thoughtfulbloke

library(ggplot2)
library(ggthemes)
library(dplyr)
colour_choice <- colorblind_pal()(3)

pasted_text = "PasteYourGameShareHre"

pasted_title <- gsub("\n\n.*", "", pasted_text)
title_short <- substr(pasted_title, 1, nchar(pasted_title) - 4)
pasted_grid <- gsub(".*[^/ ]/[^\\\n]+", "", pasted_text)

thoughtfulbloke

library(rvest)
library(dplyr)
library(lubridate)
library(readr)
library(ggplot2)
library(ggthemes)
library(ggridges)
source("davidise.R")
# saving reports from the website as html into a folder
# called news_item_html

thoughtfulbloke

library(readr)
library(dplyr)
library(ggplot2)
library(ggthemes)
library(lubridate)
source("davidise.R") # graph styling and saving at twitter size
# extract colorblind pallete in case I want to make strategic use of it
six_cols <- (colorblind_pal()(6))
# daily full demographic csvs folder
cases_folder <- list.files("daily_all_cases", pattern = "csv$", full.names = TRUE)

thoughtfulbloke

library(dplyr)
library(lubridate)
library(ggplot2)
library(ggthemes)
library(tidyr)
library(RcppRoll)

# To look at locations of interest, I am taking my own set of infectious vs isolated cases from the 1pm news conference, the csv of individual case details to get location (available as a csv linked from https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-data-and-statistics/covid-19-case-demographics ), and a collection of the location of interest updates kindly provided by @nzgizmoguy on Twitter at https://github.com/gizmoguy/locations-of-interest 

thoughtfulbloke

library(dplyr)
library(tidyr)
library(ggplot2)
library(ggthemes)
library(scales)
library(lubridate)
library(rvest)
system_time= paste("Update: ", Sys.time())
txt_data <- "
dhb, pop12+,dosed2

thoughtfulbloke

# OECD weekly all cause deaths to mortality rates
# Keep in mind, some countries (Sweden) exclude 3% of deaths from the weekly 
# dataset as the day of death is not known

library(OECD)
library(dplyr)

country1 = "NZL"
country2 = "USA"

thoughtfulbloke

# MoH is the ministry of Health all cases demographics csv file
# https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-data-and-statistics/covid-19-case-demographics#case-details
library(readr)
library(dplyr)
library(ggplot2)
library(ggthemes)
library(lubridate)

# you need to provide csv name
file_name <- "covid_cases_2021-09-14.csv"

thoughtfulbloke

davidise_graph <- function(x, savepath="~/Desktop/output", show_graph = TRUE){
  # x is an unthemed ggplot graph but all the scales & labs are applied
  # since this function wants a ggplot graph, I am assuming ggplot is loaded)
  # assume Open Sans installed as a font
  
  library(showtext)
  # sort out your own font path declarations
  font_add(family = "OpenSans", regular = "~/Library/Fonts/OpenSans-Light.ttf", 
           bolditalic = "~/Library/Fonts/OpenSans-BoldItalic.ttf",
           italic = "~/Library/Fonts/OpenSans-LightItalic.ttf",

thoughtfulbloke

# Data
# https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-data-and-statistics/covid-19-case-demographics#case-details
library(readr)
library(dplyr)
library(ggplot2)
library(ggthemes)
library(lubridate)
library(patchwork)

# you need to provide csv name

thoughtfulbloke

library(rtweet)
library(dplyr)
library(lubridate)
library(ggplot2)
library(tidytext)
library(topicmodels)
search_term <- "#nzpol"
corpus <- search_tweets(search_term, n=18000)
nzpol_utm <- corpus %>% select(screen_name, text) %>%
  unnest_tokens(word,text, token="tweets") %>%