JoFAM

# Calculation of hurricane damage as percentage of
# real GDP in the US.

# The data: 
# damage taken from:
# https://www.wunderground.com/cat6/2017-us-hurricane-damages-206-billion-highest-record
# real GDP taken from
# https://fred.stlouisfed.org/series/GDPCA
year <- c(2017,1893,2005,1928,1960,2012,1969,1947,1954,1945)
# damage in billion of dollars, adjusted to 2017 dollars

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# x is a dataset, and one wants to construct a new dataset created by x minus the 5 "quartiles",
# being 0%, 25%, 50%, 75% and 100% quantiles.
# This constructs the data
x <- rnorm(1e6)
quart <- quantile(x)

# The original code of one of my students. I loathed two things:
# - the use of a for loop when one could use outer()
# - the growing of an object
oorspronkelijk <- function(x, quart){

JoFAM

library(OECD)
library(dplyr)
library(ggplot2)

## Get the latest OECD data on decile ratios of gross earnings.
## This is the dataset that contains information on wage gaps.
oecdData <- get_dataset("DEC_I")

## Prepare the plot data
gwg <- 

JoFAM

# Currently roxygen 6.1.0 doesn't read the collate field correctly, so once you use @include anywhere
# in your package, you run into problems. The error you get, is a 'Permission denied' error. If that
# occurs, best thing to do for now is to install the previous version of roxygen until
# the problem is solved.
#
# I've made a binary build for Windows available. You can download it manually from following link:
# https://jorismeys.stackstorage.com/s/co8cKzyRemVHZef
# Alas the filesharing I use doesn't allow automatic downloading through download.file(), so you
# have to go to that link and click on Download to get the actual zip file.
#

JoFAM

``` r
library(magrittr)
library(purrr)
#> 
#> Attaching package: 'purrr'
#> The following object is masked from 'package:magrittr':
#> 
#>     set_names

library(microbenchmark)

JoFAM

library(tidyr)
library(ggplot2)

# I look at 4 different rates here. The rate for the period 2000-2018 was approximately 3 mm / year.
rates <- c(0.15,0.2,0.3, 0.4)

# This creates a tibble (dataset) with the sea level rise until 2100 depending on the rate
mydata <- tibble(
  years = seq(2018,2100, by=1),
  slow = seq_along(years)*rates[1],

JoFAM

# Download median of regional timeseries
temp <- readLines("https://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/time_series/HadCRUT.4.6.0.0.monthly_ns_avg.txt")
temp <- data.frame(do.call(rbind, strsplit(temp,"   ")),
                      stringsAsFactors = FALSE)
temp[-1] <- lapply(temp[-1], as.numeric)

temp$year <- as.numeric(gsub("/.*","",temp[,1]))
temp$month <- as.numeric(gsub(".*/","",temp[,1]))
temp$time <- temp$year + temp$month/12.5

JoFAM

## ----setup, include=FALSE------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
# Needed so the code runs later on.
datadir <- "pottmp_data"
fnames <- paste("pottmp",1980:2019,"nc", sep = ".")

## ----Download information, eval=FALSE------------------------------------
## # ONLY RUN THIS ONCE TO DOWNLOAD THE DATA
## # This downloads 39 files of appx 144 Mb each!
## # Create a directory to store the data.

JoFAM

library(shiny)

# Define server logic required to draw a histogram
shinyServer(function(input, output) {

    mysamples <- reactive({
        fdist <- switch(input$dist,
                        Normal = rnorm,
                        Exponential = rexp,
                        Poisson = function(n) rpois(n,4))

JoFAM

# Plot UAH monthly temperatures
# Joris Meys
# Date last modified: 2019-11-18

# Create a temporary filename
fname <- tempfile()

# Download data into the temporary file
download.file("https://www.nsstc.uah.edu/data/msu/v6.0/tlt/uahncdc_lt_6.0.txt",destfile = fname)