reorganisation/tidy (#34)

* reorganisation/tidy

* load script

Author: sbfnk

.github/workflows/render-report.yaml

@@ -33,7 +33,7 @@ jobs:
 
       - name: Compile the report
         run: |
-          rmarkdown::render("output/results.Rmd")
+          rmarkdown::render("report/results.Rmd")
         shell: Rscript {0}
 
       - name: Create Pull Request
@@ -46,5 +46,5 @@ jobs:
           branch: "render-report-${{ github.run_number }}"
           labels: "documentation"
           add-paths: |
-            output/results.pdf
+            report/results.pdf
           token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/test-report.yaml

@@ -34,11 +34,11 @@ jobs:
 
       - name: Compile the report
         run: |
-          rmarkdown::render("output/results.Rmd")
+          rmarkdown::render("report/results.Rmd")
         shell: Rscript {0}
 
       - name: Upload pdf as an artifact
         uses: actions/upload-artifact@v4
         with:
           name: results
-          path: output/results.pdf
+          path: report/results.pdf

R/import-data.R

@@ -1,13 +1,3 @@
-# Functions to import and save data for predictions and observations
-# Examples:
-# forecasts <- get_forecasts()
-# obs <- get_observed()
-# forecasts <- left_join(forecasts, obs,
-#                        by = c("location", "target_end_date"))
-# anomalies <- get_anomalies()
-# forecasts <- anti_join(forecasts, anomalies,
-#                        by = c("target_end_date", "location"))
-
 library(here)
 library(dplyr)
 library(readr)
@@ -16,57 +6,10 @@ library(arrow)
 library(tidyr)
 library(ggplot2)
 library(stringr)
-theme_set(theme_minimal())
-
-# Prediction data  ------------------------------------------------------
-get_forecasts <- function(data_type = "death") {
-  forecasts <- arrow::read_parquet(here("data",
-                                        "covid19-forecast-hub-europe.parquet")) |>
-    filter(grepl(data_type, target))
-
-  forecasts <- forecasts |>
-    separate(target, into = c("horizon", "target_variable"),
-             sep = " wk ahead ") |>
-    # set forecast date to corresponding submission date
-    mutate(
-      horizon = as.numeric(horizon),
-      forecast_date = target_end_date - weeks(horizon) + days(1)) |>
-    rename(prediction = value) |>
-    select(location, forecast_date,
-           horizon, target_end_date,
-           model, quantile, prediction)
-
-  # Exclusions
-  # dates should be between start of hub and until end of JHU data
-  forecasts <- forecasts |>
-    filter(forecast_date >= as.Date("2021-03-07") &
-             target_end_date <= as.Date("2023-03-10"))
-  # only keep forecasts up to 4 weeks ahead
-  forecasts <- filter(forecasts, horizon <= 4)
-
-  # only include predictions from models with all quantiles
-  rm_quantiles <- forecasts |>
-    group_by(model, forecast_date, location) |>
-    summarise(q = length(unique(quantile))) |>
-    filter(q < 23)
-  forecasts <- anti_join(forecasts, rm_quantiles,
-                    by = c("model", "forecast_date", "location"))
-  forecasts <- filter(forecasts, !is.na(quantile)) # remove "median"
-
-  # remove duplicates
-  forecasts <- forecasts |>
-    group_by_all() |>
-    mutate(duplicate = row_number()) |>
-    ungroup() |>
-    filter(duplicate == 1) |>
-    select(-duplicate)
-
-  return(forecasts)
-}
+library(purrr)
 
