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.
tidyTuesday on Global Mortality The three generic challenge graphics involve two global summaries, a raw count by type and a percentage by type. The individual county breakdowns are recorded for a predetermined year below. This can all be seen in the original. For whatever reason, I cannot open this data remotely. Here is this week’s tidyTuesday. library(skimr) library(tidyverse) library(rlang) # global_mortality <- readRDS("../../data/global_mortality.rds") global_mortality <- readRDS(url("https://github.com/robertwwalker/academic-mymod/raw/master/data/global_mortality.rds")) skim(global_mortality) Table 1: Data summary Name global_mortality Number of rows 6156 Number of columns 35 _______________________ Column type frequency: character 2 numeric 33 ________________________ Group variables None Variable type: character
EPL Scraping In a previous post, I scraped some NFL data and learned the structure of Sportrac. Now, I want to scrape the available data on the EPL. The EPL data is organized in a few distinct but potentially linked tables. The basic structure is organized around team folders. Let me begin by isolating those URLs. library(rvest) library(tidyverse) base_url <- "http://www.spotrac.com/epl/" read.base <- read_html(base_url) team.URL <- read.base %>% html_nodes(".
The NFL Data [SporTrac](http://www.sportrac.com] has a wonderful array of financial data on sports. A student going to work for the Seattle Seahawks wanted the NFL salary cap data and I also found data on the English Premier League there. Now I have a source to scrape the data from. With a source in hand, the key tool is the SelectorGadget. SelectorGadget is a browser add-in for Chrome that allows us to select text and identify the css or xpath selector to scrape the data.
I found a great example on tidyTuesday that I wanted to work on. @JakeKaupp tweeted his #tidyTuesday: a very cool slope plot of tuition changes averaged by state over the last decade. It is a very informative graphic. The only tweak is a simple embedded line plot that uses color in a creative way to show growth rates. All of the R code for this is on Jake Kaupp’s GitHub.
Pew on Rainy Day Funds and Credit Quality The Pew Charitable Trusts released a report last May (2017) that portrays rainy day funds that are well designed and deployed as a form of insurance against ratings downgrades. One the one hand, this is perfectly sensible because the alternatives do not sound like very good ideas. A poorly designed rainy day fund, for example, is going to have to fall short on either the rainy day or the fund.
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.