/
ArboMAP_forecast.Rmd
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ArboMAP_forecast.Rmd
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---
params:
forecast_date:
label: "Date to run the forecast for, e.g. 2018-08-15"
value: "2018-08-15"
state_name:
label: "Name of state"
value: "South Dakota"
state_code:
label: "Two letter state abbreviation"
value: "SD"
predictor_var1:
label: "Name of the first environmental predictor variable"
value: "tmeanc"
predictor_var2:
label: "Name of the second environmental predictor variable"
value: "vpd"
mosquito_model:
label: "Mosquito model type"
value: "stratifiedMIGR"
input: select
choices: ["simpleratio", "AUC", "MIGR", "MII", "stratifiedMIGR", "stratifiedMII"]
mosquito_doy_start:
label: "Start of mosquito modelling period (day of year)"
value: 140
mosquito_doy_end:
label: "End of mosquito modelling period (day of year)"
value: 366
file_human:
label: "Human data file"
value: !r file.path("data_human", "simulated_human_data.csv")
input: file
file_mosquito:
label: "Mosquito pool data file"
value: !r file.path("data_mosquito", "simulated_mosquito_data.csv")
input: file
file_strata:
label: "Stratification file, leave as \"\" if no strata"
value: !r file.path("data_strata", "example_strata_SD.csv")
file_county_sf:
label: "Spatial data, either file location or command. Use \"create\" to download the census spatial data once and create a file in data_spatial folder. Use \"always_download\" to download census spatial data each time."
value: !r file.path("data_spatial", "sd_counties.RDS")
file_models:
label: "Modelling formulas file"
value: !r file.path("data_models", "models.txt")
input: file
folder_weather:
label: "Folder where weather/environmental data files are located"
value: "data_weather"
year_human_start:
label: "Start year of human data to use and this will also be the start year of modelling"
value: 2004
year_human_end:
label: "End year of human data to use. This will normally be the year before the current/forecast year."
value: 2017
year_mosquito_start:
label: "Start year of mosquito data to use"
value: 2004
year_mosquito_end:
label: "End year of mosquito data to use. This is normally the same year as the current/forecast year, or the last year of available mosquito information."
value: 2018
year_weather_start:
label: "Start year of weather/environmental data to use. This should be at least the year before the start of human data."
value: 2000
year_weather_end:
label: "End year of weather/environmental data to use. This is normally the same year as the current forecast year."
value: 2018
year_compare_vis1:
label: "Year to highlight as a comparison year in certain graphs"
value: 2012
year_compare_vis2:
label: "Second year to highlight as a comparison year in certain graphs"
value: 2017
create_appendix:
label: "Create a detailed appendix?"
value: TRUE
lag_length:
label: "Number of days of weather data to include in lags"
value: 121
case_trim_alpha:
label: "Remove temporal outliers from human cases"
value: 0.02
dev_settings:
label: "Developer settings, leave as \"!r list()\""
value: !r list()
title: "West Nile Virus Forecast Report for `r params$forecast_date` \n`r params$state_name`"
subtitle: "DEMO for Arbovirus Modeling and Prediction (ArboMAP): Synthetic Data \nNot for Epidemiological Use"
author: "Dawn M. Nekorchuk, Justin K. Davis, and Michael C. Wimberly \n(mcwimberly@ou.edu) \nGeography and Environmental Sustainability, University of Oklahoma"
date: "Report compiled on `r format(Sys.time(), '%B %d, %Y')`"
header-includes:
- \usepackage{pdflscape}
- \newcommand{\blandscape}{\begin{landscape}}
- \newcommand{\elandscape}{\end{landscape}}
- \usepackage{fancyhdr}
- \pagestyle{fancy}
output:
html_document:
toc: TRUE
toc_float: TRUE
number_sections: TRUE
pdf_document:
toc: TRUE
number_sections: TRUE
---
```{r setup, include=FALSE}
#VERSION
version_text = "4.5"
#knitr
options(warn=-1)
# replace NA with a dash in kable tables
opts <- options(knitr.kable.NA = "-")
```
```{r libraries, include=FALSE}
#make sure pacman is installed
if (!require("pacman")) install.packages("pacman", repos="http://cran.us.r-project.org"); library(pacman)
#load packages, install if not installed
pacman::p_load(
#data processing, tidyverse
dplyr, readr, tidyselect, rlang, tidyr,
tibble, stringr, glue, lubridate, purrr,
#add'l data processing
zoo,
#modeling & evaluation
mgcv, pROC, #splines, parallel are used but are base packages
