inst/app/server/calibration/master.R
In jackolney/CascadeDashboard: A Shiny App for Investigating The Current Trajectory of HIV Care
# This file will source all the others and pull together a 'master data set' including:
# - Previous data on the cascade (calibration-data.R)
# - Assumptions (assumptions.R)
# - Marrakech data (as the end target) (marrakech-data.R)
# source("server/calibration/calibration-data.R", local = FALSE)
# source("server/calibration/assumptions.R", local = FALSE)
# source("server/calibration/marrakech-data.R", local = FALSE)
# Then pull all this together into one 'master function'.
# THIS, is what the model will use as data and calculate error against.
# Everything will take an argument of 'uCountry' which will be 'userCountry'
# This will be called / generated once someone clicks on the map / picks from the dropdown menu
# This function will need to run some tests on the data.set to make sure that it is sensical.
# Set country
# userCountry <- "DRC"
GetMasterDataSet <- function(userCountry) {
# Get all the data (all your base)
countryData <- GetCountryData(userCountry)
# countryAssumptions <- MakeAssumptions(userCountry, countryData)
marrakechData <- GetMarrakechData(userCountry)
# Filter out year of Marrakech data, before binding.
# int <- rbind(countryData$calib, countryAssumptions)
if (userCountry == "Kenya") {
# For Kenya we only believe, the following three indicators supplied by the Marrakech Team
mData <- marrakechData[marrakechData$indicator %in% c("PLHIV in Care", "PLHIV on ART", "PLHIV Suppressed"),]
# This is all a bit Kenya-specific... we should really change that.
# Now they need combining with the baseDataSet, but they should replace, not be an addition.
# Extract everything that isn't 2015 data.
int <- countryData$calib
intOne <- int[int$year != 2015,]
# Now extract the relevant bits of 2015
intTwo <- int[int$year == 2015 & int$indicator %in% c("PLHIV", "PLHIV Diagnosed"),]
# Combine together
# MASTER DATA SET (for calibration)
countryMasterDataSet <- rbind(intOne, intTwo, mData)
} else if (userCountry == "Tanzania") {
# This is usually just some blanket code that extract stuff from countryData$calib and prevents things from being overwritten in the background.
# Not really necessary for Tanzania (as of now).
# Now for some cheap hackery so that we can pick the smallest value of 'PLHIV on ART' in 2015 (from CTC) report.
int <- countryData$calib
intOne <- int[int$year != 2015,]
intTemp <- int[int$year == 2015 & int$indicator == "PLHIV on ART",]
intTwo <- intTemp[intTemp$value == min(intTemp$value),]
intThree <- int[int$year == 2015 & int$indicator != "PLHIV on ART",]
countryMasterDataSet <- rbind(intOne, intTwo, intThree)
