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

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

<<<<<<< HEAD

library(devtools)
library(usethis)
library(covid19interventions)

# run if you don't have covid19clark
devtools::install_github("agroimpacts/covid19clark")
library(devtools)
library(usethis)
>>>>>>> master

library(covid19interventions)

# run if you don't have covid19clark
<<<<<<< HEAD
# devtools::install_github("agroimpacts/covid19clark", build_vignettes = TRUE)

=======
devtools::install_github("agroimpacts/covid19clark")
>>>>>>> master
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
head(us_cases_daily$county)

#------------------------------------------------------------------------------------------------------------
# How many cases and deaths were recorded the date the intervention measures were put in place?
library(dplyr)
library(tidyr)
library(tidyverse)
library(stringr)

df1 <- as.data.frame(county_interventions)
df1$admin2 <- sapply(df1$admin2, tolower)
df1$admin1 <- sapply(df1$admin1, tolower)
df1$acronym <- sapply(df1$acronym, tolower)


df1$admin2_rep = str_replace(df1$admin2, " county", "")

df2 <- as.data.frame(us_cases_daily$county)
df3 <- as.data.frame(us_cases_daily$state)
#------------------------------------------------------------------------------------------------------------
#Values for day of
cima2 <- inner_join(df1,df2, by = c("SAH_County_Date" = "date",
                                    "admin1" = "state1", 
                                    "admin2_rep" = "county.x")) %>% 
  group_by(admin2_rep) %>% mutate(case_pop = cases / pop)


#------------------------------------------------------------------------------------------------------------
#new dataframe of cases one week before
before1 <- df1
before1$SAH_County_Date = before1$SAH_County_Date - 7 

before1_df <- inner_join(before1,df2, by = c("SAH_County_Date" = "date",
                                    "admin1" = "state1", 
                                    "admin2_rep" = "county.x")) %>% 
  group_by(admin2_rep) %>% mutate(case_pop = cases / pop)

#------------------------------------------------------------------------------------------------------------
#New dataframe of cases one week after
after <- df1
after$SAH_County_Date = after$SAH_County_Date + 7 

after_df <- inner_join(after,df2, by = c("SAH_County_Date" = "date",
                                    "admin1" = "state1", 
                                    "admin2_rep" = "county.x")) %>% 
  group_by(admin2_rep) %>% mutate(case_pop = cases / pop)

#------------------------------------------------------------------------------------------------------------
#State Before
state_before <- df1
state_before$SAH_State_Date= state_before$SAH_State_Date - 14 

state_before_df <- inner_join(state_before,df3, by = c("SAH_State_Date" = "date",
                                    "admin1" = "state1")) %>% 
  group_by(admin2_rep) %>% mutate(case_pop = cases / pop)

#------------------------------------------------------------------------------------------------------------
#State During
state_during <- df1
#state_before$SAH_State_Date= state_before$SAH_State_Date 

state_during_df <- inner_join(state_during,df3, by = c("SAH_State_Date" = "date",
                                    "admin1" = "state1")) %>% 
  group_by(admin2_rep) %>% mutate(case_pop = cases / pop)

#------------------------------------------------------------------------------------------------------------
#State After
state_after <- df1
state_after$SAH_State_Date = state_before$SAH_State_Date + 14 

state_after_df <- inner_join(state_after,df3, by = c("SAH_State_Date" = "date",
                                    "admin1" = "state1")) %>% 
  group_by(admin2_rep) %>% mutate(case_pop = cases / pop)

#------------------------------------------------------------------------------------------------------------
#Plot
library(ggplot2)
library(tidyverse)


g <- ggplot(data = cima2, aes(x=cima2$admin2_rep, y=case_pop)) + geom_bar( stat="identity", fill = 'steelblue')
g

coord_cartesian(
  xlim = NULL,
  ylim = NULL,
  expand = TRUE,
  default = FALSE,
  clip = "on"
)


g1 <- ggplot(data = state_before_df, aes(x=admin1, y=case_pop)) + geom_bar( stat="identity", fill = 'steelblue')
g1


library(pacman)
pacman::p_unload(pacman::p_loaded(), character.only = TRUE)

# Load libraries
library(dplyr)        # data wrangling
library(cartogram)    # for the cartogram
library(ggplot2)      # to realize the plots
library(modelr)
#library(broom)        # from geospatial format to data frame
library(tweenr)       # to create transition dataframe between 2 states
library(gganimate)    # To realize the animation
library(maptools)     # world boundaries coordinates
library(viridis) 
#what if you convert yours into a bubble plot and the magnitude of the blubles could be number of casses (edited) 
#and then the color of the bubbles could show party. (edited) 

# Get US map
#usa <- map_data("state")
gg <- ggplot()
#gg <- gg + geom_path(data = usa, aes(x = long, y = lat, group = group) , fill="#ffffff", color="#0e0e0e", size=0.15)

# your bubbles
#gg <- gg + geom_point(data=cima2, aes(x= cases, y = cases) color="#AD655F") 
#gg <- gg + labs(title="Bubbles")
# much better projection for US maps
#gg <- gg + coord_map(projection="albers", lat=39, lat1=45)
#gg <- gg + theme_map()
#gg <- gg + theme(legend.position="bottom")
#gg <- gg + theme(plot.title=element_text(size=16))
#gg

#-----------------------------------------------------------------------------------------------------------------------------
devtools::install_github("wmurphyrd/fiftystater")

library(ggplot2)
library(fiftystater)

data("fifty_states") # this line is optional due to lazy data loading
head(fifty_states)
fifty_states <- as.data.frame(fifty_states)

stateb4 <- right_join(state_before_df, fifty_states, by = c("admin1" = "id"))
statedur <- right_join(state_during_df, fifty_states, by = c("admin1" = "id"))
stateaft <- right_join(state_after_df, fifty_states, by = c("admin1" = "id"))

# map_id creates the aesthetic mapping to the state name column in your data
p <- ggplot(stateb4, aes(map_id = admin1)) + 
  # map points to the fifty_states shape data
  geom_map(aes(fill = case_pop), map = fifty_states) + 
  expand_limits(x = fifty_states$long, y = fifty_states$lat) +
  coord_map() +
  scale_x_continuous(breaks = NULL) + 
  scale_y_continuous(breaks = NULL) +
  labs(x = "", y = "") +
  theme(legend.position = "bottom", 
        panel.background = element_blank())+ theme(legend.key.width = unit(5, "cm"))
p

#How many cases and deaths were recorded the date the intervention measures were put in place?

#1.Loop through all counties in the list to find the dates and number of cases&deaths for before, during, and after interventions were put in place. 



#2.Then do a plot where the date is on the x axis and the number of cases and deaths are on the y axis.




#3.After plotting those two lines find the places that showed the highest and lowest drops due to the intervention or lack thereof.

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