Revision 552b7959
Added by Benoit Parmentier almost 9 years ago
| climate/research/oregon/interpolation/global_run_scalingup_assessment_part3.R | ||
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| 7 | 7 |
#Analyses, figures, tables and data are also produced in the script. |
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#AUTHOR: Benoit Parmentier |
| 9 | 9 |
#CREATED ON: 03/23/2014 |
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#MODIFIED ON: 02/09/2016
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#MODIFIED ON: 02/10/2016
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#Version: 5 |
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#PROJECT: Environmental Layers project |
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#COMMENTS: Initial commit, script based on part 2 of assessment, will be modified further for overall assessment |
| ... | ... | |
| 188 | 188 |
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setwd(out_dir) |
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list_outfiles <- vector("list", length=31) #collect names of output files, this should be dynamic?
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list_outfiles_names <- vector("list", length=31) #collect names of output files
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list_outfiles <- vector("list", length=35) #collect names of output files, this should be dynamic?
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list_outfiles_names <- vector("list", length=35) #collect names of output files
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counter_fig <- 0 #index of figure to collect outputs |
| 194 | 194 |
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#i <- year_predicted |
| ... | ... | |
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title(paste("RMSE with scaling for all tiles: ",model_name[i],sep=""))
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| 448 | 448 |
dev.off() |
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list_outfiles[[counter_fig+2]] <- paste("Figure3b_boxplot_overall_region_scaling_",model_name[i],"_",out_suffix,".png",sep="")
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counter_fig <- counter_fig +2 |
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| 450 | 451 |
} |
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counter_fig <- counter_fig + length(model_name) |
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#counter_fig <- counter_fig + length(model_name)
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| 452 | 453 |
r6 <-c("figure_3a","Boxplot overall accuracy with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[6]])
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| 453 | 454 |
r7 <-c("figure_3b","Boxplot overall accuracy with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[7]])
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| 454 | 455 |
r8 <-c("figure_3a","Boxplot overall accuracy with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[8]])
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| 455 | 456 |
r9 <-c("figure_3b","Boxplot overall accuracy with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[9]])
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| 456 | 457 |
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################ |
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### Figure 4: plot predicted tiff for specific date per model
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### Figure 4: plot accuracy metric by month
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## Replace by break out by season? |
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#y_var_name <-"dailyTmax" |
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#index <-244 #index corresponding to Sept 1 |
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# if (mosaic_plot==TRUE){
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# index <- 1 #index corresponding to Jan 1 |
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# date_selected <- "20100901" |
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# name_method_var <- paste(interpolation_method,"_",y_var_name,"_",sep="") |
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# |
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# pattern_str <- paste("mosaiced","_",name_method_var,"predicted",".*.",date_selected,".*.tif",sep="")
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# lf_pred_list <- list.files(pattern=pattern_str) |
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# |
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# for(i in 1:length(lf_pred_list)){
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# |
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# |
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# r_pred <- raster(lf_pred_list[i]) |
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# |
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# res_pix <- 480 |
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# col_mfrow <- 1 |
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# row_mfrow <- 1 |
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# |
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# png(filename=paste("Figure4_models_predicted_surfaces_",model_name[i],"_",name_method_var,"_",data_selected,"_",out_suffix,".png",sep=""),
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# width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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# |
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# plot(r_pred) |
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# title(paste("Mosaiced",model_name[i],name_method_var,date_selected,sep=" "))
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# dev.off() |
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# } |
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# #Plot Delta and clim... |
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# |
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# ## plotting of delta and clim for later scripts... |
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# |
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# } |
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for(i in 1:length(model_name)){
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tb_subset <- subset(tb,pred_mod==model_name[i])#mod1 is i=1, mod_kr is last |
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labels <- month.abb # abbreviated names for each month |
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## Figure 4a |
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fig_filename <- paste("Figure4a_boxplot_overall_accuracy_separated_by_month_with_outliers_",model_name[i],"_",out_suffix,".png",sep="")
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png(filename=fig_filename, |
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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boxplot(rmse~month_no,data=tb_subset,ylab=metric_name,xlab="averaged by month",axes=F)#,names=tb$pred_mod) |
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axis(1, labels = FALSE) |
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## Plot x axis labels at default tick marks |
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text(1:length(labels), par("usr")[3] - 0.25, srt = 45, adj = 1,cex=0.8,
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labels = labels, xpd = TRUE) |
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axis(2) |
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box() |
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title(paste("Overall accuracy for ", model_name[i], " by month for ",region_name,sep=""))
