solving cheks for download_utils

NAMESPACE

@@ -34,6 +34,7 @@ export(check_heaping_sawtooth)
 export(check_heaping_spoorenberg)
 export(check_heaping_whipple)
 export(dec.date)
+export(downloadAsfr)
 export(downloadSRB)
 export(downloadnLx)
 export(fetch_wpp_births)

R/utils_downloads.R

@@ -3,19 +3,42 @@
 # and potentially others.
 
 #' Extract Lx estimates from WPP2019
-#' @description We use the `FetchLifeTableWpp2019` function of the `fertestr` to extract `Lx` from `wpp2019`, interpolated to an exact date.
-#' @param nLx either `NULL` or a numeric vector of lifetable exposure. If it's the second then we just pass it back.
-#' @param location UN Pop Div `LocName` or `LocID`
-#' @param gender `"male"`, `"female"`, or `"both"`
-#' @param nLxDatesIn numeric vector of three decimal dates produced by (or passed through) `basepop_five()`
+#' @description We extract `Lx` from `wpp2019`, interpolated to exact dates. Different methods availables. 
+#' A vector of countries can handle, but with an unique sex. Row names are not indicative of countries.
+#' @param nLx numeric. either `NULL` or a numeric vector of lifetable exposure. If it's the second then we just pass it back.
+#' @param location vector. UN Pop Div `LocName` or `LocID`
+#' @param gender character. `"male"`, `"female"`, or `"both"`
+#' @param nLxDatesIn numeric. Vector of three decimal dates produced by (or passed through) `basepop_five()`
+#' @param method character. Could be `"linear"`, `"exponential"`, or `"power"`
 #'
 #' @return numeric matrix of `nLx` with `length(nLxDatesIn)` and abrdiged ages in rows.
 #' @export
-#'
-downloadnLx <- function(nLx, location, gender, nLxDatesIn) {
+#' @examples
+#' # life expectancy calculated from Lx downloaded from WPP19. Using names or codes.
+#' Lxs_name <- downloadnLx(nLx=NULL, location = "Argentina",
+#'                         gender = "both", nLxDatesIn = 1950:2030)
+#' Lxs_code <- downloadnLx(nLx=NULL, location = "32",
+#'                         gender = "both", nLxDatesIn = 1950:2030)
+#' \dontrun{
+#' plot(1950:2030, as.numeric(colSums(Lxs_name)), xlab = "Year", ylab="e0")
+#' lines(1950:2030, as.numeric(colSums(Lxs_code)))
+#' }
+#' # life expectancy for different countries
+#' Lxs_countries <- downloadnLx(nLx=NULL, location = c("Argentina","Brazil","Uruguay"),
+#' gender = "both", nLxDatesIn = 1950:2025)
+#' \dontrun{
+#' plot(1950:2025, as.numeric(colSums(Lxs_countries[1:22,])), 
+#'      t="l", xlab = "Year", ylab="e0", ylim = c(40,80))
+#' lines(1950:2025, as.numeric(colSums(Lxs_countries[23:44,])), col=2)
+#' lines(1950:2025, as.numeric(colSums(Lxs_countries[45:64,])), col=3)
+#' legend("bottomright",c("Argentina","Brazil","Uruguay"),lty=1,col=1:3)
+#' }
+downloadnLx <- function(nLx, location, gender, nLxDatesIn, method="linear") {
+
   requireNamespace("fertestr", quietly = TRUE)
   requireNamespace("magrittr", quietly = TRUE)
   verbose <- getOption("basepop_verbose", TRUE)
+
   if (!is.null(nLx)) {
     # TR: ensure colnames passed
     nLx <- as.matrix(nLx)
@@ -28,55 +51,88 @@ downloadnLx <- function(nLx, location, gender, nLxDatesIn) {
 
   if (is.null(nLx)){
 
-    if (is.null(location)) stop("You need to provide a location to download the data for nLx")
-
+    # stop/warnings
+    if (is.null(location)){
+      stop("You need to provide a location to download the data for nLx")
+    } 
+    if (!any(is_LocID(location))) {
+      location_code <- get_location_code(location)
+    }else {
+      location_code <- as.integer(location)
+    }
+
     if (verbose) {
       cat(paste0("Downloading nLx data for ", location, ", years ", paste(nLxDatesIn,collapse=", "), ", gender ", gender), sep = "\n")
     }
+    if(any(nLxDatesIn<1950,nLxDatesIn>2025)){
+      cat("Careful, extrapolating beyond range 1950-2025")
+    }
 
