Revision ea73ac07
Added by Benoit Parmentier almost 9 years ago
| climate/research/oregon/interpolation/global_run_scalingup_assessment_part3.R | ||
|---|---|---|
| 173 | 173 |
year_predicted <- list_param_run_assessment_plotting$year_predicted |
| 174 | 174 |
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| 175 | 175 |
NA_value <- NA_flag_val |
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metric_name <- "rmse" #to be added to the code later... |
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| 177 | 178 |
##################### START SCRIPT ################# |
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| 179 | 180 |
####### PART 1: Read in data ######## |
| ... | ... | |
| 187 | 188 |
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| 188 | 189 |
setwd(out_dir) |
| 189 | 190 |
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| 190 |
list_outfiles <- vector("list", length=29) #collect names of output files, this should be dynamic?
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list_outfiles_names <- vector("list", length=29) #collect names of output files
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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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| 192 | 193 |
counter_fig <- 0 #index of figure to collect outputs |
| 193 | 194 |
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| 194 | 195 |
#i <- year_predicted |
| ... | ... | |
| 447 | 448 |
list_outfiles[[counter_fig+2]] <- paste("Figure3b_boxplot_overall_region_scaling_",model_name[i],"_",out_suffix,".png",sep="")
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| 448 | 449 |
} |
| 449 | 450 |
counter_fig <- counter_fig + length(model_name) |
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r6 <-c("figure_3a","Boxplot overall accuracy with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[6]])
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r7 <-c("figure_3b","Boxplot overall accuracy with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[7]])
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r8 <-c("figure_3a","Boxplot overall accuracy with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[8]])
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r9 <-c("figure_3b","Boxplot overall accuracy with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[9]])
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| 451 | 456 |
################ |
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### Figure 4: plot predicted tiff for specific date per model |
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|
| ... | ... | |
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} |
| 517 | 522 |
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| 518 | 523 |
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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| 519 | 527 |
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| 520 | 528 |
###################### |
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### Figure 6: plot map of average RMSE per tile at centroids |
| ... | ... | |
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### Without |
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| 525 | 533 |
#list_df_ac_mod <- vector("list",length=length(lf_pred_list))
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list_df_ac_mod <- vector("list",length=2)
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list_df_ac_mod <- vector("list",length=length(model_name))
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| 527 | 535 |
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| 528 | 536 |
for (i in 1:length(model_name)){
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| 529 | 537 |
|
| ... | ... | |
| 557 | 565 |
list_outfiles[[counter_fig+i]] <- fig_filename |
| 558 | 566 |
} |
| 559 | 567 |
counter_fig <- counter_fig+length(model_name) |
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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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| 560 | 571 |
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| 561 | 572 |
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| 562 | 573 |
###################### |
| ... | ... | |
| 587 | 598 |
#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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j<-1 #for model name 1 |
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j<-1 #for model name 1,mod1
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| 591 | 602 |
for(i in 1:length(threshold_missing_day)){
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| 592 | 603 |
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| 593 | 604 |
#summary_metrics_v$n_missing <- summary_metrics_v$n == 365 |
| ... | ... | |
| 604 | 615 |
res_pix <- 960 |
| 605 | 616 |
col_mfrow <- 1 |
| 606 | 617 |
row_mfrow <- 1 |
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#only mod1 right now |
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png(filename=paste("Figure7a_ac_metrics_map_centroids_tile_",model_name[j],"_","missing_day_",threshold_missing_day[i],
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| 608 | 620 |
"_",out_suffix,".png",sep=""), |
| 609 | 621 |
width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| ... | ... | |
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list_outfiles[[counter_fig+i]] <- fig_filename |
