inst/app/server/calibration/calibration-data.R
In jackolney/CascadeDashboard: A Shiny App for Investigating The Current Trajectory of HIV Care
# require(readr)
# rm(list=ls())
# setwd("~/git/WhoCascade/CascadeApp/CascadeDashboard")
# dir()
# Setup Page -> Multiple Tabs
# Location Tab -> Please Select Country (displays a map of available countries (interactive [plotGoogleMap package])).
# 1) Pick Country.
# on next page the model will pull in all data from Marrakech, colour code boxes according to the weights assigned to them ('traffic lights').
# User can adjust 'marrakech' values and relative weights.
# clicking 'next' will run the calibration (visualise this?, or at least the final plot -> fun shit load of curves on the screen).
# Then 'parameter' page will be entered, showing the "ghost values" that the model has decided upon (in grey or something).
# The user can then leave them, as is, OR alter them (re-run calibration if so?).
# Then proceed to view results -> presented as an average perhaps, or a range (tricky).
# This 'calibration-data.R' script will include functions that are called during calibration.
# When 'country' is picked, pull out relevant data from all the data/calibration and data/marrakech.csv files.
# Assemble dynamically into a calibration-data DATA.FRAME using a standard format; i.e.
# data.frame(<year>,<country>,<category>,<value>,<source>,<details>)
# Need to specify the initial values in the model?????
# - Values for state variables in 2010?
# - CD4 distribution (SPECTRUM -- Just update script for it) [could even update it every year]
# Then run the model, check SSE between model and data, optim to minimise (weighting how?)
# Algorithm for doing this multiple times?
# (Store results for each simulation?)
# Plot results?
# Evaluate transition rates that the model decied upon.
# Model runs between 2010 and 2015.
# uses marrakech data to specify state of care (targets) in either 2015/2014/2013.
# fixed parameter values between those times.
# Spectrum tells us incidence
# Need to account for changing treatment guidelines in this period (have csv of dates, just need to configure it).
# Country Selection
# userCountry = "Kenya"
# What folders do I need to search through?
# calib.files.path <- c()
# for (i in 1:length(calib.files)) {
# print(i)
# calib.files.path[i] <- paste0("server/data/calibration/", calib.files[i])
# }
# For all files in calib.files.path, do readr and identify all those with Kenya
# readr::read_csv
# a <- readr::read_csv(calib.files.path[1])
# dplyr::filter(a, Country == "Kenya")
# Lets try that again.
# Read all csv data in at applicataion start, then we can just jump between countries as and when we select them.
# c.file.path <- "server/data/calibration"
# calib.incidence <- readr::read_csv(paste0(c.file.path, "/incident-infections.csv"), col_names = TRUE, skip = 1)
# calib.cd4 <- readr::read_csv(paste0(c.file.path, "/cd4-distribution-2010.csv"), col_names = TRUE, skip = 0)
# calib.art <- readr::read_csv(paste0(c.file.path, "/art.csv"), col_names = TRUE, skip = 1)
# calib.hiv_awareness_unaids <- readr::read_csv(paste0(c.file.path, "/hiv-awareness-unaids.csv"), col_names = TRUE, skip = 0)
# calib.not_on_art <- readr::read_csv(paste0(c.file.path, "/not-on-art.csv"), col_names = TRUE, skip = 1)
# calib.plhiv <- readr::read_csv(paste0(c.file.path, "/plhiv.csv"), col_names = TRUE, skip = 1)
# calib.previous_data <- readr::read_csv(paste0(c.file.path, "/previous-data.csv"), col_names = TRUE, skip = 0)
# calib.rates <- readr::read_csv(paste0(c.file.path, "/rates.csv"), col_names = TRUE, skip = 0)
# calib.treatment_guidelines <- readr::read_csv(paste0(c.file.path, "/treatment-guidelines.csv"), col_names = TRUE, skip = 0)
# For each find userCountry
# userCountry
# calib.incidence[[userCountry]]
# something along the lines of:
# GetCountryData <- function(uCountry) {
# This will then return a data.frame containing ALL available data.
# But how do we handle counties with values missing?
# Return a list of three things?
# [1] - Incidence and CD4 (the ESSENTIALS)
# [2] - Additional data points (varied width)
# [3] - Treatment Guideline changes for the specific country
# [4] - Rates (if available) [only will be possible for like 3 countries who supplied rates in Marrakech]
# Think we need a standard format here??
# }
# this will replace which.exist()
# build.master.df <- function(temp.names, calib.df) {
# out <- c()
# for (i in 1:length(temp.names)) {
# if (exists(temp.names[i])) {
# out <- rbind(out, get(temp.names[i]))
# }
# }
# out.list <- list(
# calib.df$incidence,
# calib.df$cd4,
# calib.df$treatment_guidelines,
# out,
# calib.df$rates
# )
# names(out.list) <- c(
# "incidence",
# "cd4",
# "treatment_guidelines",
# "calib",
# "rates"
# )
# out.list
# }
is.not.empty <- function(ListElement) {
if (!is.list(ListElement)) stop("not a list")
if (dim(ListElement)[1] == 0) {
return(FALSE)
} else {
return(TRUE)
