resistance predict

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
 Version: 0.5.0.9009
-Date: 2019-01-12
+Date: 2019-01-15
 Title: Antimicrobial Resistance Analysis
 Authors@R: c(
     person(

NAMESPACE

@@ -20,6 +20,7 @@ S3method(kurtosis,default)
 S3method(kurtosis,matrix)
 S3method(plot,frequency_tbl)
 S3method(plot,mic)
+S3method(plot,resistance_predict)
 S3method(plot,rsi)
 S3method(print,atc)
 S3method(print,frequency_tbl)
@@ -76,6 +77,7 @@ export(g.test)
 export(geom_rsi)
 export(get_locale)
 export(ggplot_rsi)
+export(ggplot_rsi_predict)
 export(guess_ab_col)
 export(guess_atc)
 export(guess_mo)
@@ -215,6 +217,7 @@ importFrom(dplyr,mutate_all)
 importFrom(dplyr,mutate_at)
 importFrom(dplyr,n)
 importFrom(dplyr,n_distinct)
+importFrom(dplyr,n_groups)
 importFrom(dplyr,progress_estimated)
 importFrom(dplyr,pull)
 importFrom(dplyr,row_number)
@@ -228,6 +231,7 @@ importFrom(dplyr,top_n)
 importFrom(dplyr,ungroup)
 importFrom(dplyr,vars)
 importFrom(grDevices,boxplot.stats)
+importFrom(graphics,arrows)
 importFrom(graphics,axis)
 importFrom(graphics,barplot)
 importFrom(graphics,hist)

NEWS.md

@@ -12,6 +12,12 @@
 * Function `mo_renamed()` to get a list of all returned values from `as.mo()` that have had taxonomic renaming
 * Function `age()` to calculate the (patients) age in years
 * Function `age_groups()` to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic antimicrobial resistance analysis per age group.
+* Function `ggplot_rsi_predict()` as well as the base R `plot()` function can now be used for resistance prediction calculated with `resistance_predict()`:
+  ```r
+  x <- resistance_predict(septic_patients, col_ab = "amox")
+  plot(x)
+  ggplot_rsi_predict(x)
+  ```
 * Functions `filter_first_isolate()` and `filter_first_weighted_isolate()` to shorten and fasten filtering on data sets with antimicrobial results, e.g.:
   ```r
   septic_patients %>% filter_first_isolate(...)

R/eucast_rules.R

@@ -231,9 +231,8 @@ eucast_rules <- function(tbl,
 
   # try to find columns based on type
   # -- mo
-  if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
+  if (is.null(col_mo)) {
-    col_mo <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
+    col_mo <- search_type_in_df(tbl = tbl, type = "mo")
-    message(blue(paste0("NOTE: Using column `", bold(col_mo), "` as input for `col_mo`.")))
   }
   if (is.null(col_mo)) {
     stop("`col_mo` must be set.", call. = FALSE)

R/first_isolate.R

@@ -23,7 +23,7 @@
 #'
 #' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
 #' @param tbl a \code{data.frame} containing isolates.
-#' @param col_date column name of the result date (or date that is was received on the lab), defaults to the first column of class \code{Date}
+#' @param col_date column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class
 #' @param col_patient_id column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive)
 #' @param col_mo column name of the unique IDs of the microorganisms (see \code{\link{mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}.
 #' @param col_testcode column name of the test codes. Use \code{col_testcode = NULL} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored.
@@ -187,9 +187,8 @@ first_isolate <- function(tbl,
 
   # try to find columns based on type
   # -- mo
-  if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
+  if (is.null(col_mo)) {
-    col_mo <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
+    col_mo <- search_type_in_df(tbl = tbl, type = "mo")
-    message(blue(paste0("NOTE: Using column `", bold(col_mo), "` as input for `col_mo`.")))
   }
   if (is.null(col_mo)) {
     stop("`col_mo` must be set.", call. = FALSE)
@@ -197,33 +196,25 @@ first_isolate <- function(tbl,
 
   # -- date
   if (is.null(col_date)) {
-    for (i in 1:ncol(tbl)) {
+    col_date <- search_type_in_df(tbl = tbl, type = "date")
-      if ("Date" %in% class(tbl %>% pull(i)) | "POSIXct" %in% class(tbl %>% pull(i))) {
-        col_date <- colnames(tbl)[i]
-        message(blue(paste0("NOTE: Using column `", bold(col_date), "` as input for `col_date`.")))
-        break
-      }
-    }
   }
   if (is.null(col_date)) {
     stop("`col_date` must be set.", call. = FALSE)
   }
-  # convert to Date (pipes for supporting tibbles too)
+  # convert to Date (pipes/pull for supporting tibbles too)
   tbl[, col_date] <- tbl %>% pull(col_date) %>% as.Date()
 
   # -- patient id
-  if (is.null(col_patient_id) & any(colnames(tbl) %like% "^(patient|patid)")) {
+  if (is.null(col_patient_id)) {
-    col_patient_id <- colnames(tbl)[colnames(tbl) %like% "^(patient|patid)"][1]
+    col_patient_id <- search_type_in_df(tbl = tbl, type = "patient_id")
-    message(blue(paste0("NOTE: Using column `", bold(col_patient_id), "` as input for `col_patient_id`.")))
