Load data

corpus_md<-create_corpus_dataset(f_corp_meta,jarprepro)
dfu<-(read.csv(f_rousage,stringsAsFactors = FALSE))
dfu$AX<-apply(dfu[c("AX")],1,base64)
df<-unique(dfu)

df_sample<-read.csv(f_onts,stringsAsFactors = FALSE)
#df_ad_raw<-read_csvs_tdl(f_impactad)
nrow(df)

## [1] 870713

#nrow(df_ad_raw)

Preprocess RO usage dataset

df$O<-gsub("http://purl.obolibrary.org/obo/","", df$ONT)
df$O<-gsub(".owl$","", df$O)
df$O<-gsub(".obo$","", df$O)
df$O<-gsub("^ontoneo/","", df$O)
df$O<-gsub("http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus","NCITNCBO", df$O)
df$O<-gsub("http://www.ebi.ac.uk/swo/","", df$O)
df$O<-gsub("http://semanticscience.org/ontology/","", df$O)
df$O<-gsub("http://anobase.vectorbase.org/mirnao/","", df$O)
df$O<-gsub("agro-edit","agro", df$O)
df$O<-gsub("caro/caro","caro", df$O)
df$O<-gsub("^mfmo$","feed", df$O)
df$O<-gsub("^ico$","ico_merged", df$O)
df$O<-gsub("^ido$","ido-merged", df$O)
df$O<-gsub("^ncro$","ncro-all-in-one", df$O)
df$O<-gsub("^obcs$","obcs_merged_inferred", df$O)
df$O<-gsub("^obib$","obib_merged_inferred", df$O)
df$O<-gsub("^omiabis$","omiabis_merged", df$O)
df$O<-gsub("^opl$","opl_inferred", df$O)
df$O<-gsub("^pco_merged$","pco_merged_inferred", df$O)
df$O<-gsub("^bcgo$","bcgo_merged_inferred", df$O)
df$O<-gsub("^DINTO$","DINTO_1.2", df$O)
df$O<-gsub("^swo$","swo_merged", df$O)
df$O<-gsub("^ogg$","ogg-merged", df$O)
df$TEMP_ANN<-ifelse(df$TEMP_ANN==""&df$RO_REL=="depends on","Dom:X-Ran:X~Time:Same~Rig:Yes-Nec:No",df$TEMP_ANN)
df$TEMP_ANN_ALL<-paste(df$TEMP_ANN,df$TEMP_INF_ANN,sep="~")
df$CAT<-ifelse(grepl("Dom:C.*Ran:C",df$TEMP_ANN_ALL),"CC",ifelse(grepl("(Dom:C.*Ran:O)|(Dom:O.*Ran:C)",df$TEMP_ANN_ALL),"OC-CO",ifelse(grepl("Dom:O.*Ran:O",df$TEMP_ANN_ALL),"OO","OT")))
df$containsnecno<-grepl("Nec:No",df$TEMP_ANN_ALL)

kable(plyr::count(df$MATCH),row.names = FALSE)

# removing broken records (that are neither exact nor smart matches)
df_broken<-(df[!(df$MATCH %in% c("exact","smart")),])
df<-(df[(df$MATCH %in% c("exact","smart")),])



# Excluding exact matches
df_ex<-(df[(df$MATCH %in% c("exact")),])
df<-(df[(df$MATCH %in% c("smart")),])

o_smart<-unique(df$ONT)
o_exact<-unique(df_ex$ONT)
# ontologies with smart matches:
length(o_smart)

## [1] 98

# ontologies with exact matches:
length(o_exact)

## [1] 68

length(o_exact)/length(o_smart)

## [1] 0.6938776

length(unique(df_ex$AX))/length(unique(df$AX))

## [1] 0.7018598

print("Broken")

## [1] "Broken"

nrow(df_broken)

## [1] 0

print("Excluded Exact matches (redundant)")

## [1] "Excluded Exact matches (redundant)"

nrow(df_ex)

## [1] 362326

print("Remaining Smart matches")