}
}
# Wrap in a function then question the data.
# uCountry = "Brazil"
GetCountryData <- function(uCountry) {
# Read in all relevant csv files
c.file.path <- "server/data/calibration"
calib.incidence <- readr::read_csv(paste0(c.file.path, "/incidence-uncertainty.csv"), col_names = TRUE, skip = 1)
calib.cd4 <- readr::read_csv(paste0(c.file.path, "/cd4-distribution-2010.csv"), col_names = TRUE, skip = 0)
calib.art <- readr::read_csv(paste0(c.file.path, "/art.csv"), col_names = TRUE, skip = 1)
calib.hiv_awareness_unaids <- readr::read_csv(paste0(c.file.path, "/hiv-awareness-unaids.csv"), col_names = TRUE, skip = 0)
calib.not_on_art <- readr::read_csv(paste0(c.file.path, "/not-on-art.csv"), col_names = TRUE, skip = 1)
calib.plhiv <- readr::read_csv(paste0(c.file.path, "/plhiv.csv"), col_names = TRUE, skip = 1)
calib.previous_data <- readr::read_csv(paste0(c.file.path, "/previous-data.csv"), col_names = TRUE, skip = 0)
calib.rates <- readr::read_csv(paste0(c.file.path, "/rates.csv"), col_names = TRUE, skip = 0)
calib.treatment_guidelines <- readr::read_csv(paste0(c.file.path, "/treatment-guidelines-cd4.csv"), col_names = TRUE, skip = 0)
calib.cd4_2015 <- readr::read_csv("server/data/projection/cd4-distribution-2015.csv", col_names = TRUE, skip = 0)
calib.pop <- readr::read_csv("server/data/projection/population.csv", col_names = TRUE, skip = 1)
# Compile data.frame
calib.df <- list()
calib.df[[1]] <- dplyr::filter(calib.incidence, country == uCountry)
calib.df[[2]] <- dplyr::filter(calib.cd4, country == uCountry)
calib.df[[3]] <- dplyr::filter(calib.treatment_guidelines, country == uCountry)
calib.df[[4]] <- dplyr::filter(calib.art, country == uCountry)
calib.df[[5]] <- dplyr::filter(calib.hiv_awareness_unaids, country == uCountry)
calib.df[[6]] <- dplyr::filter(calib.not_on_art, country == uCountry)
calib.df[[7]] <- dplyr::filter(calib.plhiv, country == uCountry)
calib.df[[8]] <- dplyr::filter(calib.previous_data, country == uCountry)
calib.df[[9]] <- dplyr::filter(calib.rates, country == uCountry)
calib.df[[10]] <- dplyr::filter(calib.cd4_2015, country == uCountry)
calib.df[[11]] <- dplyr::filter(calib.pop, country == uCountry)
# Create a vector of data names
names(calib.df) <- c(
"incidence",
"cd4",
"treatment_guidelines",
"art",
"hiv_awareness_unaids",
"not_on_art",
"plhiv",
"previous_data",
"rates",
"cd4_2015",
"pop"
)
## Incidence
if (isReallyEmpty(calib.df$incidence)) {
country <- uCountry
type <- c("Lower", "Median", "Upper")
"2010" <- as.numeric(NA)
"2011" <- as.numeric(NA)
"2012" <- as.numeric(NA)
"2013" <- as.numeric(NA)
"2014" <- as.numeric(NA)
"2015" <- as.numeric(NA)
"2016" <- as.numeric(NA)
blankIncidence <- data.frame(country, type, get("2010"), get("2011"), get("2012"), get("2013"), get("2014"), get("2015"), get("2016"))
names(blankIncidence) <- c("country", "type", as.character(seq(2010, 2016, 1)))
calib.df$incidence <- dplyr::tbl_df(blankIncidence)
}
## CD4
if (isReallyEmpty(calib.df$cd4)) {
country <- uCountry
"prop.Off.ART.500" <- as.numeric(NA)
"prop.Off.ART.350500" <- as.numeric(NA)
"prop.Off.ART.250350" <- as.numeric(NA)
"prop.Off.ART.200250" <- as.numeric(NA)
"prop.Off.ART.100200" <- as.numeric(NA)
"prop.Off.ART.50100" <- as.numeric(NA)
"prop.Off.ART.50" <- as.numeric(NA)
"prop.On.ART.500" <- as.numeric(NA)
"prop.On.ART.350500" <- as.numeric(NA)
"prop.On.ART.250350" <- as.numeric(NA)
"prop.On.ART.200250" <- as.numeric(NA)
"prop.On.ART.100200" <- as.numeric(NA)
"prop.On.ART.50100" <- as.numeric(NA)
"prop.On.ART.50" <- as.numeric(NA)
blankCD4 <- data.frame(country,
get("prop.Off.ART.500"),
get("prop.Off.ART.350500"),
get("prop.Off.ART.250350"),
get("prop.Off.ART.200250"),
get("prop.Off.ART.100200"),
get("prop.Off.ART.50100"),
get("prop.Off.ART.50"),
get("prop.On.ART.500"),
