# ==================================================================== #
# TITLE                                                                #
# Antimicrobial Resistance (AMR) Analysis                              #
#                                                                      #
# SOURCE                                                               #
# https://gitlab.com/msberends/AMR                                     #
#                                                                      #
# LICENCE                                                              #
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl)  #
#                                                                      #
# This R package is free software; you can freely use and distribute   #
# it for both personal and commercial purposes under the terms of the  #
# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
# the Free Software Foundation.                                        #
#                                                                      #
# This R package was created for academic research and was publicly    #
# released in the hope that it will be useful, but it comes WITHOUT    #
# ANY WARRANTY OR LIABILITY.                                           #
# Visit our website for more info: https://msberends.gitab.io/AMR.     #
# ==================================================================== #

#' Predict antimicrobial resistance
#'
#' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
#' @inheritParams first_isolate
#' @param col_ab column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})
#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already
#' @param year_min lowest year to use in the prediction model, dafaults to the lowest year in \code{col_date}
#' @param year_max highest year to use in the prediction model, defaults to 10 years after today
#' @param year_every unit of sequence between lowest year found in the data and \code{year_max}
#' @param minimum minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.
#' @param model the statistical model of choice. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} or \code{"linear"} (or \code{"lin"}).
#' @param I_as_R a logical to indicate whether values \code{I} should be treated as \code{R}
#' @param preserve_measurements a logical to indicate whether predictions of years that are actually available in the data should be overwritten by the original data. The standard errors of those years will be \code{NA}.
#' @param info a logical to indicate whether textual analysis should be printed with the name and \code{\link{summary}} of the statistical model.
#' @return \code{data.frame} with columns:
#' \itemize{
#'   \item{\code{year}}
#'   \item{\code{value}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise}
#'   \item{\code{se_min}, the lower bound of the standard error with a minimum of \code{0} (so the standard error will never go below 0\%)}
#'   \item{\code{se_max} the upper bound of the standard error with a maximum of \code{1} (so the standard error will never go above 100\%)}
#'   \item{\code{observations}, the total number of available observations in that year, i.e. S + I + R}
#'   \item{\code{observed}, the original observed resistant percentages}
#'   \item{\code{estimated}, the estimated resistant percentages, calculated by the model}
#' }
#' @seealso The \code{\link{portion}} function to calculate resistance, \cr \code{\link{lm}} \code{\link{glm}}
#' @rdname resistance_predict
#' @export
#' @importFrom stats predict glm lm
#' @importFrom dplyr %>% pull mutate mutate_at n group_by_at summarise filter filter_at all_vars n_distinct arrange case_when
#' @inheritSection AMR Read more on our website!
#' @examples
#' \dontrun{
#' # use it with base R:
#' resistance_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
#'                    col_ab = "amcl", col_date = "date")
#'
#' # or use dplyr so you can actually read it:
#' library(dplyr)
#' tbl %>%
#'   filter(first_isolate == TRUE,
#'          genus == "Haemophilus") %>%
#'   resistance_predict(amcl, date)
#' }
#'
#'
#' # real live example:
#' library(dplyr)
#' septic_patients %>%
#'   # get bacteria properties like genus and species
#'   left_join_microorganisms("mo") %>%
#'   # calculate first isolates
#'   mutate(first_isolate = first_isolate(.)) %>%
#'   # filter on first E. coli isolates
#'   filter(genus == "Escherichia",
#'          species == "coli",
#'          first_isolate == TRUE) %>%
#'   # predict resistance of cefotaxime for next years
#'   resistance_predict(col_ab = "cfot",
#'                      col_date = "date",
#'                      year_max = 2025,
#'                      preserve_measurements = TRUE,
#'                      minimum = 0)
#'
#' # create nice plots with ggplot
#' if (!require(ggplot2)) {
#'
#'   data <- septic_patients %>%
#'     filter(mo == as.mo("E. coli")) %>%
#'     resistance_predict(col_ab = "amox",
#'                        col_date = "date",
#'                        info = FALSE,
#'                         minimum = 15)
#'
#'   ggplot(data,
#'          aes(x = year)) +
#'     geom_col(aes(y = value),
#'              fill = "grey75") +
#'     geom_errorbar(aes(ymin = se_min,
#'                       ymax = se_max),
#'                   colour = "grey50") +
#'     scale_y_continuous(limits = c(0, 1),
#'                        breaks = seq(0, 1, 0.1),
#'                        labels = paste0(seq(0, 100, 10), "%")) +
#'     labs(title = expression(paste("Forecast of amoxicillin resistance in ",
#'                                   italic("E. coli"))),
#'          y = "%IR",
#'          x = "Year") +
#'     theme_minimal(base_size = 13)
#' }
resistance_predict <- function(tbl,
                               col_ab,
                               col_date,
                               year_min = NULL,
                               year_max = NULL,
                               year_every = 1,
                               minimum = 30,
                               model = 'binomial',
                               I_as_R = TRUE,
                               preserve_measurements = TRUE,
                               info = TRUE) {

  if (nrow(tbl) == 0) {
    stop('This table does not contain any observations.')
  }