   }
   if (is.null(col_patient_id)) {
     stop("`col_patient_id` must be set.", call. = FALSE)
   }
 
   # -- key antibiotics
-  if (is.null(col_keyantibiotics) & any(colnames(tbl) %like% "^key.*(ab|antibiotics)")) {
+  if (is.null(col_keyantibiotics)) {
-    col_keyantibiotics <- colnames(tbl)[colnames(tbl) %like% "^key.*(ab|antibiotics)"][1]
+    col_keyantibiotics <- search_type_in_df(tbl = tbl, type = "keyantibiotics")
-    message(blue(paste0("NOTE: Using column `", bold(col_keyantibiotics), "` as input for `col_keyantibiotics`. Use ", bold("col_keyantibiotics = FALSE"), " to prevent this.")))
   }
   if (isFALSE(col_keyantibiotics)) {
     col_keyantibiotics <- NULL

R/key_antibiotics.R

@@ -101,9 +101,8 @@ key_antibiotics <- function(tbl,
 
   # try to find columns based on type
   # -- mo
-  if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
+  if (is.null(col_mo)) {
-    col_mo <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
+    col_mo <- search_type_in_df(tbl = tbl, type = "mo")
-    message(blue(paste0("NOTE: Using column `", bold(col_mo), "` as input for `col_mo`.")))
   }
   if (is.null(col_mo)) {
     stop("`col_mo` must be set.", call. = FALSE)
@@ -114,7 +113,6 @@ key_antibiotics <- function(tbl,
                 GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6,
                 GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6)
   col.list <- check_available_columns(tbl = tbl, col.list = col.list, info = warnings)
-  print(col.list)
   universal_1 <- col.list[universal_1]
   universal_2 <- col.list[universal_2]
   universal_3 <- col.list[universal_3]

R/mdro.R

@@ -113,9 +113,8 @@ mdro <- function(tbl,
 
   # try to find columns based on type
   # -- mo
-  if (is.null(col_mo) & "mo" %in% lapply(tbl, class)) {
+  if (is.null(col_mo)) {
-    col_mo <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
+    col_mo <- search_type_in_df(tbl = tbl, type = "mo")
-    message(blue(paste0("NOTE: Using column `", bold(col_mo), "` as input for `col_mo`.")))
   }
   if (is.null(col_mo)) {
     stop("`col_mo` must be set.", call. = FALSE)

R/misc.R

@@ -123,3 +123,48 @@ size_humanreadable <- function(bytes, decimals = 1) {
   out <- paste(sprintf(paste0("%.", decimals, "f"), bytes / (1024 ^ factor)), size[factor + 1])
   out
 }
+
+#' @importFrom crayon blue bold
+#' @importFrom dplyr %>% pull
+search_type_in_df <- function(tbl, type) {
+  # try to find columns based on type
+  found <- NULL
+
+  # -- mo
+  if (type == "mo") {
+    if ("mo" %in% lapply(tbl, class)) {
+      found <- colnames(tbl)[lapply(tbl, class) == "mo"][1]
+    }
+  }
+  # -- key antibiotics
+  if (type == "keyantibiotics") {
+    if (any(colnames(tbl) %like% "^key.*(ab|antibiotics)")) {
+      found <- colnames(tbl)[colnames(tbl) %like% "^key.*(ab|antibiotics)"][1]
+    }
+  }
+  # -- date
+  if (type == "date") {
+    for (i in 1:ncol(tbl)) {
+      if ("Date" %in% class(tbl %>% pull(i)) | "POSIXct" %in% class(tbl %>% pull(i))) {
+        found <- colnames(tbl)[i]
+        break
+      }
+    }
+
+  }
+  # -- patient id
+  if (type == "patient_id") {
+    if (any(colnames(tbl) %like% "^(patient|patid)")) {
+      found <- colnames(tbl)[colnames(tbl) %like% "^(patient|patid)"][1]
+    }
+  }
+
+  if (!is.null(found)) {
+    msg <- paste0("NOTE: Using column `", bold(found), "` as input for `col_", type, "`.")
+    if (type == "keyantibiotics") {
+      msg <- paste(msg, "Use", bold("col_keyantibiotics = FALSE"), "to prevent this.")