#spatial, maps and graphs
tigris, sf, ggplot2, gridExtra,
viridis, ggrepel, forcats, ggpubr,
#report generation and interface
knitr, rmarkdown, shiny)
#Must use recent version of readr
if (packageVersion("readr") < "2.1"){
install.packages("readr", repos="http://cran.us.r-project.org")
}
#Must use recent version of Rcpp
if (packageVersion("Rcpp") < "1.0.7"){
install.packages("Rcpp", repos="http://cran.us.r-project.org")
}
# IF the user is trying to create a pdf,
# AND there is no LaTeX installation,
# THEN install tinytex
if (knitr::is_latex_output() &
Sys.which("pdflatex") == ""){
install.packages("tinytex", repos="http://cran.us.r-project.org")
tinytex::install_tinytex()
}
# [DEV] If warning about new TeX, need to run: tinytex::reinstall_tinytex()
# a test for this does not exist, but annual update script should take care of it
# [DEV] This may not play well if they are using xelatex or lualatex instead
# however, they need to set that in YAML headers too, so shouldn't be a problem here
#Single use functions are found in their section
# for convenience when reviewing/editing code
#for coloring text, that will work with EITHER pdf or html
#however, make sure color NAME is accepted in either
colorize <- function(x, color) {
if (knitr::is_latex_output()) {
pdf_color <- case_when(
color == "darkred" ~ "red",
color == "darkblue" ~ "blue",
TRUE ~ color
)
sprintf("\\textcolor{%s}{%s}", pdf_color, x)
} else if (knitr::is_html_output()) {
sprintf("<span style='color: %s;'>%s</span>", color, x)
} else x
}
#`r colorize("some words in red", "red")`
#https://bookdown.org/yihui/rmarkdown-cookbook/font-color.html
# [DEV] Notes on latex packages
# Used for rotating page to landscape (wide time series graph, optional for ref map)
# - \usepackage{pdflscape}
# - \newcommand{\blandscape}{\begin{landscape}}
# - \newcommand{\elandscape}{\end{landscape}}
# For nice headers and page formatting
# - \usepackage{fancyhdr}
# - \pagestyle{fancy}
```
<!-- Start of internals: data load, processing, forecast modeling, etc. -->
<!-- Code blocks: -->
<!-- dev_parameters: Checks for overrides from named list of dev parameters.
Only really accessible if rmd called from script. -->
<!-- data_load: Load all data - human, mosquito, environmental, spatial, models.
No data checks or processing here. -->
<!-- data_id_fields: add a standardized arbo_ID field based on names or fips code,
depending on fields in datasets. One or the other, not a mix -->
<!-- data_human_processing_dx: Process the human data, create diagnostics/stats -->
<!-- data_mosq_processing_dx: Process the mosquito data, create diagnostics/stats -->
<!-- mosquito_infection_model: run selected mosquito model to generate raw mir statistic -->
<!-- mir_imputation: Imputation of any missing mir statistics from unknown/no data, mir_stat sused in modeling -->
<!-- data_env_latest_history: processes the environment data
1) gets latest updated value for a day,
2) creates historical doy means -->
<!-- data_env_anomalization: calculates anomalies of environmental data via gam -->
<!-- data_combine: combine all data and format for passing off for regression -->
<!-- forecast_modeling: create the forecast models -->
<!-- Note on code comment flags: [V3]: this is what was done in version 3; [DEV] developer note; [DMN] note from Dawn Nekorchuk; DEV or <<>>: active development -->
```{r dev_parameters, echo=FALSE, include=FALSE}
#input is named list
#parameters available:
##DATA
# data_human: tbl of human case data (overrides file_human)
# data_mosquito: tbl of mosquito pool data (overrides file_mosquito)
# data_strata: tbl of strata (overrides file_stratification)
# data_weather: tbl of weather data (overrides folder_weather & processing)
# data_sf: sf object of counties for state (overrides file_county_sf)
# model_formulas: model formulas to run (overrides file_models)
##CACHED MODELS
# save_models: TRUE/FALSE: Will create a list of saved model objects (using rest of input params)
# models_cached: must be named list of model objects, named from modeling file to pattern match (output of save_models)
##REGRESSION
# mir_exactfit: T/F for how to handle missing MIR values in unknown years
# T - Original V3 with impute unknown MIR <- 0 and add exactfit
# F - using a MIR ~ total human cases linear model, no exactfit term.
# reg_function: "GAM" (hook for future possibility)
##SAVING (for extra research files)
#out_folder: "."