## [1] "Remaining Smart matches"

nrow(df)

## [1] 508387

# Variable with number of all ontologies in dataset
ct_o<-length(unique(df$O))

Dataset characteristics

d0<-unique(df[c("O","ONT_TOTAL_AX","AX")])

## Percentage of axioms with temporal annotations across ontologies
ct_ov_o<-plyr::count(d0[c("O","ONT_TOTAL_AX")])
ct_ov_o$pc<-round((ct_ov_o$freq/ct_ov_o$ONT_TOTAL_AX)*100,2)
summary(ct_ov_o$pc)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.040   5.485  18.000  25.780  39.000  98.510

ct_ov_o<-ct_ov_o[order(-ct_ov_o$pc),]
kable(ct_ov_o,row.names = FALSE)

## Distribution of ontologies
options(scipen=1000)
ov_o_size<-unique(d0[c("O","ONT_TOTAL_AX")])
summary(ov_o_size$ONT_TOTAL_AX)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     42.0    708.5   2191.0  14960.0   6218.0 281400.0

#ggplot(ov_o_size,aes(ONT_TOTAL_AX)) + geom_histogram(bins = 10) + scale_x_log10()
ct_cat<-plyr::count(df$CAT)
kable(ct_cat,row.names = FALSE)

### The impact of smart matching (difference of top ten relation by coverage)
ds<-unique(df[c("O","AX","RO_REL","CAT")])
de<-unique(df_ex[c("O","AX","RO_REL","CAT")])
coverage_rel_wosmart<-compute_coverage_rel(de[c("O","RO_REL","CAT")],ct_o)
coverage_rel<-compute_coverage_rel(ds[c("O","RO_REL","CAT")],ct_o)
top10<-head(coverage_rel[order(-coverage_rel$coverage),],10)
top10e<-coverage_rel_wosmart[coverage_rel_wosmart$x %in% top10$x,]
top10e$source<-"exact"
top10$source<-"smart"
top<-merge(top10,top10e,all = TRUE)
top$CAT<-NULL
top$freq<-NULL
top<-reshape(top,direction = "wide",timevar = "source",idvar = "x")
names(top)<-c("Relation","Exact","Smart")
top$pdiff<-round(((top$Smart-top$Exact)/top$Exact)*100,2)
top<-top[order(-top$Smart),]
kable(top,row.names = FALSE)

print(xtable(top,digits=c(0,0,2,2,2)),include.rownames=FALSE,file="coverage_rel_exact_v_smart.tex")

## Histogram of smart matches over ontology size
#### y axis = number of ontologies, x axis: % of (axioms that use RO relations) / (total axioms)
ct_o_smart<-plyr::count(unique(ds[c("O","AX")])[c("O")])
ct_o_smart<-merge(ct_o_smart,ov_o_size,by="O")
ct_o_smart$rat<-ct_o_smart$freq/ct_o_smart$ONT_TOTAL_AX
p<-ggplot(ct_o_smart,aes(rat)) + geom_histogram() + xlab("% of axioms with smart matches") + ylab("Number of ontologies")
theme_tdl(p)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggsave("hist_matches_d_ontsize.jpg",width = 3,height = 4,units="in")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

#### y axis = number of ontologies, x axis: number of DIFFERENT ro relations used in ontology
ct_o_ro<-plyr::count(unique(ds[c("O","RO_REL")])[c("O")])
p<-ggplot(ct_o_ro,aes(freq)) + geom_histogram() + xlab("Nr. of RO relations used") + ylab("Number of ontologies")
theme_tdl(p)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggsave("hist_ro_rel_ont.jpg",width = 3,height = 4,units="in")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

### Check for duplicates
xx<-plyr::count(dfu[!(names(dfu) %in% c("AX"))])
yy<-xx[xx$freq>1,]

### Number of exact duplicate records in raw dataset
nrow(yy)

## [1] 4

head(yy)

