diff --git a/.gitignore b/.gitignore
index d005b88..e7077bb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -33,6 +33,5 @@ inst/shiny/install*
 _modules
 _shiny
 .Renviron
-lie*.csv
 lie*.rds
 .httr-oauth
diff --git a/inst/extdata/test_data/lie.csv b/inst/extdata/test_data/lie.csv
new file mode 100644
index 0000000..267368b
--- /dev/null
+++ b/inst/extdata/test_data/lie.csv
@@ -0,0 +1,12 @@
+"","area","response"
+"1","Triesen",1
+"2","Schellenberg",2
+"3","Gamprin",3
+"4","Triesenberg",4
+"5","Eschen",5
+"6","Ruggell",6
+"7","Mauren",7
+"8","Schaan",8
+"9","Balzers",9
+"10","Planken",10
+"11","Vaduz",11
diff --git a/inst/shiny/modules/fit_fit.R b/inst/shiny/modules/fit_fit.R
index dc95beb..d683c16 100644
--- a/inst/shiny/modules/fit_fit.R
+++ b/inst/shiny/modules/fit_fit.R
@@ -119,7 +119,9 @@ fit_fit_module_server <- function(id, common, parent_session, map) {
     common$meta$fit_fit$iterations <- as.numeric(input$iterations)
     common$meta$fit_fit$field <- input$field
     common$meta$fit_fit$iid <- input$iid
-    common$meta$fit_fit$priors <- input$priors
+    if (input$priors){
+      common$meta$fit_fit$priors <- TRUE
+    }
     common$meta$fit_fit$mean_intercept <- as.numeric(input$mean_intercept)
     common$meta$fit_fit$sd_intercept <- as.numeric(input$sd_intercept)
     common$meta$fit_fit$mean_slope <- as.numeric(input$mean_slope)
@@ -141,16 +143,16 @@ fit_fit_module_server <- function(id, common, parent_session, map) {
       req(common$fit$sd_out)
 
       parameter <- sd <- obs <- pred <- NULL
-      posteriors <- as.data.frame(summary(common$fit$sd_out, select = 'fixed'))
+      posteriors <- as.data.frame(summary(common$fit$sd_out, select = "fixed"))
       posteriors <- dplyr::mutate(posteriors, name = rownames(posteriors))
-      names(posteriors) <- c('mean', 'sd', 'parameter')
-      posteriors$fixed <- grepl('slope', posteriors$parameter)
-      posteriors$type <- ifelse(posteriors$fixed, 'Slope', 'Other')
+      names(posteriors) <- c("mean", "sd", "parameter")
+      posteriors$fixed <- grepl("slope", posteriors$parameter)
+      posteriors$type <- ifelse(posteriors$fixed, "Slope", "Other")
 
       # Check name lengths match before substituting.
       lengths_match <- terra::nlyr(common$fit$data$covariate_rasters) == sum(posteriors$fixed)
       if(lengths_match){
-        posteriors$parameter[grepl('slope', posteriors$parameter)] <- names(common$fit$data$covariate_rasters)
+        posteriors$parameter[grepl("slope", posteriors$parameter)] <- names(common$fit$data$covariate_rasters)
       }
 
       # Unique types for faceting
@@ -163,13 +165,13 @@ fit_fit_module_server <- function(id, common, parent_session, map) {
         plotly::plot_ly(subset_data,
                 y = ~parameter,
                 x = ~mean,
-                type = 'scatter',
-                mode = 'markers',
-                marker = list(color = 'black'),
+                type = "scatter",
+                mode = "markers",
+                marker = list(color = "black"),
                 error_x = list(array = ~sd, color = "blue")) |>
           plotly::layout(title = list(text = type, x = 0.5),
-                 xaxis = list(title = 'SD', showline = TRUE, zeroline = FALSE),
-                 yaxis = list(title = 'Parameter', showline = TRUE, zeroline = FALSE,
+                 xaxis = list(title = "SD", showline = TRUE, zeroline = FALSE),
+                 yaxis = list(title = "Parameter", showline = TRUE, zeroline = FALSE,
                               range = c(-1, nrow(subset_data))),
                  margin = list(t = 100))
       })
@@ -190,18 +192,18 @@ fit_fit_module_server <- function(id, common, parent_session, map) {
       report <- common$fit$obj$report()
 
       # Form of the observed and predicted results depends on the likelihood function used
-      if( common$fit$model_setup$family == 'gaussian') {
+      if( common$fit$model_setup$family == "gaussian") {
         observed_data = report$polygon_response_data/report$reportnormalisation
         predicted_data = report$reportprediction_rate
-        title <- 'In sample performance: incidence rate'
-      } else if( common$fit$model_setup$family == 'binomial') {
+        title <- "In sample performance: incidence rate"
+      } else if( common$fit$model_setup$family == "binomial") {
         observed_data =  common$fit$data$polygon_data$response / common$fit$data$polygon_data$N
         predicted_data = report$reportprediction_rate
-        title <- 'In sample performance: prevalence rate'
-      } else if( common$fit$model_setup$family == 'poisson') {
+        title <- "In sample performance: prevalence rate"
+      } else if( common$fit$model_setup$family == "poisson") {
         observed_data = report$polygon_response_data/report$reportnormalisation
         predicted_data = report$reportprediction_rate
-        title <- 'In sample performance: incidence rate'
+        title <- "In sample performance: incidence rate"
       }
 
       data <- data.frame(obs = observed_data, pred = predicted_data)
@@ -213,11 +215,11 @@ fit_fit_module_server <- function(id, common, parent_session, map) {
       identity_line <- data.frame(x = x_range, y = x_range)
 