-    . <- NULL
+    # handle sex
+    sex_code <- ifelse(tolower(gender) == "both", "b", 
+                       ifelse(tolower(gender) == "female", "f", 
+                              ifelse(tolower(gender) == "male", "m", NA)))
+    Sex_mortlaws <- ifelse(sex_code == "b", "total", tolower(gender))
+    stopifnot(`Invalid sex name, please set it to 'both', 'male' or 'female'` = !is.na(sex_code))
 
-    ind_invalidyear <- which(nLxDatesIn < 1955)
-    if (length(ind_invalidyear) != 0) {
-      invalid_yrs <- paste0(nLxDatesIn[ind_invalidyear], collapse = ", ")
-      cat("nLxDate(s)", invalid_yrs, "is/are below 1955. Capping at 1955\n")
-      nLxDatesIn[ind_invalidyear] <- 1955
-    }
+    # initial data
+    lt_wpp19 <-DemoToolsData::WPP2019_lt
 
-    nLx <-
-      lapply(nLxDatesIn, function(x) {
-        fertestr::FetchLifeTableWpp2019(location, x, gender)$Lx
-      }) %>%
-      do.call("cbind", .) %>%
-      as.matrix()
+    # filter and matrix shape
+    lt_ctry <- lt_wpp19[lt_wpp19$LocID %in% location_code &
+                          lt_wpp19$Sex %in% sex_code,] %>% as.data.frame() %>% 
+                reshape(data = ., 
+                        direction = "wide", idvar = c("LocID","AgeStart","Sex"), 
+                        timevar = "Year", v.names = "mx", drop = c("AgeSpan","lx"))
+
+    # intert/extrap rates and built life tables for each combination location/Sex/Year 
+    .<-NULL
+    out <- cbind(lt_ctry[,c(1:3)],
+                 interp(lt_ctry[,-c(1:3)], 
+                        seq(1953,2023,5), as.numeric(nLxDatesIn), 
+                        extrap = TRUE, method = method) %>% 
+                        as.data.frame() %>% 
+                   setNames(as.character(nLxDatesIn))
+                 ) %>% 
+          split(., list(lt_ctry$LocID, lt_ctry$Sex)) %>% 
+          lapply(function(X){
+            Age <- X[["AgeStart"]]
+            apply(X[,-c(1:3)] %>% 
+                    as.data.frame()%>% setNames(as.character(nLxDatesIn)), 2,
+                    function(S){
+                        MortalityLaws::LifeTable(x = Age,
+                                                 mx = S,
+                                                 lx0 = 1,
+                                                 sex = Sex_mortlaws)$lt$Lx
+                  })
+          }) %>% 
+          do.call("rbind", .)
 
-    colnames(nLx) <- nLxDatesIn
-    n             <- nrow(nLx)
-    Age           <- c(0,1,seq(5,(n-2)*5,by=5))
-    rownames(nLx) <- Age
-    return(nLx)
+    # combination as rowname
+    rownames(out) <- lt_ctry$AgeStart
+
+    return(out)
   }
 }
 
 #' Extract ASFR estimates from WPP2019
-#' @description We use the `FetchFertilityWpp2019` function of the `fertestr` to extract `asfr` from `wpp2019`, interpolated to an exact date.
-#' @param location UN Pop Div `LocName` or `LocID`
-#' @param AsfrDatesIn numeric vector of decimal dates.
-#' @param extrap_50s logical, shall we extrapolate between 1950 and 1955?
+#' @description We extract `ASFRx` from `wpp2019`, interpolated to exact dates. Different methods availables.
+#' A vector of countries can handle, but with an unique sex. Row names are not indicative of countries.
+#' @param Asfrmat numeric.
+#' @param location vector. UN Pop Div `LocName` or `LocID`
+#' @param AsfrDatesIn numeric. Vector of decimal dates.
+#' @param method character. Could be `"linear"`, `"exponential"`, or `"power"`
 #'
 #' @return numeric matrix interpolated asfr
 #' @export
 #' @examples
-#' # Kenya example
-#' kenya_noext <- downloadAsfr(Asfrmat = NULL, location = "Kenya", AsfrDatesIn = 1950:2050)
-#' kenya_ext <- downloadAsfr(Asfrmat = NULL, location = "Kenya", AsfrDatesIn = 1950:2050, T)
+#' # Total fertility ratio calculated from ASFRx downloaded from WPP19. 
+#' # See `downloadnLx` for analogous examples on multiple countries or using codes instead of names. 
+#' ASFR_Arg <- downloadAsfr(Asfrmat = NULL, location = "Argentina", AsfrDatesIn = 1950:2025)
 #' \dontrun{
-#' plot(1950:2050, colSums(kenya_noext) * 5, t="l", main="TFR Kenya")
-#' lines(1950:2050, colSums(kenya_ext) * 5, col=2)
-#' legend("topright",c("NoExtr","Extr"),lty=1,col = 1:2)
+#' plot(1950:2025, as.numeric(colSums(ASFR_Arg))*5, xlab = "Year", ylab="TFR", ylim=c(1.5,4), t="l")
 #' }
-
-downloadAsfr <- function(Asfrmat, location = NULL, AsfrDatesIn, extrap_50s = FALSE) {
+downloadAsfr <- function(Asfrmat, location = NULL, AsfrDatesIn, method="linear") {
   requireNamespace("fertestr", quietly = TRUE)
   verbose <- getOption("basepop_verbose", TRUE)
 