##        ONT_TOTAL_AX       RO_REL      OBO_REL
## 93370        117057      part of      part_of
## 93371        117057      part of      part_of
## 189473       117057 derives from derives from
## 189474       117057 derives from derives from
##                                                                   TEMP_INF_ANN
## 93370                                                                         
## 93371                                                                         
## 189473 Time:Diff~Dom:C-Ran:IC~Ran:Changed~Dom:C-Ran:C~Dom:Changed~Dom:IC-Ran:C
## 189474 Time:Diff~Dom:C-Ran:IC~Ran:Changed~Dom:C-Ran:C~Dom:Changed~Dom:IC-Ran:C
##                                           ONT
## 93370  http://purl.obolibrary.org/obo/clo.owl
## 93371  http://purl.obolibrary.org/obo/clo.owl
## 189473 http://purl.obolibrary.org/obo/clo.owl
## 189474 http://purl.obolibrary.org/obo/clo.owl
##                                        RO_REL_IRI  RO_REL_HC OBO_REL_HC
## 93370  http://purl.obolibrary.org/obo/BFO_0000050 -978027865 -978027865
## 93371  http://purl.obolibrary.org/obo/BFO_0000050 -978027865 -978027865
## 189473  http://purl.obolibrary.org/obo/RO_0001000 1643394301 1643394301
## 189474  http://purl.obolibrary.org/obo/RO_0001000 1643394301 1643394301
##        MATCH      AX_HC                                OBO_REL_IRI
## 93370  exact 1812853289 http://purl.obolibrary.org/obo/BFO_0000050
## 93371  smart 1812853289 http://purl.obolibrary.org/obo/BFO_0000050
## 189473 exact 1812853289  http://purl.obolibrary.org/obo/RO_0001000
## 189474 smart 1812853289  http://purl.obolibrary.org/obo/RO_0001000
##                                             TEMP_ANN  X freq
## 93370  Dom:X-Ran:X~Time:Same~Rig:Yes-Nec:No~TI:AHFAT NA    2
## 93371  Dom:X-Ran:X~Time:Same~Rig:Yes-Nec:No~TI:AHFAT NA    2
## 189473   Dom:IC-Ran:IC~Time:Past~Dom:Birth~Ran:Death NA    2
## 189474   Dom:IC-Ran:IC~Time:Past~Dom:Birth~Ran:Death NA    2

Coverage: Relation

coverage_rel<-compute_coverage_rel(df[c("O","RO_REL","CAT")],ct_o)
d_coverage_rel<-preparePlotRanksDf(coverage_rel,"x","coverage")

plotRanks(coverage_rel,"x","coverage",xlab="Relation",ylab="Coverage",ncol=1)

ggsave("coverage_rel.jpg",width = 3,height = 4,units="in")

top10<-head(coverage_rel[order(-coverage_rel$coverage),],10)
top10relcov<-top10$x
all<-coverage_rel[order(coverage_rel$CAT,-coverage_rel$coverage),]
print(xtable(all,digits=c(0,0,2,2,2)),include.rownames=FALSE,file="coverage_rel.tex")
print(xtable(top10,digits=c(0,0,2,2,2)),include.rownames=FALSE,file="coverage_rel_top10.tex")

# Summary stats over ALL temporal features
summary(all$coverage)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.020   1.020   2.040   5.728   6.120  79.590

sd(all$coverage)

## [1] 9.375772

# Summary stats BY DOMAIN RANGE over ALL temporal features
ddply(all,~CAT,summarise,mean=mean(coverage),median=median(coverage),sd=sd(coverage), min=min(coverage),max=max(coverage))

##     CAT      mean median        sd  min   max
## 1    CC  4.413980   2.04  5.801800 1.02 29.59
## 2 OC-CO  6.844583   3.06  7.538174 1.02 27.55
## 3    OO  5.555000   3.06  5.482081 1.02 18.37
## 4    OT 26.734000   2.04 35.949403 1.02 79.59

kable(all,row.names = FALSE)

kable(top10,row.names = FALSE)