| 624 | 636 |
} |
| 625 | 637 |
counter_fig <- counter_fig+length(threshold_missing_day) #currently 4 days... |
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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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### Potential |
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png(filename=paste("Figure7b_number_daily_predictions_per_models","_",out_suffix,".png",sep=""),
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| 629 | 646 |
width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| ... | ... | |
| 634 | 651 |
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list_outfiles[[counter_fig+1]] <- paste("Figure7b_number_daily_predictions_per_models","_",out_suffix,".png",sep="")
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| 636 | 653 |
counter_fig <- counter_fig + 1 |
| 654 |
r18 <-c("figure_7b","Number of daily predictions per_models","mod1",metric_name,region_name,year_predicted,list_outfiles[[9]])
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| 637 | 655 |
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| 638 | 656 |
table(tb$pred_mod) |
| 639 | 657 |
table(tb$index_d) |
| ... | ... | |
| 648 | 666 |
as.character(unique(test$tile_id)) #141 tiles |
| 649 | 667 |
|
| 650 | 668 |
dim(subset(test,test$predicted==365 & test$pred_mod=="mod1")) |
| 651 |
histogram(subset(test, test$pred_mod=="mod1")$predicted) |
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#histogram(subset(test, test$pred_mod=="mod1")$predicted)
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| 652 | 670 |
unique(subset(test, test$pred_mod=="mod1")$predicted) |
| 653 | 671 |
table((subset(test, test$pred_mod=="mod1")$predicted)) |
| 654 | 672 |
|
| ... | ... | |
| 661 | 679 |
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| 662 | 680 |
list_outfiles[[counter_fig+1]] <- paste("Figure7c_histogram_number_daily_predictions_per_models","_",out_suffix,".png",sep="")
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| 663 | 681 |
counter_fig <- counter_fig + 1 |
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r19 <-c("figure_7c","Histogram number daily predictions per models","mod1",metric_name,region_name,year_predicted,list_outfiles[[9]])
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| 664 | 683 |
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#table(tb) |
| 666 | 686 |
## Figure 7b |
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#png(filename=paste("Figure7b_number_daily_predictions_per_models","_",out_suffix,".png",sep=""),
|
| ... | ... | |
| 680 | 700 |
##### Figure 8: Breaking down accuracy by regions!! ##### |
| 681 | 701 |
|
| 682 | 702 |
#summary_metrics_v <- merge(summary_metrics_v,df_tile_processed,by="tile_id") |
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################## |
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| 683 | 705 |
##First plot with all models together |
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|
| 685 | 707 |
## Figure 8a |
| ... | ... | |
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col_mfrow <- 1 |
| 688 | 710 |
row_mfrow <- 1 |
| 689 | 711 |
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| 690 |
png(filename=paste("Figure8a_boxplot_overall_separated_by_region_with_oultiers_","_",out_suffix,".png",sep=""),
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fig_filename <- paste("Figure8a_boxplot_overall_separated_by_region_with_oultiers_",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) |
| 692 | 715 |
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| 693 | 716 |
p<- bwplot(rmse~pred_mod | reg, data=tb, |
| ... | ... | |
| 695 | 718 |
print(p) |
| 696 | 719 |
dev.off() |
| 697 | 720 |
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list_outfiles[[counter_fig+1]] <- paste("Figure8a_boxplot_overall_separated_by_region_with_oultiers_",model_name[i],"_",out_suffix,".png",sep="")
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list_outfiles[[counter_fig+1]] <- fig_filename
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counter_fig <- counter_fig + 1 |
| 700 | 723 |
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| 701 | 724 |
## Figure 8b |
| 702 |
png(filename=paste("Figure8b_boxplot_overall_separated_by_region_scaling_","_",out_suffix,".png",sep=""),
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fig_filename <- paste("Figure8b_boxplot_overall_separated_by_region_scaling_",out_suffix,".png",sep="")
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png(filename=fig_filename, |
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| 703 | 727 |
width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 704 | 728 |
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| 705 | 729 |
#boxplot(rmse~pred_mod,data=tb,ylim=c(0,5),outline=FALSE)#,names=tb$pred_mod) |
| ... | ... | |
| 709 | 733 |
print(p) |
| 710 | 734 |
dev.off() |
| 711 | 735 |
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list_outfiles[[counter_fig+1]] <- paste("Figure8b_boxplot_overall_separated_by_region_scaling_",model_name[i],"_",out_suffix,".png",sep="")
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list_outfiles[[counter_fig+1]] <- fig_filename
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| 713 | 737 |
counter_fig <- counter_fig + 1 |
