the bottom of this post. paste0("https://github.com/robertwwalker/rww-science/raw/master/content/R/COVID/data/OregonCOVID",Sys.Date(),".RData") ## [1] "https://github.com/robertwwalker/rww-science/raw/master/content/R/COVID/data/OregonCOVID2020-03-24.RData" load(url(paste0("https://github.com/robertwwalker/rww-science/raw/master/content/R/COVID/data/OregonCOVID",Sys.Date(),".RData"))) A base map Load the tigris library then grab the map as an sf object; there is a geom_sf that makes them easy to work with.
Oregon COVID data The Oregon data are available from OHA here. I cut and pasted the first two days because it was easy with datapasta. As it goes on, it was easier to write a script that I detail elsewhere that I can self-update. urbnmapr The Urban Institute has an excellent state and county mapping package. I want to make use of the county-level data and plot the starter map.
The Office library(tidyverse) office_ratings <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-03-17/office_ratings.csv') A First Plot The number of episodes for the Office by season. library(janitor) TableS <- office_ratings %>% tabyl(season) p1 <- TableS %>% ggplot(., aes(x=as.factor(season), y=n, fill=as.factor(season))) + geom_col() + labs(x="Season", y="Episodes", title="The Office: Episodes") + guides(fill=FALSE) p1 Ratings How are the various seasons and episodes rated? p2 <- office_ratings %>% ggplot(., aes(x=as.factor(season), y=imdb_rating, fill=as.factor(season), color=as.factor(season))) + geom_violin(alpha=0.3) + guides(fill=FALSE, color=FALSE) + labs(x="Season", y="IMDB Rating") + geom_point() p2 Patchwork Using patchwork, we can combine multiple plots.
R to Import COVID Data library(tidyverse) library(gganimate) COVID.states <- read.csv(url("http://covidtracking.com/api/states/daily.csv")) 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.