Oregon COVID data The Oregon data are available from OHA here. I cut and pasted the first two days because it was easy with datapasta. As it goes on, it was easier to write a script that I detail elsewhere that I can self-update.
urbnmapr The Urban Institute has an excellent state and county mapping package. I want to make use of the county-level data and plot the starter map.
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.
Driving Directions from R There is no reason that maps with driving directions cannot be produced in R. Given the directions api from Google, it should be doable. As it happens, I was surprised how easy it was. Let me try to map a simple A to B location. First, to the locations; I will specify two. It is possible to geolocate addresses for this also, I happened to have the GPS coordinates in hand.
tidyTuesday on the Carbon Footprint of Feeding the Planet The tidyTuesday for this week relies on data scraped from the Food and Agricultural Organization of the United Nations. The blog post for obtaining the data can be found on r-tastic. The scraping exercise is nice and easy to follow and explored a case of cleaning up a very messy data structure. I took this exercise as practice for using pivot_wider and pivot_longer.
Trees in San Francisco This week’s data cover trees in San Francisco.
sf_trees <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-01-28/sf_trees.csv') library(tidyverse); library(ggmap); library(skimr) skim(sf_trees) Table 1: Data summary Name sf_trees Number of rows 192987 Number of columns 12 _______________________ Column type frequency: character 6 Date 1 numeric 5 ________________________ Group variables None Variable type: character
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.
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!
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 3.2.1 ✓ purrr 0.3.3 ## ✓ tibble 2.1.3 ✓ dplyr 0.