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Created Oct 4, 2020
Regression examples and figures for the blog https://tonyladson.wordpress.com/2020/10/05/errors-in-variables-regression/
View gist:286077221cc07022a77aa74d0a6c211c
 library(tidyverse) library(deming) # Functions --------------------------------------------------------------- # given a point (x1, y1) and a line defined by a slope m and intercept c1 # function to return the point x on the line where a line drawn perpendicular to x1, y1 # will intercept the line
Last active Jun 2, 2020
Sampling distribution of floods. See https://tonyladson.wordpress.com/2020/06/02/sampling-distribution-of-the-1-flood/
View Flood_sampling_distribution.R
 library(tidyverse) #pbinom(q, size, prob, lower.tail = TRUE, log.p = FALSE) # Probability of 1, 1% flood in 100 years dbinom(1, size = 100, prob = 0.01, log = FALSE) choose(100,1) *0.01^1*0.99^99
View Issues_column_graph.R
 library(tidyverse) issues <- tribble(~issue, ~pc, 'Inflows', 6, 'Demand changes', 6, 'System operation', 18, 'Equity of water sharing arrangements', 10, 'State entitlements and policies', 10, 'Environmental water', 11, 'Reliability of different licences', 4, 'Miscellaneous', 4,
Last active May 23, 2020
Polynomial interpolation of ARF values between 12 and 24 hours. See https://tonyladson.wordpress.com/2020/05/23/smooth-interpolation-of-arf-curves/
View ARF_poly_interp.R
 # Smooth interpolation between long and short duration ARFs using a cubic. library(tidyverse) library(pracma) devtools::source_url("https://gist.githubusercontent.com/TonyLadson/fc870cf7ebfe39ea3d1a812bcc53c8fb/raw/d8112631a92a32be749cabe334a22931c035711e/ARF2019.R?raw=TRUE")
Created May 12, 2020
Code to produce Figure 2 for the Blog https://tonyladson.wordpress.com/2020/04/05/arr2019-areal-reduction-factors/
View ARF_map.R
 library(tidyverse) xdf <- tribble(~area_min, ~area_max, ~dur_min, ~dur_max, ~type, 10, 1000, 0, 12, 'short', 10, 30000, 24, 168, 'long', 10, 30000, 12, 24, 'interpolation', 0, 1, 0, 168, 'ARF = 1', 1, 10, 0, 12, 'Interp3', 1, 10, 12, 24, 'Interp2', 1, 10, 24, 168, 'Interp1')
Created May 12, 2020
Code to reproduce the figures in the blog: ARR2019 – Areal Reduction Factors: interpolating between short and long duration ARFs
View ARF_interp.R
 # Load the functions we need devtools::source_url("https://gist.githubusercontent.com/TonyLadson/fc870cf7ebfe39ea3d1a812bcc53c8fb/raw/d8112631a92a32be749cabe334a22931c035711e/ARF2019.R?raw=TRUE") #source(file.path('ARR2019_ARF', "ARF_2019.R")) #source(file.path('ARR2019_ARF', "ARF_tests.R")) # Check that we pass tests # Functions and constants ------------------------------------------------------
Created Apr 19, 2020
Investigating short duration Areal Reduction Factors https://tonyladson.wordpress.com/2020/04/19/arr2019-areal-reduction-factors-wobbles-in-short-duration-arfs/
View ARF_sd.R
 library(tidyverse) library(optimx) devtools::source_url("https://gist.githubusercontent.com/TonyLadson/fc870cf7ebfe39ea3d1a812bcc53c8fb/raw/d8112631a92a32be749cabe334a22931c035711e/ARF2019.R?raw=TRUE") #source(file.path('ARR2019_ARF', "ARF_2019.R")) #source(file.path('ARR2019_ARF', "ARF_tests.R")) # Check that we pass tests
Last active Apr 17, 2020
Areal reduction factors - some edge cases https://tonyladson.wordpress.com/2020/04/14/arr2019-areal-reduction-factors-some-edge-cases/
View ARF_edge_cases.R
 library(tidyverse) devtools::source_url("https://gist.githubusercontent.com/TonyLadson/fc870cf7ebfe39ea3d1a812bcc53c8fb/raw/d8112631a92a32be749cabe334a22931c035711e/ARF2019.R?raw=TRUE") #source(file.path('ARR2019_ARF', "ARF_2019.R")) #source(file.path('ARR2019_ARF', "ARF_tests.R")) # Functions and data ------------------------------------------------------
Created Apr 13, 2020
Function to calculate The Australian Rainfall and Runoff 2019 Areal Reduction Factors see https://tonyladson.wordpress.com/2020/04/05/arr2019-areal-reduction-factors/
View ARF2019.R
 library(stringr) library(testthat) region_names <- c("East Coast North", "Semi-arid Inland QLD", "Tasmania", "SW WA", "Central NSW", "SE Coast", "Southern Semi-arid", "Southern Temperate", "Northern Coastal", "Inland Arid") params <- structure(list(`East Coast North` = c(0.327, 0.241, 0.448, 0.36, 0.00096, 0.48, -0.21, 0.012, -0.0013),
Created Apr 11, 2020 — forked from jennybc/2020-03-29_sane-legend.R
Make the legend order = data order, with forcats::fct_reorder2()
View 2020-03-29_sane-legend.R
 library(tidyverse) library(patchwork) dat_wide <- tibble( x = 1:3, top = c(4.5, 4, 5.5), middle = c(4, 4.75, 5), bottom = c(3.5, 3.75, 4.5) )