       # Create scatter plot and add identity line
-      obspred_plot <- plotly::plot_ly(data, x = ~obs, y = ~pred, type = 'scatter', mode = 'markers') |>
-        plotly::add_lines(data = identity_line, x = ~x, y = ~y, line = list(color = 'blue')) |>
+      obspred_plot <- plotly::plot_ly(data, x = ~obs, y = ~pred, type = "scatter", mode = "markers") |>
+        plotly::add_lines(data = identity_line, x = ~x, y = ~y, line = list(color = "blue")) |>
         plotly::layout(title = list(text = title, x = 0.5),
-               xaxis = list(title = 'Observed', showline = TRUE, zeroline = FALSE),
-               yaxis = list(title = 'Predicted', showline = TRUE, zeroline = FALSE),
+               xaxis = list(title = "Observed", showline = TRUE, zeroline = FALSE),
+               yaxis = list(title = "Predicted", showline = TRUE, zeroline = FALSE),
                margin = list(t = 100),
                showlegend = FALSE)
 
@@ -286,7 +288,7 @@ fit_fit_module_rmd <- function(common) {
     fit_iterations = common$meta$fit_fit$iterations,
     fit_field = common$meta$fit_fit$field,
     fit_iid = common$meta$fit_fit$iid,
-    fit_priors_knit = common$meta$fit_fit$priors,
+    fit_priors_knit = !is.null(common$meta$fit_fit$priors),
     fit_priors = common$meta$fit_fit$priors,
     fit_mean_intercept = common$meta$fit_fit$mean_intercept,
     fit_sd_intercept = common$meta$fit_fit$sd_intercept,
diff --git a/inst/shiny/modules/pred_pred.R b/inst/shiny/modules/pred_pred.R
index 573d70f..a6617e3 100644
--- a/inst/shiny/modules/pred_pred.R
+++ b/inst/shiny/modules/pred_pred.R
@@ -86,7 +86,9 @@ pred_pred_module_server <- function(id, common, parent_session, map) {
     }
     # METADATA ####
     common$meta$pred_pred$used <- TRUE
-    common$meta$pred_pred$uncertain <- input$uncertain
+    if (input$uncertain){
+      common$meta$pred_pred$uncertain <- input$uncertain
+    }
     if (is.null(input$iid)){
       common$meta$pred_pred$iid <- FALSE
     }
@@ -172,7 +174,7 @@ pred_pred_module_map <- function(map, common) {
       raster_map(map, common, common$pred[[tolower(variable)]], variable)
     }
   }
-  if (common$meta$pred_pred$uncertain){
+  if (!is.null(common$meta$pred_pred$uncertain)){
     raster_map(map, common, common$pred$uncertainty$predictions_ci$`lower CI`, "Lower credible interval")
     raster_map(map, common, common$pred$uncertainty$predictions_ci$`upper CI`, "Upper credible interval")
   }
diff --git a/tests/testthat/test-downloads.R b/tests/testthat/test-downloads.R
index e496ff7..74acd08 100644
--- a/tests/testthat/test-downloads.R
+++ b/tests/testthat/test-downloads.R
@@ -20,26 +20,47 @@ test_that("{shinytest2} recording: e2e_empty_markdown", {
   })
 
 # this is very temperamental
-test_that("{shinytest2} recording: e2e_markdown_from_uploads", {
+test_that("{shinytest2} recording: e2e_markdown_from_complete_analysis", {
 
   skip_on_ci()
 
-  app <- shinytest2::AppDriver$new(app_dir = system.file("shiny", package = "disagapp"), name = "e2e_markdown_from_uploads", timeout = 60000)
+  app <- shinytest2::AppDriver$new(app_dir = system.file("shiny", package = "disagapp"), name = "e2e_complete_analysis", timeout = 120000)
+
   app$set_inputs(tabs = "resp")
-  app$set_inputs(respSel = "resp_shape")
-  app$upload_file("resp_shape-shape" = shpdf$datapath)
-  app$set_inputs("resp_shape-resp_var" = "inc")
-  app$click("resp_shape-run")
+  app$set_inputs(respSel = "resp_download")
+  app$upload_file("resp_download-spread" = df_path)
+  app$set_inputs(`resp_download-area_column` = "area")
+  app$set_inputs(`resp_download-response_column` = "response")
+  app$set_inputs(`resp_download-country` = "Liechtenstein")
+  app$click("resp_download-run")
+
+  common <- app$get_value(export = "common")
+  expect_is(common$shape, "sf")
 
