doseresNMA:

#' Data preparation
#' @description Puts data into appropriate form for subsequent analysis using BUGSnet.
#' @param arm.data Data with 1 row for each study arm.
#' @param varname.t A string indicating the name of the treatment variable.
#' @param varname.s A string indicating the name of the study variable.
#'
#' @return \code{data.prep} returns an object of class \code{BUGSnetData} which is a list containing the following components:
#' @return \code{arm.data} - A tibble containing the arm level study data
#' @return \code{treatments} - A list of all treatments in the network
#' @return \code{studies} - A list of all studies in the network
#' @return \code{n.arms} - A tibble containing the number of arms for each study
#' @return \code{varname.t} - A string containing the name of the treatment variable
#' @return \code{varname.s} - A string containing the name of the study variable
#'
#' @examples
#' data(diabetes.sim)
#'
#' diabetes.slr <- data.prep(arm.data = diabetes.sim,
#' varname.t = "Treatment",
#' varname.s = "Study")
#'
#' diabetes.slr$arm.data
#' diabetes.slr$treatments
#' diabetes.slr$studies
#' @export
#' @seealso \code{\link{net.plot}}, \code{\link{net.tab}}, \code{\link{data.plot}}, \code{\link{nma.model}}


data.prep <- function(arm.data,
                      #patient.data = NULL,
                      varname.t,
                      varname.s
){

  varname.t.quo <- rlang::quo(!! as.name(varname.t))
  varname.s.quo <- rlang::quo(!! as.name(varname.s))


  arm.data %<>% mutate(!! varname.t.quo := as.character(!! varname.t.quo),
                       !! varname.s.quo := as.character(!! varname.s.quo))

  bdata <- structure(list(arm.data=arm.data,
                          #patient.data = patient.data,
                          treatments=arm.data %>% select(varname.t) %>% unique %>% arrange(!! varname.t.quo),
                          studies=arm.data %>% select(varname.s) %>% unique %>% arrange(!! varname.s.quo),
                          n.arms= arm.data %>% group_by(!! varname.s.quo) %>% summarize(n.arms = n()) %>% ungroup() %>% arrange,
                          varname.t = varname.t,
                          varname.s = varname.s),
                     class = "BUGSnetData")

  return(bdata)
}
###CURRENT ISSUES
####1) comparison.charac() needs overall mean for continuous variables

network.charac <- function(data.nma, outcome, N, type.outcome, time){

  # Check if number of events and participants is integer
  tmp.check1 <- data.nma$arm.data %>% select(outcome)
  tmp.check2 <- data.nma$arm.data %>% select(N)

  if(type.outcome == "binomial" && all(tmp.check1%%1!=0)) {
    stop('The "outcome" variable (number of events) must be an integer')
  }
  if(all(tmp.check2%%1!=0)) {
    stop('The "N" variable (number of participants) must be an integer')
  }

  outcome2 <- rlang::quo(!! as.name(outcome))#this may be redundant now
  N2 <- rlang::quo(!! as.name(N))

  n.interventions <- data.nma$arm.data %>% select(data.nma$varname.t) %>% unique() %>% nrow()

  n.studies <- data.nma$arm.data %>% select(data.nma$varname.s) %>% unique() %>% nrow()

  n.patients <- data.nma$arm.data %>% select(N) %>% sum()

  tmp1 <- data.nma$arm.data %>%
    select(data.nma$varname.s, data.nma$varname.t) %>%
    nest(data=c(data.nma$varname.t))

  cnt <- data.nma$arm.data %>%
    select(data.nma$varname.s, data.nma$varname.t) %>%
    count_(data.nma$varname.s)

  tmp1 <- bind_cols(tmp1, cnt) %>%
    filter(n>1)
  pairs <- tmp1[1,"data"] %>% unlist %>% sort %>% combn(2)
  for(i in 2:nrow(tmp1)){
    pairs <- tmp1[i,"data"] %>% unlist %>% sort %>% combn(2) %>% cbind(pairs)
  }

  n.pairwise.direct <- unique(pairs, MARGIN=2) %>% ncol()

  edgesANDnodes <- network.structure(data.nma)
  edges <- edgesANDnodes[[1]]
  nodes <- edgesANDnodes[[2]]
  net <- graph_from_data_frame(d=edges, vertices=nodes, directed=F)

  n.pairwise <- sum(sapply(clusters(net)$csize, function(n) n*(n-1)/2))

  tmp2 <- data.nma$arm.data %>% select(data.nma$varname.s) %>% table()
  n.2arm.studies <- sum(tmp2 == 2)

