R/subProcessors.R

In jsicherman/Gemma-API: A wrapper for Gemma's Restful API to access curated gene expression data and differential expression analyses

#' @keywords internal
processDate <- function(x){
    switch(as.character(is.character(x)),
           `TRUE` = lubridate::ymd_hms(x), # parse ISO 8601 format
           `FALSE` = as.POSIXct(x / 1e3, origin = "1970-01-01"))
}


read_gzipped_tsv <- function(content){

    tmp <- tempfile() # Make a temp file
    writeBin(content, tmp) # Save to that file
    tmp2 <- gzfile(tmp)
    ret <- tmp2 %>%
        readLines() %>%
        .[which(!startsWith(., "#"))[1]:length(.)] %>%
        # Strip comments
        paste0(collapse = "\n") %>%
        paste0('\n') %>%
        {
            fread(text = .)
        }
    close(tmp2)
    unlink(tmp)
    
    return(ret)
}


#' Replace missing data with NAs
#' @param x Data
#' @param natype type of NA to replace the missing data with
#' @return Data or NA in case of an out of bounds error
#'
#' @keywords internal
checkBounds <- function(x,natype = NA){
    tryCatch(x, error = function(e){
        if(e$message == "subscript out of bounds"){
            return(natype)
        } else{
            stop(e$message)
        }
    })
}


#' Access the field in a list
#'
#' This function accesses named field within the elements of a list. If an element
#' lacks the field, it's filled in by natype.
#'
#' @param d Input data list
#' @param field Field name to access in each element
#' @param natype What to fill in when field is unavailable
#' @return A vector of elements
#' @keywords internal
accessField <- function(d, field, natype = NA){
    field <- lapply(d,function(e){
        out <- checkBounds(e[[field]], natype)
        if(is.null(out)){
            out <- natype
        }
        return(out)
    }) %>% unlist
    if(is.null(field)){
        field <- array(dim=0)
    }
    return(field)
}

#' Avoid NULLS as data.table columns
#'
#' @param x A value that might be null
#' @param natype What to fill in when data is unavailable
#' @return x as is or natypee
#' @keywords internal
nullCheck <- function(x,natype= NA){
    if(is.null(x)){
        return(natype)
    } else{
        return(x)
    }
}


#' Check for empty arguments
#' 
#' Gemma functions accept typed NAs instead of simple NULLS. I believe this was
#' done as a non-standard-in-R way of specifying data types for the users. Usually
#' this is fine but it makes checking for emptyness a bit annoying since a user
#' can still provide NULLs to make them empty
#' @param x A parameter that can be NA or NULL when empty
#' @keywords internal
isEmpty <- function(x){
    is.null(x) || all(is.na(x))
}

#' Processes JSON as a factor
#'
#' @param d The JSON to process
#'
#' @return A processed data.table
#'
#' @keywords internal
processCharacteristicValueObject <- function(d){
    data.table(
        category = d %>% accessField('category',NA_character_),
        category.URI = d %>% accessField('categoryUri',NA_character_),
        value = d %>% accessField('value',NA_character_),
        value.URI = d %>% accessField('valueUri',NA_character_),
        value.ID = d %>% accessField('valueId',NA_character_)
    )
}


processStatementValueObject <- function(d){
    data.table(
        category = d %>% accessField('category',NA_character_),
        category.URI = d %>% accessField('categoryUri',NA_character_),
        subject = d %>% accessField('subject', NA_character_),
        subject.URI = d %>% accessField('subjectUri',NA_character_),
        subject.ID = d %>% accessField('subjectId',NA_character_),
        predicate = d %>% accessField('predicate',NA_character_),
        predicate.URI = d %>% accessField('predicateUri',NA_character_),
        object = d %>% accessField('object',NA_character_),
        object.URI = d %>% accessField('objectUri',NA_character_)
    )
}


processFactorValueValueObject <- function(d){
    processFactorValueBasicValueObject(d)
}



processFactorValueBasicValueObject <- function(d){
    if(is.null(d)){
        return(data.table(
            category = array(dim=0),
            category.URI = array(dim=0),
            value = array(dim=0),
            value.URI = array(dim=0),
            predicate = array(dim=0),
            predicate.URI = array(dim=0),
            object = array(dim=0),
            object.URI = array(dim=0),
            summary = array(dim=0),
            ID = array(dim = 0),
            factor.ID = array(dim=0),
            factor.category = array(dim=0),
            factor.category.URI = array(dim=0)
        ))
    } else if(!is.null(d$measurement)){
        data.table(
            category = nullCheck(d$category,natype = NA_character_),
            category.URI = nullCheck(d$categoryUri,natype = NA_character_),
            value = nullCheck(d$measurement$value,NA_character_),
            value.URI = NA_character_,
            predicate = NA_character_,
            predicate.URI = NA_character_,
            object = NA_character_,
            object.URI = NA_character_,
            summary = NA_character_,
            ID = d$id %>% nullCheck(NA_integer_),
            factor.ID = d$experimentalFactorId %>% nullCheck(NA_integer_),
            factor.category = d$experimentalFactorCategory$category %>% nullCheck(NA_character_),
            factor.category.URI = d$experimentalFactorCategory$categoryUri %>% nullCheck(NA_character_)
        )
        
    } else{

        characteristics <- d$characteristics %>% processCharacteristicValueObject()
        statements <- d$statements %>% processStatementValueObject()
        # remove characteristics already covered by statements
        characteristics <- characteristics[!characteristics$value.ID %in% statements$subject.ID,]
        # edit characteristics fields to match statements
        statements %>% data.table::setnames(old = c("subject",'subject.URI','subject.ID'),
                                            new = c("value","value.URI","value.ID"),skip_absent = TRUE)
        out <- rbind(characteristics,statements,fill= TRUE)
        out$summary <- d$summary %>% nullCheck(NA_character_)
        out$ID <- d$id %>% nullCheck(NA_integer_)
        out$factor.ID <- d$experimentalFactorId %>% nullCheck(NA_character_)
        out$factor.category <- d$experimentalFactorCategory$category %>% nullCheck(NA_character_)
        out$factor.category.URI <- d$experimentalFactorCategory$categoryUri %>% nullCheck(NA_character_)
        out <- out[,!"value.ID"]
        
        
        return(out)
    }
}


#' A blank processor that returns data as is
#'
#' @param data any data
#' @return Data as is
#' @keywords internal
blank_processor <- function(data){
    return(data)
}