get("prop.On.ART.350500"),
get("prop.On.ART.250350"),
get("prop.On.ART.200250"),
get("prop.On.ART.100200"),
get("prop.On.ART.50100"),
get("prop.On.ART.50"))
names(blankCD4) <- c("country", "prop.Off.ART.500", "prop.Off.ART.350500", "prop.Off.ART.250350", "prop.Off.ART.200250", "prop.Off.ART.100200", "prop.Off.ART.50100", "prop.Off.ART.50", "prop.On.ART.500", "prop.On.ART.350500", "prop.On.ART.250350", "prop.On.ART.200250", "prop.On.ART.100200", "prop.On.ART.50100", "prop.On.ART.50")
calib.df$cd4 <- dplyr::tbl_df(blankCD4)
}
## CD4 2015
if (isReallyEmpty(calib.df$cd4_2015)) {
country <- uCountry
"prop.Off.ART.500" <- as.numeric(NA)
"prop.Off.ART.350500" <- as.numeric(NA)
"prop.Off.ART.250350" <- as.numeric(NA)
"prop.Off.ART.200250" <- as.numeric(NA)
"prop.Off.ART.100200" <- as.numeric(NA)
"prop.Off.ART.50100" <- as.numeric(NA)
"prop.Off.ART.50" <- as.numeric(NA)
"prop.On.ART.500" <- as.numeric(NA)
"prop.On.ART.350500" <- as.numeric(NA)
"prop.On.ART.250350" <- as.numeric(NA)
"prop.On.ART.200250" <- as.numeric(NA)
"prop.On.ART.100200" <- as.numeric(NA)
"prop.On.ART.50100" <- as.numeric(NA)
"prop.On.ART.50" <- as.numeric(NA)
blankCD4 <- data.frame(country,
get("prop.Off.ART.500"),
get("prop.Off.ART.350500"),
get("prop.Off.ART.250350"),
get("prop.Off.ART.200250"),
get("prop.Off.ART.100200"),
get("prop.Off.ART.50100"),
get("prop.Off.ART.50"),
get("prop.On.ART.500"),
get("prop.On.ART.350500"),
get("prop.On.ART.250350"),
get("prop.On.ART.200250"),
get("prop.On.ART.100200"),
get("prop.On.ART.50100"),
get("prop.On.ART.50"))
names(blankCD4) <- c("country", "prop.Off.ART.500", "prop.Off.ART.350500", "prop.Off.ART.250350", "prop.Off.ART.200250", "prop.Off.ART.100200", "prop.Off.ART.50100", "prop.Off.ART.50", "prop.On.ART.500", "prop.On.ART.350500", "prop.On.ART.250350", "prop.On.ART.200250", "prop.On.ART.100200", "prop.On.ART.50100", "prop.On.ART.50")
calib.df$cd4_2015 <- dplyr::tbl_df(blankCD4)
}
## Treatment Guidelines
if (isReallyEmpty(calib.df$treatment_guidelines)) {
country <- uCountry
less200 <- as.integer(NA)
less250 <- as.integer(NA)
less350 <- as.integer(NA)
less500 <- as.integer(NA)
more500 <- as.integer(NA)
blankGuidelines <- data.frame(country, less200, less250, less350, less500, more500)
calib.df$treatment_guidelines <- dplyr::tbl_df(blankGuidelines)
}
## Population
if (isReallyEmpty(calib.df$pop)) {
country <- uCountry
indicator <- "Population aged 15-49 in 2015"
value <- as.integer(NA)
blankPop <- data.frame(country, indicator, value)
calib.df$pop <- dplyr::tbl_df(blankPop)
}
## All of the below goes into data[["calib"]]
# list temp.names
temp.names <- c(
"temp.art",
"temp.hiv_awareness_unaids",
"temp.not_on_art",
"temp.plhiv",
"temp.previous_data"
)
for (i in 1:length(temp.names)) {
if (exists(temp.names[i])) {
rm(list = temp.names[i], pos = ".GlobalEnv")
}
}
# Reshape data
# calib.art
if (is.not.empty(calib.df$art)) {
temp.art <- reshape2::melt(calib.df$art[c("country","indicator","2010","2011","2012","2013","2014","2015")],
id.vars = c("country", "indicator"),
variable.name = "year",
value.name = "value")
temp.art$weight <- "green"
temp.art$source <- "Spectrum"
}
# calib.not_on_art
if (is.not.empty(calib.df$not_on_art)) {
temp.not_on_art <- reshape2::melt(calib.df$not_on_art[c("country","indicator","2010","2011","2012","2013","2014","2015")],
id.vars = c("country", "indicator"),
variable.name = "year",
value.name = "value")
temp.not_on_art$weight <- "green"
temp.not_on_art$source <- "Spectrum"
}
# calib.plhiv
if (is.not.empty(calib.df$plhiv)) {
temp.plhiv <- reshape2::melt(calib.df$plhiv[c("country","indicator","2010","2011","2012","2013","2014","2015")],
id.vars = c("country","indicator"),
variable.name = "year",
value.name = "value")
temp.plhiv$weight <- "green"
temp.plhiv$source <- "Spectrum"