#out_name_base: "ArboMAP_forecast_"
#dev_write_output: write out a bunch of files into dev folder for testing and checking
##EVALUATION
#model_evaluation: TRUE/FALSE output model evaluation statistics
##REPORT
#highlight: list of counties to 'highlight' in report output.
# Needs to match names OR fips as in shapefile (depending on ID field used for arbo_ID)
#highlight_mask: T/F, whether to highlight (red outline, FALSE, default) or mask out rest of state (TRUE)
#NOTE: If not using dev parameter, do NOT include in list or instead set to NULL
# The test checks if the value is NULL. Not NA or "" or things like that.
# dev_settings itself is set to an empty list in default params above
##DATA
if (!is.null(params$dev_settings$data_human)){
#override data
data_human <- params$dev_settings$data_human
} else {
#load from file, data_load code block
data_human <- NULL
}
if (!is.null(params$dev_settings$data_mosquito)){
#override data
data_mosquito <- params$dev_settings$data_mosquito
} else {
#load from file, data_load code block
data_mosquito <- NULL
}
if (!is.null(params$dev_settings$data_strata)){
#override data
data_strata <- params$dev_settings$data_strata
} else {
#load from file, data_load code block
data_strata <- NULL
}
# RAW, as processing will still happen on override data, except for deduplication
if (!is.null(params$dev_settings$data_weather)){
#override data
data_env_raw <- params$dev_settings$data_weather
} else {
#load from file, data_load code block
data_env_raw <- NULL
}
if (!is.null(params$dev_settings$data_sf)){
#override data
data_sf <- params$dev_settings$data_sf
} else {
#load from file, data_load code block
data_sf <- NULL
}
if (!is.null(params$dev_settings$model_formulas)){
#override data
models_raw <- params$dev_settings$model_formulas
#create named list from tbl
model_formulas <- models_raw %>%
dplyr::pull(2, name = 1)
#just the model names for later use
model_names <- names(model_formulas)
} else {
#load from file, data_load code block
model_formulas <- NULL
}
##CACHED MODELS
if (!is.null(params$dev_settings$save_models)){
#use param given
save_models <- params$dev_settings$save_models
} else {
#default is FALSE
save_models <- FALSE
}
#models_cached testing and possible use is done inside of forecast regression section
if (!is.null(params$dev_settings$models_cached)){
#use param given
models_cached <- params$dev_settings$models_cached
} else {
models_cached <- NULL
}
##REGRESSION
if (!is.null(params$dev_settings$mir_exactfit)){
#use param given
mir_exactfit <- params$dev_settings$mir_exactfit
} else {
#default is FALSE
mir_exactfit <- FALSE
}
if (!is.null(params$dev_settings$reg_function)){
#use param given
reg_function <- params$dev_settings$reg_function
} else {
#default is the current and only way via mgcv bam
#this is being setup so that more may be added easily in the future [DMN]
reg_function <- "GAM"
}
##SAVING (for extra research files)
if (!is.null(params$dev_settings$out_folder)){
#use param given
out_folder <- params$dev_settings$out_folder
#create out_folder if necessary
dir.create(out_folder, recursive = TRUE)
} else {
out_folder <- "."
}
if (!is.null(params$dev_settings$out_name_base)){
#use param given
out_name_base <- params$dev_settings$out_name_base
} else {
#default
#out_name_base <- "ArboMAP_forecast_"
out_name_base <- paste0(params$mosquito_model, "_", Sys.time() %>% format("%Y%m%d%H%M"))
}
if (!is.null(params$dev_settings$dev_write_output)){
#use param given
dev_write_output <- params$dev_settings$dev_write_output
} else {
dev_write_output <- FALSE
}
##EVALUATION
if (!is.null(params$dev_settings$model_evaluation)){
#use param given
model_evaluation <- params$dev_settings$model_evaluation
} else {
#default is to not save extra files,
# only currently used in research comparing many models
model_evaluation <- FALSE
}
##REPORT
if (!is.null(params$dev_settings$highlight)){
#use param given
highlight <- params$dev_settings$highlight
#set flag to true
highlight_flag <- TRUE
} else {
#default is empty list (NULL)
highlight <- c()
