In agroimpacts/covid19interventions: Package for a COVID-19 related spatial interventions database for the U.S.

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(covid19interventions)
library(rvest)
library(dplyr)
# Install the package
# Load the package
library(lubridate)
library(ggplot2)
# run if you don't have covid19clark
devtools::install_github("agroimpacts/covid19clark", build_vignettes = TRUE )
library(covid19clark)

# data load
# covid interventions by county
data("county_interventions")
# covid cases
data("us_cases_daily")

# interventions
head(county_interventions)
# covid cases by county
str(us_cases_daily)

# state stay at home order dates
sah_states <- sah %>% filter(county == 'All')
# counties in states with no stay at home orders
sah_counties <- sah %>% filter(county != 'All')

options(scipen=999)
df1 <- data.frame(county_interventions)
print (df1)

df1$Intervention_Date<- coalesce(df1$SAH_County_Date, df1$SAH_State_Date)
df1
print (df1)

library(ggplot2)
library(tidyverse)
df3 <- df1 %>%
 select(Intervention_Date, population) %>%
filter (Intervention_Date <= as.Date("2020-04-10"))
g <- ggplot(data = df3, aes(x=Intervention_Date, y=population)) + geom_bar( stat="identity", fill = 'steelblue') 
g

library(ggplot2)
library(tidyverse)
df3 <- df1 %>%
 select(interve, population) %>%
filter (interve <= as.Date("2020-04-10"))
g <- ggplot(data = df3, aes(x=population, y=interve)) + geom_point() + geom_smooth(method="lm", se= FALSE)
g

agroimpacts/covid19interventions documentation built on May 6, 2020, 12:35 a.m.