Coverage: Temporal Annotation

coverage_ann<-compute_coverage_ann(df[df$TEMP_ANN!="",c("O","TEMP_ANN","CAT")],ct_o)
coverage_ann$ann_id<-paste("A",1:nrow(coverage_ann),sep="")
plotRanks(coverage_ann,"ann_id","coverage",xlab="Annotation",ylab="Coverage")

ggsave("coverage_ann.jpg",width = 3,height = 2.5,units="in")

top10<-head(coverage_ann[order(-coverage_ann$coverage),],10)
top10anncov<-top10$x
all<-coverage_ann[order(coverage_ann$CAT,-coverage_ann$coverage),]

# Summary stats over ALL temporal features
summary(all$coverage)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.020   2.040   4.080   9.168  14.290  85.710

sd(all$coverage)

## [1] 12.71982

# Summary stats BY DOMAIN RANGE over ALL temporal features
ddply(all,~CAT,summarise,mean=mean(coverage),median=median(coverage),sd=sd(coverage), min=min(coverage),max=max(coverage))

##     CAT      mean median        sd  min   max
## 1    CC  8.832187   4.08  9.949514 1.02 34.69
## 2 OC-CO  8.216316   4.08  8.089142 1.02 27.55
## 3    OO  6.777857   3.57  6.387612 1.02 21.43
## 4    OT 29.930000   3.06 48.317664 1.02 85.71

kable(all,row.names = FALSE)

kable(top10,row.names = FALSE)

coverage_ann_xt<-coverage_ann
coverage_ann_xt$x<-NULL
print(xtable(all[,c("ann_id","freq","coverage","CAT")],digits=c(0,0,2,2,2)),include.rownames=FALSE,file="coverage_ann.tex")
print(xtable(top10[,c("ann_id","freq","coverage","CAT")],digits=c(0,0,2,2,2)),include.rownames=FALSE,file="coverage_ann_top10.tex")

Coverage: Temporal Attributes

coverage_feat<-compute_coverage_feat(df[c("O","TEMP_ANN_ALL","CAT")],ct_o)

## Warning in "\"\"": attributes are not identical across measure variables;
## they will be dropped

plotRanks(coverage_feat,"x","coverage",xlab="Temporal Feature",ylab="Coverage",ncol = 1)

ggsave("coverage_feat.jpg",width = 3,height = 4,units="in")
top10<-head(coverage_feat[order(-coverage_feat$coverage),],10)
top10attcov<-top10$x
all<-coverage_feat[order(coverage_feat$CAT,-coverage_feat$coverage),]

# Summary stats over ALL temporal features
summary(all$coverage)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1.02    5.10   15.31   23.45   36.73   85.71

sd(all$coverage)

## [1] 23.22989

# Summary stats BY DOMAIN RANGE over ALL temporal features
ddply(all,~CAT,summarise,mean=mean(coverage),median=median(coverage),sd=sd(coverage), min=min(coverage),max=max(coverage))

##     CAT     mean median       sd  min   max
## 1    CC 25.31258  19.39 21.22622 1.02 69.39
## 2 OC-CO 18.90095  13.27 14.03151 3.06 46.94
## 3    OO 11.54375   5.10 13.33598 1.02 41.84
## 4    OT 69.18000  85.71 36.96220 3.06 85.71

kable(all,row.names = FALSE)

kable(top10,row.names = FALSE)

print(xtable(all,digits=c(0,0,2,2,2)),include.rownames=FALSE,file="coverage_att.tex")
print(xtable(top10,digits=c(0,0,2,2,2)),include.rownames=FALSE,file="coverage_att_top10.tex")

Impact: Relations

impact_rel<-compute_impact_rel(df[c("O","CAT","ONT_TOTAL_AX","RO_REL")])
plotRanks(impact_rel,"RO_REL","impact",xlab="Relation",ylab="Impact (Basic)",ncol=1)

ggsave("impact_rel.jpg",width = 3,height = 4,units="in")
top10<-head(impact_rel[order(-impact_rel$impact),],10)
top10relim<-top10$RO_REL
all<-impact_rel[order(impact_rel$CAT,-impact_rel$impact),]