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#p<- bwplot(rmse~year_predicted | reg , data=tb_subset,ylim=c(0,5), |
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#main="RMSE per model and region over all tiles") |
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#print(p) |
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dev.off() |
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list_outfiles[[counter_fig+1]] <- fig_filename |
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counter_fig <- counter_fig + 1 |
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fig_filename <- paste("Figure4b_boxplot_overall_separated_by_month_scaling_",model_name[i],"_",out_suffix,".png",sep="")
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png(filename=fig_filename, |
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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boxplot(rmse~month_no,data=tb_subset,ylim=c(0,5),outline=FALSE,ylab=metric_name, |
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xlab="averaged by month",axes=F)#,names=tb$pred_mod) |
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axis(1, labels = FALSE) |
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## Plot x axis labels at default tick marks |
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text(1:length(labels), par("usr")[3] - 0.25, srt = 45, adj = 1,cex=0.8,
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labels = labels, xpd = TRUE) |
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axis(2) |
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box() |
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title(paste("Overall accuracy for ", model_name[i], " by month for ",region_name,sep=""))
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#p<- bwplot(rmse~year_predicted | reg , data=tb_subset,ylim=c(0,5), |
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#main="RMSE per model and region over all tiles") |
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#print(p) |
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dev.off() |
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list_outfiles[[counter_fig+1]] <- fig_filename |
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counter_fig <- counter_fig + 1 |
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} |
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#counter_fig <- counter_fig + length(model_name) |
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r10 <-c("figure_4a","Boxplot overall accuracy by month with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[10]])
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r11 <-c("figure_4b","Boxplot overall accuracy by month with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[11]])
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r12 <-c("figure_4a","Boxplot overall accuracy by month with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[12]])
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r13 <-c("figure_4b","Boxplot overall accuracy by month with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[13]])
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###################### |
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### Figure 5: plot accuracy ranked |
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|
| ... | ... | |
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title(paste("RMSE ranked by tile for ",model_name[i],sep=""))
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dev.off() |
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list_outfiles[[counter_fig+i]] <- fig_filename |
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list_outfiles[[counter_fig+1]] <- fig_filename |
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counter_fig <- counter_fig + 1 |
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} |
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counter_fig <- counter_fig + length(model_name) |
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#counter_fig <- counter_fig + length(model_name)
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r10 <-c("figure_5","Barplot of accuracy metrics ranked by tiles","mod1",metric_name,region_name,year_predicted,list_outfiles[[8]])
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r11 <-c("figure_5","Barplot of accuracy metrics ranked by tiles","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[9]])
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r14 <-c("figure_5","Barplot of accuracy metrics ranked by tiles","mod1",metric_name,region_name,year_predicted,list_outfiles[[14]])
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r15 <-c("figure_5","Barplot of accuracy metrics ranked by tiles","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[15]])
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| 530 | 553 |
###################### |
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### Figure 6: plot map of average RMSE per tile at centroids |
| ... | ... | |
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} |
| 572 | 595 |
counter_fig <- counter_fig+length(model_name) |
| 573 | 596 |
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r12 <-c("figure_6","Average accuracy metrics map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[8]])
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r13 <-c("figure_6","Average accuracy metrics map at centroids","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[9]])
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r16 <-c("figure_6","Average accuracy metrics map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[16]])
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r17 <-c("figure_6","Average accuracy metrics map at centroids","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[17]])
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| 577 | 600 |
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###################### |
| ... | ... | |
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nb<-nrow(subset(summary_metrics_v,model_name=="mod1")) |
| 595 | 618 |
sum(subset(summary_metrics_v,model_name=="mod1")$n_missing)/nb #33/35 |
| 596 | 619 |
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## Make this a figure... |
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#plot(summary_metrics_v) |
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#Make this a function later so that we can explore many thresholds... |
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#Problem here |
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#Browse[3]> c |
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#Error in grid.Call.graphics(L_setviewport, pvp, TRUE) : |
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#non-finite location and/or size for viewport |
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| 605 | 620 |
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j<-1 #for model name 1,mod1 |
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for(i in 1:length(threshold_missing_day)){
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| ... | ... | |
| 613 | 628 |
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fig_filename <- paste("Figure7a_ac_metrics_map_centroids_tile_",model_name[j],"_","missing_day_",threshold_missing_day[i],
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| 615 | 630 |
"_",out_suffix,".png",sep="") |
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list_outfiles[[counter_fig+i]] <- fig_filename |
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| 616 | 632 |
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| 617 | 633 |
if(sum(summary_metrics_v_subset$n_missing) > 0){#then there are centroids to plot!!!
