diff --git a/scripts/00_dpc_analyisis/dpc_analyisis.R b/scripts/00_dpc_analyisis/dpc_analyisis.R
index 26acfbab..7fca0e8b 100644
--- a/scripts/00_dpc_analyisis/dpc_analyisis.R
+++ b/scripts/00_dpc_analyisis/dpc_analyisis.R
@@ -2,7 +2,7 @@
 
 # Install necessary libraries - packages  
 packagesPrev<- installed.packages()[,"Package"] # Check and get a list of installed packages in this machine and R version
-packagesNeed<- list("magrittr", "terra", "raster", "sf", "fasterize", "pbapply", "this.path", "rjson", "units") # Define the list of required packages to run the script
+packagesNeed<- list("magrittr", "terra", "raster", "sf", "fasterize", "pbapply", "this.path", "rjson") # Define the list of required packages to run the script
 lapply(packagesNeed, function(x) {   if ( ! x %in% packagesPrev ) { install.packages(x, force=T)}    }) # Check and install required packages that are not previously installed
 
 # Load libraries
@@ -23,7 +23,8 @@ lapply(packagesList, library, character.only = TRUE)  # Load libraries - package
 Sys.getenv("SCRIPT_LOCATION")
 
 # Option 2: Recommended for debugging purposes to be used as a testing environment. This is designed to facilitate script testing and correction
-# outputFolder<- {x<- this.path::this.path();  file_prev<-  paste0(gsub("/scripts.*", "/output", x), gsub("^.*/scripts", "", x)  ); options<- tools::file_path_sans_ext(file_prev) %>% {c(paste0(., ".R"), paste0(., "_R"))}; folder_out<- options %>% {.[file.exists(.)]} %>% {.[which.max(sapply(., function(info) file.info(info)$mtime))]}; folder_final<- list.files(folder_out, full.names = T) %>% {.[which.max(sapply(., function(info) file.info(info)$mtime))]} }
+# outputFolder<- {x<- this.path::this.path();  file_prev<-  paste0(gsub("/scripts.*", "/output", x), gsub("^.*/scripts", "", x)  ); options<- tools::file_path_sans_ext(file_prev) %>% {c(., paste0(., ".R"), paste0(., "_R"))}; folder_out<- options %>% {.[file.exists(.)]} %>% {.[which.max(sapply(., function(info) file.info(info)$mtime))]}; folder_final<- list.files(folder_out, full.names = T) %>% {.[which.max(sapply(., function(info) file.info(info)$mtime))]} }
+
 
 
 
@@ -33,9 +34,8 @@ Sys.getenv("SCRIPT_LOCATION")
 input <- rjson::fromJSON(file=file.path(outputFolder, "input.json")) # Load input file
 
 # This section adjusts the input values based on specific conditions to rectify and prevent errors in the input paths
-input<- lapply(input, function(x) if( grepl("/output/", x) ){
-  sub(".*/output/", "/output/", x) %>%  {gsub("/output.*", ., outputFolder)}}else{x} ) # Ajuste input 1
-
+input<- lapply(input, function(x) if(!is.null(x)){ if( grepl("/", x)  ){ 
+  sub("/output/.*", "/output", outputFolder) %>% dirname() %>%  file.path(x) %>% {gsub("//+", "/", .)}  }else{x}} ) # adjust input 1
 
 
 
@@ -47,6 +47,7 @@ input<- lapply(input, function(x) if( grepl("/output/", x) ){
 output<- tryCatch({
   
   
+  
   units::units_options(set_units_mode = "standard")
   
   group<- input$column_spatial_unit
@@ -54,13 +55,12 @@ output<- tryCatch({
   spatial_unit_data<- read.csv(input$data_spatial_unit) %>%
     dplyr::mutate(group_n= paste0(!!!dplyr::syms(group)) %>% {gsub(" ", "_", .)}) %>% 
     dplyr::select(c(input$column_spatial_unit, input$column_date, input$column_area, "group_n")) %>%
-    dplyr::filter(!duplicated(group_n)) %>% dplyr::mutate(spatial_unit= "yes_spatial_unit_data")
-  
-  spatial_unit_data$date<- as.Date(spatial_unit_data$created_date, format = "%Y-%m-%d")
-  
-  
-  start_date<- {if(input$time_start == "NA"){min(spatial_unit_data$date)}else{input$time_start}} %>% lubridate::floor_date(unit = input$time_interval)
-  end_date<- {if(input$time_end == "NA"){max(spatial_unit_data$date)}else{input$time_end}}  %>% lubridate::ceiling_date(unit = input$time_interval)
+    dplyr::filter(!duplicated(group_n)) %>% dplyr::mutate(spatial_unit= "yes_spatial_unit", )
+
+  input$area_study_area<- input$area_study_area %>% {if( is.null(.)  ){sum( spatial_unit_data[,input$column_area] )*2 } else {.}}
+
+  start_date<- {if( is.null(input$time_start) ){min(spatial_unit_data$date)}else{input$time_start}} %>% lubridate::floor_date(unit = input$time_interval)
+  end_date<- {if( is.null(input$time_end) ){max(spatial_unit_data$date)}else{input$time_end}}  %>% lubridate::ceiling_date(unit = input$time_interval)
   
   
   period_time<- paste(timechange:::parse_rounding_unit(input$time_interval), collapse = " "  )
@@ -80,9 +80,9 @@ output<- tryCatch({
     data_period<- lapply(new_periods, function(j) dplyr::mutate(spatial_unit_data_v2[i,], period= j)) %>% plyr::rbind.fill()
   }) %>% plyr::rbind.fill()
   