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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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####### |
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##Second, plot for each model separately |
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| 717 | 746 |
for(i in 1:length(model_name)){
|
| ... | ... | |
| 723 | 752 |
col_mfrow <- 1 |
| 724 | 753 |
row_mfrow <- 1 |
| 725 | 754 |
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| 726 |
fig_filename <- paste("Figure8c_boxplot_overall_separated_by_region_with_oultiers_",model_name[i],"_",out_suffix,".png",sep="")
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fig_filename <- paste("Figure8c_boxplot_overall_accuracy_separated_by_region_with_outliers_",model_name[i],"_",out_suffix,".png",sep="")
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| 727 | 756 |
png(filename=fig_filename, |
| 728 | 757 |
width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 729 | 758 |
|
| ... | ... | |
| 736 | 765 |
counter_fig <- counter_fig + 1 |
| 737 | 766 |
|
| 738 | 767 |
## Figure 8d |
| 739 |
fig_filename <- paste("Figure8d_boxplot_overall_separated_by_region_scaling_",model_name[i],"_",out_suffix,".png",sep="")
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fig_filename <- paste("Figure8d_boxplot_overall_accuracy_separated_by_region_scaling_",model_name[i],"_",out_suffix,".png",sep="")
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| 740 | 769 |
png(filename=fig_filename, |
| 741 | 770 |
width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 742 | 771 |
|
| ... | ... | |
| 751 | 780 |
counter_fig <- counter_fig + 1 |
| 752 | 781 |
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| 753 | 782 |
} |
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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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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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| 755 | 788 |
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##################################################### |
| 757 | 790 |
#### Figure 9: plotting boxplot by year and regions ########### |
| ... | ... | |
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col_mfrow <- 1 |
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row_mfrow <- 1 |
| 763 | 796 |
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png(filename=paste("Figure9a_boxplot_overall_separated_by_region_year_with_oultiers_",model_name[i],"_",out_suffix,".png",sep=""),
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png(filename=paste("Figure9a_boxplot_overall_separated_by_year_and_model_with_oultiers_",out_suffix,".png",sep=""),
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| 765 | 798 |
width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 766 | 799 |
#This will need to be changed, the layout is difficult at this stage |
| 767 | 800 |
#p<- bwplot(rmse~pred_mod + reg + year_predicted, data=tb, |
| 768 | 801 |
# main="RMSE per model and region over all tiles") |
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p<- bwplot(rmse~pred_mod | reg + year_predicted, data=tb,
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p<- bwplot(rmse~pred_mod | year_predicted, data=tb, |
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| 770 | 803 |
main="RMSE per model and region over all tiles") |
| 771 | 804 |
print(p) |
| 772 | 805 |
dev.off() |
| 773 | 806 |
|
| 774 | 807 |
## Figure 9b |
| 775 |
png(filename=paste("Figure8b_boxplot_overall_separated_by_region_year_scaling_",model_name[i],"_",out_suffix,".png",sep=""),
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png(filename=paste("Figure9b_boxplot_overall_separated_by_year_and_model_scaling_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
| 777 | 810 |
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| 778 | 811 |
#boxplot(rmse~pred_mod,data=tb,ylim=c(0,5),outline=FALSE)#,names=tb$pred_mod) |
| 779 | 812 |
#title("RMSE per model over all tiles")
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p<- bwplot(rmse~pred_mod | reg + year_predicted, data=tb,ylim=c(0,5),
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p<- bwplot(rmse~pred_mod | year_predicted, data=tb,ylim=c(0,5), |
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| 781 | 814 |
main="RMSE per model and region over all tiles") |
| 782 | 815 |
print(p) |
| 783 | 816 |
dev.off() |
| 784 | 817 |
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list_outfiles[[counter_fig+1]] <- paste("Figure9a_boxplot_overall_separated_by_region_year_with_oultiers_",model_name[i],"_",out_suffix,".png",sep="")
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counter_fig <- counter_fig + 1 |
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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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## Figure 9c |
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fig_filename <- paste("Figure9c_boxplot_overall_accuracy_separated_by_year_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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| 826 |
boxplot(rmse~year_predicted,data=tb_subset,ylab=metric_name,xlab="year predicted")#,names=tb$pred_mod) |
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title(paste("Overall accuracy for ", model_name[i], " by year for ",region_name,sep=""))