+    }
+    message(blue(msg))
+  }
+  found
+}

R/resistance_predict.R

@@ -23,17 +23,18 @@
 #'
 #' 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
+#' @inheritParams graphics::plot
 #' @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 col_date column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class
 #' @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 model the statistical model of choice. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} (or \code{"poisson"}) 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:
+#' @return \code{data.frame} with extra class \code{"resistance_predict"} 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}
@@ -47,42 +48,23 @@
 #' @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
+#' @importFrom dplyr %>% pull mutate mutate_at n group_by_at summarise filter filter_at all_vars n_distinct arrange case_when n_groups
 #' @inheritSection AMR Read more on our website!
 #' @examples
-#' \dontrun{
+#' x <- resistance_predict(septic_patients, col_ab = "amox", year_min = 2010)
-#' # use it with base R:
+#' plot(x)
-#' resistance_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
+#' ggplot_rsi_predict(x)
-#'                    col_ab = "amcl", col_date = "date")
 #'
-#' # or use dplyr so you can actually read it:
+#' # use dplyr so you can actually read it:
 #' library(dplyr)
-#' tbl %>%
+#' x <- septic_patients %>%
-#'   filter(first_isolate == TRUE,
+#'   filter_first_isolate() %>%
-#'          genus == "Haemophilus") %>%
+#'   filter(mo_genus(mo) == "Staphylococcus") %>%
-#'   resistance_predict(amcl, date)
+#'   resistance_predict("peni")
-#' }
+#' plot(x)
 #'
 #'
-#' # real live example:
+#' # create nice plots with ggplot yourself
-#' 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 %>%
@@ -90,7 +72,7 @@
 #'     resistance_predict(col_ab = "amox",
 #'                        col_date = "date",
 #'                        info = FALSE,
-#'                         minimum = 15)
+#'                        minimum = 15)
 #'
 #'   ggplot(data,
 #'          aes(x = year)) +
@@ -110,7 +92,7 @@
 #' }
 resistance_predict <- function(tbl,
                                col_ab,
-                               col_date,
+                               col_date = NULL,
                                year_min = NULL,
                                year_max = NULL,
                                year_every = 1,
@@ -128,23 +110,23 @@ resistance_predict <- function(tbl,
     stop('Column ', col_ab, ' not found.')
   }
 
+  # -- date
+  if (is.null(col_date)) {
+    col_date <- search_type_in_df(tbl = tbl, type = "date")
+  }
+  if (is.null(col_date)) {
+    stop("`col_date` must be set.", call. = FALSE)
+  }
+
   if (!col_date %in% colnames(tbl)) {
     stop('Column ', col_date, ' not found.')
   }
-  if ('grouped_df' %in% class(tbl)) {
+
+  if (n_groups(tbl) > 1) {
     # 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
@@ -154,13 +136,23 @@ resistance_predict <- function(tbl,
   }
 
   df <- tbl %>%
-    mutate(year = tbl %>% pull(col_date) %>% year()) %>%
+    mutate_at(col_ab, as.rsi) %>%
+    mutate_at(col_ab, droplevels) %>%
+    mutate_at(col_ab, funs(
+      if (I_as_R == TRUE) {
+        gsub("I", "R", .)
+      } else {
+        gsub("I", "S", .)
+      }
+      )) %>%
+    filter_at(col_ab, all_vars(!is.na(.))) %>%
+    mutate(year = 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(.)], "'.",
+         df %>% pull(col_ab) %>% unique(), "'.",
          call. = FALSE)
   }
 
@@ -168,8 +160,11 @@ resistance_predict <- function(tbl,
   df <- df %>%
     filter(!is.na(antibiotic)) %>%
     tidyr::spread(antibiotic, observations, fill = 0) %>%
-    mutate(total = R + S) %>%
+    filter((R + S) >= minimum)
-    filter(total >= minimum)
+  df_matrix <- df %>%
+    ungroup() %>%
+    select(R, S) %>%
+    as.matrix()
 
   if (NROW(df) == 0) {
     stop('There are no observations.')