} else if (userCountry == "Zimbabwe") {
# By hand, removing the indicators that get in the way of everything and cause trouble.
# See issue #10 on GitHub for more details about my thinking on this.
intOne <- marrakechData[marrakechData$indicator %in% c("PLHIV Diagnosed", "PLHIV in Care", "PLHIV Suppressed"),]
int <- countryData$calib
int[int$year == 2015 & int$indicator != "PLHIV on ART",]
intTwo <- int[int$indicator != "PLHIV not on ART",]
intThree <- rbind(intTwo[intTwo$year == 2015 & intTwo$indicator != "PLHIV on ART",], intTwo[intTwo$year != 2015,])
countryMasterDataSet <- rbind(intOne, intThree)
# New Data From Croatia (Global HIV Cascade Workshop 2016)
# new_care <- new_data(country = "Zimbabwe", year = 2015, indicator = "PLHIV in Care", value = 903011, weight = "amber", source = "Program Data (adjusted ART data)")
# new_art <- new_data(country = "Zimbabwe", year = 2015, indicator = "PLHIV on ART", value = 879271, weight = "amber", source = "Program Data (DHIS)")
# new_supp <- new_data(country = "Zimbabwe", year = 2015, indicator = "PLHIV Suppressed", value = 782551, weight = "red", source = "Surveillance")
# countryMasterDataSet <- replace_or_append(datOne = countryMasterDataSet, datTwo = new_care)
# countryMasterDataSet <- replace_or_append(datOne = countryMasterDataSet, datTwo = new_art)
# countryMasterDataSet <- replace_or_append(datOne = countryMasterDataSet, datTwo = new_supp)
# New Data from PHIA
# 16/01/17 - Updated to now be calibrating to PERCENTAGES for the PHIA
# PHIA_dx <- new_data(country = "Zimbabwe", year = 2015, indicator = "PLHIV Diagnosed", value = 1057915, weight = "green", source = "PHIA")
# PHIA_tx <- new_data(country = "Zimbabwe", year = 2015, indicator = "PLHIV on ART", value = 918271, weight = "green", source = "PHIA")
# PHIA_vs <- new_data(country = "Zimbabwe", year = 2015, indicator = "PLHIV Suppressed", value = 794304, weight = "green", source = "PHIA")
# countryMasterDataSet <- replace_or_append(datOne = countryMasterDataSet, datTwo = PHIA_dx)
# countryMasterDataSet <- replace_or_append(datOne = countryMasterDataSet, datTwo = PHIA_tx)
# countryMasterDataSet <- replace_or_append(datOne = countryMasterDataSet, datTwo = PHIA_vs)
} else if (userCountry == "Brazil") {
# copy in the countryData
int <- countryData$calib
# Remove stuff from Jaun's thesis (superceded by latest Spectrum)
noDST <- int[int$source != "Data from Department of DST",]
# Remove other UNAIDS data
noOther <- noDST[noDST$source != "http://www.unaids.org/sites/default/files/en/dataanalysis/knowyourresponse/countryprogressreports/2014countries/BRA_narrative_report_2014.pdf",]
# Ignore Spectrum stuff in 2013
not2013 <- noOther[noOther$year != 2013,]
# Get the 2013 Spectrum PLHIV estimate
plhiv_2013 <- noOther[noOther$year == 2013 & noOther$indicator == "PLHIV",]
# Pull in MarrakechData
intOne <- marrakechData
# Use all except the PLHIV estimate
intTwo <- intOne[intOne$indicator != "PLHIV",]
# Bind together
countryMasterDataSet <- rbind(not2013, intTwo, plhiv_2013)
} else if (userCountry == "Cambodia") {
int <- countryData$calib
intOne <- marrakechData
# Override, 2013 PLHIV on ART value & pull in Marrakech data and use to overwrite
intTwo <- int[int$year == 2013,][2:4,]
countryMasterDataSet <- rbind(int[int$year != 2013 & int$year != 2014,], intTwo, intOne)
} else if (userCountry == "DRC") {
# Pull in data.structure
int <- countryData$calib
# Ignore PLHIV estimate from Marrakech, as it conflicts with
int2 <- marrakechData[marrakechData$indicator != "PLHIV",]
# Want to keep the DRC estimate of numbers on ART from 2014
int3 <- int[int$year == 2014 & int$indicator != "PLHIV on ART",]
# Now want int for all years except 2014
int4 <- int[int$year != 2014,]
# Pull it all together
countryMasterDataSet <- rbind(int2, int3, int4)