-  spatial_unit_no_area<- dplyr::select(spatial_unit_area, c("group_n", "period", "area_spatial")) %>%
+  spatial_unit_no_area<- spatial_unit_area %>%
     dplyr::distinct() %>% dplyr::group_by(period) %>%
-    dplyr::summarize( area_spatial =  input$area_study_area - sum(area_spatial)) %>% 
+    dplyr::summarise( !!input$column_area :=  input$area_study_area - sum( .data[[ input$column_area ]] )) %>% 
     dplyr::mutate(spatial_unit= "no_spatial_unit")
   
   
@@ -91,6 +91,9 @@ output<- tryCatch({
   
   
   
+  spatial_units_periods<- read.csv("C:/Users/LENOVO/Documents/pa_ejemplo_victor_v2.csv")
+  
+  
   #Define the decades reported for the protected areas creation to start the analysis
   decades = unique(spatial_units_periods$period) 
   
@@ -98,8 +101,9 @@ output<- tryCatch({
   result_dPC = as.data.frame(matrix(NA,ncol = 4, nrow = 0))
   colnames(result_dPC) = c("Period", "group_n","PC_out","PC")
   
-  dPC = as.data.frame(matrix(NA,ncol = 6, nrow = 0))
-  colnames(dPC) = c("Period", "group_n", "name", "PC_out","PC" , "dPC")
+
+  # 
+  
   
   
   
@@ -125,6 +129,8 @@ output<- tryCatch({
     }
     
     
+    
+    
     #Calculate the PC value
     PC = addition/(area_consult*area_consult)
     #result_pc= data.frame(Period = i,PC=PC)
@@ -148,31 +154,41 @@ output<- tryCatch({
         add = add+sum(prod)
       }
       
-      
       PC_out_id = add/(area_consult*area_consult)
       result_PC_out = data.frame(Period = i,group_n = ids[k], PC_out = PC_out_id, PC = PC)
       result_dPC = rbind.data.frame(result_dPC,result_PC_out)
     }
     
-    result_dPC2<- result_dPC
-    result_dPC2$dPC = (((result_dPC$PC- result_dPC$PC_out))/result_dPC$PC)*100
     
-    #Calculate the dPC index by period of time
-    for (i in decades) {
-      filter = result_dPC[result_dPC$Period==decades[i],]
-      order = filter[with(filter, order(-filter$dPC)), ]
-      if(nrow(order)>=5){
-        dpc_decade_df = order[5,]
-      } else{dpc_decade_df = order}
+    result_dPC2<- result_dPC
+    result_dPC2$dPC<- (((result_dPC$PC- result_dPC$PC_out))/result_dPC$PC) %>% sapply(function(x) {if(is.na(x)){0}else{x*100}} )
       
-      dPC = rbind.data.frame(dPC,dpc_decade_df)
-    }
+
+  }
+  
+  
+  
+  
+  #Calculate the dPC index by period of time
+  dPC = as.data.frame(matrix(NA,ncol = 3, nrow = 0))
+  colnames(dPC) = c("Period", "id_pa","dPC")
+  
+  #Calculate the dPC index by period of time
+  for (i in as.character(decades)) {
+    filter = result_dPC2[result_dPC2$Period== i,]
+    order = filter[with(filter, order(-filter$dPC)), ]
+    if(nrow(order)>=5){
+      dpc_decade_df = order[5,]
+    } else{dpc_decade_df = order}
     
-    ########################PEDAZO QUE FALTA################################## 
-     
+    dPC = rbind.data.frame(dPC,dpc_decade_df)
   }
   
   
+  
+  
+  
+  
   # Define and export the output values
   
   dPC_template<- dplyr::select(spatial_unit_data,c("group_n", group)) %>% dplyr::distinct()
diff --git a/scripts/00_dpc_analyisis/dpc_analyisis.yml b/scripts/00_dpc_analyisis/dpc_analyisis.yml
index 3e8fc641..f8d89b38 100644
--- a/scripts/00_dpc_analyisis/dpc_analyisis.yml
+++ b/scripts/00_dpc_analyisis/dpc_analyisis.yml
@@ -19,7 +19,7 @@ inputs:
     label: Area column name
     description: "String refering to the name of the column in the data spatial units file that contains the area of each spatial unit or nodes that intersects the area of interest. If there are several areas for the same id, the areas will be added. Values must be numeric otherwise they will be ignored."
     type: text
-    example: "area_spatial"
+    example: "area_ha"
   column_date:
     label: Date column name 
     description: "String refering to the name of the column that contains the date. The date is a mandatory field if you want an temporal analysis by time periods. The date can be entered as 'Year-Month-Day', just year, or just year-month; if given in a different format the row is ignored. If (NULL) is not specified, a single period is assumed for the entire data set and the following arguments will be ignored."
@@ -34,12 +34,10 @@ inputs:
     label: Start of time interval
     description: "Refering to the starting date of the time interval. If this value is null or not provided, the minimum date from the column_date will be automatically taken as the starting date for the analysis. This argument is ignored if column_date=NULL."
     type: text
-    example: NA
   time_end:
     label: End of time interval
     description: "Refering to the ending date of the time interval. If this value is null or not provided, the minimum date from the column_date will be automatically taken as the starting date for the analysis. This argument is ignored if column_date=NULL."
     type: text
-    example: NA
 outputs:
   dpc_result:
     label: Final table with dpC result