}
# calib.hiv_awareness_unaids (remember this is a proportion of PLHIV)
# Needs to be matched to PLHIV from the SAME YEAR.
if (is.not.empty(calib.df$hiv_awareness_unaids)) {
temp.hiv_awareness_unaids <- calib.df$hiv_awareness_unaids[c("country","indicator","year","value","source")]
# Need to match PLHIV, if it doesn't exist for a particular year then we delete the value
if (exists("temp.plhiv") & exists("temp.hiv_awareness_unaids")) {
for (i in 1:dim(temp.hiv_awareness_unaids)[1]) {
t.plhiv <- dplyr::filter(temp.plhiv, year == temp.hiv_awareness_unaids$year[i])$value
temp.hiv_awareness_unaids[[i,"value"]] <- temp.hiv_awareness_unaids[[i,"value"]] * t.plhiv
temp.hiv_awareness_unaids$weight <- "green"
}
} else {
rm(temp.hiv_awareness_unaids)
}
}
# calib.previous_data
if (is.not.empty(calib.df$previous_data)) {
temp.previous_data <- dplyr::filter(calib.df$previous_data[c("country","indicator","year","value","weight","source")], year >= 2010)
}
# Assemble master.data.frame
# master.df <- build.master.df(temp.names, calib.df)
out <- c()
for (i in 1:length(temp.names)) {
if (exists(temp.names[i])) {
out <- rbind(out, get(temp.names[i]))
}
}
if (length(out) == 0) {
country <- uCountry
indicator <- c(rep("PLHIV", 6),
rep("PLHIV Diagnosed", 6),
rep("PLHIV in Care", 6),
rep("PLHIV on ART", 6),
rep("PLHIV Suppressed", 6))
year <- rep(seq(2010, 2015, 1), 5)
value <- as.numeric(NA)
weight <- factor(x = NA, levels = c("red", "amber", "green"))
source <- as.character(NA)
out <- data.frame(country, indicator, year, value, weight, source, stringsAsFactors = FALSE)
}
out.list <- list(
calib.df$incidence,
calib.df$cd4,
calib.df$treatment_guidelines,
out,
calib.df$rates,
calib.df$cd4_2015,
calib.df$pop
)
names(out.list) <- c(
"incidence",
"cd4",
"treatment_guidelines",
"calib",
"rates",
"cd4_2015",
"pop"
)
out.list
}
# test <- GetCountryData("Brazil")
# master.df needs to include
# incidence
# cd4
# rates
# treatment guidelines
# Below function called when resetting treatment guidelines
GetTreatmentGuidelines <- function(uCountry) {
# Read in all relevant csv files
c.file.path <- "server/data/calibration"
calib.treatment_guidelines <- readr::read_csv(paste0(c.file.path, "/treatment-guidelines-cd4.csv"), col_names = TRUE, skip = 0)
# Compile data.frame
treatment_guidelines <- dplyr::filter(calib.treatment_guidelines, country == uCountry)
# Return vector
treatment_guidelines
}
new_data <- function(country, year, indicator, value, weight, source) {
if (!is.character(country)) stop("country is not a character.")
if (!is.numeric(year)) stop("year is not a character.")
if (!is.character(indicator)) stop("indicator is not a character.")
if (!is.numeric(value)) stop("value is not a character.")
if (!is.character(weight)) stop("weight is not a character.")
if (!is.character(source)) stop("soruce is not a character.")
# Check if valid indicator
indicator_list <- c("PLHIV", "PLHIV Diagnosed", "PLHIV in Care", "PLHIV on ART", "PLHIV Suppressed")
if (!any(indicator_list == indicator)) stop("Not a valid indicator type")
new_dat <- data.frame(country, year, indicator, value, weight, source)
new_dat
}
replace_or_append <- function(datOne, datTwo) {
# find out if indicator for datTwo exists in datOne
if (any(as.character(datOne[datOne$year == datTwo$year, "indicator"]) == as.character(datTwo$indicator))) {
# REPLACE
datOne[datOne$year == datTwo$year & datOne$indicator == as.character(datTwo$indicator), "value"] <- as.numeric(datTwo$value)
datOne[datOne$year == datTwo$year & datOne$indicator == as.character(datTwo$indicator), "weight"] <- as.character(datTwo$weight)
datOne[datOne$year == datTwo$year & datOne$indicator == as.character(datTwo$indicator), "source"] <- as.character(datTwo$source)
} else {
# APPEND
datOne <- rbind(datOne, datTwo)
}
datOne
}
jackolney/CascadeDashboard documentation built on May 18, 2019, 7:56 a.m.
