New York Times data for the US The New York Times has a wonderful compilation of United States on the novel coronavirus. The data update automatically so the following graphics were generated with data retrieved at 2020-06-04 13:22:37.
The Basic State of Things options(scipen=9) library(tidyverse); library(hrbrthemes); library(patchwork); library(plotly); library(ggdark); library(ggrepel) CTP <- read.csv("https://covidtracking.com/api/v1/states/daily.csv") state.data <- read_csv(url("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")) Rect.NYT <- complete(state.data, state,date) Rect.NYT <- Rect.NYT %>% group_by(state) %>% mutate(New.
Beer Distribution The #tidyTuesday for March 31, 2020 is on beer. The essential elements and a method for pulling the data are shown:
A Comment on Scraping .pdf The Tweet
The details on how the data were obtained are a nice overview of scraping .pdf files. The code for doing it is at the bottom of the page. @thomasmock has done a great job commenting his way through it.
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.
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.
Fariss Data Is neat and complete.
load("FarissHRData.RData") skimr::skim(HR.Data) Table 1: Data summary Name HR.Data Number of rows 11717 Number of columns 27 _______________________ Column type frequency: factor 1 numeric 26 ________________________ Group variables None Variable type: factor
skim_variable n_missing complete_rate ordered n_unique top_counts COW_YEAR 0 1 FALSE 11717 100: 1, 100: 1, 100: 1, 100: 1 Variable type: numeric
Archigos Is an amazing collaboration that produced a comprehensive dataset of world leaders going pretty far back; see Archigos on the web. For thinking about leadership, it is quite natural. In this post, I want to do some reshaping into country year and leader year datasets and explore the basic confines of Archigos. I also want to use gganimate for a few things. So what do we know?
library(lubridate) library(tidyverse) library(ggthemes) library(stringr) library(gganimate) library(emoGG) library(emojifont) library(haven) Archigos <- read_dta(url("http://www.