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.
mathart A cool package for math generated art that I just discovered. Here is the install code for it install.packages(c("devtools", "mapproj", "tidyverse", "ggforce", "Rcpp")) devtools::install_github("marcusvolz/mathart") devtools::install_github("marcusvolz/ggart") devtools::install_github("gsimchoni/kandinsky") Load some libraries library(mathart) library(ggart) library(ggforce) library(Rcpp) library(tidyverse) Generate some Art? This is quite fun to do. set.seed(12341) terminals <- data.frame(x = runif(10, 0, 10000), y = runif(10, 0, 10000)) df <- 1:10000 %>% map_df(~weiszfeld(terminals, c(points$x[.], points$y[.])), .id = "id") p <- ggplot() + geom_point(aes(x, y), points, size = 1, alpha = 0.
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 3.3.3 ✓ purrr 0.3.4 ## ✓ tibble 3.0.6 ✓ dplyr 1.
The Economist’s Errors and Credit Where Credit is Due The Economist is serious about their use of data visualization and they have occasionally owned up to errors in their visualizations. They can be deceptive, uninformative, confusing, excessively busy, and present a host of other barriers to clean communication. Their blog post on their errors is great. I have drawn the following example from a #tidyTuesday earlier this year that explores this.
Pizza Ratings The #tidyTuesday for this week involves pizza shop ratings data. The data come from a variety of sources; it is price, ratings, and similar data for pizza restaurants. The actual contents vary depending on the data source. I will begin by loading the data and summarizing what data seem to be available so that we can figure out what we can do with it.
So this Robert Mueller guy wrote a report I may as well analyse it a bit. There are tons of things that we might wish to discover about the report; my goal is not at that. First, let me see if I can get a hold of the data. I grabbed the report directly from the Department of Justice website. You can follow this link. The report is really long and making sense of it could be done in an absolute ton of ways.
Scraping NFL data Note: An original version of this post had issues induced by overtime games. There is a better way to handle all of this that I learned from a brief analysis of a tie game between Cleveland and Pittsburgh in Week One. The nflscrapR package is designed to make data on NFL games more easily available. To install the package, we need to grab it from github.