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# require(readr)
# rm(list=ls())
# setwd("~/git/WhoCascade/CascadeApp/CascadeDashboard")
# dir()
# Setup Page -> Multiple Tabs
# Location Tab -> Please Select Country (displays a map of available countries (interactive [plotGoogleMap package])).
# 1) Pick Country.
# on next page the model will pull in all data from Marrakech, colour code boxes according to the weights assigned to them ('traffic lights').
# User can adjust 'marrakech' values and relative weights.
# clicking 'next' will run the calibration (visualise this?, or at least the final plot -> fun shit load of curves on the screen).
# Then 'parameter' page will be entered, showing the "ghost values" that the model has decided upon (in grey or something).
# The user can then leave them, as is, OR alter them (re-run calibration if so?).
# Then proceed to view results -> presented as an average perhaps, or a range (tricky).
# This 'calibration-data.R' script will include functions that are called during calibration.
# When 'country' is picked, pull out relevant data from all the data/calibration and data/marrakech.csv files.
# Assemble dynamically into a calibration-data DATA.FRAME using a standard format; i.e.
# data.frame(<year>,<country>,<category>,<value>,<source>,<details>)
# Need to specify the initial values in the model?????
# - Values for state variables in 2010?
# - CD4 distribution (SPECTRUM -- Just update script for it) [could even update it every year]
# Then run the model, check SSE between model and data, optim to minimise (weighting how?)
# Algorithm for doing this multiple times?
# (Store results for each simulation?)
# Plot results?
# Evaluate transition rates that the model decied upon.
# Model runs between 2010 and 2015.
# uses marrakech data to specify state of care (targets) in either 2015/2014/2013.
# fixed parameter values between those times.
# Spectrum tells us incidence
# Need to account for changing treatment guidelines in this period (have csv of dates, just need to configure it).
# Country Selection
# userCountry = "Kenya"
# What folders do I need to search through?
# calib.files.path <- c()
# for (i in 1:length(calib.files)) {
# print(i)
# calib.files.path[i] <- paste0("server/data/calibration/", calib.files[i])
# }
# For all files in calib.files.path, do readr and identify all those with Kenya
# readr::read_csv
# a <- readr::read_csv(calib.files.path[1])
# dplyr::filter(a, Country == "Kenya")
# Lets try that again.
# Read all csv data in at applicataion start, then we can just jump between countries as and when we select them.
# c.file.path <- "server/data/calibration"
# calib.incidence <- readr::read_csv(paste0(c.file.path, "/incident-infections.csv"), col_names = TRUE, skip = 1)
# calib.cd4 <- readr::read_csv(paste0(c.file.path, "/cd4-distribution-2010.csv"), col_names = TRUE, skip = 0)
# calib.art <- readr::read_csv(paste0(c.file.path, "/art.csv"), col_names = TRUE, skip = 1)
# calib.hiv_awareness_unaids <- readr::read_csv(paste0(c.file.path, "/hiv-awareness-unaids.csv"), col_names = TRUE, skip = 0)
# calib.not_on_art <- readr::read_csv(paste0(c.file.path, "/not-on-art.csv"), col_names = TRUE, skip = 1)
# calib.plhiv <- readr::read_csv(paste0(c.file.path, "/plhiv.csv"), col_names = TRUE, skip = 1)
# calib.previous_data <- readr::read_csv(paste0(c.file.path, "/previous-data.csv"), col_names = TRUE, skip = 0)
# calib.rates <- readr::read_csv(paste0(c.file.path, "/rates.csv"), col_names = TRUE, skip = 0)
# calib.treatment_guidelines <- readr::read_csv(paste0(c.file.path, "/treatment-guidelines.csv"), col_names = TRUE, skip = 0)
# For each find userCountry
# userCountry
# calib.incidence[[userCountry]]
# something along the lines of:
# GetCountryData <- function(uCountry) {
# This will then return a data.frame containing ALL available data.
# But how do we handle counties with values missing?
# Return a list of three things?
# [1] - Incidence and CD4 (the ESSENTIALS)
# [2] - Additional data points (varied width)
# [3] - Treatment Guideline changes for the specific country
# [4] - Rates (if available) [only will be possible for like 3 countries who supplied rates in Marrakech]
# Think we need a standard format here??
# }
# this will replace which.exist()
# build.master.df <- function(temp.names, calib.df) {
# out <- c()
# for (i in 1:length(temp.names)) {
# if (exists(temp.names[i])) {
# out <- rbind(out, get(temp.names[i]))
# }
# }
# out.list <- list(
# calib.df$incidence,
# calib.df$cd4,
# calib.df$treatment_guidelines,
# out,
# calib.df$rates
# )
# names(out.list) <- c(
# "incidence",
# "cd4",
# "treatment_guidelines",
# "calib",
# "rates"
# )
# out.list
# }
is.not.empty <- function(ListElement) {
if (!is.list(ListElement)) stop("not a list")
if (dim(ListElement)[1] == 0) {
return(FALSE)
} else {
return(TRUE)
