TonyLadson

# see  https://tonyladson.wordpress.com/2015/02/17/quality-codes-in-hydrologic-data/

library(treemap)
library(RColorBrewer)
library(ggplot2)
library(grid)
library(lubridate)

# Generate some data

TonyLadson

# Usage
# LeadingNA(x)
#
# x is a data frame
# Value
#  A data frame, with two rows, leading NAs and trailing NAs

LeadingNA <- function(x){
  num.col <- ncol(x)
  trailing.na <- rep(0, times = num.col)

TonyLadson

# Usage
#  MissingMap(df, date.column, var.column)
#  df = data.frame that contains a column of dates and column of variables that may have missing values
#  date.column = quoted name of the column cotaining dates
#  var.column = quoted name of the column containing the variable
#
# Produces a tile plot showing here missing values occur in the sequence of dates
# https://tonyladson.wordpress.com/2014/12/21/mapping-missing-data/

MissingMap <- function(df, date.column, var.column){ 

TonyLadson

BwTheme = theme_bw() + 
  theme(
    panel.background = element_rect(fill="gray98"),
    axis.title.x = element_text(colour="grey20", size=20, margin=margin(20,0,0,0)),
    axis.text.x = element_text(colour="grey20",size=14),
    axis.title.y = element_text(colour="grey20",size=20, margin = margin(0,20,0,0)),
    axis.text.y = element_text(colour="grey20",size=14),
    legend.title = element_text(colour="grey20",size=12),
    plot.margin = unit(c(0.5, 0.5, 1, 0.5), "cm")) # top, right, bottom, left

TonyLadson

# Function to return delta as used to estimate
# confidence intervals for flood frequency analysis
 
# Usage
#   Delta(g, aep)
#
# g = skewness
# aep = Annual exceedance probability
 
#

TonyLadson

##########################################################################################################################
#
# Delta values
#
# tony.ladson at gmail.com
# 11 March 2015
#
# Used to determine confidence intervals for the Log Pearson III distribution
# The table of delta values is taken from Table 2.4, Book IV, Section 2 of Australian Rainfall and Runoff third edition.
# Confidence interval calculations are based on 

TonyLadson

isGaugeNum <- function(x) {

# returns true if value is missing, or a gauge number i.e. 6 digits followed by a capital letter
# otherwise returns false

  if(!is.character(x)) return(FALSE)
  if(is.na(x)) return(TRUE)
  if(str_count(x) != 7) return(FALSE)
  str_detect(x, "^[0-9]{6}[A-Z]{1}") # 6 digits followed by a capital letter
 

TonyLadson

isNewSiteNum <- function(x) {
  
  # returns true if value is missing, or a site number i.e. 5 capitals follows by 4 digits
  # otherwise returns false
  
  if(is.na(x)) return(TRUE)
  if(!is.character(x)) return(FALSE)
  
  if(str_count(x) != 9) return(FALSE)
  str_detect(x, "^[A-Z]{5}[:digit:]{4}") # 5 capitals followed by 4 digits

TonyLadson

dbinom(0,100,0.01) # zero 1% floods in 100 years
dbinom(1,100,0.01) # one 1% floods in 100 years
dbinom(2,100,0.01) # two 1% floods in 100 years
dbinom(3,100,0.01) # three 1% floods in 100 years
dbinom(4,100,0.01) # four 1% floods in 100 years
dbinom(5,100,0.01) # five 1% floods in 100 years

# at least 1 1% flood in 100 years
1 - pbinom(0,100, 0.01) 

TonyLadson

dpois(0,1)  # prob of zero 100-year floods in 100 years
dpois(0,1)  # prob of zero 100-year floods in 100 years
[1] 0.3678794
dpois(1,1)  # prob of one 100-year floods in 100 years
[1] 0.3678794
dpois(2,1)  # prob of two 100-year floods in 100 years
[1] 0.1839397


# simulation