R/gtr_project_person_function.R

In MatthewSmith430/GtR: Access Gateway to Research data via the API

#' @title gtr_project_person
#'
#' @description This function extracts project information, including individual collaborators
#' @param url project URL from gtr.ukri.org
#' @export
#' @return Dataframe with projects and all individual collaborators

gtr_project_person<-function(url){
  firmTEST<-httr::GET(url)
  firmTEXT<-httr::content(firmTEST, as="text")
  JLfirm<-jsonlite::fromJSON(firmTEXT, flatten=TRUE)

  ORG<-JLfirm$projectOverview$projectComposition

  pt<-ORG$project$title

  imp<-ORG$project$output

  PUB<-nrow(ORG$project$publication)
  if (purrr::is_empty(PUB)==TRUE){
    PUB<-0
  }else{PUB<-PUB}

  if(purrr::is_empty(imp)==TRUE){
    names_imp1<-c("collaborationOutput",
                  "intellectualPropertyOutput",
                  "policyInfluenceOutput",
                  "productOutput",
                  "researchMaterialOutput",
                  "artisticAndCreativeProductOutput",
                  "softwareAndTechnicalProductOutput",
                  "researchDatabaseAndModelOutput",
                  "spinOutOutput",
                  "impactSummaryOutput",
                  "furtherFundingOutput",
                  "otherResearchOutput",
                  "exploitationOutput",
                  "disseminationOutput")
    imp_info<-dplyr::tibble(name=names_imp1,len=0)
  }else{
    imp_l<-list()
    for (j in 1:length(imp)){
      imp_l[[j]]<-length(imp[[j]])
    }
    imp_info<-data.frame(name=names(imp),len=unlist(imp_l))
  }



  CO<-dplyr::filter(imp_info,
                    imp_info$name=="collaborationOutput")
  if(CO$len!=0){
    CO_imp<-nrow(imp[["collaborationOutput"]])
  }else{
    CO_imp<-0
  }

  IP<-dplyr::filter(imp_info,
                    imp_info$name=="intellectualPropertyOutput")
  if(IP$len!=0){
    IP_imp<-nrow(imp[["intellectualPropertyOutput"]])
  }else{
    IP_imp<-0
  }

  POL<-dplyr::filter(imp_info,
                     imp_info$name=="policyInfluenceOutput")
  if(POL$len!=0){
    POL_imp<-nrow(imp[["policyInfluenceOutput"]])
  }else{
    POL_imp<-0
  }

  PROD<-dplyr::filter(imp_info,
                      imp_info$name=="productOutput")
  if(PROD$len!=0){
    PROD_imp<-nrow(imp[["productOutput"]])
  }else{
    PROD_imp<-0
  }

  RM<-dplyr::filter(imp_info,
                    imp_info$name=="researchMaterialOutput")
  if(RM$len!=0){
    RM_imp<-nrow(imp[["researchMaterialOutput"]])
  }else{
    RM_imp<-0
  }

  ART<-dplyr::filter(imp_info,
                     imp_info$name=="artisticAndCreativeProductOutput")
  if(ART$len!=0){
    ART_imp<-nrow(imp[["artisticAndCreativeProductOutput"]])
  }else{
    ART_imp<-0
  }

  SOFT<-dplyr::filter(imp_info,
                      imp_info$name=="softwareAndTechnicalProductOutput")
  if(SOFT$len!=0){
    SOFT_imp<-nrow(imp[["softwareAndTechnicalProductOutput"]])
  }else{
    SOFT_imp<-0
  }

  RESDATA<-dplyr::filter(imp_info,
                         imp_info$name=="researchDatabaseAndModelOutput")
  if(RESDATA$len!=0){
    RESDATA_imp<-nrow(imp[["researchDatabaseAndModelOutput"]])
  }else{
    RESDATA_imp<-0
  }

  SPIN<-dplyr::filter(imp_info,
                      imp_info$name=="spinOutOutput")
  if(SPIN$len!=0){
    SPIN_imp<-nrow(imp[["spinOutOutput"]])
  }else{
    SPIN_imp<-0
  }

  SUM<-dplyr::filter(imp_info,
                     imp_info$name=="impactSummaryOutput")
  if(SUM$len!=0){
    SUM_imp<-nrow(imp[["impactSummaryOutput"]])
  }else{
    SUM_imp<-0
  }

  FUR<-dplyr::filter(imp_info,
                     imp_info$name=="furtherFundingOutput")
  if(FUR$len!=0){
    FUR_imp<-nrow(imp[["furtherFundingOutput"]])
  }else{
    FUR_imp<-0
  }