# DHS Spectrum value in 2013, do we want?
# Ignore the high PLHIV diagnosed value
# value <- rbind(int[int$year == 2013 & int$indicator != "PLHIV Diagnosed",], int[int$year == 2013 & int$indicator == "PLHIV Diagnosed",][2,])
# countryMasterDataSet <- rbind(int[int$year != 2013 & int$year != 2014,], value, marrakechData)
} else if (userCountry == "India") {
int <- countryData$calib
countryMasterDataSet <- rbind(int, marrakechData)
} else if (userCountry == "Myanmar") {
int <- countryData$calib
# Ignore the values that we pulled in from 'aidsdatahub' (we weighted all these as amber in any case)
intOne <- int[int$weight != "amber",]
countryMasterDataSet <- rbind(intOne[intOne$year != 2014,], marrakechData)
} else if (userCountry == "Pakistan") {
int <- countryData$calib
# Ignore the values that we pulled in from 'aidsdatahub' (we weighted all these as amber in any case)
intOne <- int[int$weight != "amber",]
# Ignoring most of the data from Pakistan as very little info was supplied on it (but will use the estimate of PLHIV in Care)
countryMasterDataSet <- rbind(intOne, marrakechData[marrakechData$indicator == "PLHIV in Care",])
} else if (userCountry == "Thailand") {
int <- countryData$calib
# Ignore the values that we pulled in from 'aidsdatahub' (we weighted all these as amber in any case)
intOne <- int[int$weight != "amber",]
countryMasterDataSet <- rbind(marrakechData, intOne[intOne$year != 2014,])
} else if (userCountry == "Vietnam") {
# Pull in countryData
int <- countryData$calib
# Ignore some of the values that we pulled from other sources
plhiv <- int[int$indicator == "PLHIV" & int$source == "Spectrum",]
diag <- int[int$indicator == "PLHIV Diagnosed",]
care <- int[int$indicator == "PLHIV In Care",]
art <- int[int$indicator == "PLHIV on ART" & int$source == "Spectrum",]
data <- rbind(plhiv, diag, care, art)
# merge and ignore 2014 data as we rely on Marrakech estimates for that.
countryMasterDataSet <- rbind(data[data$year != 2014,], marrakechData)
} else if (userCountry == "Cameroon") {
int <- countryData$calib
countryMasterDataSet <- rbind(int[int$year != 2014,], marrakechData)
} else if (userCountry == "Cote d'Ivoire") {
int <- countryData$calib
countryMasterDataSet <- rbind(int[int$year != 2014,], marrakechData)
} else if (userCountry == "Ethiopia") {
int <- countryData$calib
countryMasterDataSet <- rbind(int[int$year != 2014,], marrakechData)
} else if (userCountry == "Haiti") {
int <- countryData$calib
countryMasterDataSet <- rbind(marrakechData[marrakechData$indicator %in% c("PLHIV Diagnosed", "PLHIV in Care", "PLHIV Suppressed"),], int)
} else if (userCountry == "Indonesia") {
# Pull in countryData
int <- countryData$calib
# Ignore anything that came from AIDS datahub
intOne <- int[int$source != "http://www.aidsdatahub.org/Country-Profiles/Indonesia",]
# marrakechData is just for 2014 and covers all the indicators
countryMasterDataSet <- rbind(rbind(intOne[intOne$year != 2014,], marrakechData))
} else if (userCountry == "Malawi") {
int <- countryData$calib
countryMasterDataSet <- rbind(marrakechData[marrakechData$indicator %in% c("PLHIV Diagnosed", "PLHIV in Care", "PLHIV Suppressed"),], int)
} else if (userCountry == "Morocco") {
int <- countryData$calib
countryMasterDataSet <- rbind(marrakechData[marrakechData$indicator %in% c("PLHIV Diagnosed", "PLHIV in Care", "PLHIV Suppressed"),], int)
} else if (userCountry == "Mozambique") {
int <- countryData$calib
countryMasterDataSet <- rbind(marrakechData[marrakechData$indicator %in% c("PLHIV in Care", "PLHIV Suppressed"),], int)
} else if (userCountry == "Nigeria") {
int <- countryData$calib
countryMasterDataSet <- rbind(marrakechData[marrakechData$indicator %in% c("PLHIV Diagnosed", "PLHIV in Care"),], int)
} else if (userCountry == "Philippines") {
# Pull in countryData
int <- countryData$calib
# Ignore anything that came from AIDS datahub
intOne <- int[int$source != "http://www.aidsdatahub.org/Country-Profiles/Philippines",]