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| 828 |
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#p<- bwplot(rmse~year_predicted | reg , data=tb_subset,ylim=c(0,5), |
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#main="RMSE per model and region over all tiles") |
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| 831 |
#print(p) |
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dev.off() |
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| 834 |
list_outfiles[[counter_fig+1]] <- fig_filename |
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counter_fig <- counter_fig + 1 |
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fig_filename <- paste("Figure9d_boxplot_overall_separated_by_year_scaling_",model_name[i],"_",out_suffix,".png",sep="")
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png(filename=fig_filename, |
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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| 841 |
boxplot(rmse~year_predicted,data=tb_subset,ylim=c(0,5),outline=FALSE,ylab=metric_name,xlab="year predicted")#,names=tb$pred_mod) |
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| 842 |
title(paste("Overall accuracy for ", model_name[i], " by year for ",region_name,sep=""))
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| 843 |
#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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| 845 |
#print(p) |
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dev.off() |
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| 847 |
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| 848 |
list_outfiles[[counter_fig+1]] <- fig_filename |
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counter_fig <- counter_fig + 1 |
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| 850 |
} |
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| 852 |
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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| 853 |
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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| 854 |
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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| 855 |
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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| 856 |
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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| 857 |
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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| 787 | 858 |
|
| 788 | 859 |
############################################################## |
| 789 | 860 |
############## Prepare object to return |
| 790 | 861 |
############## Collect information from assessment ########## |
| 791 | 862 |
|
| 792 | 863 |
# This is hard coded and can be improved later on for flexibility. It works for now... |
| 793 |
comments_str <- c("tile processed for the region",
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| 794 |
"boxplot with outliers", |
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| 795 |
"boxplot with outliers", |
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| 796 |
"boxplot scaling by tiles", |
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| 797 |
"boxplot scaling by tiles", |
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| 798 |
"boxplot overall region with outliers", |
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| 799 |
"boxplot overall region with scaling", |
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| 800 |
"boxplot overall region with outliers", |
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| 801 |
"boxplot overall region with scaling", |
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| 802 |
"Barplot of accuracy metrics ranked by tile", |
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| 803 |
"Barplot of accuracy metrics ranked by tile", |
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| 804 |
"Average accuracy metrics map at centroids", |
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| 805 |
"Average accuracy metrics map at centroids", |
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| 806 |
"Number of missing day threshold1 map centroids", |
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| 807 |
"Number of missing day threshold2 map centroids", |
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| 808 |
"Number of missing day threshold3 map centroids", |
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| 809 |
"Number of missing day threshold4 map centroids", |
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| 810 |
"number_daily_predictions_per_model", |
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| 811 |
"histogram number_daily_predictions_per_models", |
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| 812 |
"boxplot overall separated by region with_outliers", |
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| 813 |
"boxplot overall separated by region with_scaling", |
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| 814 |
"boxplot overall separated by region with_outliers", |
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| 815 |
"boxplot overall separated by region with_scaling") |
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| 816 |
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| 817 |