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| 618 | 634 |
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| ... | ... | |
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} |
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list_outfiles[[counter_fig+i]] <- fig_filename |
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#list_outfiles[[counter_fig+i]] <- fig_filename
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} |
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counter_fig <- counter_fig+length(threshold_missing_day) #currently 4 days... |
| 644 | 660 |
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r14 <-c("figure_7","Number of missing days threshold1 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[8]])
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r15 <-c("figure_7","Number of missing days threshold2 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[9]])
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r16 <-c("figure_7","Number of missing days threshold3 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[8]])
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r17 <-c("figure_7","Number of missing days threshold4 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[9]])
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r18 <-c("figure_7","Number of missing days threshold1 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[18]])
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r19 <-c("figure_7","Number of missing days threshold2 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[19]])
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r20 <-c("figure_7","Number of missing days threshold3 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[20]])
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r21 <-c("figure_7","Number of missing days threshold4 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[21]])
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### Potential |
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png(filename=paste("Figure7b_number_daily_predictions_per_models","_",out_suffix,".png",sep=""),
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| ... | ... | |
| 657 | 673 |
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list_outfiles[[counter_fig+1]] <- paste("Figure7b_number_daily_predictions_per_models","_",out_suffix,".png",sep="")
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| 659 | 675 |
counter_fig <- counter_fig + 1 |
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r18 <-c("figure_7b","Number of daily predictions per_models","mod1",metric_name,region_name,year_predicted,list_outfiles[[9]])
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r22 <-c("figure_7b","Number of daily predictions per_models","mod1",metric_name,region_name,year_predicted,list_outfiles[[22]])
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| 661 | 677 |
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table(tb$pred_mod) |
| 663 | 679 |
table(tb$index_d) |
| ... | ... | |
| 685 | 701 |
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| 686 | 702 |
list_outfiles[[counter_fig+1]] <- paste("Figure7c_histogram_number_daily_predictions_per_models","_",out_suffix,".png",sep="")
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| 687 | 703 |
counter_fig <- counter_fig + 1 |
| 688 |
r19 <-c("figure_7c","Histogram number daily predictions per models","mod1",metric_name,region_name,year_predicted,list_outfiles[[9]])
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r23 <-c("figure_7c","Histogram number daily predictions per models","mod1",metric_name,region_name,year_predicted,list_outfiles[[23]])
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| 689 | 705 |
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#table(tb) |
| 692 | 707 |
## Figure 7b |
| 693 | 708 |
#png(filename=paste("Figure7b_number_daily_predictions_per_models","_",out_suffix,".png",sep=""),
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| ... | ... | |
| 743 | 758 |
counter_fig <- counter_fig + 1 |
| 744 | 759 |
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| 745 | 760 |
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r20 <-c("figure 8a","Boxplot overall accuracy by model separated by region with outliers",NA,metric_name,region_name,year_predicted,list_outfiles[[20]])
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r21 <-c("figure 8b","Boxplot overall accuracy by model separated by region with scaling",NA,metric_name,region_name,year_predicted,list_outfiles[[21]])
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r24 <-c("figure 8a","Boxplot overall accuracy by model separated by region with outliers",NA,metric_name,region_name,year_predicted,list_outfiles[[24]])
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r25 <-c("figure 8b","Boxplot overall accuracy by model separated by region with scaling",NA,metric_name,region_name,year_predicted,list_outfiles[[25]])
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| 748 | 763 |
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| 749 | 764 |
####### |
| 750 | 765 |
##Second, plot for each model separately |
| ... | ... | |
| 787 | 802 |
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| 788 | 803 |
} |
| 789 | 804 |
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r22 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[22]])