# marrakechData is just for 2015 and covers all the indicators
countryMasterDataSet <- rbind(rbind(intOne[intOne$year != 2015,], marrakechData))
} else if (userCountry == "Russia") {
int <- countryData$calib
intOne <- int[int$year == 2015 & int$indicator %in% c("PLHIV", "PLHIV Suppressed"),]
countryMasterDataSet <- rbind(intOne, marrakechData, int[int$year != 2015,])
} else if (userCountry == "South Africa") {
#THEMBISA
# Just overwrite Spectrum estimates here in:
# $calib (yes)
# $pop (yes)
# $incidence (yes, need 95% CI)
# Draft code
thembisa.cascade <- data.frame(readr::read_csv("server/data/calibration/thembisa-cascade.csv", col_names = TRUE, skip = 0), stringsAsFactors = FALSE)
thembisa.incidence <- data.frame(readr::read_csv("server/data/calibration/thembisa-incidence.csv", col_names = TRUE, skip = 1), stringsAsFactors = FALSE)
thembisa.population <- data.frame(readr::read_csv("server/data/calibration/thembisa-population.csv", col_names = TRUE, skip = 0), stringsAsFactors = FALSE)
# countryData$calib overload
int <- countryData$calib
t1 <- thembisa.cascade
t2 <- int[int$source != "Spectrum",]
countryMasterDataSet <- rbind(marrakechData[marrakechData$indicator %in% c("PLHIV Diagnosed", "PLHIV Suppressed"),], t1, t2)
# countryData$incidence
inc <- countryData$incidence
inc[1,3:9] <- thembisa.incidence[1,3:9]
inc[2,3:9] <- thembisa.incidence[2,3:9]
inc[3,3:9] <- thembisa.incidence[3,3:9]
countryData$incidence <- inc
# countryData$pop
countryData$pop[["value"]] <- thembisa.population[["value"]]
# Then overload the Spectrum estimates here (allow me to be able to comment out this section and return to the SPECTRUM estimates)
# SPECTRUM
# (BLANKED OUT WHILE WE USE THEMBISA)
# int <- countryData$calib
# countryMasterDataSet <- rbind(marrakechData[marrakechData$indicator %in% c("PLHIV Diagnosed", "PLHIV Suppressed"),], int)
} else if (userCountry == "South Sudan") {
int <- countryData$calib
countryMasterDataSet <- rbind(marrakechData[marrakechData$indicator %in% c("PLHIV Diagnosed", "PLHIV in Care"),], int)
} else if (userCountry == "Uganda") {
int <- countryData$calib
countryMasterDataSet <- rbind(int[int$year != 2014,], marrakechData)
} else if (userCountry == "Ukraine") {
int <- countryData$calib
countryMasterDataSet <- rbind(int[int$year != 2015,], marrakechData)
} else {
countryMasterDataSet <- countryData$calib
}
# Overwrite calib on countryData
countryData$calib <- countryMasterDataSet[countryMasterDataSet$indicator != "PLHIV not on ART",]
# Only allow certain countries to 'proceed', i.e. return 'countryData'
# This will be removed eventually, but good for testing right now.
# if (userCountry %in% c("Kenya", "Tanzania", "Zimbabwe")) {
# countryData
# } else {
# stop("Country not approved for use by this model.")
# }
countryData
}
# GetMasterDataSet("Kenya")[["calib"]]
# Output testing
# test <- countryMasterDataSet[countryMasterDataSet$indicator != "PLHIV not on ART",]
# ggplot(test, aes(x = year, y = value)) + geom_bar(aes(fill = indicator), stat = "identity", position = "dodge")
# BLANK MASTER DATA SET
GetBlankMasterDataSet <- function(newName) {
oldData <- GetMasterDataSet("Kenya")
# Incidence
oldData$incidence[,"country"] <- newName
oldData$incidence[,as.character(seq(2010,2016,1))] <- as.numeric(NA)
# CD4
oldData$cd4[,"country"] <- newName
oldData$cd4[, c("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")
] <- NA
# CD4 2015
oldData$cd4_2015[,"country"] <- newName
oldData$cd4_2015[, c("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")
] <- NA
# Treatment Guidelines
oldData$treatment_guidelines[,"country"] <- newName
oldData$treatment_guidelines[,c("less200", "less250", "less350", "less500", "more500")] <- as.numeric(NA)
# Population
oldData$pop[,"country"] <- newName
oldData$pop[,"value"] <- as.numeric(NA)
# Calibration
country <- newName
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)
oldData$calib <- data.frame(country, indicator, year, value, weight, source, stringsAsFactors = FALSE)
# Rates (not used)
oldData
}
jackolney/CascadeDashboard documentation built on May 18, 2019, 7:56 a.m.