  n.multiarm.studies <- sum(tmp2 > 2)

  tble <- tibble(Characteristic=c("Number of Interventions",
                                  "Number of Studies",
                                  "Total Number of Patients in Network",
                                  "Total Possible Pairwise Comparisons",
                                  "Total Number of Pairwise Comparisons With Direct Data",
                                  "Is the network connected?",
                                  "Number of Two-arm Studies",
                                  "Number of Multi-Arms Studies"),
                 Value= c(n.interventions %>% as.character,
                          n.studies,
                          n.patients,
                          n.pairwise,
                          n.pairwise.direct,
                          is.connected(net),
                          n.2arm.studies,
                          n.multiarm.studies))

  if(type.outcome=="binomial"){

    n.events <- data.nma$arm.data %>% select(outcome) %>% sum

    tmp3 <- data.nma$arm.data %>%
      select(data.nma$varname.s, outcome) %>%
      mutate(event0=(as.integer((!! outcome2)==0)), one=1) %>%
      group_by_(data.nma$varname.s) %>%
      summarise(s=sum(event0), n.arms=sum(one)) %>%
      mutate(no.0=(s==0), some.0=(s!=0), all.0=(s==n.arms))

    n.studies.no.0 <- tmp3$no.0 %>% sum

    n.studies.some.0 <- tmp3$some.0 %>% sum

    n.studies.all.0 <- tmp3$all.0 %>% sum

    add.tble <- tibble(Characteristic=c("Total Number of Events in Network",
                                        "Number of Studies With  No Zero Events",
                                        "Number of Studies With At Least One Zero Event",
                                        "Number of Studies with All Zero Events"),
                       Value= c(n.events %>% as.character,
                                n.studies.no.0,
                                n.studies.some.0,
                                n.studies.all.0))

    return(bind_rows(tble,add.tble))

  }
  if(type.outcome %in% c("rate", "rate2")){

    n.events <- data.nma$arm.data %>% select(outcome) %>% sum

    tmp3 <- data.nma$arm.data %>%
      select(data.nma$varname.s, outcome) %>%
      mutate(event0=(as.integer((!! outcome2)==0)), one=1) %>%
      group_by_(data.nma$varname.s) %>%
      summarise(s=sum(event0), n.arms=sum(one)) %>%
      mutate(no.0=(s==0), some.0=(s!=0), all.0=(s==n.arms))

    n.studies.no.0 <- tmp3$no.0 %>% sum

    n.studies.some.0 <- tmp3$some.0 %>% sum

    n.studies.all.0 <- tmp3$all.0 %>% sum

    mean.person.time.Fup <- data.nma$arm.data %>% select(time) %>% colMeans %>% round(digits=2)
    str.mean.person.time.Fup <- ifelse(type.outcome=="rate", "Mean person-time of studies", "Mean person follow up time")

    add.tble <- tibble(Characteristic=c("Total Number of Events in Network",
                                        "Number of Studies With  No Zero Events",
                                        "Number of Studies With At Least One Zero Event",
                                        "Number of Studies with All Zero Events",
                                        str.mean.person.time.Fup),
                       Value= c(n.events %>% as.character,
                                n.studies.no.0,
                                n.studies.some.0,
                                n.studies.all.0,
                                mean.person.time.Fup %>% as.character))

    return(bind_rows(tble,add.tble))

  }
  if (type.outcome=="continuous"){

    just.N <- data.nma$arm.data %>% select(N)
    just.outcome <- data.nma$arm.data %>% select(outcome)

    total <- sum(just.N*just.outcome)

    w.average <- total/n.patients

    add.tble <- tibble(Characteristic="Average Outcome",
                       Value=signif(w.average, digits=4) %>% as.character)

    return(bind_rows(tble,add.tble))
  }
}

intervention.charac <- function(data.nma, outcome, N, type.outcome, time=NULL) {
  outcome2 <- rlang::quo(!! as.name(outcome))
  N2 <- rlang::quo(!! as.name(N))

  if ("n" %in% colnames(data.nma$arm.data)) {
    n.studies <- data.nma$arm.data %>% select(-n) %>% count_(data.nma$varname.t) %>% rename(n.studies = n)
  } else {
    n.studies <- data.nma$arm.data %>% count_(data.nma$varname.t) %>% rename(n.studies = n)
  }

  n.patients <- data.nma$arm.data %>%
    group_by_(data.nma$varname.t) %>%
    summarise(n.patients = as.integer(sum(!! N2)))

  tmp.tbl <- left_join(n.studies, n.patients, by=data.nma$varname.t)