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r23 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[23]])
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| 792 |
r24 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[24]])
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r25 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[25]])
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r26 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[26]])
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r27 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[27]])
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r28 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[28]])
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| 808 |
r29 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[29]])
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| 794 | 809 |
|
| 795 | 810 |
##################################################### |
| 796 | 811 |
#### Figure 9: plotting boxplot by year and regions ########### |
| ... | ... | |
| 799 | 814 |
res_pix <- 480 |
| 800 | 815 |
col_mfrow <- 1 |
| 801 | 816 |
row_mfrow <- 1 |
| 802 |
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| 803 |
png(filename=paste("Figure9a_boxplot_overall_separated_by_year_and_model_with_oultiers_",out_suffix,".png",sep=""),
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| 817 |
fig_filename <- paste("Figure9a_boxplot_overall_separated_by_year_and_model_with_oultiers_",out_suffix,".png",sep="")
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| 818 |
png(filename= fig_filename,
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| 804 | 819 |
width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 805 | 820 |
#This will need to be changed, the layout is difficult at this stage |
| 806 | 821 |
#p<- bwplot(rmse~pred_mod + reg + year_predicted, data=tb, |
| ... | ... | |
| 809 | 824 |
main="RMSE per model and region over all tiles") |
| 810 | 825 |
print(p) |
| 811 | 826 |
dev.off() |
| 812 |
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| 827 |
list_outfiles[[counter_fig+1]] <- fig_filename |
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| 828 |
counter_fig <- counter_fig + 1 |
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| 829 |
|
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| 813 | 830 |
## Figure 9b |
| 814 |
png(filename=paste("Figure9b_boxplot_overall_separated_by_year_and_model_scaling_",out_suffix,".png",sep=""),
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| 831 |
fig_filename <- paste("Figure9b_boxplot_overall_separated_by_year_and_model_scaling_",out_suffix,".png",sep="")
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| 832 |
png(filename= fig_filename, |
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| 815 | 833 |
width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 816 | 834 |
|
| 817 | 835 |
#boxplot(rmse~pred_mod,data=tb,ylim=c(0,5),outline=FALSE)#,names=tb$pred_mod) |
| ... | ... | |
| 820 | 838 |
main="RMSE per model and region over all tiles") |
| 821 | 839 |
print(p) |
| 822 | 840 |
dev.off() |
| 841 |
list_outfiles[[counter_fig+1]] <- fig_filename |
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| 842 |
counter_fig <- counter_fig + 1 |
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| 823 | 843 |
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| 824 | 844 |
for(i in 1:length(model_name)){
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| 825 | 845 |
|
| ... | ... | |
| 855 | 875 |
counter_fig <- counter_fig + 1 |
| 856 | 876 |
} |
| 857 | 877 |
|
| 858 |
r26 <-c("figure 9a","Boxplot overall accuracy separated_by year and model with oultiers",NA,metric_name,region_name,year_predicted,list_outfiles[[22]])
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| 859 |
r27 <-c("figure 9b","Boxplot overall accuracy separated_by year and model with scaling",NA,metric_name,region_name,year_predicted,list_outfiles[[23]])
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| 860 |
r28 <-c("figure 9c","Boxplot overall accuracy separated by year with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[24]])
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| 861 |
r29 <-c("figure 9d","Boxplot overall accuracy separated by year with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[25]])
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| 862 |
r30 <-c("figure 9c","Boxplot overall accuracy separated by year with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[24]])
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r31 <-c("figure 9d","Boxplot overall accuracy separated by year with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[25]])
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| 878 |
r30 <-c("figure 9a","Boxplot overall accuracy separated_by year and model with oultiers",NA,metric_name,region_name,year_predicted,list_outfiles[[30]])
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r31 <-c("figure 9b","Boxplot overall accuracy separated_by year and model with scaling",NA,metric_name,region_name,year_predicted,list_outfiles[[31]])
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| 880 |
r32 <-c("figure 9c","Boxplot overall accuracy separated by year with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[32]])