  ORO<-dplyr::filter(imp_info,
                     imp_info$name=="otherResearchOutput")
  if(ORO$len!=0){
    ORO_imp<-nrow(imp[["otherResearchOutput"]])
  }else{
    ORO_imp<-0
  }

  EXPL<-dplyr::filter(imp_info,
                      imp_info$name=="exploitationOutput")
  if(EXPL$len!=0){
    EXPL_imp<-nrow(imp[["exploitationOutput"]])
  }else{
    EXPL_imp<-0
  }

  DISS<-dplyr::filter(imp_info,
                      imp_info$name=="disseminationOutput")
  if(DISS$len!=0){
    DISS_imp<-nrow(imp[["disseminationOutput"]])
  }else{
    DISS_imp<-0
  }

  top<-ORG$project$researchTopic
  sub1<-ORG$project$researchSubject

  PROJ<-tibble::tibble(project_title=ORG$project$title,
                       project_ref=ORG$project$grantReference,
                       abstract_text=ORG$project$abstractText,
                       project_value=ORG$project$fund$valuePounds,
                       start_date=ORG$project$fund$start,
                       end_date=ORG$project$fund$end,
                       topic=paste(top$text,collapse="_"),
                       subject=paste(sub1$text,collapse="_"),
                       num_collaboration_output=CO_imp,
                       num_intellectual_property_output=IP_imp,
                       num_policy_influence_output=POL_imp,
                       num_research_material_output=RM_imp,
                       num_artistic_creative_product_output=ART_imp,
                       num_software_technical_product_output=SOFT_imp,
                       num_research_database_model_output=RESDATA_imp,
                       num_spinout_output=SPIN_imp,
                       num_impact_summary_output=SUM_imp,
                       num_further_funding_output=FUR_imp,
                       num_other_research_output=ORO_imp,
                       num_exploitation_output=EXPL_imp,
                       num_dissemination_output=DISS_imp,
                       num_publications=PUB
  )
  #OM<-ORG$organisationRole
  PR<-ORG$personRole




  OMR<- sapply(unlist(PR$role,
                      recursive=FALSE, use.names=FALSE), "[", 1)
  PersonRole<-tibble::tibble(person_id=unlist(PR$id),
                         first_name=unlist(PR$firstName),
                         surname=unlist(PR$surname),
                         #other_name=unlist(PR$otherNames),
                      person_role=OMR)
  #OM$
  first_g<-gender::gender(names=PersonRole$first_name,
                  method="ssa",year="2012")
  PR<-merge(PersonRole,first_g,by.x="first_name",by.y="name",all.x=TRUE)
  PersonRole<-dplyr::select(PR,-c(proportion_male,proportion_female,year_min,year_max))
  PIG<-dplyr::filter(PersonRole,PersonRole$person_role=="PRINCIPAL_INVESTIGATOR")
  if(length(PIG$person_role)==0){
    PIgender<-NA
  }else if(length(PIG$person_role)>1){
    PIgender<-PIG$gender[1]
  }else{PIgender<-PIG$gender}

  PersonRole<-dplyr::mutate(PersonRole,PI_gender=PIgender)
  DG<-table(PersonRole$gender)
  DG<-as.data.frame(DG)
  TOT_people<-sum(DG$Freq)
  DGM<-dplyr::filter(DG,DG$Var1=="male")
  TOT_male<-DGM$Freq

  DGF<-dplyr::filter(DG,DG$Var1=="female")
  TOT_female<-DGF$Freq


  if(purrr::is_empty(TOT_female)==TRUE){
    TOT_female<-0
  }else{TOT_female<-TOT_female}
  if(purrr::is_empty(TOT_male)==TRUE){
    TOT_male<-0
  }else{TOT_male<-TOT_male}

  FEM_PROP<-TOT_female/TOT_people
  PersonRole<-dplyr::mutate(PersonRole,
                            total_people=TOT_people,
                            total_male=TOT_male,
                            total_female=TOT_female,
                            proportion_female=FEM_PROP,
                            proportion_male=1-FEM_PROP)

  #NR<-nrow(PersonRole)-nrow(PROJ)
  #PROJ[nrow(PROJ)+NR,] <- NA

  DF1<-dplyr::bind_cols(PROJ,PersonRole)
  DF2<-tidyr::fill(DF1,colnames(PROJ))




  DF2$project_title<-gsub("&amp;","and",DF2$project_title)

  return(DF2)
}