# Summary stats over ALL temporal features
summary(all$impact)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##  0.000000  0.002347  0.014800  0.225400  0.115400 11.520000

sd(all$impact)

## [1] 1.027628

# Summary stats BY DOMAIN RANGE over ALL temporal features
ddply(all,~CAT,summarise,mean=mean(impact),median=median(impact),sd=sd(impact), min=min(impact),max=max(impact))

##     CAT      mean      median        sd          min       max
## 1    CC 0.1060933 0.007959184 0.3140803 0.0000000000  2.066224
## 2 OC-CO 0.1267007 0.027295918 0.1550766 0.0001020408  0.560102
## 3    OO 0.2440986 0.065459184 0.5318933 0.0000000000  2.239592
## 4    OT 2.9697755 0.276224490 4.9457643 0.0047959184 11.518980

kable(all,row.names = FALSE)

kable(top10,row.names = FALSE)

print(xtable(all,digits=c(3)),include.rownames=FALSE,file="impact_rel.tex")
print(xtable(top10,digits=c(2)),include.rownames=FALSE,file="impact_rel_top10.tex")

Impact: Annotations

impact_anno<-compute_impact_anno(df[c("O","CAT","ONT_TOTAL_AX","TEMP_ANN")])
impact_anno<-merge(impact_anno,unique(coverage_ann[,c("x","ann_id")]),by.x = "TEMP_ANN",by.y = "x")
plotRanks(impact_anno,"ann_id","impact",xlab="Annotation",ylab="Impact (Basic)")

ggsave("impact_anno.jpg",width = 3,height = 2.5,units="in")

top10<-head(impact_anno[order(-impact_anno$impact),],10)
top10annim<-top10$TEMP_ANN
all<-impact_anno[order(impact_anno$CAT,-impact_anno$impact),]

# Summary stats over ALL temporal features
summary(all$impact)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##  0.000102  0.005077  0.036480  0.480600  0.277000 14.550000

sd(all$impact)

## [1] 1.801924

# Summary stats BY DOMAIN RANGE over ALL temporal features
ddply(all,~CAT,summarise,mean=mean(impact),median=median(impact),sd=sd(impact), min=min(impact),max=max(impact))

##     CAT      mean     median        sd          min        max
## 1    CC 0.3249235 0.02397959 0.6010122 0.0002040816  2.2267347
## 2 OC-CO 0.1600430 0.14510204 0.1601661 0.0001020408  0.5654082
## 3    OO 0.3138484 0.09397959 0.5946116 0.0002040816  2.2395918
## 4    OT 4.9497279 0.29571429 8.3142032 0.0047959184 14.5486735

kable(all,row.names = FALSE)

kable(top10,row.names = FALSE)

impact_anno_x<-merge(impact_anno,unique(coverage_ann[,c("x","ann_id")]),by.x = "TEMP_ANN",by.y = "x")
print(xtable(all[,c("ann_id","impact","CAT")],digits=c(3)),include.rownames=FALSE,file="impact_ann.tex")
print(xtable(top10[,c("ann_id","impact","CAT")],digits=c(2)),include.rownames=FALSE,file="impact_ann_top10.tex")

Impact: Attributes

impact_feat<-compute_impact_feat(df[c("O","CAT","ONT_TOTAL_AX","TEMP_ANN_ALL")])

## Warning in "\"\"": attributes are not identical across measure variables;
## they will be dropped

plotRanks(impact_feat,"TEMP_FEAT","impact",xlab="Temporal Feature",ylab="Impact (Basic)",ncol = 1)

ggsave("impact_feat.jpg",width = 3,height = 4,units="in")

top10<-head(impact_feat[order(-impact_feat$impact),],10)
top10attim<-top10$TEMP_FEAT
all<-impact_feat[order(impact_feat$CAT,-impact_feat$impact),]