}
}
# Wrap in a function then question the data.
# uCountry = "Brazil"
GetCountryData <- function(uCountry) {
# Read in all relevant csv files
c.file.path <- "server/data/calibration"
calib.incidence <- readr::read_csv(paste0(c.file.path, "/incidence-uncertainty.csv"), col_names = TRUE, skip = 1)
calib.cd4 <- readr::read_csv(paste0(c.file.path, "/cd4-distribution-2010.csv"), col_names = TRUE, skip = 0)
calib.art <- readr::read_csv(paste0(c.file.path, "/art.csv"), col_names = TRUE, skip = 1)
calib.hiv_awareness_unaids <- readr::read_csv(paste0(c.file.path, "/hiv-awareness-unaids.csv"), col_names = TRUE, skip = 0)
calib.not_on_art <- readr::read_csv(paste0(c.file.path, "/not-on-art.csv"), col_names = TRUE, skip = 1)
calib.plhiv <- readr::read_csv(paste0(c.file.path, "/plhiv.csv"), col_names = TRUE, skip = 1)
calib.previous_data <- readr::read_csv(paste0(c.file.path, "/previous-data.csv"), col_names = TRUE, skip = 0)
calib.rates <- readr::read_csv(paste0(c.file.path, "/rates.csv"), col_names = TRUE, skip = 0)
calib.treatment_guidelines <- readr::read_csv(paste0(c.file.path, "/treatment-guidelines-cd4.csv"), col_names = TRUE, skip = 0)
calib.cd4_2015 <- readr::read_csv("server/data/projection/cd4-distribution-2015.csv", col_names = TRUE, skip = 0)
calib.pop <- readr::read_csv("server/data/projection/population.csv", col_names = TRUE, skip = 1)
# Compile data.frame
calib.df <- list()
calib.df[[1]] <- dplyr::filter(calib.incidence, country == uCountry)
calib.df[[2]] <- dplyr::filter(calib.cd4, country == uCountry)
calib.df[[3]] <- dplyr::filter(calib.treatment_guidelines, country == uCountry)
calib.df[[4]] <- dplyr::filter(calib.art, country == uCountry)
calib.df[[5]] <- dplyr::filter(calib.hiv_awareness_unaids, country == uCountry)
calib.df[[6]] <- dplyr::filter(calib.not_on_art, country == uCountry)
calib.df[[7]] <- dplyr::filter(calib.plhiv, country == uCountry)
calib.df[[8]] <- dplyr::filter(calib.previous_data, country == uCountry)
calib.df[[9]] <- dplyr::filter(calib.rates, country == uCountry)
calib.df[[10]] <- dplyr::filter(calib.cd4_2015, country == uCountry)
calib.df[[11]] <- dplyr::filter(calib.pop, country == uCountry)
# Create a vector of data names
names(calib.df) <- c(
"incidence",
"cd4",
"treatment_guidelines",
"art",
"hiv_awareness_unaids",
"not_on_art",
"plhiv",
"previous_data",
"rates",
"cd4_2015",
"pop"
)
## Incidence
if (isReallyEmpty(calib.df$incidence)) {
country <- uCountry
type <- c("Lower", "Median", "Upper")
"2010" <- as.numeric(NA)
"2011" <- as.numeric(NA)
"2012" <- as.numeric(NA)
"2013" <- as.numeric(NA)
"2014" <- as.numeric(NA)
"2015" <- as.numeric(NA)
"2016" <- as.numeric(NA)
blankIncidence <- data.frame(country, type, get("2010"), get("2011"), get("2012"), get("2013"), get("2014"), get("2015"), get("2016"))
names(blankIncidence) <- c("country", "type", as.character(seq(2010, 2016, 1)))
calib.df$incidence <- dplyr::tbl_df(blankIncidence)
}
## CD4
if (isReallyEmpty(calib.df$cd4)) {
country <- uCountry
"prop.Off.ART.500" <- as.numeric(NA)
"prop.Off.ART.350500" <- as.numeric(NA)
"prop.Off.ART.250350" <- as.numeric(NA)
"prop.Off.ART.200250" <- as.numeric(NA)
"prop.Off.ART.100200" <- as.numeric(NA)
"prop.Off.ART.50100" <- as.numeric(NA)
"prop.Off.ART.50" <- as.numeric(NA)
"prop.On.ART.500" <- as.numeric(NA)
"prop.On.ART.350500" <- as.numeric(NA)
"prop.On.ART.250350" <- as.numeric(NA)
"prop.On.ART.200250" <- as.numeric(NA)
"prop.On.ART.100200" <- as.numeric(NA)
"prop.On.ART.50100" <- as.numeric(NA)
"prop.On.ART.50" <- as.numeric(NA)
blankCD4 <- data.frame(country,
get("prop.Off.ART.500"),
get("prop.Off.ART.350500"),
get("prop.Off.ART.250350"),
get("prop.Off.ART.200250"),
get("prop.Off.ART.100200"),
get("prop.Off.ART.50100"),
get("prop.Off.ART.50"),
get("prop.On.ART.500"),