@@ -185,41 +180,44 @@ resistance_predict <- function(tbl,
     year_max <- year(Sys.Date()) + 10
   }
 
-  years_predict <- seq(from = year_min, to = year_max, by = year_every)
+  years <- list(year = 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))
+    model <- "binomial"
+    model_lm <- with(df, glm(df_matrix ~ year, family = binomial))
     if (info == TRUE) {
       cat('\nLogistic regression model (logit) with binomial distribution')
       cat('\n------------------------------------------------------------\n')
-      print(summary(logitmodel))
+      print(summary(model_lm))
     }
 
-    predictmodel <- predict(logitmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
+    predictmodel <- predict(model_lm, newdata = years, type = "response", se.fit = TRUE)
     prediction <- predictmodel$fit
     se <- predictmodel$se.fit
 
-  } else if (model == 'loglin') {
+  } else if (model %in% c('loglin', 'poisson')) {
-    loglinmodel <- with(df, glm(R ~ year, family = poisson))
+    model <- "poisson"
+    model_lm <- 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))
+      print(summary(model_lm))
     }
 
-    predictmodel <- predict(loglinmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
+    predictmodel <- predict(model_lm, newdata = years, 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))
+    model <- "linear"
+    model_lm <- with(df, lm((R / (R + S)) ~ year))
     if (info == TRUE) {
       cat('\nLinear regression model')
       cat('\n-----------------------\n')
-      print(summary(linmodel))
+      print(summary(model_lm))
     }
 
-    predictmodel <- predict(linmodel, newdata = with(df, list(year = years_predict)), se.fit = TRUE)
+    predictmodel <- predict(model_lm, newdata = years, se.fit = TRUE)
     prediction <- predictmodel$fit
     se <- predictmodel$se.fit
 
@@ -228,64 +226,117 @@ resistance_predict <- function(tbl,
   }
 
   # prepare the output dataframe
-  prediction <- data.frame(year = years_predict, value = prediction, stringsAsFactors = FALSE)
+  df_prediction <- data.frame(year = unlist(years),
+                              value = prediction,
+                              stringsAsFactors = FALSE) %>%
 
-  prediction$se_min <- prediction$value - se
+    mutate(se_min = value - se,
-  prediction$se_max <- prediction$value + se
+           se_max = value + se)
 
-  if (model == 'loglin') {
+  if (model == 'poisson') {
-    prediction$value <- prediction$value %>%
+    df_prediction <- df_prediction %>%
-      format(scientific = FALSE) %>%
+      mutate(value = value %>%
-      as.integer()
+               format(scientific = FALSE) %>%
-    prediction$se_min <- prediction$se_min %>% as.integer()
+               as.integer(),
-    prediction$se_max <- prediction$se_max %>% as.integer()
+             se_min = as.integer(se_min),
-
+             se_max = as.integer(se_max))
-    colnames(prediction) <- c('year', 'amountR', 'se_max', 'se_min')
   } else {
-    prediction$se_max[which(prediction$se_max > 1)] <- 1
+    df_prediction <- df_prediction %>%
+      # se_max not above 1
+      mutate(se_max = ifelse(se_max > 1, 1, se_max))
   }
-  prediction$se_min[which(prediction$se_min < 0)] <- 0
+  df_prediction <- df_prediction %>%
-  prediction$observations = NA
+    # se_min not below 0
+    mutate(se_min = ifelse(se_min < 0, 0, se_min))
 
-  total <- prediction
+  df_observations <- df %>%
+    ungroup() %>%
+    transmute(year,
+              observations = R + S,
+              observed = R / (R + S))
+  df_prediction <- df_prediction %>%
+    left_join(df_observations, by = "year") %>%
+    mutate(estimated = value)
 
   if (preserve_measurements == TRUE) {
     # replace estimated data by observed data
-    if (I_as_R == TRUE) {
+    df_prediction <- df_prediction %>%
-      if (!'I' %in% colnames(df)) {
+      mutate(value = ifelse(!is.na(observed), observed, value),
-        df$I <- 0
+             se_min = ifelse(!is.na(observed), NA, se_min),
-      }
+             se_max = ifelse(!is.na(observed), NA, se_max))
-      df$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)) {
+  df_prediction <- df_prediction %>%
-    total <- total %>%
+    mutate(value = case_when(value > 1 ~ 1,
-      mutate(value = case_when(value > 1 ~ 1,
+                             value < 0 ~ 0,
-                               value < 0 ~ 0,
+                             TRUE ~ value)) %>%
-                               TRUE ~ value))
+    arrange(year)
-  }
-
-  total %>% arrange(year)
 
+  structure(
+    .Data = df_prediction,
+    class = c("resistance_predict", "data.frame"),
+    I_as_R = I_as_R,
+    model_title = model,
+    model = model_lm,
+    ab = col_ab
+  )
 }
 
 #' @rdname resistance_predict
 #' @export
 rsi_predict <- resistance_predict
+
+#' @exportMethod plot.mic
+#' @export
+#' @importFrom dplyr %>% group_by summarise
+#' @importFrom graphics plot axis arrows
+#' @rdname resistance_predict
+plot.resistance_predict <- function(x, main = paste("Resistance prediction of", attributes(x)$ab), ...) {
+  if (attributes(x)$I_as_R == TRUE) {
+    ylab <- "%IR"
+  } else {
+    ylab <- "%R"
+  }
+  plot(x = x$year,
+       y = x$value,
+       ylim = c(0, 1),
+       yaxt = "n", # no y labels
+       pch = 19, # closed dots
+       ylab = paste0("Percentage (", ylab, ")"),
+       xlab = "Year",
+       main = main,
+       sub = paste0("(model: ", attributes(x)$model_title, ")"))
+
+  axis(side = 2, at = seq(0, 1, 0.1), labels = paste0(0:10 * 10, "%"))
+
+  # arrows hack: https://stackoverflow.com/a/22037078/4575331
+  arrows(x0 = x$year,
+         y0 = x$se_min,
+         x1 = x$year,
+         y1 = x$se_max, length = 0.05, angle = 90, code = 3)
+}
+
+#' @rdname resistance_predict
+#' @export
+ggplot_rsi_predict <- function(x, main = paste("Resistance prediction of", attributes(x)$ab), ...) {
+
+  if (!"resistance_predict" %in% class(x)) {
+    stop("`x` must be a resistance prediction model created with resistance_predict().")