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# This file will source all the others and pull together a 'master data set' including:
# - Previous data on the cascade (calibration-data.R)
# - Assumptions (assumptions.R)
# - Marrakech data (as the end target) (marrakech-data.R)
# source("server/calibration/calibration-data.R", local = FALSE)
# source("server/calibration/assumptions.R", local = FALSE)
# source("server/calibration/marrakech-data.R", local = FALSE)
# Then pull all this together into one 'master function'.
# THIS, is what the model will use as data and calculate error against.
# Everything will take an argument of 'uCountry' which will be 'userCountry'
# This will be called / generated once someone clicks on the map / picks from the dropdown menu
# This function will need to run some tests on the data.set to make sure that it is sensical.
# Set country
# userCountry <- "DRC"
GetMasterDataSet <- function(userCountry) {
# Get all the data (all your base)
countryData <- GetCountryData(userCountry)
# countryAssumptions <- MakeAssumptions(userCountry, countryData)
marrakechData <- GetMarrakechData(userCountry)
# Filter out year of Marrakech data, before binding.
# int <- rbind(countryData$calib, countryAssumptions)
if (userCountry == "Kenya") {
# For Kenya we only believe, the following three indicators supplied by the Marrakech Team
mData <- marrakechData[marrakechData$indicator %in% c("PLHIV in Care", "PLHIV on ART", "PLHIV Suppressed"),]
# This is all a bit Kenya-specific... we should really change that.
# Now they need combining with the baseDataSet, but they should replace, not be an addition.
# Extract everything that isn't 2015 data.
int <- countryData$calib
intOne <- int[int$year != 2015,]
# Now extract the relevant bits of 2015
intTwo <- int[int$year == 2015 & int$indicator %in% c("PLHIV", "PLHIV Diagnosed"),]
# Combine together
# MASTER DATA SET (for calibration)
countryMasterDataSet <- rbind(intOne, intTwo, mData)
} else if (userCountry == "Tanzania") {
# This is usually just some blanket code that extract stuff from countryData$calib and prevents things from being overwritten in the background.
# Not really necessary for Tanzania (as of now).
# Now for some cheap hackery so that we can pick the smallest value of 'PLHIV on ART' in 2015 (from CTC) report.
int <- countryData$calib
intOne <- int[int$year != 2015,]
intTemp <- int[int$year == 2015 & int$indicator == "PLHIV on ART",]
intTwo <- intTemp[intTemp$value == min(intTemp$value),]
intThree <- int[int$year == 2015 & int$indicator != "PLHIV on ART",]
countryMasterDataSet <- rbind(intOne, intTwo, intThree)