get("prop.On.ART.350500"),
get("prop.On.ART.250350"),
get("prop.On.ART.200250"),
get("prop.On.ART.100200"),
get("prop.On.ART.50100"),
get("prop.On.ART.50"))
names(blankCD4) <- c("country", "prop.Off.ART.500", "prop.Off.ART.350500", "prop.Off.ART.250350", "prop.Off.ART.200250", "prop.Off.ART.100200", "prop.Off.ART.50100", "prop.Off.ART.50", "prop.On.ART.500", "prop.On.ART.350500", "prop.On.ART.250350", "prop.On.ART.200250", "prop.On.ART.100200", "prop.On.ART.50100", "prop.On.ART.50")
calib.df$cd4 <- dplyr::tbl_df(blankCD4)
}
## CD4 2015
if (isReallyEmpty(calib.df$cd4_2015)) {
country <- uCountry
"prop.Off.ART.500" <- as.numeric(NA)
"prop.Off.ART.350500" <- as.numeric(NA)
"prop.Off.ART.250350" <- as.numeric(NA)
"prop.Off.ART.200250" <- as.numeric(NA)
"prop.Off.ART.100200" <- as.numeric(NA)
"prop.Off.ART.50100" <- as.numeric(NA)
"prop.Off.ART.50" <- as.numeric(NA)
"prop.On.ART.500" <- as.numeric(NA)
"prop.On.ART.350500" <- as.numeric(NA)
"prop.On.ART.250350" <- as.numeric(NA)
"prop.On.ART.200250" <- as.numeric(NA)
"prop.On.ART.100200" <- as.numeric(NA)
"prop.On.ART.50100" <- as.numeric(NA)
"prop.On.ART.50" <- as.numeric(NA)
blankCD4 <- data.frame(country,
get("prop.Off.ART.500"),
get("prop.Off.ART.350500"),
get("prop.Off.ART.250350"),
get("prop.Off.ART.200250"),
get("prop.Off.ART.100200"),
get("prop.Off.ART.50100"),
get("prop.Off.ART.50"),
get("prop.On.ART.500"),
get("prop.On.ART.350500"),
get("prop.On.ART.250350"),
get("prop.On.ART.200250"),
get("prop.On.ART.100200"),
get("prop.On.ART.50100"),
get("prop.On.ART.50"))
names(blankCD4) <- c("country", "prop.Off.ART.500", "prop.Off.ART.350500", "prop.Off.ART.250350", "prop.Off.ART.200250", "prop.Off.ART.100200", "prop.Off.ART.50100", "prop.Off.ART.50", "prop.On.ART.500", "prop.On.ART.350500", "prop.On.ART.250350", "prop.On.ART.200250", "prop.On.ART.100200", "prop.On.ART.50100", "prop.On.ART.50")
calib.df$cd4_2015 <- dplyr::tbl_df(blankCD4)
}
## Treatment Guidelines
if (isReallyEmpty(calib.df$treatment_guidelines)) {
country <- uCountry
less200 <- as.integer(NA)
less250 <- as.integer(NA)
less350 <- as.integer(NA)
less500 <- as.integer(NA)
more500 <- as.integer(NA)
blankGuidelines <- data.frame(country, less200, less250, less350, less500, more500)
calib.df$treatment_guidelines <- dplyr::tbl_df(blankGuidelines)
}
## Population
if (isReallyEmpty(calib.df$pop)) {
country <- uCountry
indicator <- "Population aged 15-49 in 2015"
value <- as.integer(NA)
blankPop <- data.frame(country, indicator, value)
calib.df$pop <- dplyr::tbl_df(blankPop)
}
## All of the below goes into data[["calib"]]
# list temp.names
temp.names <- c(
"temp.art",
"temp.hiv_awareness_unaids",
"temp.not_on_art",
"temp.plhiv",
"temp.previous_data"
)
for (i in 1:length(temp.names)) {
if (exists(temp.names[i])) {
rm(list = temp.names[i], pos = ".GlobalEnv")
}
}
# Reshape data
# calib.art
if (is.not.empty(calib.df$art)) {
temp.art <- reshape2::melt(calib.df$art[c("country","indicator","2010","2011","2012","2013","2014","2015")],
id.vars = c("country", "indicator"),
variable.name = "year",
value.name = "value")
temp.art$weight <- "green"
temp.art$source <- "Spectrum"
}
# calib.not_on_art
if (is.not.empty(calib.df$not_on_art)) {
temp.not_on_art <- reshape2::melt(calib.df$not_on_art[c("country","indicator","2010","2011","2012","2013","2014","2015")],
id.vars = c("country", "indicator"),
variable.name = "year",
value.name = "value")
temp.not_on_art$weight <- "green"
temp.not_on_art$source <- "Spectrum"
}
# calib.plhiv
if (is.not.empty(calib.df$plhiv)) {
temp.plhiv <- reshape2::melt(calib.df$plhiv[c("country","indicator","2010","2011","2012","2013","2014","2015")],
id.vars = c("country","indicator"),
variable.name = "year",
value.name = "value")
temp.plhiv$weight <- "green"
temp.plhiv$source <- "Spectrum"