  if(type.outcome %in% c("bin","binom","binomial","binary")){

    n.events <- data.nma$arm.data %>%
      group_by_(data.nma$varname.t) %>%
      summarise(n.events = as.integer(sum(!! outcome2)))

    tmp.rate <- data.nma$arm.data %>%
      mutate(tmp.rate = (!! outcome2) / (!! N2)) %>%
      group_by_(data.nma$varname.t) %>%
      summarise(min.outcome = min(tmp.rate), max.outcome = max(tmp.rate))

    tmp.tbl <- left_join(n.studies, n.events, by=data.nma$varname.t) %>%
      left_join(., n.patients, by=data.nma$varname.t) %>%
      left_join(., tmp.rate, by=data.nma$varname.t) %>%
      mutate(av.outcome=n.events/n.patients)

  } else if(type.outcome %in% c("cont", "continuous")){

    tmp.outcome <- data.nma$arm.data %>%
      mutate(tmp.outcome = (!! outcome2), w.outcome=(!! outcome2)*(!! N2)) %>%
      group_by_(data.nma$varname.t) %>%
      summarise(min.outcome = min(tmp.outcome), max.outcome = max(tmp.outcome), w.outcome=sum(w.outcome))

    tmp.tbl <- left_join(n.studies, n.patients, by=data.nma$varname.t) %>%
      left_join(., tmp.outcome, by=data.nma$varname.t) %>%
      mutate(av.outcome=w.outcome/n.patients) %>%
      select(-w.outcome)

  } else if (type.outcome =="rate"){
    time2 <- rlang::quo(!! as.name(time))

    person.time <- data.nma$arm.data %>%
      group_by_(data.nma$varname.t) %>%
      summarise(person.time.fup = as.integer(sum(!! time2)))

    n.events <- data.nma$arm.data %>%
      group_by_(data.nma$varname.t) %>%
      summarise(n.events = as.integer(sum(!! outcome2)))

    tmp.proportion <- data.nma$arm.data %>%
      mutate(tmp.proportion = (!! outcome2) / (!! N2)) %>%
      group_by_(data.nma$varname.t) %>%
      summarise(min.proportion = min(tmp.proportion), max.proportion = max(tmp.proportion))

    tmp.rate <- data.nma$arm.data %>%
      mutate(tmp.rate = (!! outcome2) / (!! time2)) %>%
      group_by_(data.nma$varname.t) %>%
      summarise(min.event.rate = min(tmp.rate), max.event.rate = max(tmp.rate))

    tmp.tbl <- left_join(n.studies, n.events, by=data.nma$varname.t) %>%
      left_join(., n.patients, by=data.nma$varname.t) %>%
      #left_join(., tmp.proportion, by=data.nma$varname.t) %>%
      left_join(., person.time, by=data.nma$varname.t) %>%
      left_join(., tmp.rate, by=data.nma$varname.t) %>%
      mutate(events.per.person=n.events/n.patients) %>%
      mutate(av.event.rate=n.events/person.time.fup)
  } else if (type.outcome =="rate2"){
    time2 <- quo(!! as.name(time))

    person.time <- data.nma$arm.data %>%
      group_by_(data.nma$varname.t) %>%
      summarise(person.time.fup = as.integer(sum((!! time2)*(!! N2))))

    n.events <- data.nma$arm.data %>%
      group_by_(data.nma$varname.t) %>%
      summarise(n.events = as.integer(sum(!! outcome2)))

    tmp.rate <- data.nma$arm.data %>%
      mutate(tmp.rate = (!! outcome2) / ((!! time2)*(!! N2))) %>%
      group_by_(data.nma$varname.t) %>%
      summarise(min.event.rate = min(tmp.rate), max.event.rate = max(tmp.rate))

    tmp.tbl <- left_join(n.studies, n.events, by=data.nma$varname.t) %>%
      left_join(., n.patients, by=data.nma$varname.t) %>%
      #left_join(., tmp.proportion, by=data.nma$varname.t) %>%
      left_join(., person.time, by=data.nma$varname.t) %>%
      left_join(., tmp.rate, by=data.nma$varname.t) %>%
      mutate(events.per.person=n.events/n.patients) %>%
      mutate(overall.event.rate=n.events/person.time.fup)
  }

  return(tmp.tbl)
}

comparison.charac <- function(data.nma, outcome, N, type.outcome, time=NULL) {

  tmp1 <- by.comparison(data.nma=data.nma, outcome=outcome, type.outcome, N=N, time=time)