+  }
+
+  if (attributes(x)$I_as_R == TRUE) {
+    ylab <- "%IR"
+  } else {
+    ylab <- "%R"
+  }
+  suppressWarnings(
+    ggplot2::ggplot(x, ggplot2::aes(x = year, y = value)) +
+      ggplot2::geom_col() +
+      ggplot2::geom_errorbar(ggplot2::aes(ymin = se_min, ymax = se_max)) +
+      scale_y_percent() +
+      labs(title = main,
+           y = paste0("Percentage (", ylab, ")"),
+           x = "Year",
+           caption = paste0("(model: ", attributes(x)$model_title, ")"))
+  )
+}

docs/news/index.html

@@ -238,6 +238,8 @@
 <li>Function <code><a href="../reference/mo_renamed.html">mo_renamed()</a></code> to get a list of all returned values from <code><a href="../reference/as.mo.html">as.mo()</a></code> that have had taxonomic renaming</li>
 <li>Function <code><a href="../reference/age.html">age()</a></code> to calculate the (patients) age in years</li>
 <li>Function <code><a href="../reference/age_groups.html">age_groups()</a></code> to split ages into custom or predefined groups (like children or elderly). This allows for easier demographic antimicrobial resistance analysis per age group.</li>
+<li>Function <code><a href="../reference/resistance_predict.html">ggplot_rsi_predict()</a></code> as well as the base R <code><a href="https://www.rdocumentation.org/packages/graphics/topics/plot">plot()</a></code> function can now be used for resistance prediction calculated with <code><a href="../reference/resistance_predict.html">resistance_predict()</a></code>: <code>r   x &lt;- resistance_predict(septic_patients, col_ab = "amox")   plot(x)   ggplot_rsi_predict(x)</code>
+</li>
 <li>Functions <code><a href="../reference/first_isolate.html">filter_first_isolate()</a></code> and <code><a href="../reference/first_isolate.html">filter_first_weighted_isolate()</a></code> to shorten and fasten filtering on data sets with antimicrobial results, e.g.: <code>r   septic_patients %&gt;% filter_first_isolate(...)   # or   filter_first_isolate(septic_patients, ...)</code> is equal to: <code>r   septic_patients %&gt;% mutate(only_firsts = first_isolate(septic_patients, ...)) %&gt;% filter(only_firsts == TRUE) %&gt;% select(-only_firsts)</code>
 </li>
 <li>New vignettes about how to conduct AMR analysis, predict antimicrobial resistance, use the <em>G</em>-test and more. These are also available (and even easier readable) on our website: <a href="https://msberends.gitlab.io/AMR" class="uri">https://msberends.gitlab.io/AMR</a>.</li>

docs/reference/first_isolate.html

@@ -250,7 +250,7 @@
     </tr>
     <tr>
       <th>col_date</th>
-      <td><p>column name of the result date (or date that is was received on the lab), defaults to the first column of class <code>Date</code></p></td>
+      <td><p>column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class</p></td>
     </tr>
     <tr>
       <th>col_patient_id</th>

docs/reference/index.html

@@ -415,7 +415,7 @@
       </tr><tr>
         
         <td>
-          <p><code><a href="resistance_predict.html">resistance_predict()</a></code> <code><a href="resistance_predict.html">rsi_predict()</a></code> </p>
+          <p><code><a href="resistance_predict.html">resistance_predict()</a></code> <code><a href="resistance_predict.html">rsi_predict()</a></code> <code><a href="resistance_predict.html">plot(<i>&lt;resistance_predict&gt;</i>)</a></code> <code><a href="resistance_predict.html">ggplot_rsi_predict()</a></code> </p>
         </td>
         <td><p>Predict antimicrobial resistance</p></td>
       </tr><tr>

docs/reference/resistance_predict.html

@@ -227,14 +227,22 @@
     
     </div>
 
-    <pre class="usage"><span class='fu'>resistance_predict</span>(<span class='no'>tbl</span>, <span class='no'>col_ab</span>, <span class='no'>col_date</span>, <span class='kw'>year_min</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
+    <pre class="usage"><span class='fu'>resistance_predict</span>(<span class='no'>tbl</span>, <span class='no'>col_ab</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_min</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
   <span class='kw'>year_max</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_every</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>30</span>,
   <span class='kw'>model</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>, <span class='kw'>I_as_R</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>preserve_measurements</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
   <span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
 
-<span class='fu'>rsi_predict</span>(<span class='no'>tbl</span>, <span class='no'>col_ab</span>, <span class='no'>col_date</span>, <span class='kw'>year_min</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_max</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
+<span class='fu'>rsi_predict</span>(<span class='no'>tbl</span>, <span class='no'>col_ab</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_min</span> <span class='kw'>=</span> <span class='kw'>NULL</span>,