} else if (userCountry == "Zimbabwe") {
# By hand, removing the indicators that get in the way of everything and cause trouble.
# See issue #10 on GitHub for more details about my thinking on this.
intOne <- marrakechData[marrakechData$indicator %in% c("PLHIV Diagnosed", "PLHIV in Care", "PLHIV Suppressed"),]
int <- countryData$calib
int[int$year == 2015 & int$indicator != "PLHIV on ART",]
intTwo <- int[int$indicator != "PLHIV not on ART",]
intThree <- rbind(intTwo[intTwo$year == 2015 & intTwo$indicator != "PLHIV on ART",], intTwo[intTwo$year != 2015,])
countryMasterDataSet <- rbind(intOne, intThree)
# New Data From Croatia (Global HIV Cascade Workshop 2016)
# new_care <- new_data(country = "Zimbabwe", year = 2015, indicator = "PLHIV in Care", value = 903011, weight = "amber", source = "Program Data (adjusted ART data)")
# new_art <- new_data(country = "Zimbabwe", year = 2015, indicator = "PLHIV on ART", value = 879271, weight = "amber", source = "Program Data (DHIS)")
# new_supp <- new_data(country = "Zimbabwe", year = 2015, indicator = "PLHIV Suppressed", value = 782551, weight = "red", source = "Surveillance")
# countryMasterDataSet <- replace_or_append(datOne = countryMasterDataSet, datTwo = new_care)
# countryMasterDataSet <- replace_or_append(datOne = countryMasterDataSet, datTwo = new_art)
# countryMasterDataSet <- replace_or_append(datOne = countryMasterDataSet, datTwo = new_supp)
# New Data from PHIA
# 16/01/17 - Updated to now be calibrating to PERCENTAGES for the PHIA
# PHIA_dx <- new_data(country = "Zimbabwe", year = 2015, indicator = "PLHIV Diagnosed", value = 1057915, weight = "green", source = "PHIA")
# PHIA_tx <- new_data(country = "Zimbabwe", year = 2015, indicator = "PLHIV on ART", value = 918271, weight = "green", source = "PHIA")
# PHIA_vs <- new_data(country = "Zimbabwe", year = 2015, indicator = "PLHIV Suppressed", value = 794304, weight = "green", source = "PHIA")
# countryMasterDataSet <- replace_or_append(datOne = countryMasterDataSet, datTwo = PHIA_dx)
# countryMasterDataSet <- replace_or_append(datOne = countryMasterDataSet, datTwo = PHIA_tx)
# countryMasterDataSet <- replace_or_append(datOne = countryMasterDataSet, datTwo = PHIA_vs)
} else if (userCountry == "Brazil") {
# copy in the countryData
int <- countryData$calib
# Remove stuff from Jaun's thesis (superceded by latest Spectrum)
noDST <- int[int$source != "Data from Department of DST",]
# Remove other UNAIDS data
noOther <- noDST[noDST$source != "http://www.unaids.org/sites/default/files/en/dataanalysis/knowyourresponse/countryprogressreports/2014countries/BRA_narrative_report_2014.pdf",]
# Ignore Spectrum stuff in 2013
not2013 <- noOther[noOther$year != 2013,]
# Get the 2013 Spectrum PLHIV estimate
plhiv_2013 <- noOther[noOther$year == 2013 & noOther$indicator == "PLHIV",]
# Pull in MarrakechData
intOne <- marrakechData
# Use all except the PLHIV estimate
intTwo <- intOne[intOne$indicator != "PLHIV",]
# Bind together
countryMasterDataSet <- rbind(not2013, intTwo, plhiv_2013)
} else if (userCountry == "Cambodia") {
int <- countryData$calib
intOne <- marrakechData
# Override, 2013 PLHIV on ART value & pull in Marrakech data and use to overwrite
intTwo <- int[int$year == 2013,][2:4,]
countryMasterDataSet <- rbind(int[int$year != 2013 & int$year != 2014,], intTwo, intOne)
} else if (userCountry == "DRC") {
# Pull in data.structure
int <- countryData$calib
# Ignore PLHIV estimate from Marrakech, as it conflicts with
int2 <- marrakechData[marrakechData$indicator != "PLHIV",]
# Want to keep the DRC estimate of numbers on ART from 2014
int3 <- int[int$year == 2014 & int$indicator != "PLHIV on ART",]
# Now want int for all years except 2014
int4 <- int[int$year != 2014,]
# Pull it all together
countryMasterDataSet <- rbind(int2, int3, int4)