figure_no <- c("figure_1","figure_2a","figure_2a","figure_2b","figure_2b","figure_3a","figure_3a","figure_3b","figure_3b",
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| 818 |
"figure_5", "figure_5","figure_6","figure_6","Figure_7a","Figure_7a","Figure_7a","Figure_7a","Figure_7b", |
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| 819 |
"Figure_7c","Figure 8a","Figure 8a","Figure 8b","Figure 8b") |
|
| 820 |
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| 821 |
col_model_name <- c(NA,"mod1","mod_kr","mod1","mod_kr","mod1","mod_kr","mod1","mod_kr","mod1","mod_kr", |
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| 822 |
"mod1","mod_kr","mod1","mod1","mod1","mod1","mod1","mod1",NA,NA,"mod1","mod1") |
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| 823 |
col_reg <- rep(region_name,length(list_outfiles)) |
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| 824 |
col_year_predicted <- rep(year_predicted,length(list_outfiles)) |
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| 825 |
|
|
| 826 | 864 |
#This data.frame contains all the files from the assessment |
| 827 |
df_assessment_figures_files <- data.frame(figure_no=figure_no, |
|
| 828 |
comment = comments_str, |
|
| 829 |
model_name=col_model_name, |
|
| 830 |
reg=col_reg, |
|
| 831 |
year_predicted=col_year_predicted, |
|
| 832 |
filename=unlist(list_outfiles)) |
|
| 865 |
|
|
| 866 |
#Should have this at the location of the figures!!! will be done later? |
|
| 867 |
#r1 <-c("figure_1","Tiles processed for the region",NA,NA,region_name,year_predicted,list_outfiles[[1]])
|
|
| 868 |
#r2 <-c("figure_2a","Boxplot of accuracy with outliers by tiles","mod1",metric_name,region_name,year_predicted,list_outfiles[[2]])
|
|
| 869 |
#r3 <-c("figure_2a","boxplot of accuracy with outliers by tiles","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[3]])
|
|
| 870 |
#r4 <-c("figure_2b","Boxplot of accuracy with scaling by tiles","mod1",metric_name,region_name,year_predicted,list_outfiles[[4]])
|
|
| 871 |
#r5 <-c("figure_2b","Boxplot of accuracy with scaling by tiles","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[5]])
|
|
| 872 |
#r6 <-c("figure_3a","Boxplot overall accuracy with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[6]])
|
|
| 873 |
#r7 <-c("figure_3b","Boxplot overall accuracy with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[7]])
|
|
| 874 |
#r8 <-c("figure_3a","Boxplot overall accuracy with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[8]])
|
|
| 875 |
#r9 <-c("figure_3b","Boxplot overall accuracy with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[9]])
|
|
| 876 |
#r10 <-c("figure_5","Barplot of accuracy metrics ranked by tiles","mod1",metric_name,region_name,year_predicted,list_outfiles[[10]])
|
|
| 877 |
#r11 <-c("figure_5","Barplot of accuracy metrics ranked by tiles","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[11]])
|
|
| 878 |
#r12 <-c("figure_6","Average accuracy metrics map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[12]])
|
|
| 879 |
#r13 <-c("figure_6","Average accuracy metrics map at centroids","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[13]])
|
|
| 880 |
#r14 <-c("figure_7","Number of missing days threshold1 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[14]])
|
|
| 881 |
#r15 <-c("figure_7","Number of missing days threshold2 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[15]])
|
|
| 882 |
#r16 <-c("figure_7","Number of missing days threshold3 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[16]])
|
|
| 883 |
#r17 <-c("figure_7","Number of missing days threshold4 map at centroids","mod1",metric_name,region_name,year_predicted,list_outfiles[[17]])
|
|
| 884 |
#r18 <-c("figure_7b","Number of daily predictions per_models","mod1",metric_name,region_name,year_predicted,list_outfiles[[18]])
|
|
| 885 |
#r19 <-c("figure_7c","Histogram number daily predictions per models","mod1",metric_name,region_name,year_predicted,list_outfiles[[19]])
|
|
| 886 |
#r20 <-c("figure 8a","Boxplot overall accuracy by model separated by region with outliers",NA,metric_name,region_name,year_predicted,list_outfiles[[20]])
|
|
| 887 |
#r21 <-c("figure 8b","Boxplot overall accuracy by model separated by region with scaling",NA,metric_name,region_name,year_predicted,list_outfiles[[21]])
|
|
| 888 |
#r22 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[22]])
|
|
| 889 |
#r23 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[23]])
|
|
| 890 |
#r24 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[24]])
|
|
| 891 |
#r25 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[25]])
|
|
| 892 |
|
|
| 893 |
#Assemble all the figures description and information in a data.frame for later use |
|
| 894 |
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, |
|
| 895 |
r25,r26,r27,r28,r29,r30,r31) |
|
| 896 |
df_assessment_figures_files <- as.data.frame(do.call(rbind,list_rows)) |
|
| 897 |
names(df_assessment_figures_files) <- c("figure_no","comment","model_name","reg","metric_name","year_predicted","filename")
|
|
| 833 | 898 |
|
| 834 | 899 |
###Prepare files for copying back? |
| 835 | 900 |
df_assessment_figures_files_names <- file.path(out_dir,paste("df_assessment_figures_files_",region_name,"_",year_predicted,"_",out_suffix,".txt",sep=""))
|
Also available in: Unified diff
assessment part3, tracking figures and listing in table