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.
Updating Hugo: It’s Changed Alot… Many key features of hugo have been in a state of flux since I began this blogdown a few years ago. It was time to update hugo and the academic theme that I have built around and customized. A number of things broke. The config.toml and the like In my original website, there was only one configuation file. Now it has split into four parts; for blogdown, config.
First, I wanted to acquire the distribution of letters and then play with that. I embedded the result here. The second step is to import the tidyTuesday data. library(tidyverse) Letter.Freq <- data.frame(stringsAsFactors=FALSE, Letter = c("E", "T", "A", "O", "I", "N", "S", "R", "H", "D", "L", "U", "C", "M", "F", "Y", "W", "G", "P", "B", "V", "K", "X", "Q", "J", "Z"), Frequency = c(12.02, 9.1, 8.12, 7.68, 7.31, 6.95, 6.28, 6.
Mapping Points in R My goal is a streamlined and self-contained freeware map maker with points denoting addresses. It is a three step process that involves: Get a map. Geocode the addresses into latitude and longitude. Combine the the two with a first map layer and a second layer on top that contains the points. From there, it is pretty easy to get fancy using ggplotly to put relevant text hovers into place.
Adoptable Dogs # devtools::install_github("thebioengineer/tidytuesdayR", force=TRUE) tuesdata51 <- tidytuesdayR::tt_load(2019, week = 51) dog_moves <- tuesdata51$dog_moves dog_des <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-12-17/dog_descriptions.csv') library(tidyverse); library(scatterpie) library(rgeos) library(maptools) library(rgdal); library(usmap); library(ggthemes) The Base Map My.Map <- us_map(regions = "states") Base.Plot <- ggplot() + geom_polygon(data=My.Map, aes(x=x, y=y, group=group), fill="white", color="black") + theme_map() Base.Plot A fifty state map to plot this information on. New.Dat <- left_join(My.Map, dog_moves, by= c("full" = "location")) ggplot() + geom_polygon(data=New.
tidyTuesday: December 10, 2019 Replicating plots from simplystatistics. One nice twist is the development of a tidytuesdayR package to grab the necessary data in an easy way. You can install the package via github. I will also use fiftystater and ggflags. devtools::install_github("thebioengineer/tidytuesdayR") devtools::install_github("ellisp/ggflags") devtools::install_github("wmurphyrd/fiftystater") tuesdata <- tidytuesdayR::tt_load(2019, week = 50) ## --- Downloading #TidyTuesday Information for 2019-12-10 ---- ## --- Identified 4 files available for download ---- ## --- Downloading files --- ## Warning in identify_delim(temp_file): Not able to detect delimiter for the file.
International Murders Are among the data for analysis in the tidyTuesday for December 10, 2019. These are made for a map. library(tidyverse) library(leaflet) library(stringr) library(sf) library(here) library(widgetframe) library(htmlwidgets) library(htmltools) options(digits = 3) set.seed(1234) theme_set(theme_minimal()) library(tidytuesdayR) tuesdata <- tt_load(2019, week = 50) murders <- tuesdata$gun_murders There isn’t much data so it should make this a bit easier. Now for some data. As it happens, the best way I currently know how to do this is going to involve acquiring a spatial frame.