-  <span class='kw'>year_every</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>30</span>, <span class='kw'>model</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>, <span class='kw'>I_as_R</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
+  <span class='kw'>year_max</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>year_every</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>30</span>,
-  <span class='kw'>preserve_measurements</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</pre>
+  <span class='kw'>model</span> <span class='kw'>=</span> <span class='st'>"binomial"</span>, <span class='kw'>I_as_R</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>preserve_measurements</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
+  <span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
+
+<span class='co'># S3 method for resistance_predict</span>
+<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>x</span>,
+  <span class='kw'>main</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"Resistance prediction of"</span>, <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/attributes'>attributes</a></span>(<span class='no'>x</span>)$<span class='no'>ab</span>), <span class='no'>...</span>)
+
+<span class='fu'>ggplot_rsi_predict</span>(<span class='no'>x</span>, <span class='kw'>main</span> <span class='kw'>=</span> <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/paste'>paste</a></span>(<span class='st'>"Resistance prediction of"</span>,
+  <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/attributes'>attributes</a></span>(<span class='no'>x</span>)$<span class='no'>ab</span>), <span class='no'>...</span>)</pre>
     
     <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
     <table class="ref-arguments">
@@ -249,7 +257,7 @@
     </tr>
     <tr>
       <th>col_date</th>
-      <td><p>column name of the date, will be used to calculate years if this column doesn't consist of years already</p></td>
+      <td><p>column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class</p></td>
     </tr>
     <tr>
       <th>year_min</th>
@@ -269,7 +277,7 @@
     </tr>
     <tr>
       <th>model</th>
-      <td><p>the statistical model of choice. Valid values are <code>"binomial"</code> (or <code>"binom"</code> or <code>"logit"</code>) or <code>"loglin"</code> or <code>"linear"</code> (or <code>"lin"</code>).</p></td>
+      <td><p>the statistical model of choice. Valid values are <code>"binomial"</code> (or <code>"binom"</code> or <code>"logit"</code>) or <code>"loglin"</code> (or <code>"poisson"</code>) or <code>"linear"</code> (or <code>"lin"</code>).</p></td>
     </tr>
     <tr>
       <th>I_as_R</th>
@@ -283,11 +291,21 @@
       <th>info</th>
       <td><p>a logical to indicate whether textual analysis should be printed with the name and <code><a href='https://www.rdocumentation.org/packages/base/topics/summary'>summary</a></code> of the statistical model.</p></td>
     </tr>
+    <tr>
+      <th>x</th>
+      <td><p>the coordinates of points in the plot. Alternatively, a
+      single plotting structure, function or <em>any <span style="R">R</span> object with a
+        <code>plot</code> method</em> can be provided.</p></td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td><p>parameters passed on to the <code>first_isolate</code> function</p></td>
+    </tr>
     </table>
     
     <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
 
-    <p><code>data.frame</code> with columns:</p><ul>
+    <p><code>data.frame</code> with extra class <code>"resistance_predict"</code> with columns:</p><ul>
 <li><p><code>year</code></p></li>
 <li><p><code>value</code>, the same as <code>estimated</code> when <code>preserve_measurements = FALSE</code>, and a combination of <code>observed</code> and <code>estimated</code> otherwise</p></li>
 <li><p><code>se_min</code>, the lower bound of the standard error with a minimum of <code>0</code> (so the standard error will never go below 0%)</p></li>
@@ -311,37 +329,20 @@ On our website <a href='https://msberends.gitlab.io/AMR'>https://msberends.gitla
 
     <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
     <pre class="examples"><span class='co'># NOT RUN {</span>
-<span class='co'># use it with base R:</span>
+<span class='no'>x</span> <span class='kw'>&lt;-</span> <span class='fu'>resistance_predict</span>(<span class='no'>septic_patients</span>, <span class='kw'>col_ab</span> <span class='kw'>=</span> <span class='st'>"amox"</span>, <span class='kw'>year_min</span> <span class='kw'>=</span> <span class='fl'>2010</span>)
-<span class='fu'>resistance_predict</span>(<span class='kw'>tbl</span> <span class='kw'>=</span> <span class='no'>tbl</span>[<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/which'>which</a></span>(<span class='no'>first_isolate</span> <span class='kw'>==</span> <span class='fl'>TRUE</span> <span class='kw'>&amp;</span> <span class='no'>genus</span> <span class='kw'>==</span> <span class='st'>"Haemophilus"</span>),],