@@ -85,47 +141,43 @@ downloadAsfr <- function(Asfrmat, location = NULL, AsfrDatesIn, extrap_50s = FAL
     return(Asfrmat)
   }
 
-  if (is.null(location)) stop("You need to provide a location to download the data for Asfrmat")
-
-  ind_invalidyear <- which(AsfrDatesIn < 1955)
-  if (length(ind_invalidyear) != 0) {
-    invalid_yrs <- paste0(AsfrDatesIn[ind_invalidyear], collapse = ", ")
-    if(!extrap_50s) cat("AsfrDate(s)", invalid_yrs, "is/are below 1955. Capping at 1955\n")
-    AsfrDatesIn[ind_invalidyear] <- 1955
+  # stop/warnings
+  if (is.null(location)){
+    stop("You need to provide a location to download the data for Asfrmat")
+  } 
+  if (!any(is_LocID(location))) {
+    location_code <- get_location_code(location)
+  }else {
+    location_code <- as.integer(location)
   }
-
   if (verbose) {
-    cat(paste0("Downloading Asfr data for ", 
-               loc_message(location), 
-               ", years ", 
-               paste0(AsfrDatesIn),collapse=", "), sep = "\n")
+    cat(paste0("Downloading ASFR data for ", location, ", years ", paste(AsfrDatesIn,collapse=", ")), sep = "\n")
   }
-
-  tmp <-
-    lapply(AsfrDatesIn, function(x) {
-      res        <- fertestr::FetchFertilityWpp2019(location, x)["asfr"]
-      names(res) <- NULL
-      as.matrix(res)[2:nrow(res), , drop = FALSE]
-    })
-
-  Asfrmat           <- do.call(cbind, tmp)
-  colnames(Asfrmat) <- AsfrDatesIn
-
-  # IW: add extrap option extrap_50s
-  # IW: maybe no need to call fertestr::FetchFertilityWpp2019 and then fix first interval.
-    # just load all WPP2019_asfr, select location, interpolate with interp(...,extrap=TRUE), 
-    # and return only requested years. What do you think?
-  if(extrap_50s){
-    minor_years <- sort(unique(AsfrDatesIn))[1:2]
-    if(sum(1955:1960 %in% minor_years)==1) stop("Need two points at least between 1955 and 1960 in AsfrDatesIn to extrapolate.")
-    Asfrmat_1950   <- interp(Asfrmat[,as.character(minor_years)],
-                            minor_years,
-                            as.numeric(1950:1955), extrap = TRUE)  
-    Asfrmat <- cbind(Asfrmat_1950, 
-                     Asfrmat[,as.numeric(colnames(Asfrmat))>1955])
+  if(any(AsfrDatesIn<1950,AsfrDatesIn>2025)){
+    cat("Careful, extrapolating beyond range 1950-2025")
   }
 
-  Asfrmat
+  # initial data
+  asfr_wpp19    <-DemoToolsData::WPP2019_asfr
+
+  # spread format
+  asfr_ctry     <- asfr_wpp19[asfr_wpp19$LocID %in% location_code,] %>% 
+                      as.data.frame() %>% 
+                      reshape(direction = "wide", idvar = c("LocID","AgeStart"), 
+                              timevar = "Year", v.names = "ASFR")
+
+  # interp/extrap
+  out <- interp(asfr_ctry[,-c(1:3)], seq(1953,2023,5),
+                as.numeric(AsfrDatesIn), 
+                extrap = TRUE, method = method) %>% 
+                as.data.frame() %>% 
+                setNames(as.character(AsfrDatesIn)) %>% 
+                as.matrix()
+
+  # combination as rowname
+  rownames(out) <- asfr_ctry$AgeStart
+
+  return(out)
 }
 
 #' Extract SRB estimates from WPP2019