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| 881 |
r33 <-c("figure 9d","Boxplot overall accuracy separated by year with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[33]])
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| 882 |
r34 <-c("figure 9c","Boxplot overall accuracy separated by year with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[34]])
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|
| 883 |
r35 <-c("figure 9d","Boxplot overall accuracy separated by year with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[35]])
|
|
| 864 | 884 |
|
| 865 | 885 |
############################################################## |
| 866 | 886 |
############## Prepare object to return |
| ... | ... | |
| 869 | 889 |
# This is hard coded and can be improved later on for flexibility. It works for now... |
| 870 | 890 |
#This data.frame contains all the files from the assessment |
| 871 | 891 |
|
| 872 |
#Should have this at the location of the figures!!! will be done later? |
|
| 873 |
#r1 <-c("figure_1","Tiles processed for the region",NA,NA,region_name,year_predicted,list_outfiles[[1]])
|
|
| 874 |
#r2 <-c("figure_2a","Boxplot of accuracy with outliers by tiles","mod1",metric_name,region_name,year_predicted,list_outfiles[[2]])
|
|
| 875 |
#r3 <-c("figure_2a","boxplot of accuracy with outliers by tiles","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[3]])
|
|
| 876 |
#r4 <-c("figure_2b","Boxplot of accuracy with scaling by tiles","mod1",metric_name,region_name,year_predicted,list_outfiles[[4]])
|
|
| 877 |
#r5 <-c("figure_2b","Boxplot of accuracy with scaling by tiles","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[5]])
|
|
| 878 |
#r6 <-c("figure_3a","Boxplot overall accuracy with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[6]])
|
|
| 879 |
#r7 <-c("figure_3b","Boxplot overall accuracy with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[7]])
|
|
| 880 |
#r8 <-c("figure_3a","Boxplot overall accuracy with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[8]])
|
|
| 881 |
#r9 <-c("figure_3b","Boxplot overall accuracy with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[9]])
|
|
| 882 |
#r10 <-c("figure_5","Barplot of accuracy metrics ranked by tiles","mod1",metric_name,region_name,year_predicted,list_outfiles[[10]])
|
|
| 883 |
#r11 <-c("figure_5","Barplot of accuracy metrics ranked by tiles","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[11]])
|
|
| 884 |
#r12 <-c("figure_6","Average accuracy metrics map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[12]])
|
|
| 885 |
#r13 <-c("figure_6","Average accuracy metrics map at centroids","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[13]])
|
|
| 886 |
#r14 <-c("figure_7","Number of missing days threshold1 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[14]])
|
|
| 887 |
#r15 <-c("figure_7","Number of missing days threshold2 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[15]])
|
|
| 888 |
#r16 <-c("figure_7","Number of missing days threshold3 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[16]])
|
|
| 889 |
#r17 <-c("figure_7","Number of missing days threshold4 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[17]])
|
|
| 890 |
#r18 <-c("figure_7b","Number of daily predictions per_models","mod1",metric_name,region_name,year_predicted,list_outfiles[[18]])
|
|
| 891 |
#r19 <-c("figure_7c","Histogram number daily predictions per models","mod1",metric_name,region_name,year_predicted,list_outfiles[[19]])
|
|
| 892 |
#r20 <-c("figure 8a","Boxplot overall accuracy by model separated by region with outliers",NA,metric_name,region_name,year_predicted,list_outfiles[[20]])
|
|
| 893 |
#r21 <-c("figure 8b","Boxplot overall accuracy by model separated by region with scaling",NA,metric_name,region_name,year_predicted,list_outfiles[[21]])
|
|
| 894 |
#r22 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[22]])
|
|
| 895 |
#r23 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[23]])
|
|
| 896 |
#r24 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[24]])
|
|
| 897 |
#r25 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[25]])
|
|
| 898 |
|
|
| 899 | 892 |
#Assemble all the figures description and information in a data.frame for later use |
| 900 | 893 |
list_rows <-list(r1,r2,r3,r4,r5,r6,r7,r8,r9,r10,r11,r12,r13,r14,r15,r16,r17,r18,r19,r20,r21,r22,r23,r24, |
| 901 |
r25,r26,r27,r28,r29,r30,r31) |
|
| 894 |
r25,r26,r27,r28,r29,r30,r31,r32,r33,r34,r35)
|
|
| 902 | 895 |
df_assessment_figures_files <- as.data.frame(do.call(rbind,list_rows)) |
| 903 | 896 |
names(df_assessment_figures_files) <- c("figure_no","comment","model_name","reg","metric_name","year_predicted","filename")
|
| 904 | 897 |
|
| ... | ... | |
| 912 | 905 |
###################################################### |
| 913 | 906 |
##### Prepare objet to return #### |
| 914 | 907 |
|
| 915 |
assessment_obj <- list(df_assessment_files, df_assessment_figures_files) |
|
| 908 |
assessment_obj <- list(list_df_assessment_files, df_assessment_figures_files)
|
|
| 916 | 909 |
names(assessment_obj) <- c("df_assessment_files", "df_assessment_figures_files")
|
| 917 | 910 |
## Prepare list of files to return... |
| 918 | 911 |
return(assessment_obj) |
Also available in: Unified diff
assessment part3, solving figures count and clean up