highlight_flag <- FALSE
}
if (!is.null(params$dev_settings$highlight_mask)){
#use param given
highlight_mask <- params$dev_settings$highlight_mask
} else {
#default is FALSE
highlight_mask <- FALSE
}
## OTHER
#[V3] [DEV] imputemissingdistricts was hard coded as FALSE in v3 production code
# updated names and code, and added dev option for renamed impute_human_missing_districts, default = FALSE
# DEV This looks like it samples existing districts as data for the missing districts.
# I can't think of a time where this would be wanted. May be able to delete in future. [DMN]
if (!is.null(params$dev_settings$impute_human_missing_districts)){
#use param given
impute_human_missing_districts <- params$dev_settings$impute_human_missing_districts
} else {
#should stay FALSE unless you are very sure
impute_human_missing_districts <- FALSE
}
#Important references
#https://stackoverflow.com/questions/25407102/conditionally-display-a-block-of-text-in-r-markdown/46686678#46686678
#https://stackoverflow.com/questions/25240541/how-to-add-newpage-in-rmarkdown-in-a-smart-way
#https://stackoverflow.com/questions/25849814/rstudio-rmarkdown-both-portrait-and-landscape-layout-in-a-single-pdf
#https://bookdown.org/yihui/rmarkdown-cookbook/child-document.html
#https://yihui.org/knitr/
```
```{r data_load, echo=FALSE, include=FALSE, cache=FALSE}
#Loads data from file locations given in parameters
#Note: does NOT do any data checks
if (is.null(data_human)){
data_human <- readr::read_csv(params$file_human,
show_col_types = FALSE)
}
if (is.null(data_mosquito)){
data_mosquito <- readr::read_csv(params$file_mosquito,
show_col_types = FALSE)
}
if (is.null(data_strata)){
#if given a strata file, which is optional
if (!params$file_strata == ""){
data_strata <- readr::read_csv(params$file_strata,
show_col_types = FALSE)
}
}
if (is.null(data_sf)){
if (params$file_county_sf == "create"){
#if user set to "create" then we will download tigris shapefile and save for future use
#download tigris, internet required
data_sf <- tigris::counties(state = params$state_code, cb = TRUE)
#save out for use next time
#make folder if does not exist (if exists, just shows warning, suppressed)
dir.create("data_spatial", showWarnings = FALSE)
saveRDS(data_sf, file.path("data_spatial", paste0(params$state_code, "_counties.RDS")))
} else if (params$file_county_sf == "always_download"){
#if "always_download" then we will download tigris shapefile each time (no save), internet required
data_sf <- tigris::counties(state = params$state_code, cb = TRUE)
} else {
#read in file from params
data_sf <- readRDS(params$file_county_sf)
}
}#end is.null
if (is.null(model_formulas)){
models_raw <- readr::read_csv(params$file_models,
col_names = FALSE, show_col_types = FALSE,
quote = "\"")
#create named list from tbl
model_formulas <- models_raw %>%
dplyr::pull(2, name = 1)
#just the model names for later use
model_names <- names(model_formulas)
}
# Weather data
# Raw read in with prep for taking most recent value for day
# See data_weather_latest block for that processing
if (is.null(data_env_raw)){
# Reading in of data & file modified time
#get list of csv files (NOT in subfolders)
env_csv_files_raw <- list.files(path = params$folder_weather,
pattern="*.csv$",
full.names = TRUE, recursive = FALSE)
#keep the names of only csv files that are not empty
#not likely relevant here, however does no harm to check
file_condition <- sapply(env_csv_files_raw, function(x) {length(readr::count_fields(x, readr::tokenizer_csv())) > 1})
env_csv_files <- env_csv_files_raw[file_condition]
#read in all data files, and add the time the file was last modified
data_env_raw <- env_csv_files %>%
lapply(function(x) {
readr::read_csv(x, show_col_types = FALSE) %>%
#add last modified time
dplyr::mutate(file_time = file.info(x)$mtime)}) %>%
#bind list items into one dataset
dplyr::bind_rows()
}
#Record row count of data as read in
dx_mosq_nrow_0load <- nrow(data_mosquito)
dx_human_nrow_0load <- nrow(data_human)
dx_env_nrow_0load <- nrow(data_env_raw) #note PRE de-duplication
```
```{r map_dimensions_sizes, echo=FALSE, include=FALSE, cache=FALSE}
#Because this can be run for any state,
# different states have different dimensions
# and some are wider than tall and vice versa
# Calculating where reference map will be rotated, and
# appropriate maps sizes
#determine if spatial data is wider than it is tall