# Summary stats over ALL temporal features
summary(all$impact)

##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
##  0.000204  0.095510  0.560900  2.069000  1.916000 14.850000

sd(all$impact)

## [1] 3.692601

# Summary stats BY DOMAIN RANGE over ALL temporal features
ddply(all,~CAT,summarise,mean=mean(impact),median=median(impact),sd=sd(impact), min=min(impact),max=max(impact))

##     CAT       mean     median        sd          min       max
## 1    CC  2.0942363  0.8082653 2.8314447 0.0006122449 10.397653
## 2 OC-CO  0.6648299  0.5172449 0.6285685 0.0663265306  2.375918
## 3    OO  0.8335842  0.1363776 1.4952231 0.0002040816  4.393469
## 4    OT 11.7601224 14.5534694 6.4100931 0.2957142857 14.849286

kable(all,row.names = FALSE)

kable(top10,row.names = FALSE)

#write.csv(impact_feat,file="impact_feat.csv")
#print(xtable(impact_feat[order(impact_feat$CAT,-impact_feat$impact),],digits=c(0,0,2,2)),include.rownames=FALSE)

print(xtable(all[,],digits=c(3)),include.rownames=FALSE,file="impact_att.tex")
print(xtable(top10[,],digits=c(2)),include.rownames=FALSE,file="impact_att_top10.tex")

Correlation: Coverage and Impact

plotCovImRel(coverage_rel,impact_rel,"RO_REL",top10relcov,top10relim)

## Warning in "\"\"": Transformation introduced infinite values in continuous
## x-axis

ggsave("cor_rel_imp_cov.jpg",width = 3,height = 4,units="in")

## Warning in "\"\"": Transformation introduced infinite values in continuous
## x-axis

plotCovImRel(coverage_ann,impact_anno,"TEMP_ANN",top10anncov,top10annim)

ggsave("cor_ann_imp_cov.jpg",width = 3,height = 4,units="in")

plotCovImRel(coverage_feat,impact_feat,"TEMP_FEAT",top10attcov,top10attim)

ggsave("cor_feat_imp_cov.jpg",width = 3,height = 4,units="in")

rel<-merge(coverage_rel[,c("x","coverage","CAT")],impact_rel[,c("RO_REL","impact","CAT")],by.x=c("x","CAT"),by.y=c("RO_REL","CAT"))
ann<-merge(coverage_ann[,c("ann_id","coverage","CAT")],impact_anno[,c("ann_id","impact","CAT")],by=c("ann_id","CAT"))
att<-merge(coverage_feat[,c("x","coverage","CAT")],impact_feat[,c("TEMP_FEAT","impact","CAT")],by.x=c("x","CAT"),by.y=c("TEMP_FEAT","CAT"))

# Actual correlations of impact and coverage for temporal features
cor(rel$coverage,rel$impact,method="pearson")

## [1] 0.8307543

cor(ann$coverage,ann$impact,method="pearson")

## [1] 0.8428761

cor(att$coverage,att$impact,method="pearson")

## [1] 0.9068331

Requirements analysis pre-processing

## Warning in "\"\"": attributes are not identical across measure variables;
## they will be dropped

Requirements Analysis

asis_output("## Requirments Overview")

languages$ID<-as.numeric(gsub("R","",languages$LID))
languages<-languages[order(languages$ID),]
kable(languages,row.names = FALSE)

#print(xtable(languages,digits=c(0,0,2)),include.rownames=FALSE)
print(xtable(languages[c("LID","L")],digits=c(0,0,2)),include.rownames=FALSE,file="languages.tex")

asis_output("## Annotations Overview")

kable(annotations,row.names = FALSE)

l<-annotations
l$TEMP_ANN<-gsub("~"," ",l$TEMP_ANN)
l$TEMP_INF_ANN<-gsub("~"," ",l$TEMP_INF_ANN)
print(xtable(l,digits=c(0,0,0,0)),include.rownames=FALSE,file="annotations.tex")

asis_output("## Requirements Importance Overview")

kable(ct_need_suff,row.names = FALSE)