thoughtfulbloke

library(dplyr)
library(lubridate)
library(ggplot2)

EUcdc <- read.csv("https://opendata.ecdc.europa.eu/covid19/casedistribution/csv",
                  stringsAsFactors = FALSE, fileEncoding = "UTF-8-BOM")
# NZ level 4 lockdown 26 March
#Boris Handshakes 3 March,
EUcdc %>%
  filter(countriesAndTerritories %in% c("United_Kingdom", "New_Zealand")) %>%

thoughtfulbloke

library(readr)
library(dplyr)
library(tidyr)
library(lubridate)
library(ggplot2)
# from the downloaded apple mobiblity data of the day
mobl <- read_csv("applemobilitytrends-2020-05-03.csv")
# colums added for each day, so get number of coluns in current data
extant <- ncol(mobl)
# graph specific choices

thoughtfulbloke

library(dplyr)
library(ggplot2)
library(scales)
library(patchwork)

# random data, ordered
set.seed(2020)
series1_ind <- sort(sample(0:100, 25, replace=TRUE))
series2_ind <- sort(sample(1000:30000, 25, replace=TRUE))
example <- data_frame(step=1:25, series1_ind, series2_ind)

thoughtfulbloke

# https://www.mbie.govt.nz/immigration-and-tourism/tourism-research-and-data/tourism-data-releases/monthly-regional-tourism-estimates/regional-tourism-estimates/regional-tourism-estimates-key-pivot-table/
# Regional Tourism Estimates key pivot table

library(readxl)
library(tidyr)
library(dplyr)
library(ggplot2)
library(forcats)
tourism <- read_excel("rte-pivot-table-ye-march-2015.xlsx", sheet="Database")
TA_tour <- tourism %>% filter(YEMar == 2015) %>%

thoughtfulbloke

"constituency","party","candidate","attribute","value"
"Aberavon","Labour","Stephen Kinnock","Votes:",17008
"Aberavon","Labour","Stephen Kinnock","Vote share %:",53.8
"Aberavon","Labour","Stephen Kinnock","Vote share change:",-14.3
"Aberavon","Conservative","Charlotte Lang","Votes:",6518
"Aberavon","Conservative","Charlotte Lang","Vote share %:",20.6
"Aberavon","Conservative","Charlotte Lang","Vote share change:",2.9
"Aberavon","The Brexit Party","Glenda Davies","Votes:",3108
"Aberavon","The Brexit Party","Glenda Davies","Vote share %:",9.8
"Aberavon","The Brexit Party","Glenda Davies","Vote share change:",9.8

thoughtfulbloke

library(readr)
geo_mean <- read_csv("https://www.mbie.govt.nz/assets/Data-Files/Building-and-construction/Tenancy-and-housing/Rental-bond-data/Territorial-Authority/ta-geometric-mean.csv",
                     col_types = cols(
                       .default = col_double(),
                       Month = col_date(format = "")
                     ))
library(dplyr)
library(ggplot2)
geo_mean %>% filter(Month >= as.Date("2009-01-01")) %>%
mutate(govt = c(rep("National", 105), rep("Labour",25))) %>%

thoughtfulbloke

# tweets are assumed to be gathered for this

library(stringr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggthemes)
library(ggrepel)
library(Rtsne)
disc <- read.csv("discussion.csv", stringsAsFactors = FALSE)

thoughtfulbloke

#globe map without countries distorted by polygon points being clipped by the "edge of the world"
#This is my hacking around of the fuller script
#https://github.com/r-spatial/sf/blob/master/demo/twitter.R
#linked to from section 8.1 of https://keen-swartz-3146c4.netlify.com/plotting.html

library(sf)
library(maptools)
library(animation)

data(wrld_simpl)

thoughtfulbloke

library(Rtsne)
library(vroom)
library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
library(ggrepel)
library(janitor)

# data from https://www.dragonfly.co.nz/news/2019-11-12-boty.html

thoughtfulbloke

library(ggplot2)
library(dplyr)
library(gganimate)
exhale <- 5 # seconds
exhaled_pause <- 3 # seconds
inhale <- 4 # seconds
inhaled_pause <- 4 # seconds
frames_per_second = 16

exh <- data.frame(stage = rep("exhale", exhale*frames_per_second),