+<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>x</span>)
-                   <span class='kw'>col_ab</span> <span class='kw'>=</span> <span class='st'>"amcl"</span>, <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='st'>"date"</span>)
+<span class='fu'>ggplot_rsi_predict</span>(<span class='no'>x</span>)
 
-<span class='co'># or use dplyr so you can actually read it:</span>
+<span class='co'># use dplyr so you can actually read it:</span>
 <span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/library'>library</a></span>(<span class='no'>dplyr</span>)
-<span class='no'>tbl</span> <span class='kw'>%&gt;%</span>
+<span class='no'>x</span> <span class='kw'>&lt;-</span> <span class='no'>septic_patients</span> <span class='kw'>%&gt;%</span>
-  <span class='fu'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span>(<span class='no'>first_isolate</span> <span class='kw'>==</span> <span class='fl'>TRUE</span>,
+  <span class='fu'><a href='first_isolate.html'>filter_first_isolate</a></span>() <span class='kw'>%&gt;%</span>
-         <span class='no'>genus</span> <span class='kw'>==</span> <span class='st'>"Haemophilus"</span>) <span class='kw'>%&gt;%</span>
+  <span class='fu'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span>(<span class='fu'><a href='mo_property.html'>mo_genus</a></span>(<span class='no'>mo</span>) <span class='kw'>==</span> <span class='st'>"Staphylococcus"</span>) <span class='kw'>%&gt;%</span>
-  <span class='fu'>resistance_predict</span>(<span class='no'>amcl</span>, <span class='no'>date</span>)
+  <span class='fu'>resistance_predict</span>(<span class='st'>"peni"</span>)
-<span class='co'># }</span><span class='co'># NOT RUN {</span>
+<span class='fu'><a href='https://www.rdocumentation.org/packages/graphics/topics/plot'>plot</a></span>(<span class='no'>x</span>)
 
-<span class='co'># real live example:</span>
-<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/library'>library</a></span>(<span class='no'>dplyr</span>)
-<span class='no'>septic_patients</span> <span class='kw'>%&gt;%</span>
-  <span class='co'># get bacteria properties like genus and species</span>
-  <span class='fu'><a href='join.html'>left_join_microorganisms</a></span>(<span class='st'>"mo"</span>) <span class='kw'>%&gt;%</span>
-  <span class='co'># calculate first isolates</span>
-  <span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span>(<span class='kw'>first_isolate</span> <span class='kw'>=</span> <span class='fu'><a href='first_isolate.html'>first_isolate</a></span>(<span class='no'>.</span>)) <span class='kw'>%&gt;%</span>
-  <span class='co'># filter on first E. coli isolates</span>
-  <span class='fu'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span>(<span class='no'>genus</span> <span class='kw'>==</span> <span class='st'>"Escherichia"</span>,
-         <span class='no'>species</span> <span class='kw'>==</span> <span class='st'>"coli"</span>,
-         <span class='no'>first_isolate</span> <span class='kw'>==</span> <span class='fl'>TRUE</span>) <span class='kw'>%&gt;%</span>
-  <span class='co'># predict resistance of cefotaxime for next years</span>
-  <span class='fu'>resistance_predict</span>(<span class='kw'>col_ab</span> <span class='kw'>=</span> <span class='st'>"cfot"</span>,
-                     <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='st'>"date"</span>,
-                     <span class='kw'>year_max</span> <span class='kw'>=</span> <span class='fl'>2025</span>,
-                     <span class='kw'>preserve_measurements</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>,
-                     <span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>0</span>)
 
-<span class='co'># create nice plots with ggplot</span>
+<span class='co'># create nice plots with ggplot yourself</span>
 <span class='kw'>if</span> (!<span class='fu'><a href='https://www.rdocumentation.org/packages/base/topics/library'>require</a></span>(<span class='no'>ggplot2</span>)) {
 
   <span class='no'>data</span> <span class='kw'>&lt;-</span> <span class='no'>septic_patients</span> <span class='kw'>%&gt;%</span>
@@ -349,7 +350,7 @@ On our website <a href='https://msberends.gitlab.io/AMR'>https://msberends.gitla
     <span class='fu'>resistance_predict</span>(<span class='kw'>col_ab</span> <span class='kw'>=</span> <span class='st'>"amox"</span>,
                        <span class='kw'>col_date</span> <span class='kw'>=</span> <span class='st'>"date"</span>,
                        <span class='kw'>info</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
-                        <span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>15</span>)
+                       <span class='kw'>minimum</span> <span class='kw'>=</span> <span class='fl'>15</span>)
 
   <span class='fu'><a href='https://ggplot2.tidyverse.org/reference/ggplot.html'>ggplot</a></span>(<span class='no'>data</span>,