 # Observed data ---------------------------------------------------------
-# Get raw values
-get_observed <- function(data_type = "death") {
+walk(c("case", "death"), \(data_type) {
   file_name <- paste0("truth_JHU-Incident%20", str_to_title(data_type), "s.csv")
   obs <- read_csv(paste0(
     "https://raw.githubusercontent.com/covid19-forecast-hub-europe/",
@@ -98,67 +41,13 @@ get_observed <- function(data_type = "death") {
                                                   "Stable")))))
   obs <- obs |>
     select(location, target_end_date, observed, trend)
-  return(obs)
-}
-
-# Observed data ---------------------------------------------------------
-# Get raw values
-get_pop <- function() {
-  pop <- read_csv(paste0(
-    "https://raw.githubusercontent.com/european-modelling-hubs/",
-    "covid19-forecast-hub-europe/main/data-locations/locations_eu.csv"
-  ), show_col_types = FALSE) |>
-    select(location, population)
-  return(pop)
-}
-
-# Plot observed data and trend classification
-plot_observed <- function() {
-  obs <- import_observed()
-  obs |>
-    ggplot(aes(x = target_end_date, y = log(observed))) +
-    geom_point(col = trend) +
-    geom_line(alpha = 0.3) +
-    scale_x_date() +
-    labs(x = NULL, y = "Log observed", col = "Trend",
-         caption = "Trend (coloured points) of weekly change in 3-week moving average") +
-    theme(legend.position = "bottom", ) +
-    facet_wrap(facets = "location", ncol = 1,
-               strip.position = "left")
-
-  ggsave(filename = here("output/fig-trends.pdf"),
-         height = 50, width = 15, limitsize = FALSE)
-}
-
-# Anomalies
-get_anomalies <- function() {
-  read_csv("https://raw.githubusercontent.com/covid19-forecast-hub-europe/covid19-forecast-hub-europe/5a2a8d48e018888f652e981c95de0bf05a838135/data-truth/anomalies/anomalies.csv") |>
-    filter(target_variable == "inc death") |>
-    select(-target_variable) |>
-    mutate(anomaly = TRUE)
+  write_csv(obs, here("data", paste0("observed-", data_type, ".csv")))
 }
 
-# Plot anomalies
-plot_anomalies <- function() {
-  obs <- get_observed()
-  anomalies <- get_anomalies()
-
-  obs <- left_join(obs, anomalies) |>
-    group_by(location) |>
-    mutate(anomaly = replace_na(anomaly, FALSE))
-
-  obs |>
-    ggplot(aes(x = target_end_date,
-               y = log(observed),
-               col = anomaly)) +
-    geom_line() +
-    geom_point(size = 0.3) +
-    scale_x_date() +
-    labs(x = NULL, y = "Log observed") +
-    theme(legend.position = "bottom", ) +
-    facet_wrap(facets = "location", ncol = 1,
-               strip.position = "left")
-
-  ggsave(filename = here("output/fig-anomalies.pdf"),
-         height = 50, width = 15, limitsize = FALSE)
-}
+# Population data ---------------------------------------------------------
+pop <- read_csv(paste0(
+  "https://raw.githubusercontent.com/european-modelling-hubs/",
+  "covid19-forecast-hub-europe/main/data-locations/locations_eu.csv"
+), show_col_types = FALSE) |>
+  select(location, population)
+write_csv(pop, here("data", paste0("populations.csv")))

R/model-plots.R

@@ -0,0 +1,75 @@
+library("purrr")
+library("dplyr")
+library("ggplot2")
+library("patchwork")
+library("gammit")
+source(here("R", "prep-data.R"))
+source(here("R", "descriptive.R"))
+
+plot_models <- function(fits, scores, x_labels = TRUE) {
+  outcomes <- unique(scores$outcome_target)
+  classification <- classify_models() |>
+    rename(group = model)
+  targets <- table_targets(scores) |>
+    select(group = Model, CountryTargets) |>
+    distinct()
+  plots <- map(fits, function(fit) {
+    plot <- extract_ranef(fit) |>
+      filter(group_var == "Model") |>
+      left_join(classification) |>
+      left_join(targets) |>
+      mutate(group = sub(".*-", "", group)) |> ## remove institution identifier
+      select(-group_var) |>
+      arrange(-value) |>
+      mutate(group = factor(group, levels = unique(as.character(group)))) |>
+      ggplot(aes(x = group, col = classification, shape = CountryTargets)) +
+      geom_point(aes(y = value)) +
+      geom_linerange(aes(ymin = lower_2.5, ymax = upper_97.5)) +
+      geom_hline(yintercept = 0, lty = 2) +
+      labs(y = "Partial effect", x = "Model", colour = NULL, shape = NULL) +
+      scale_colour_brewer(type = "qual", palette = 2) +
+      theme(
+        legend.position = "bottom",
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)
+      ) +
+      coord_flip()
+    if (!x_labels) {
+      plot <- plot +
+        theme(
+          axis.text.y = element_blank(),
+          axis.ticks.y = element_blank()
+        )
+    }
+    return(plot)
+  })
+  ## remove legends
+  if (length(plots) > 1) {
+    for (i in seq_len(length(plots) - 1)) {
+      plots[[i]] <- plots[[i]] + theme(legend.position = "none")
+    }
+  }
+  for (i in seq_along(plots)) {
+    plots[[i]] <- plots[[i]] + ggtitle(outcomes[i])
+  }
+  Reduce(`+`, plots) + plot_layout(ncol = 2)
+}
+
+plot_effects <- function(fits, scores) {
+  map(fits, extract_ranef) |>
+    bind_rows(.id = "outcome_target") |>
+    filter(!(group_var %in% c("Model", "location"))) |>
+    mutate(group = factor(group, levels = unique(as.character(rev(group))))) |>
+    ggplot(aes(x = group, col = group_var)) +
+    geom_point(aes(y = value)) +
+    geom_linerange(aes(ymin = lower_2.5, ymax = upper_97.5)) +
+    geom_hline(yintercept = 0, lty = 2, alpha = 0.25) +
+    facet_wrap(~outcome_target, scales = "free_y") +
+    labs(y = "Partial effect", x = NULL, colour = NULL, shape = NULL) +
+    scale_colour_brewer(type = "qual", palette = "Set1") +
+    theme(
+      legend.position = "bottom",
+      strip.background = element_blank(),
+      axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)
+    ) +
+    coord_flip()
+}