  add.patients <- tmp1 %>% select(paste0(N,".e"), paste0(N,".c")) %>% rowSums()
  add.patients <- cbind.data.frame(tmp1$comparison, add.patients, stringsAsFactors = FALSE)
  names(add.patients) <- c("comparison", "n.patients")
  n.patients <- add.patients %>% group_by(comparison) %>% summarise(n.patients = as.integer(sum(n.patients)))

  add.outcomes <- tmp1 %>% select(paste0(outcome,".e"), paste0(outcome,".c")) %>% rowSums()
  add.outcomes <- cbind.data.frame(tmp1$comparison, add.outcomes, stringsAsFactors = FALSE)
  names(add.outcomes) <- c("comparison", "n.outcomes")
  n.outcomes <- add.outcomes %>% group_by(comparison) %>% summarise(n.outcomes = as.integer(sum(n.outcomes)))

  n.studies <- tmp1 %>% count(comparison) %>% rename(n.studies=n)

  tmp2 <- left_join (n.studies, n.patients, by="comparison")

  if (type.outcome %in% c("bin","binom","binomial","binary")){
    tmp2 %<>% left_join(., n.outcomes, by="comparison")%>%
      mutate(proportion=n.outcomes/n.patients)
  } else if(type.outcome == "rate"){
    add.time <- tmp1 %>% select(paste0(time,".e"), paste0(time,".c")) %>% rowSums()
    add.time <- cbind.data.frame(tmp1$comparison, add.time, stringsAsFactors = FALSE)
    names(add.time) <- c("comparison", "n.time")

    n.time <- add.time %>% group_by(comparison) %>% summarise(patient_time = as.integer(sum(n.time)))

    tmp2 %<>% left_join(.,n.outcomes, by="comparison")%<>%
      left_join(., n.time, by="comparison") %>%
      mutate(proportion=n.outcomes/n.patients)%>%
      mutate(event.rate=n.outcomes/patient_time)

  } else if(type.outcome == "rate2"){
    add.time <- tmp1 %>% select(paste0(time,".e"), paste0(time,".c")) %>% rowSums()
    add.time <- cbind.data.frame(tmp1$comparison, add.time, stringsAsFactors = FALSE)
    names(add.time) <- c("comparison", "n.time")

    n.time <- add.time %>% group_by(comparison) %>% summarise(patient_time = as.integer(sum(n.time)))

    tmp2 %<>% left_join(.,n.outcomes, by="comparison")%<>%
      left_join(., n.time, by="comparison") %>%
      mutate(proportion=n.outcomes/n.patients)%>%
      mutate(patient_time=patient_time * n.patients)%>%
      mutate(event.rate=n.outcomes/patient_time)

  } else if (type.outcome =="continuous"){
    #total.outcome <- tmp1%>%
    #mutate(total.outcome=(paste0(outcome,".e")*paste0(N,".e")+ paste0(outcome,".c")*paste0(N,".c"))/(paste0(N,".e")+paste0(N,".c") ))

    #tmp2 %<>% left_join(., n.outcomes, by="comparison")

  }
  return(tmp2)
}

#' Generate Network Characteristics
#' @description Generates tables of network characteristics
#' @param data A \code{BUGSnetData} object produced by \code{data.prep()}
#' @param outcome A string indicating the name of the outcome variable
#' @param N A string indicating the name of the variable containing the number of participants in each arm
#' @param type.outcome A string. Options are: "binomial", "continuous", "rate" (e.g # of events and # person-time reported),
#' "rate2" (e.g # events and followup time reported)
#' @param time A string required when type.outcome = "rate" or "rate2". Name of variable
#'   indicating person-time followup (e.g person years) or study followup time
#' @return \code{network} - Table of network characteristics
#' @return \code{intervention} - Summary statistics broken down by treatment
#' @return \code{comparison} - Summary statistics broken down by treatment comparison
#' @examples
#' data(diabetes.sim)
#'
#' diabetes.slr <- data.prep(arm.data = diabetes.sim,
#' varname.t = "Treatment",
#' varname.s = "Study")
#'
#' network.char <- net.tab(data = diabetes.slr,
#' outcome = "diabetes",
#' N = "n",
#' type.outcome = "rate2",
#' time = "followup")
#' @seealso \code{\link{data.prep}}
#' @export



net.tab <- function(data, outcome, N, type.outcome, time=NULL){
  return(list(network = network.charac(data, outcome, N, type.outcome,time),
              intervention = intervention.charac(data, outcome, N, type.outcome, time),
              comparison = comparison.charac(data, outcome, N, type.outcome, time)))

  if(class(data) != "BUGSnetData")
    stop("\'data\' must be a valid BUGSnetData object created using the data.prep function.")
}