          <span class='fu'><a href='https://ggplot2.tidyverse.org/reference/aes.html'>aes</a></span>(<span class='kw'>x</span> <span class='kw'>=</span> <span class='no'>year</span>)) +

man/first_isolate.Rd

@@ -26,7 +26,7 @@ filter_first_weighted_isolate(tbl, col_date = NULL,
 \arguments{
 \item{tbl}{a \code{data.frame} containing isolates.}
 
-\item{col_date}{column name of the result date (or date that is was received on the lab), defaults to the first column of class \code{Date}}
+\item{col_date}{column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class}
 
 \item{col_patient_id}{column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive)}
 

man/resistance_predict.Rd

@@ -3,23 +3,32 @@
 \name{resistance_predict}
 \alias{resistance_predict}
 \alias{rsi_predict}
+\alias{plot.resistance_predict}
+\alias{ggplot_rsi_predict}
 \title{Predict antimicrobial resistance}
 \usage{
-resistance_predict(tbl, col_ab, col_date, year_min = NULL,
+resistance_predict(tbl, col_ab, col_date = NULL, year_min = NULL,
   year_max = NULL, year_every = 1, minimum = 30,
   model = "binomial", I_as_R = TRUE, preserve_measurements = TRUE,
   info = TRUE)
 
-rsi_predict(tbl, col_ab, col_date, year_min = NULL, year_max = NULL,
+rsi_predict(tbl, col_ab, col_date = NULL, year_min = NULL,
-  year_every = 1, minimum = 30, model = "binomial", I_as_R = TRUE,
+  year_max = NULL, year_every = 1, minimum = 30,
-  preserve_measurements = TRUE, info = TRUE)
+  model = "binomial", I_as_R = TRUE, preserve_measurements = TRUE,
+  info = TRUE)
+
+\method{plot}{resistance_predict}(x,
+  main = paste("Resistance prediction of", attributes(x)$ab), ...)
+
+ggplot_rsi_predict(x, main = paste("Resistance prediction of",
+  attributes(x)$ab), ...)
 }
 \arguments{
 \item{tbl}{a \code{data.frame} containing isolates.}
 
 \item{col_ab}{column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})}
 
-\item{col_date}{column name of the date, will be used to calculate years if this column doesn't consist of years already}
+\item{col_date}{column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class}
 
 \item{year_min}{lowest year to use in the prediction model, dafaults to the lowest year in \code{col_date}}
 
@@ -29,16 +38,22 @@ rsi_predict(tbl, col_ab, col_date, year_min = NULL, year_max = NULL,
 
 \item{minimum}{minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.}
 
-\item{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"}).}
+\item{model}{the statistical model of choice. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} (or \code{"poisson"}) or \code{"linear"} (or \code{"lin"}).}
 
 \item{I_as_R}{a logical to indicate whether values \code{I} should be treated as \code{R}}
 
 \item{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}.}
 
 \item{info}{a logical to indicate whether textual analysis should be printed with the name and \code{\link{summary}} of the statistical model.}
+
+\item{x}{the coordinates of points in the plot. Alternatively, a
+      single plotting structure, function or \emph{any \R object with a
+        \code{plot} method} can be provided.}
+
+\item{...}{parameters passed on to the \code{first_isolate} function}
 }
 \value{
-\code{data.frame} with columns:
+\code{data.frame} with extra class \code{"resistance_predict"} 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}
@@ -59,39 +74,20 @@ On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://
 }
 
 \examples{
-\dontrun{
+x <- resistance_predict(septic_patients, col_ab = "amox", year_min = 2010)
-# use it with base R:
+plot(x)
-resistance_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
+ggplot_rsi_predict(x)
-                   col_ab = "amcl", col_date = "date")
 
-# or use dplyr so you can actually read it:
+# use dplyr so you can actually read it:
 library(dplyr)
-tbl \%>\%
+x <- septic_patients \%>\%
-  filter(first_isolate == TRUE,
+  filter_first_isolate() \%>\%
-         genus == "Haemophilus") \%>\%
+  filter(mo_genus(mo) == "Staphylococcus") \%>\%
-  resistance_predict(amcl, date)
+  resistance_predict("peni")
-}
+plot(x)
 
 
-# real live example:
+# create nice plots with ggplot yourself
-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 \%>\%
@@ -99,7 +95,7 @@ if (!require(ggplot2)) {
     resistance_predict(col_ab = "amox",
                        col_date = "date",
                        info = FALSE,
-                        minimum = 15)
+                       minimum = 15)
 
   ggplot(data,
          aes(x = year)) +

tests/testthat/test-resistance_predict.R

@@ -32,6 +32,11 @@ test_that("prediction of rsi works", {
   # amox resistance will increase according to data set `septic_patients`
   expect_true(amox_R[3] < amox_R[20])
 
+  x <- resistance_predict(septic_patients, col_ab = "amox", year_min = 2010)
+  plot(x)
+  ggplot_rsi_predict(x)
+  expect_error(ggplot_rsi_predict(septic_patients))
+
   library(dplyr)
 
   expect_output(rsi_predict(tbl = filter(septic_patients, mo == "B_ESCHR_COL"),