# DHS Spectrum value in 2013, do we want?
# Ignore the high PLHIV diagnosed value
# value <- rbind(int[int$year == 2013 & int$indicator != "PLHIV Diagnosed",], int[int$year == 2013 & int$indicator == "PLHIV Diagnosed",][2,])
# countryMasterDataSet <- rbind(int[int$year != 2013 & int$year != 2014,], value, marrakechData)
} else if (userCountry == "India") {
int <- countryData$calib
countryMasterDataSet <- rbind(int, marrakechData)
} else if (userCountry == "Myanmar") {
int <- countryData$calib
# Ignore the values that we pulled in from 'aidsdatahub' (we weighted all these as amber in any case)
intOne <- int[int$weight != "amber",]
countryMasterDataSet <- rbind(intOne[intOne$year != 2014,], marrakechData)
} else if (userCountry == "Pakistan") {
int <- countryData$calib
# Ignore the values that we pulled in from 'aidsdatahub' (we weighted all these as amber in any case)
intOne <- int[int$weight != "amber",]
# Ignoring most of the data from Pakistan as very little info was supplied on it (but will use the estimate of PLHIV in Care)
countryMasterDataSet <- rbind(intOne, marrakechData[marrakechData$indicator == "PLHIV in Care",])
} else if (userCountry == "Thailand") {
int <- countryData$calib
# Ignore the values that we pulled in from 'aidsdatahub' (we weighted all these as amber in any case)
intOne <- int[int$weight != "amber",]
countryMasterDataSet <- rbind(marrakechData, intOne[intOne$year != 2014,])
} else if (userCountry == "Vietnam") {
# Pull in countryData
int <- countryData$calib
# Ignore some of the values that we pulled from other sources
plhiv <- int[int$indicator == "PLHIV" & int$source == "Spectrum",]
diag <- int[int$indicator == "PLHIV Diagnosed",]
care <- int[int$indicator == "PLHIV In Care",]
art <- int[int$indicator == "PLHIV on ART" & int$source == "Spectrum",]
data <- rbind(plhiv, diag, care, art)
# merge and ignore 2014 data as we rely on Marrakech estimates for that.
countryMasterDataSet <- rbind(data[data$year != 2014,], marrakechData)
} else if (userCountry == "Cameroon") {
int <- countryData$calib
countryMasterDataSet <- rbind(int[int$year != 2014,], marrakechData)
} else if (userCountry == "Cote d'Ivoire") {
int <- countryData$calib
countryMasterDataSet <- rbind(int[int$year != 2014,], marrakechData)
} else if (userCountry == "Ethiopia") {
int <- countryData$calib
countryMasterDataSet <- rbind(int[int$year != 2014,], marrakechData)
} else if (userCountry == "Haiti") {
int <- countryData$calib
countryMasterDataSet <- rbind(marrakechData[marrakechData$indicator %in% c("PLHIV Diagnosed", "PLHIV in Care", "PLHIV Suppressed"),], int)
} else if (userCountry == "Indonesia") {
# Pull in countryData
int <- countryData$calib
# Ignore anything that came from AIDS datahub
intOne <- int[int$source != "http://www.aidsdatahub.org/Country-Profiles/Indonesia",]
# marrakechData is just for 2014 and covers all the indicators
countryMasterDataSet <- rbind(rbind(intOne[intOne$year != 2014,], marrakechData))
} else if (userCountry == "Malawi") {
int <- countryData$calib
countryMasterDataSet <- rbind(marrakechData[marrakechData$indicator %in% c("PLHIV Diagnosed", "PLHIV in Care", "PLHIV Suppressed"),], int)
} else if (userCountry == "Morocco") {
int <- countryData$calib
countryMasterDataSet <- rbind(marrakechData[marrakechData$indicator %in% c("PLHIV Diagnosed", "PLHIV in Care", "PLHIV Suppressed"),], int)
} else if (userCountry == "Mozambique") {
int <- countryData$calib
countryMasterDataSet <- rbind(marrakechData[marrakechData$indicator %in% c("PLHIV in Care", "PLHIV Suppressed"),], int)
} else if (userCountry == "Nigeria") {
int <- countryData$calib
countryMasterDataSet <- rbind(marrakechData[marrakechData$indicator %in% c("PLHIV Diagnosed", "PLHIV in Care"),], int)
} else if (userCountry == "Philippines") {
# Pull in countryData
int <- countryData$calib
# Ignore anything that came from AIDS datahub
intOne <- int[int$source != "http://www.aidsdatahub.org/Country-Profiles/Philippines",]