   app$set_inputs(tabs = "cov")
-  app$set_inputs(covSel = "cov_upload")
-  app$upload_file("cov_upload-cov" = covdf$datapath)
-  app$click("cov_upload-run")
+  app$set_inputs(covSel = "cov_bioclim")
+  app$set_inputs(`cov_bioclim-variables` = c("Mean temperature", "Total precipitation"))
+  app$click(selector = "#cov_bioclim-run")
+  app$wait_for_value(input = "cov_bioclim-complete")
+
+  app$set_inputs(main = "Save")
+  save_file <- app$get_download("core_save-save_session", filename = save_path)
+  common <- readRDS(save_file)
+  common$covs <- unwrap_terra(common$covs)
+  expect_length(common$covs, 2)
 
   app$set_inputs(tabs = "agg")
-  app$set_inputs(aggSel = "agg_upload")
-  app$upload_file("agg_upload-agg" = aggdf$datapath)
-  app$click("agg_upload-run")
+  app$set_inputs(aggSel = "agg_worldpop")
+  app$set_inputs("agg_worldpop-method" = "Unconstrained")
+  app$set_inputs("agg_worldpop-country" = "Liechtenstein")
+  app$click(selector = "#agg_worldpop-run")
+  app$wait_for_value(input = "agg_worldpop-complete")
+
+  app$set_inputs(main = "Save")
+  save_file <- app$get_download("core_save-save_session", filename = save_path)
+  common <- readRDS(save_file)
+  common$agg <- unwrap_terra(common$agg)
+  expect_is(common$agg, "SpatRaster")
 
   app$set_inputs(tabs = "prep")
   app$set_inputs(prepSel = "prep_mesh")
@@ -47,41 +68,56 @@ test_that("{shinytest2} recording: e2e_markdown_from_uploads", {
   app$set_inputs("prep_mesh-mesh_offset" = c(0.1, 0.3))
   app$click(selector = "#prep_mesh-run")
   app$wait_for_value(input = "prep_mesh-complete")
+  app$set_inputs(main = "Save")
+  save_file <- app$get_download("core_save-save_session", filename = save_path)
+  common <- readRDS(save_file)
+  expect_is(common$mesh, "inla.mesh")
 
+  app$set_inputs(tabs = "prep")
   app$set_inputs(prepSel = "prep_summary")
   app$click("prep_summary-run")
-  app$set_inputs("prep_summary-resample_layer" = "EVI.tif")
+  app$set_inputs("prep_summary-resample_layer" = "Mean temperature")
   app$click("prep_summary-resample")
 
   app$set_inputs(prepSel = "prep_scale")
   app$click("prep_scale-run")
-
-  app$set_inputs(tabs = "prep")
   app$set_inputs(prepSel = "prep_final")
-  app$set_inputs("prep_final-id_var" = "ID_2")
-  app$set_inputs("prep_final-resp_var" = "inc")
+  app$set_inputs(`prep_final-id_var` = "shapeName")
   app$click("prep_final-run")
 
+  app$set_inputs(main = "Save")
+  save_file <- app$get_download("core_save-save_session", filename = save_path)
+  common <- readRDS(save_file)
+  common$covs_prep <- unwrap_terra(common$covs_prep)
+  expect_length(common$covs_prep, 1)
+  expect_is(common$covs_prep, "SpatRaster")
+  expect_is(common$prep, "disag_data")
+
   app$set_inputs(tabs = "fit")
   app$click("fit_fit-run")
+  app$set_inputs(main = "Save")
+  save_file <- app$get_download("core_save-save_session", filename = save_path)
+  common <- readRDS(save_file)
+  expect_is(common$fit, "disag_model")
 
   app$set_inputs(tabs = "pred")
+  app$set_inputs(predSel = "pred_pred")
   app$click("pred_pred-run")
+  app$set_inputs(main = "Save")
+  save_file <- app$get_download("core_save-save_session", filename = save_path)
+  common <- readRDS(save_file)
+  common$pred$`prediction (rate)` <- unwrap_terra(common$pred$`prediction (rate)`)
+  expect_is(common$pred$`prediction (rate)`, "SpatRaster")
 
   app$set_inputs(tabs = "rep")
   app$set_inputs(repSel = "rep_markdown")
   sess_file <- app$get_download("rep_markdown-dlRMD")
   expect_false(is.null(sess_file))
   lines <- readLines(sess_file)
-
-  target_line <- grep("shapefile_directory <- ", lines)
-  lines[target_line] <- 'shapefile_directory <- system.file("extdata", "shapes", package="disagapp")'
-  target_line <- grep("covariate_directory <- ", lines)
-  lines[target_line] <- 'covariate_directory <- system.file("extdata", "covariates", package="disagapp")'
-  target_line <- grep("aggregation_directory <- ", lines)
-  lines[target_line] <- 'aggregation_directory <- system.file("extdata", "aggregation", package="disagapp")'
-
+  target_line <- grep("response_directory <- ", lines)
+  lines[target_line] <- 'response_directory <- system.file("extdata", "test_data", package="disagapp")'
   writeLines(lines, sess_file)
+
   rmarkdown::render(sess_file)
   html_file <- gsub("Rmd", "html", sess_file)
   expect_gt(file.info(html_file)$size, 1000)