}
# calib.hiv_awareness_unaids (remember this is a proportion of PLHIV)
# Needs to be matched to PLHIV from the SAME YEAR.
if (is.not.empty(calib.df$hiv_awareness_unaids)) {
temp.hiv_awareness_unaids <- calib.df$hiv_awareness_unaids[c("country","indicator","year","value","source")]
# Need to match PLHIV, if it doesn't exist for a particular year then we delete the value
if (exists("temp.plhiv") & exists("temp.hiv_awareness_unaids")) {
for (i in 1:dim(temp.hiv_awareness_unaids)[1]) {
t.plhiv <- dplyr::filter(temp.plhiv, year == temp.hiv_awareness_unaids$year[i])$value
temp.hiv_awareness_unaids[[i,"value"]] <- temp.hiv_awareness_unaids[[i,"value"]] * t.plhiv
temp.hiv_awareness_unaids$weight <- "green"
}
} else {
rm(temp.hiv_awareness_unaids)
}
}
# calib.previous_data
if (is.not.empty(calib.df$previous_data)) {
temp.previous_data <- dplyr::filter(calib.df$previous_data[c("country","indicator","year","value","weight","source")], year >= 2010)
}
# Assemble master.data.frame
# master.df <- build.master.df(temp.names, calib.df)
out <- c()
for (i in 1:length(temp.names)) {
if (exists(temp.names[i])) {
out <- rbind(out, get(temp.names[i]))
}
}
if (length(out) == 0) {
country <- uCountry
indicator <- c(rep("PLHIV", 6),
rep("PLHIV Diagnosed", 6),
rep("PLHIV in Care", 6),
rep("PLHIV on ART", 6),
rep("PLHIV Suppressed", 6))
year <- rep(seq(2010, 2015, 1), 5)
value <- as.numeric(NA)
weight <- factor(x = NA, levels = c("red", "amber", "green"))
source <- as.character(NA)
out <- data.frame(country, indicator, year, value, weight, source, stringsAsFactors = FALSE)
}
out.list <- list(
calib.df$incidence,
calib.df$cd4,
calib.df$treatment_guidelines,
out,
calib.df$rates,
calib.df$cd4_2015,
calib.df$pop
)
names(out.list) <- c(
"incidence",
"cd4",
"treatment_guidelines",
"calib",
"rates",
"cd4_2015",
"pop"
)
out.list
}
# test <- GetCountryData("Brazil")
# master.df needs to include
# incidence
# cd4
# rates
# treatment guidelines
# Below function called when resetting treatment guidelines
GetTreatmentGuidelines <- function(uCountry) {
# Read in all relevant csv files
c.file.path <- "server/data/calibration"
calib.treatment_guidelines <- readr::read_csv(paste0(c.file.path, "/treatment-guidelines-cd4.csv"), col_names = TRUE, skip = 0)
# Compile data.frame
treatment_guidelines <- dplyr::filter(calib.treatment_guidelines, country == uCountry)
# Return vector
treatment_guidelines
}
new_data <- function(country, year, indicator, value, weight, source) {
if (!is.character(country)) stop("country is not a character.")
if (!is.numeric(year)) stop("year is not a character.")
if (!is.character(indicator)) stop("indicator is not a character.")
if (!is.numeric(value)) stop("value is not a character.")
if (!is.character(weight)) stop("weight is not a character.")
if (!is.character(source)) stop("soruce is not a character.")
# Check if valid indicator
indicator_list <- c("PLHIV", "PLHIV Diagnosed", "PLHIV in Care", "PLHIV on ART", "PLHIV Suppressed")
if (!any(indicator_list == indicator)) stop("Not a valid indicator type")
new_dat <- data.frame(country, year, indicator, value, weight, source)
new_dat
}
replace_or_append <- function(datOne, datTwo) {
# find out if indicator for datTwo exists in datOne
if (any(as.character(datOne[datOne$year == datTwo$year, "indicator"]) == as.character(datTwo$indicator))) {
# REPLACE
datOne[datOne$year == datTwo$year & datOne$indicator == as.character(datTwo$indicator), "value"] <- as.numeric(datTwo$value)
datOne[datOne$year == datTwo$year & datOne$indicator == as.character(datTwo$indicator), "weight"] <- as.character(datTwo$weight)
datOne[datOne$year == datTwo$year & datOne$indicator == as.character(datTwo$indicator), "source"] <- as.character(datTwo$source)
} else {
# APPEND
datOne <- rbind(datOne, datTwo)
}
datOne
}
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