code/covid_cases.R

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params <-
list(month = "April May June", year = 2020L, geo_id = "NL BE")

## ----setup, include=FALSE----------------------------------------------------------------------
knitr::opts_chunk$set(
  echo = FALSE,
  warning = FALSE,
  message = FALSE,
  error = FALSE
  
  )


## ----------------------------------------------------------------------------------------------
## Packages
library(utils)
library(tidyverse)
library(tools)


## ---- echo=TRUE--------------------------------------------------------------------------------
## Data
#read the Dataset sheet into “R”. The dataset will be called "data".
#data <- read_csv(
#  "https://opendata.ecdc.europa.eu/covid19/casedistribution/csv", 
#  na = "")
source(
  here::here(
    "data-raw",
    "D010",
    "supporting",
    "covid_ecdc_cases_geography.R"))


## ---- include=FALSE----------------------------------------------------------------------------

## Filter for `r params$month` and `r params$geo_id`
## create a conversion table to go from month name to month integer
convert_months <- tibble(
  month_name = c(
    "january",
    "february",
    "march",
    "april",
    "may",
    "june",
    "july",
    "august",
    "september",
    "october",
    "november",
    "december"),
  month_number = c(1:12))

  m_names <- str_split(tolower(params$month), pattern = " ") %>% unlist()


  month_id <- convert_months %>%
    dplyr::filter(
      month_name %in% m_names) %>% 
    dplyr::select(month_number)
  
  id <- month_id$month_number

geo_ids <- str_split(string = params$geo_id, pattern = " ") %>% unlist()

data_filter <- data %>%
  dplyr::filter(
    month %in% id,
     geoId %in% geo_ids
)
  
char_col <- map_lgl(
  .x = data_filter,
  .f = is.numeric 
)

map(
  .x = data_filter[, !char_col],
  .f = unique
)

country_names <- data_filter$countriesAndTerritories %>% unique() %>%
  paste(collapse = ", ")

Caps <- function(x) {
s <- strsplit(x, " ")[[1]]
paste(toupper(substring(s, 1,1)), substring(s, 2),
sep="", collapse=" ")
}

cap_cases <- paste("Cases for", params$month, "and", country_names)



## ---- fig.cap= cap_cases-----------------------------------------------------------------------
data_filter <- data_filter %>% 
  mutate(date_time = lubridate::dmy(dateRep))

#names(data_filter)
plot_cases <- data_filter %>%
  ggplot(aes(x = date_time, y = cases)) +
  geom_point(aes(colour = geoId)) +
  geom_line(aes(group = geoId, colour = geoId))

plot_cases



## ---- fig.cap=paste("Deaths for", params$month, "and" , country_names)-------------------------
data_filter <- data_filter %>% 
  mutate(date_time = lubridate::dmy(dateRep))

#names(data_filter)
plot <- data_filter %>%
  ggplot(aes(x = date_time, y = deaths)) +
  geom_point(aes(colour = geoId)) +
  geom_line(aes(group = geoId, colour = geoId))

plot