  if (!col_ab %in% colnames(tbl)) {
    stop('Column ', col_ab, ' not found.')
  }

  if (!col_date %in% colnames(tbl)) {
    stop('Column ', col_date, ' not found.')
  }
  if ('grouped_df' %in% class(tbl)) {
    # no grouped tibbles please, mutate will throw errors
    tbl <- base::as.data.frame(tbl, stringsAsFactors = FALSE)
  }

  if (I_as_R == TRUE) {
    tbl[, col_ab] <- gsub('I', 'R', tbl %>% pull(col_ab))
  }

  tbl <- tbl %>%
    mutate_at(col_ab, as.rsi) %>%
    filter_at(col_ab, all_vars(!is.na(.)))
  tbl[, col_ab] <- droplevels(tbl[, col_ab])

  year <- function(x) {
    if (all(grepl('^[0-9]{4}$', x))) {
      x
    } else {
      as.integer(format(as.Date(x), '%Y'))
    }
  }

  df <- tbl %>%
    mutate(year = tbl %>% pull(col_date) %>% year()) %>%
    group_by_at(c('year', col_ab)) %>%
    summarise(n())

  if (df %>% pull(col_ab) %>% n_distinct(na.rm = TRUE) < 2) {
    stop("No variety in antimicrobial interpretations - all isolates are '",
         df %>% pull(col_ab) %>% unique() %>% .[!is.na(.)], "'.",
         call. = FALSE)
  }

  colnames(df) <- c('year', 'antibiotic', 'observations')
  df <- df %>%
    filter(!is.na(antibiotic)) %>%
    tidyr::spread(antibiotic, observations, fill = 0) %>%
    mutate(total = R + S) %>%
    filter(total >= minimum)

  if (NROW(df) == 0) {
    stop('There are no observations.')
  }

  year_lowest <- min(df$year)
  if (is.null(year_min)) {
    year_min <- year_lowest
  } else {
    year_min <- max(year_min, year_lowest, na.rm = TRUE)
  }
  if (is.null(year_max)) {
    year_max <- year(Sys.Date()) + 10
  }

  years_predict <- seq(from = year_min, to = year_max, by = year_every)

  if (model %in% c('binomial', 'binom', 'logit')) {
    logitmodel <- with(df, glm(cbind(R, S) ~ year, family = binomial))
    if (info == TRUE) {
      cat('\nLogistic regression model (logit) with binomial distribution')
      cat('\n------------------------------------------------------------\n')
      print(summary(logitmodel))
    }

    predictmodel <- predict(logitmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
    prediction <- predictmodel$fit
    se <- predictmodel$se.fit

  } else if (model == 'loglin') {
    loglinmodel <- with(df, glm(R ~ year, family = poisson))
    if (info == TRUE) {
      cat('\nLog-linear regression model (loglin) with poisson distribution')
      cat('\n--------------------------------------------------------------\n')
      print(summary(loglinmodel))
    }

    predictmodel <- predict(loglinmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
    prediction <- predictmodel$fit
    se <- predictmodel$se.fit

  } else if (model %in% c('lin', 'linear')) {
    linmodel <- with(df, lm((R / (R + S)) ~ year))
    if (info == TRUE) {
      cat('\nLinear regression model')
      cat('\n-----------------------\n')
      print(summary(linmodel))
    }

    predictmodel <- predict(linmodel, newdata = with(df, list(year = years_predict)), se.fit = TRUE)
    prediction <- predictmodel$fit
    se <- predictmodel$se.fit

  } else {
    stop('No valid model selected.')
  }

  # prepare the output dataframe
  prediction <- data.frame(year = years_predict, value = prediction, stringsAsFactors = FALSE)

  prediction$se_min <- prediction$value - se
  prediction$se_max <- prediction$value + se

  if (model == 'loglin') {
    prediction$value <- prediction$value %>%
      format(scientific = FALSE) %>%
      as.integer()
    prediction$se_min <- prediction$se_min %>% as.integer()
    prediction$se_max <- prediction$se_max %>% as.integer()

    colnames(prediction) <- c('year', 'amountR', 'se_max', 'se_min')
  } else {
    prediction$se_max[which(prediction$se_max > 1)] <- 1
  }
  prediction$se_min[which(prediction$se_min < 0)] <- 0
  prediction$observations = NA

  total <- prediction

  if (preserve_measurements == TRUE) {
    # replace estimated data by observed data
    if (I_as_R == TRUE) {
      if (!'I' %in% colnames(df)) {
        df$I <- 0
      }
      df$value <- df$R / rowSums(df[, c('R', 'S', 'I')])
    } else {
      df$value <- df$R / rowSums(df[, c('R', 'S')])
    }
    measurements <- data.frame(year = df$year,
                               value = df$value,
                               se_min = NA,
                               se_max = NA,
                               observations = df$total,
                               stringsAsFactors = FALSE)
    colnames(measurements) <- colnames(prediction)

    total <- rbind(measurements,
                   prediction %>% filter(!year %in% df$year))
    if (model %in% c('binomial', 'binom', 'logit')) {
      total <- total %>% mutate(observed = ifelse(is.na(observations), NA, value),
                                estimated = prediction$value)
    }
  }

  if ("value" %in% colnames(total)) {
    total <- total %>%
      mutate(value = case_when(value > 1 ~ 1,
                               value < 0 ~ 0,
                               TRUE ~ value))
  }

  total %>% arrange(year)

}

#' @rdname resistance_predict
#' @export
rsi_predict <- resistance_predict