sf_box_dist <- data_sf %>%
#get bounding box with xmin, xmax, ymin, ymax
sf::st_bbox() %>%
#convert into sfc object
sf::st_as_sfc() %>%
#to turn into points
sf::st_cast("POINT") %>%
#calculate all distances between points
sf::st_distance()
#points:
#1: xmin, ymin,
#2: xmax, ymin
#3: xmax, ymax
#4: xmin, ymax
#5: point 1
# Want distance xmin to xmax, so can use distance between pts 1 & 2
# Want distance ymin to ymax, so can use distance between pts 1 & 4
#TRUE if wider (x distance > y distance), to be used with dynamic sizing/page orientation
sf_wider <- sf_box_dist[1,2] > sf_box_dist[1,4]
# apparently can't use ! in knitr headers, so making separate object for inverse
sf_taller <- !sf_wider
#aspect ratio is height/width
sf_asp_ratio <- sf_box_dist[1,4] / sf_box_dist[1,2]
#calculate width of reference map
# which will either be on a landscape or portrait page
# assuming US letter size paper
ref_map_width <- if (sf_wider){10} else {7}
ref_map_height <- ref_map_width * sf_asp_ratio #note returns units obj not numeric
#however, if the calc height is too tall to most likely fit on the page, recalculate
max_height_portrait <- 7
max_height_landscape <- 5
if (sf_taller &
ref_map_height > units::as_units(max_height_portrait)){
ref_map_height <- max_height_portrait
ref_map_width <- ref_map_height / sf_asp_ratio
} else if (sf_wider &
ref_map_height > units::as_units(max_height_landscape)){
ref_map_height <- max_height_landscape
ref_map_width <- ref_map_height / sf_asp_ratio
}
```
```{r data_id_fields, echo=FALSE, include=FALSE, cache=FALSE}
#ID fields:
# If FIPS field is in all, will use fips (FULL 5 character version)
# Else use original county/district name matching
# Accepted field names here, there will be preferred,
# but this gives some flexibility
# Processing happens after read in, will create "arbo_ID" used afterwards
# Processing includes wrangling fips to match across all files
# Each list in DESCENDING order of priority
# will only take the field that appears first
field_fips_accepted <- c("fips", "FIPS", "fips_code", "FIPS_CODE")
field_names_accepted <- c("county", "district", "parish", "Parish")
#ID Functions
confirm_id_fields <- function(fld_vector){
#Does any of the accepted fields (of a particular type)
# exist in all 3 or 4 datasets
# strata is OPTIONAL
if (!is.null(data_strata)){
#4 datasets
my_count <- sum(
any(fld_vector %in% names(data_human)),
any(fld_vector %in% names(data_mosquito)),
any(fld_vector %in% names(data_env_raw)),
any(fld_vector %in% names(data_strata))
)
#true/false
use_fld <- my_count == 4L
} else {
#3 datasets
my_count <- sum(
any(fld_vector %in% names(data_human)),
any(fld_vector %in% names(data_mosquito)),
any(fld_vector %in% names(data_env_raw))
)
#true/false
use_fld <- my_count == 3L
}
return(use_fld)
}
create_id_field <- function(my_tbl, fld_vector){
#the field names in the dataset that match the accepted names
# [[1]] takes the first
field_to_copy <- intersect(fld_vector, names(my_tbl))[[1]]
updated_tbl <- my_tbl %>%
#slight weirdness with dynamic field name in tidyverse
#using glue {{}}
#dplyr::mutate(arbo_ID = {{field_to_copy}})
#glue solution stopped working 2022-03-14? unknown reasons but only RHS
#using pseudo-base solution instead
dplyr::mutate(arbo_ID = .data[[field_to_copy]])
}
standarize_fips <- function(my_tbl, fips_vector = field_fips_accepted){
# Create new field arbo_ID which will be used for matching now on
arbo_tbl <- create_id_field(my_tbl, fips_vector)
# FIPS codes, while use numbers, should be considered text/string
# e.g. numerical math is meaningless
#1. convert to character
#2. convert to standard 5 character (2 state + 3 county) format
# if length 5, good
# if length 4, then full but probably read in as number and state has leading 0
# -> pad 0 in front
# if length 3 -> add state code
# if length 2, then county code but read as number and county has leading 0
# -> pad 0 in front to 3, and add state code
#grab state fips code from shp, as string
state_fips <- data_sf$STATEFP %>% unique() %>% as.character()
#attempt to handle possible read-ins and formats of fips codes in user data
arbo_tbl <- arbo_tbl %>%
dplyr::mutate(arbo_ID = as.character(arbo_ID),
