dimension parameters names #22

tunaatlas_scripts/generation/convert_units.R

@@ -4,7 +4,7 @@ convert_units = function (con, df_input, df_conversion_factor, codelist_geoident
   cat(paste0("\n BEGIN tunaatlas::convert_units() => converting units and measures"))
   columns_df_input = colnames(df_input)
   df_input <- data.table(df_input)
-  units_source <- unique(df_conversion_factor$unit)
+  units_source <- unique(df_conversion_factor$measurement_unit)
   units_target <- unique(df_conversion_factor$unit_target)
   df_conversion_factor$conversion_factor = as.numeric(df_conversion_factor$conversion_factor)
   if ("geographic_identifier" %in% colnames(df_conversion_factor)) {
@@ -85,15 +85,15 @@ convert_units = function (con, df_input, df_conversion_factor, codelist_geoident
     df_input <- left_join(df_input, data_zone_0)
     df_input$conv_factor_df_geo_id[which(!is.na(df_input$zone0))] <- "0"
     df_input <- df_input %>% dplyr::select(-zone0, -unit_target)
-    class(df_input$value) <- "numeric"
+    class(df_input$measurement_value) <- "numeric"
   }
   if (nrow(df_input) == nrow(left_join(df_input, df_conversion_factor) %>% 
                              filter(is.na(conversion_factor)))) {
-    if (length(intersect(unique(df_conversion_factor$gear), 
-                         unique(df_input$gear))) == 0) {
+    if (length(intersect(unique(df_conversion_factor$gear_type), 
+                         unique(df_input$gear_type))) == 0) {
       df_conversion_factor_no_gear <- df_conversion_factor %>% 
         group_by_at(setdiff(colnames(df_conversion_factor), 
-                            "gear")) %>% summarise(conversion_factor = mean(conversion_factor))
+                            "gear_type")) %>% summarise(conversion_factor = mean(conversion_factor))
       df_input <- left_join(df_input, df_conversion_factor_no_gear)
     }
   }
@@ -108,12 +108,12 @@ convert_units = function (con, df_input, df_conversion_factor, codelist_geoident
   #   summarise(sum_unit_source_before_conversion = sum(value)) %>% 
   #   filter(!is.na(unit_target))
   index.not_na.conv_factor <- which(!is.na(df_input$conversion_factor))
-  df_input$value[index.not_na.conv_factor] <- df_input$value[index.not_na.conv_factor] *
+  df_input$measurement_value[index.not_na.conv_factor] <- df_input$measurement_value[index.not_na.conv_factor] *
     df_input$conversion_factor[index.not_na.conv_factor]
   # stats_after_conversion <- df_input %>% group_by(unit, unit_target) %>% 
   #   summarise(sum_unit_target_after_conversion = sum(value)) %>% 
   #   filter(!is.na(unit_target))
-  df_input$unit[index.not_na.conv_factor] <- df_input$unit_target[index.not_na.conv_factor]
+  df_input$measurement_unit[index.not_na.conv_factor] <- df_input$unit_target[index.not_na.conv_factor]
   df_input <- df_input %>% dplyr::select(all_of(columns_df_input))
   # sum_after_conversion <- df_input %>% group_by(unit) %>% 
   #   summarise(sum_value_after_conversion = sum(value))

tunaatlas_scripts/generation/function_raising_georef_to_nominal.R

@@ -29,7 +29,7 @@ function_raising_georef_to_nominal<-function(con, opts,entity,
 
     cat("filter by source_authority\n")
     dataset_to_raise<-dataset_to_raise[which(dataset_to_raise$source_authority %in% source_authority_filter),]
-    config$logger.info(paste0("Total catch for dataset_to_raise before raising  is ",sum(dataset_to_compute_rf$measurement_value),"  \n"))
+    config$logger.info(paste0("Total catch for dataset_to_raise before raising  is ",sum(dataset_to_raise$measurement_value),"  \n"))
 
     dataset_to_compute_rf<-dataset_to_compute_rf[which(dataset_to_compute_rf$source_authority %in% source_authority_filter),]
     config$logger.info(paste0("Total catch for dataset_to_compute_rf before raising  is ",sum(dataset_to_compute_rf$measurement_value),"  \n"))
@@ -49,8 +49,8 @@ function_raising_georef_to_nominal<-function(con, opts,entity,
 
     cat(paste0("raise_get_rf function has",nrow(df_rf),"rows \n"))
     cat(paste0(" write csv file to check   \n"))
-    filename <- paste0("/tmp/DFPartialInfo_rf_",gsub(Sys.time(),pattern = " ", replacement = "_"),".csv")
-    write.csv(x = df_rf, file = filename)
+    # filename <- paste0("DFPartialInfo_rf_",gsub(Sys.time(),pattern = " ", replacement = "_"),".csv")
+    # write.csv(x = df_rf, file = filename)
 
     cat("function raise_get_rf has been executed ! \n")
     config$logger.info(paste0("Rows number in df_rf ",nrow(df_rf),"  \n"))
@@ -65,7 +65,7 @@ function_raising_georef_to_nominal<-function(con, opts,entity,
 
     # raise dataset
     cat("Executing raise_incomplete_dataset_to_total_dataset \n")
-    source("https://raw.githubusercontent.com/firms-gta/geoflow-tunaatlas/master/sardara_functions/raise_incomplete_dataset_to_total_dataset.R")
+    source("~/Documents/geoflow-tunaatlas/sardara_functions/raise_incomplete_dataset_to_total_dataset.R")
 
     data_raised<-raise_incomplete_dataset_to_total_dataset(df_input_incomplete = dataset_to_raise,
                                                            df_input_total = nominal_dataset_df,
@@ -90,11 +90,11 @@ function_raising_georef_to_nominal<-function(con, opts,entity,
   if (raising_raise_only_for_PS_LL==TRUE){
 
     # @juldebar =>  check if gear codes are correct
-    gears_PS_LL<-dbGetQuery(con,"SELECT distinct(src_code) FROM gear.gear_mapping_view WHERE trg_codingsystem='geargroup_tunaatlas' AND trg_code IN ('PS','LL')")$src_code
+    gears_PS_LL<-dbGetQuery(con,"SELECT distinct(src_code) FROM gear_type.gear_type_mapping_view WHERE trg_codingsystem='geargroup_tunaatlas' AND trg_code IN ('PS','LL')")$src_code
     # config$logger.info("Now filtering gear codes with those returned by SLQ query",  gears_PS_LL)
 
-    dataset_not_PS_LL<-dataset_to_raise %>% filter(!(gear_type %in% gears_PS_LL))
-    dataset_to_raise<-dataset_to_raise %>% filter(gear_type %in% gears_PS_LL)
+    dataset_not_PS_LL<-dataset_to_raise %>% dplyr::filter(!(gear_type %in% gears_PS_LL))
+    dataset_to_raise<-dataset_to_raise %>% dplyr::filter(gear_type %in% gears_PS_LL)
 
     config$logger.info(paste0("Since option raising_raise_only_for_PS_LL==TRUE, kept rows number is  ",nrow(dataset_to_raise)," and number of removed rows is ",nrow(dataset_not_PS_LL),"\n"))