R/model-wis.R

@@ -5,13 +5,8 @@ library(readr)
 library(tidyr)
 library(purrr)
 library(mgcv)
-library(gratia) # devtools::install_github('gavinsimpson/gratia')
-library(broom)
-library(ggplot2)
-library(broom)
-library(gammit)
-theme_set(theme_classic())
 source(here("R", "prep-data.R"))
+source(here("R", "descriptive.R"))
 
 # --- Get data ---
 data <- prep_data(scoring_scale = "log")
@@ -60,70 +55,4 @@ m.fits <- outcomes |>
   )
 })
 
-plot_models <- function(fits, scores, x_labels = TRUE) {
-  outcomes <- unique(scores$outcome_target)
-  classification <- classify_models() |>
-    rename(group = model)
-  targets <- table_targets(scores) |>
-    select(group = Model, CountryTargets) |>
-    distinct()
-  plots <- map(fits, function(fit) {
-    plot <- extract_ranef(fit) |>
-      filter(group_var == "Model") |>
-      left_join(classification) |>
-      left_join(targets) |>
-      mutate(group = sub(".*-", "", group)) |> ## remove institution identifier
-      select(-group_var) |>
-      arrange(-value) |>
-      mutate(group = factor(group, levels = unique(as.character(group)))) |>
-      ggplot(aes(x = group, col = classification, shape = CountryTargets)) +
-      geom_point(aes(y = value)) +
-      geom_linerange(aes(ymin = lower_2.5, ymax = upper_97.5)) +
-      geom_hline(yintercept = 0, lty = 2) +
-      labs(y = "Partial effect", x = "Model", colour = NULL, shape = NULL) +
-      scale_colour_brewer(type = "qual", palette = 2) +
-      theme(
-        legend.position = "bottom",
-        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)
-      ) +
-      coord_flip()
-    if (!x_labels) {
-      plot <- plot +
-        theme(
-          axis.text.y = element_blank(),
-          axis.ticks.y = element_blank()
-        )
-    }
-    return(plot)
-  })
-  ## remove legends
-  if (length(plots) > 1) {
-    for (i in seq_len(length(plots) - 1)) {
-      plots[[i]] <- plots[[i]] + theme(legend.position = "none")
-    }
-  }
-  for (i in seq_along(plots)) {
-    plots[[i]] <- plots[[i]] + ggtitle(outcomes[i])
-  }
-  Reduce(`+`, plots) + plot_layout(ncol = 2)
-}
-
-plot_effects <- function(fits, scores) {
-  map(fits, extract_ranef) |>
-    bind_rows(.id = "outcome_target") |>
-    filter(!(group_var %in% c("Model", "location"))) |>
-    mutate(group = factor(group, levels = unique(as.character(rev(group))))) |>
-    ggplot(aes(x = group, col = group_var)) +
-    geom_point(aes(y = value)) +
-    geom_linerange(aes(ymin = lower_2.5, ymax = upper_97.5)) +
-    geom_hline(yintercept = 0, lty = 2, alpha = 0.25) +
-    facet_wrap(~outcome_target, scales = "free_y") +
-    labs(y = "Partial effect", x = NULL, colour = NULL, shape = NULL) +
-    scale_colour_brewer(type = "qual", palette = "Set1") +
-    theme(
-      legend.position = "bottom",
-      strip.background = element_blank(),
-      axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)
-    ) +
-    coord_flip()
-}
+saveRDS(m.fits, here("output", "fits.rds"))