arbo_ID = dplyr::case_when(
stringr::str_length(arbo_ID) == 4 ~ stringr::str_pad(arbo_ID, width = 5,
side = "left", pad = "0"),
stringr::str_length(arbo_ID) == 3 ~ paste0(.env$state_fips, arbo_ID),
stringr::str_length(arbo_ID) == 2 ~ paste0(.env$state_fips,
stringr::str_pad(arbo_ID, width = 3,
side = "left", pad = "0")),
TRUE ~ arbo_ID))
return(arbo_tbl)
}
simplifynames <- function(priornames=NULL) {
#ORIGINAL name matching
# convert to lower case
priornames <- tolower(priornames)
# remove spaces
priornames <- gsub(pattern=" ", replacement="", x=priornames, fixed=TRUE)
# remove other offending placename modifiers
priornames <- gsub(pattern="county", replacement="", x=priornames, fixed=TRUE)
priornames <- gsub(pattern="parish", replacement="", x=priornames, fixed=TRUE)
priornames <- gsub(pattern="par.", replacement="", x=priornames, fixed=TRUE)
priornames <- gsub(pattern="(zone)", replacement="", x=priornames, fixed=TRUE)
priornames <- gsub(pattern="lower", replacement="", x=priornames, fixed=TRUE)
priornames <- gsub(pattern="upper", replacement="", x=priornames, fixed=TRUE)
priornames <- gsub(pattern="southern", replacement="", x=priornames, fixed=TRUE)
priornames <- gsub(pattern="northern", replacement="", x=priornames, fixed=TRUE)
priornames <- gsub(pattern="saint", replacement="", x=priornames, fixed=TRUE)
priornames <- gsub(pattern="st", replacement="", x=priornames, fixed=TRUE)
priornames <- gsub(pattern=".", replacement="", x=priornames, fixed=TRUE)
# return names
return(priornames)
}
standarize_names <- function(my_tbl, names_vector = field_names_accepted){
#Create new field arbo_ID which will be used for matching now on
arbo_tbl <- create_id_field(my_tbl, names_vector)
#Use original name simplification
arbo_tbl <- arbo_tbl %>%
dplyr::mutate(arbo_ID = simplifynames(arbo_ID))
}
# Set up arbo_ID
if (confirm_id_fields(field_fips_accepted)){
# use fips as arbo_ID
id_as_fips <- TRUE
id_as_names <- FALSE
data_human <- standarize_fips(data_human)
data_mosquito <- standarize_fips(data_mosquito)
data_env_raw <- standarize_fips(data_env_raw)
if (!is.null(data_strata)){
data_strata <- standarize_fips(data_strata)
}
# data_sf will be dealt with separately, as it is standard format
#census shape: GEOID is 5 char FIPS code
data_sf <- data_sf %>%
dplyr::mutate(arbo_ID = GEOID)
} else if (confirm_id_fields(field_names_accepted)){
# use county names as arbo_ID
id_as_fips <- FALSE
id_as_names <- TRUE
data_human <- standarize_names(data_human)
data_mosquito <- standarize_names(data_mosquito)
data_env_raw <- standarize_names(data_env_raw)
if (!is.null(data_strata)){
data_strata <- standarize_names(data_strata)
}
# data_sf will be dealt with separately, as it is standard format
#census shape: NAME is county name
data_sf <- data_sf %>%
dplyr::mutate(arbo_ID = simplifynames(NAME))
}#end arbo_ID setup
#Create a crosswalk from arbo_ID to 'pretty' names (data_sf$NAME)
id_crosswalk <- data_sf %>%
sf::st_drop_geometry() %>%
dplyr::select(NAME, GEOID, arbo_ID) %>%
dplyr::rename(FIPS = GEOID)
#If county highlights (dev), then set up proper IDs here
if (highlight_flag){
#using names, need to crosswalk from user given name to simplename arbo_ID
# user given names MUST match shapefile standard name (NAME)
if (id_as_names){
highlight_ids <- id_crosswalk$arbo_ID[match(highlight, id_crosswalk$NAME)]
} else {
#if using fips, use as is
highlight_ids <- highlight
}
}
```
```{r data_dates, echo=FALSE, include=FALSE, cache=FALSE}
# Dates in data
# "date_obs" will become the standard date field name of the observed
# "date_epi" will become the standard date for modelling weeks (END date of epiweek)
# Filtering on years given in parameters
# Setting up week-related variables, in CDC epiweeks
##Functions
# epiwday borrowed from epidemiar, originally written by Chris Merkord, defaults changed here
epiwday <- function(x, system = "CDC") {
week_type <- match.arg(system, c("ISO", "CDC"))
if (week_type == "ISO") {
as.integer(lubridate::wday(x - 1))
} else if (week_type == "CDC") {
as.integer(lubridate::wday(x))
}
}
# make_date_yw borrowed from epidemiar, originally written by Chris Merkord, defaults changed here
make_date_yw <- function(year = 1970L, week = 1L, weekday = 7L, system = "CDC") {
#year: epidemiological year
#week: epidemiological week number (1--53).
