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.
Philadelphia Map Use ggmap for the base layer. library(ggmap); library(osmdata); library(tidyverse) PHI <- get_map(getbb("Philadelphia, PA"), maptype = "stamen", zoom=12) Get the Tickets Data TidyTuesday covers 1.26 million parking tickets in Philadelphia. tickets <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-12-03/tickets.csv") ## Parsed with column specification: ## cols( ## violation_desc = col_character(), ## issue_datetime = col_datetime(format = ""), ## fine = col_double(), ## issuing_agency = col_character(), ## lat = col_double(), ## lon = col_double(), ## zip_code = col_double() ## ) Two Lines of Code Left library(lubridate); library(ggthemes) tickets <- tickets %>% mutate(Day = wday(issue_datetime, label=TRUE)) # use lubridate to extract the day of the month.
Searching and Mapping the Census Searching for the Asian Population via the Census To use tidycensus, there are limitations imposed by the available tables. There is ACS – a survey of about 3 million people – and the two main decennial census files [SF1] and [SF2]. I will search SF1 for the Asian population. library(tidycensus); library(kableExtra) library(tidyverse); library(stringr) v10 <- load_variables(2010, "sf1", cache = TRUE) v10 %>% filter(str_detect(concept, "ASIAN")) %>% filter(str_detect(label, "Female")) %>% kable() %>% scroll_box(width = "100%") name label concept P012D026 Total!
Hashtag OKBoomer The generational banter that has followed the use of #OKBoomer reminded me of an interesting feature of US population data. I believe it to be true that Generation X has never and will never be the largest generation of Americans. There are tons of Millenials and Baby Boomers alike, though the rate of decline in the latter means that the former are about to surpass them. Or perhaps they have.
Pre-Talk Slides Set the RStudio to install blogdown. install.packages("blogdown") and to get a working local version of Hugo, the static site generator at the heart of this, blogdown::install_hugo() Motivation Credit where credit is due; Alison Hill motivated all of this with her wealth of resources. Indeed, I redesigned the flow for today because of her brilliant R-Ladies Canberra seminar [I found the resource links on Twitter and the idea is
Ode to Griffin Park Griffin Park’s famous four corners make it the only football ground in England with four pubs on the four corners surrounding the ground. Here’s a link to the curated twitter thread. In the Beginning The Griffin To the Brook The Brook Sussex Best at the Brook The Garden Up New Road The New Road Gate at Griffin Park The New Inn Doom Bar Rules on the Main – Ealing – Road
Chris Fariss has some really neat data on international human rights that are really interesting to visualize for insight into the development of human rights over time. The data arise from measurement models applied to a variety of underlying indicators and they are presented in rather easy to work with forms. The basic structure is panel data. Let’s see what we have. Fariss Data Is neat and complete.