# marrakechData is just for 2015 and covers all the indicators
countryMasterDataSet <- rbind(rbind(intOne[intOne$year != 2015,], marrakechData))
} else if (userCountry == "Russia") {
int <- countryData$calib
intOne <- int[int$year == 2015 & int$indicator %in% c("PLHIV", "PLHIV Suppressed"),]
countryMasterDataSet <- rbind(intOne, marrakechData, int[int$year != 2015,])
} else if (userCountry == "South Africa") {
#THEMBISA
# Just overwrite Spectrum estimates here in:
# $calib (yes)
# $pop (yes)
# $incidence (yes, need 95% CI)
# Draft code
thembisa.cascade <- data.frame(readr::read_csv("server/data/calibration/thembisa-cascade.csv", col_names = TRUE, skip = 0), stringsAsFactors = FALSE)
thembisa.incidence <- data.frame(readr::read_csv("server/data/calibration/thembisa-incidence.csv", col_names = TRUE, skip = 1), stringsAsFactors = FALSE)
thembisa.population <- data.frame(readr::read_csv("server/data/calibration/thembisa-population.csv", col_names = TRUE, skip = 0), stringsAsFactors = FALSE)
# countryData$calib overload
int <- countryData$calib
t1 <- thembisa.cascade
t2 <- int[int$source != "Spectrum",]
countryMasterDataSet <- rbind(marrakechData[marrakechData$indicator %in% c("PLHIV Diagnosed", "PLHIV Suppressed"),], t1, t2)
# countryData$incidence
inc <- countryData$incidence
inc[1,3:9] <- thembisa.incidence[1,3:9]
inc[2,3:9] <- thembisa.incidence[2,3:9]
inc[3,3:9] <- thembisa.incidence[3,3:9]
countryData$incidence <- inc
# countryData$pop
countryData$pop[["value"]] <- thembisa.population[["value"]]
# Then overload the Spectrum estimates here (allow me to be able to comment out this section and return to the SPECTRUM estimates)
# SPECTRUM
# (BLANKED OUT WHILE WE USE THEMBISA)
# int <- countryData$calib
# countryMasterDataSet <- rbind(marrakechData[marrakechData$indicator %in% c("PLHIV Diagnosed", "PLHIV Suppressed"),], int)
} else if (userCountry == "South Sudan") {
int <- countryData$calib
countryMasterDataSet <- rbind(marrakechData[marrakechData$indicator %in% c("PLHIV Diagnosed", "PLHIV in Care"),], int)
} else if (userCountry == "Uganda") {
int <- countryData$calib
countryMasterDataSet <- rbind(int[int$year != 2014,], marrakechData)
} else if (userCountry == "Ukraine") {
int <- countryData$calib
countryMasterDataSet <- rbind(int[int$year != 2015,], marrakechData)
} else {
countryMasterDataSet <- countryData$calib
}
# Overwrite calib on countryData
countryData$calib <- countryMasterDataSet[countryMasterDataSet$indicator != "PLHIV not on ART",]
# Only allow certain countries to 'proceed', i.e. return 'countryData'
# This will be removed eventually, but good for testing right now.
# if (userCountry %in% c("Kenya", "Tanzania", "Zimbabwe")) {
# countryData
# } else {
# stop("Country not approved for use by this model.")
# }
countryData
}
# GetMasterDataSet("Kenya")[["calib"]]
# Output testing
# test <- countryMasterDataSet[countryMasterDataSet$indicator != "PLHIV not on ART",]
# ggplot(test, aes(x = year, y = value)) + geom_bar(aes(fill = indicator), stat = "identity", position = "dodge")
# BLANK MASTER DATA SET
GetBlankMasterDataSet <- function(newName) {
oldData <- GetMasterDataSet("Kenya")
# Incidence
oldData$incidence[,"country"] <- newName
oldData$incidence[,as.character(seq(2010,2016,1))] <- as.numeric(NA)
# CD4
oldData$cd4[,"country"] <- newName
oldData$cd4[, c("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")
] <- NA
# CD4 2015
oldData$cd4_2015[,"country"] <- newName
oldData$cd4_2015[, c("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")
] <- NA
# Treatment Guidelines
oldData$treatment_guidelines[,"country"] <- newName
oldData$treatment_guidelines[,c("less200", "less250", "less350", "less500", "more500")] <- as.numeric(NA)
# Population
oldData$pop[,"country"] <- newName
oldData$pop[,"value"] <- as.numeric(NA)
# Calibration
country <- newName
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)
oldData$calib <- data.frame(country, indicator, year, value, weight, source, stringsAsFactors = FALSE)
# Rates (not used)
oldData
}
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