RamiKrispin

snippet dd
	devtools::document()
snippet m
	`%>%` <- magrittr::`%>%`
snippet l
	lubridate::
snippet >
	%>%
snippet p
	plotly::

RamiKrispin

data("covid19sf_vaccine_demo")
d1 <- covid19sf_vaccine_demo
head(d1)
`%>%` <- magrittr::`%>%`
d1a <- covid19sf_vaccine_demo %>%
  dplyr::filter(administering_provider_type == "All Providers",
                demographic_group == "Age Bracket",
                age_group == "All") %>%
  dplyr::mutate(only_1st = total_1st_doses - total_2nd_doses,
                not_vaccinated = subgroup_population - total_series_completed - only_1st,

RamiKrispin

x <- "Plan_Fc.Plan_Fc_Doppler_US"

s <- unlist(regexec(pattern = "\\.", text = x)) + 1
e <- nchar(x)
substr(x = x, start = s, stop = e)

RamiKrispin

library(cdcfluview)
national_ili <- ilinet("national", years = c(1997:2017))
mydata.ts <- ts(data = national_ili$total_patients, end = c(2018,39), frequency= 52)

plot(mydata.ts,type="o",col="blue") 
plot(forecast(mydata.ts, h = 65))

library(coronavirus)
library(dplyr)
library(forecast)

RamiKrispin

library(coronavirus)
library(dplyr)
raw <- refresh_coronavirus_jhu() 
head(raw)
df <- raw %>%
  filter(location == "Israel",
         data_type == "cases_new") %>%
  select(date, new_cases = value) %>%
  arrange(date)
head(df)

RamiKrispin

# Train ARIMA model
md <- arima(x = AirPassengers, order = c(2, 1, 2), seasonal = list(order = c(0, 1, 0)))
# Set number of simulations
n <- 200

#Run simulation
sim <- parallel::mclapply(1:n,
                   mc.cores = 12,
                   function(i){
  fc_sim <- simulate(md, 24)

RamiKrispin

df <- data.frame(date = seq.Date(from = as.Date("2020-01-01"), 
                                 length.out = 100, 
                                 by = "week")) %>%
  dplyr::mutate(week_index = 1 + (as.numeric(date) - as.numeric(min(date)))/7)


head(df)

RamiKrispin

library(coronavirus)
library(dplyr)
library(plotly)
df <- refresh_coronavirus_jhu()
head(df)

df1 <-  df %>%
  filter(data_type == "deaths_new") %>%
  group_by(location) %>%
  summarise(total = sum(value),

RamiKrispin

library(USgas)
data("us_total")
# Set labels
ne <- c("Connecticut", "Maine", "Massachusetts",
        "New Hampshire", "Rhode Island", "Vermont")

# Filter the data
ne_gas <-  us_total[which(us_total$state %in% ne),]

# Transform to wide format

RamiKrispin

# Based on this example - https://jkunst.com/highcharter/articles/fontawesome.html
# Data from the USgrid package - https://github.com/RamiKrispin/USgrid
# Icons from - https://fontawesome.com/icons?d=gallery&p=2


library(fontawesome)
library(highcharter)
library(stringr)
library(dplyr) # to wokr with list columns
library(purrr) # to wokr with list columns