fix to averaging function

output-data/output-for-interactive/regions_line_chart.csv

@@ -1,9 +1,9 @@
 location,date,estimate,estimate_top_95,estimate_top_50,estimate_bot_50,estimate_bot_95,official_covid_deaths,known_excess_deaths
-Africa,2020-01-01,635.004,1213.731,635.004,84.774,-105.854,0,FALSE
-Asia,2020-01-01,-264.284,593.793,-264.284,-1010.258,-1262.044,0,FALSE
+Africa,2020-01-01,635.004,1213.731,635.004,84.774,-105.854,NA,FALSE
+Asia,2020-01-01,-264.284,593.793,-264.284,-1010.258,-1262.044,NA,FALSE
 North America,2020-01-01,0,0,0,0,0,0,FALSE
-Europe,2020-01-01,-0.097,0.098,-0.019,-0.137,-0.265,0,FALSE
-Oceania,2020-01-01,-1.123,18.817,8.33,-2.1,-6.971,0,FALSE
+Europe,2020-01-01,-0.097,0.098,-0.019,-0.137,-0.265,NA,FALSE
+Oceania,2020-01-01,-1.123,18.817,8.33,-2.1,-6.971,NA,FALSE
 Latin America and Caribbean,2020-01-01,4.743,17.654,4.743,-2.11,-10.561,0,FALSE
 Africa,2020-01-06,635.159,1208.065,635.159,93.664,-104.669,0,FALSE
 Asia,2020-01-06,-264.465,582.615,-264.465,-1028.321,-1218.65,0,FALSE
@@ -1271,9 +1271,9 @@ Asia,2024-01-15,3447.157,13333.182,4458.04,-364.871,-3140.861,14.571,FALSE
 Oceania,2024-01-15,-4.213,76.902,21.477,-4.702,-52.266,6.143,FALSE
 North America,2024-01-15,-42.596,803.993,-17.617,-270.076,-607.42,0.143,FALSE
 Europe,2024-01-15,-314.841,873.244,-150.58,-968.157,-1431.248,0,FALSE
-Africa,2024-01-22,648.486,2909.879,1237.354,-151.207,-1143.123,0,FALSE
-Oceania,2024-01-22,-1.782,79.209,25.373,-3.464,-48.447,0,FALSE
+Africa,2024-01-22,648.486,2909.879,1237.354,-151.207,-1143.123,NA,FALSE
+Oceania,2024-01-22,-1.782,79.209,25.373,-3.464,-48.447,NA,FALSE
 Latin America and Caribbean,2024-01-22,-102.833,968.074,248.648,-194.171,-1106.512,0,FALSE
-Asia,2024-01-22,3613.075,13323.487,4468.164,-504.769,-3279.726,0,FALSE
+Asia,2024-01-22,3613.075,13323.487,4468.164,-504.769,-3279.726,NA,FALSE
 North America,2024-01-22,-42.596,803.993,-1.676,-277.762,-607.42,0,FALSE
-Europe,2024-01-22,-314.859,865.85,-151.577,-968.079,-1431.248,0,FALSE
+Europe,2024-01-22,-314.859,865.85,-151.577,-968.079,-1431.248,NA,FALSE

output-data/output-for-interactive/regions_line_chart_per_100k.csv

@@ -1272,8 +1272,8 @@ Latin America and Caribbean,2024-01-15,-0.016,0.147,0.038,-0.028,-0.167,0,FALSE
 Africa,2024-01-15,0.045,0.204,0.087,0.005,-0.072,0,FALSE
 Asia,2024-01-15,0.073,0.284,0.095,-0.008,-0.067,0,FALSE
 North America,2024-01-22,-0.011,0.213,-0,-0.074,-0.161,0,FALSE
-Europe,2024-01-22,-0.042,0.116,-0.02,-0.13,-0.192,0,FALSE
-Oceania,2024-01-22,-0.004,0.176,0.056,-0.008,-0.108,0,FALSE
+Europe,2024-01-22,-0.042,0.116,-0.02,-0.13,-0.192,NA,FALSE
+Oceania,2024-01-22,-0.004,0.176,0.056,-0.008,-0.108,NA,FALSE
 Latin America and Caribbean,2024-01-22,-0.016,0.147,0.038,-0.029,-0.168,0,FALSE
-Africa,2024-01-22,0.045,0.204,0.087,-0.011,-0.08,0,FALSE
-Asia,2024-01-22,0.077,0.284,0.095,-0.011,-0.07,0,FALSE
+Africa,2024-01-22,0.045,0.204,0.087,-0.011,-0.08,NA,FALSE
+Asia,2024-01-22,0.077,0.284,0.095,-0.011,-0.07,NA,FALSE