#weekday: epidemiological weekday number (1--7). Day 1 is a Monday in
# the ISO-8601 WHO system and a Sunday in the CDC system. DEFAULT is LAST day of week (7)
#system: String indicating the standard (WHO ISO-8601 or CDC epiweeks) ["ISO" or "CDC"].
week_type <- match.arg(system, c("ISO", "CDC"))
lengths <- vapply(list(year, week, weekday), length, 1, USE.NAMES = FALSE)
if (min(lengths) == 0L) as.Date(integer(), lubridate::origin)
# recycle arguments
N <- max(lengths)
y <- rep_len(as.integer(year), N)
w <- rep_len(as.integer(week), N)
d <- rep_len(as.integer(weekday), N)
out <-
ifelse(
is.na(y) | is.na(w) | is.na(d), NA,
{
jan1 <- lubridate::make_date(y, 1, 1)
wday <- epiwday(jan1, week_type)
to_add <- ifelse(wday <= 4, 1, 8) - wday
wk1 <- jan1 + to_add
day1 <- wk1 + (w - 1) * 7
day1 + d - 1
}
)
as.Date(out, lubridate::origin)
}
# find last epiweek in a year: will be either 52 or 53
get_last_epiweek <- function(year){
#first pass, get epiweek of Dec 31 of the year
dec31 <- as.Date(paste0(year, "-12-31"), "%Y-%m-%d")
dec31_epiweek <- lubridate::epiweek(dec31)
# Dec 31 might fall into 1st week of following year
if (dec31_epiweek == 1){
#if so, then get epiweek of the week prior instead
prev_epiweek <- lubridate::epiweek(dec31 - lubridate::weeks(1))
return(prev_epiweek)
} else {return(dec31_epiweek)}
}
# Note: tryCatch not helpful b/c of as.Date return values
# > as.Date("2019-6-4")
# [1] "2019-06-04"
# > as.Date("2019-6-4", "%m/%d/%Y")
# [1] NA
# > as.Date("6/4/2019")
# [1] "0006-04-20"
# also note that tryCatch doesn't work inside mutate plain
# need rowwise or other solution to use that
# So below solution is not quite as robust as it could be,
# but it should be pretty good
data_human <- data_human %>%
#try old specified format first
#gives NA when given dates like "2019-6-4"
dplyr::mutate(date_obs = as.Date(date, format = "%m/%d/%Y"),
#test for NA and let as.Date guess this time
#MUST use ifelse, not if_else b/c that evaluates all
# and as.Date will throw error if given the old format without pattern
# however ifelse strips date format, so must cast it afterwards
# note: zoo package used for default origin for as.Date()
date_obs = as.Date(ifelse(is.na(date_obs),
as.Date(date),
date_obs)))
data_mosquito <- data_mosquito %>%
#try old specified format first
#gives NA when given dates like "2019-6-4"
dplyr::mutate(date_obs = as.Date(col_date, format = "%m/%d/%Y"),
#test for NA and let as.Date guess this time
#MUST use ifelse, not if_else b/c that evaluates all
# and as.Date will throw error if given the old format without pattern
# however ifelse strips date format, so must cast it afterwards
# note: zoo package used for default origin for as.Date()
date_obs = as.Date(ifelse(is.na(date_obs),
as.Date(col_date),
date_obs)))
## Filter data by year parameters
# More data may be present in the files than what we want to use
# e.g. incomplete year data
# Adds useful date parts as fields for use here and later
# Note: Epi year is used for filtering for consistency with using epiweeks
data_human <- data_human %>%
#year field for filtering
dplyr::mutate(#epi year
year_epi = lubridate::epiyear(date_obs),
#epiweek
week_epi = lubridate::epiweek(date_obs),
date_epi = make_date_yw(year = year_epi, week = week_epi, weekday = 7),
#also doy
doy = lubridate::yday(date_obs)) %>%
#filter year range from parameter input
dplyr::filter(year_epi >= params$year_human_start &
year_epi <= params$year_human_end)
data_mosquito <- data_mosquito %>%
#date fields for filtering (here and later in mosq modelling)
dplyr::mutate(year_cal = lubridate::year(date_obs),
#epi year and week
year_epi = lubridate::epiyear(date_obs),
week_epi = lubridate::epiweek(date_obs),
date_epi = make_date_yw(year = year_epi,
week = week_epi,
weekday = 7),
#also doy, used later in mosq modelling
doy = lubridate::yday(date_obs)) %>%