output-data/output-for-interactive/timestamp.csv

@@ -1,2 +1,2 @@
 timestamp
-2024-01-23T10:55:49Z
+2024-01-23T11:31:04Z

output-data/output-for-interactive/world_line_chart.csv

@@ -1,5 +1,5 @@
 location,date,estimate,estimate_top_95,estimate_top_50,estimate_bot_50,estimate_bot_95,official_covid_deaths,known_excess_deaths
-World,2020-01-01,374.243,1384.672,374.243,-844.36,-1390.743,0,FALSE
+World,2020-01-01,374.243,1384.672,374.243,-844.36,-1390.743,NA,FALSE
 World,2020-01-06,373.124,1370.299,373.124,-895.181,-1315.737,0.5,FALSE
 World,2020-01-13,134.855,1289.904,134.855,-1117.36,-1615.393,0.143,FALSE
 World,2020-01-20,29.609,1073.973,29.609,-1457.189,-2158.155,0.286,FALSE
@@ -211,4 +211,4 @@ World,2023-12-25,7015.2,19766.107,9413.021,1752.837,-1197.669,209.286,FALSE
 World,2024-01-01,5580.444,20646.006,7699.299,-189.356,-3906.676,165.143,FALSE
 World,2024-01-08,4849.286,20881.452,6184.286,-754.917,-4553.517,118.286,FALSE
 World,2024-01-15,3630.215,19349.324,5675.962,-1131.606,-6227.168,23.857,FALSE
-World,2024-01-22,3798.849,19362.117,5436.865,-1878.987,-5874.573,0,FALSE
+World,2024-01-22,3798.849,19362.117,5436.865,-1878.987,-5874.573,NA,FALSE

output-data/output-for-interactive/world_line_chart_per_100k.csv

@@ -211,4 +211,4 @@ World,2023-12-25,0.088,0.249,0.118,0.022,-0.015,0.003,FALSE
 World,2024-01-01,0.07,0.26,0.097,-0.002,-0.049,0.002,FALSE
 World,2024-01-08,0.061,0.263,0.078,-0.009,-0.057,0.001,FALSE
 World,2024-01-15,0.046,0.243,0.071,-0.014,-0.078,0,FALSE
-World,2024-01-22,0.048,0.244,0.068,-0.024,-0.074,0,FALSE
+World,2024-01-22,0.048,0.244,0.068,-0.024,-0.074,NA,FALSE

scripts/1_excess_deaths_global_estimates_data_generation.R

@@ -151,7 +151,7 @@ country_daily_data <- data.frame(country_daily_data[order(country_daily_data$dat
 seven_day_average <- function(x){
   temp <- x
   for(i in 1:length(x)){
-    x[i] <- sum(temp[max(c(1, i-6)):min(c(length(x), i))], na.rm = T)/length(temp[max(c(1, i-6)):min(c(length(x), i))])
+    x[i] <- ifelse(any(!is.na(temp[max(c(1, i-6)):min(c(length(x), i))])), sum(temp[max(c(1, i-6)):min(c(length(x), i))], na.rm = T)/length(temp[max(c(1, i-6)):min(c(length(x), i))]), NA)
   }
   x
 }

scripts/4_excess_deaths_global_estimates_export_for_interactive.R

@@ -45,8 +45,6 @@ country_daily_data <- merge(data.frame(country_daily_data), expand.grid(iso_code
                                                                         date = unique_dates),
                             all = T)
 
-
-
 country_daily_data <- country_daily_data[order(country_daily_data$date, country_daily_data$iso_code), ]
 
 columns_to_fill <- c("location", "continent", "population", "aged_65_older", "aged_70_older")
@@ -67,7 +65,7 @@ country_daily_data <- data.frame(country_daily_data[order(country_daily_data$dat
 seven_day_average <- function(x){
   temp <- x
   for(i in 1:length(x)){
-    x[i] <- sum(temp[max(c(1, i-6)):min(c(length(x), i))], na.rm = T)/length(temp[max(c(1, i-6)):min(c(length(x), i))])
+    x[i] <- ifelse(any(!is.na(temp[max(c(1, i-6)):min(c(length(x), i))])), sum(temp[max(c(1, i-6)):min(c(length(x), i))], na.rm = T)/length(temp[max(c(1, i-6)):min(c(length(x), i))]), NA)
   }
   x
 }
@@ -89,19 +87,19 @@ fill_if_known_in_last_90 <- function(x){
 country_daily_data$total_deaths <- as.numeric(ave(country_daily_data$total_deaths, country_daily_data$location, FUN = fill_if_known_in_last_90))
 country_daily_data$total_vaccinations <- as.numeric(ave(country_daily_data$total_vaccinations, country_daily_data$location, FUN = fill_if_known_in_last_90))
 
-## Use last known value for new deaths (if known within the last 30 days):
+## Use last known value for new deaths (if known within the last 14 days):
 country_daily_data <- country_daily_data[order(country_daily_data$date), ]
 
-fill_if_known_in_last_30 <- function(x){
+fill_if_known_in_last_14 <- function(x){
   temp <- x
   for(i in 1:length(x)){
     if(is.na(x[i])){
-      x[i] <- rev(c(NA, na.omit(temp[max(c(1, i-30)):(i-1)])))[1]
+      x[i] <- rev(c(NA, na.omit(temp[max(c(1, i-14)):(i-1)])))[1]
     }
   }
   x
 }
-country_daily_data$new_deaths <- as.numeric(ave(country_daily_data$new_deaths, country_daily_data$location, FUN = fill_if_known_in_last_30))
+country_daily_data$new_deaths <- as.numeric(ave(country_daily_data$new_deaths, country_daily_data$location, FUN = fill_if_known_in_last_14))
 
 # Ensure stable covariates are not missing:
 for(i in c("location", "continent", 'aged_65_older', "aged_70_older")){