#filter year range from parameter input
dplyr::filter(year_epi >= params$year_mosquito_start &
year_epi <= params$year_mosquito_end)
#Note: doy filtering happens in code block data_mosq_processing_dx below
data_env_raw <- data_env_raw %>%
#filter year range from parameter input
#already has year field from GEE, however
# will ALSO have epi year
dplyr::mutate(date_obs = as.Date(paste(year, doy, sep = "-"),
"%Y-%j"),
#epi year and week
year_epi = lubridate::epiyear(date_obs),
week_epi = lubridate::epiweek(date_obs),
date_epi = make_date_yw(year = year_epi,
week = week_epi,
weekday = 7)) %>%
dplyr::filter(year_epi >= params$year_weather_start &
year_epi <= params$year_weather_end)
## Dates for forecasts and functions
# Now using CDC epiweeks (MMWR week)
# The date associated with the epiweek will be the LAST day of the epiweek
#Forecast week
#week of forecast, given by user
date_request <- as.Date(params$forecast_date, "%Y-%m-%d")
#epi week of requested date
epiweek_request <- lubridate::epiweek(date_request)
#epi year of requested date
epiyear_request <- lubridate::epiyear(date_request)
#ENDING date of the requested epi week
date_epiweek_request <- make_date_yw(year = epiyear_request,
week = epiweek_request,
weekday = 7)
#day of year of the ending date of the requested epiweek
doy_dt_epiwk_req <- lubridate::yday(date_epiweek_request)
#Modelling years
# Used to be separate parameter, but is really driven by other inputs
# To avoid confusion and mismatches years in strange ways,
# now being set internally
# Start year is the same of the start of human data.
# Can't model before human data, and to move modelling start, change start of human data
year_modeling_start <- params$year_human_start
# End year is the year of the forecast date
year_modeling_end <- epiyear_request
#Various other dates
#ending date of the FIRST epiweek in the MIN modelling year
date_min_model <- make_date_yw(year = year_modeling_start,
week = 1,
weekday = 7)
#ending date of LAST epiweek in the MAX modelling year
date_max_model <- make_date_yw(year = year_modeling_end,
week = get_last_epiweek(year_modeling_end), #could be 52 or 53
weekday = 7)
```
```{r data_human_processing_dx, echo=FALSE, include=FALSE, cache=FALSE}
## Human
#Record row count (in human_year_start through human_year_end)
data_human_range <- data_human
dx_human_nrow_1range <- nrow(data_human_range)
#Clean human data
# remove any with unmatched county info
# especially necessary for doing regression modeling
data_human <- data_human %>%
dplyr::filter(arbo_ID %in% unique(data_sf$arbo_ID),
#remove any without a good date
(!is.na(date_obs)))
#Record row count post cleaning
dx_human_nrow_2clean <- nrow(data_human)
#any rows unmatched
dx_human_unmatched <- data_human_range %>%
#let it match columns on its own
dplyr::anti_join(data_human) %>%
#file original date field to display in table
dplyr::select(arbo_ID, date)
#gather list of counties in cleaned human data
dx_human_counties <- data_human %>%
dplyr::pull(arbo_ID) %>% unique() %>% sort()
#min/max years of data actually PRESENT in the cleaned input data
# (within the user-supplied range of years to use in modeling)
# If set up normally, these should align, but it is not necessarily true
human_exists_yr_min <- min(data_human$year_epi, na.rm = TRUE)
human_exists_yr_max <- max(data_human$year_epi, na.rm = TRUE)
human_exists_yr_list <- data_human$year_epi %>% unique() %>% sort()
#are there years between requested start/end years that no data were observed for?
#NOTE: These may be zero-case years! (and therefore fine). Or potentially missing data.
human_yrs_missing <- setdiff(params$year_human_start:params$year_human_end,
human_exists_yr_list)
#[V3] [DEV] imputemissingdistricts was hard coded as FALSE in v3 production code
# updated names and code, and added dev option for renamed impute_human_missing_districts, default = FALSE