(v0.8.0.9030) depend on tidyr >= 1.0.0

R/bug_drug_combinations.R

@@ -32,7 +32,7 @@
 #' @inheritParams rsi_df
 #' @inheritParams base::formatC
 #' @importFrom dplyr %>% rename group_by select mutate filter summarise ungroup
-#' @importFrom tidyr spread
+#' @importFrom tidyr pivot_longer
 #' @details The function \code{format} calculates the resistance per bug-drug combination. Use \code{combine_IR = FALSE} (default) to test R vs. S+I and \code{combine_IR = TRUE} to test R+I vs. S. 
 #' 
 #' The language of the output can be overwritten with \code{options(AMR_locale)}, please see \link{translate}.
@@ -80,7 +80,7 @@ bug_drug_combinations <- function(x,
              FUN(...)) %>% 
     group_by(mo) %>% 
     select_if(is.rsi) %>% 
-    gather("ab", "value", -mo) %>% 
+    pivot_longer(-mo, names_to = "ab") %>% 
     group_by(mo, ab) %>% 
     summarise(S = sum(value == "S", na.rm = TRUE),
               I = sum(value == "I", na.rm = TRUE),
@@ -93,7 +93,7 @@ bug_drug_combinations <- function(x,
 }
 
 #' @importFrom dplyr everything rename %>% ungroup group_by summarise mutate_all arrange everything lag
-#' @importFrom tidyr spread
+#' @importFrom tidyr pivot_wider
 #' @importFrom cleaner percentage
 #' @exportMethod format.bug_drug_combinations
 #' @export
@@ -135,7 +135,7 @@ format.bug_drug_combinations <- function(x,
     }
     ab_txt
   }
-
+  
   y <- x %>%
     mutate(ab = as.ab(ab),
            ab_txt = give_ab_name(ab = ab, format = translate_ab, language = language)) %>% 
@@ -146,8 +146,9 @@ format.bug_drug_combinations <- function(x,
     mutate(txt = paste0(percentage(isolates / total, decimal.mark = decimal.mark, big.mark = big.mark), 
                         " (", trimws(format(isolates, big.mark = big.mark)), "/", 
                         trimws(format(total, big.mark = big.mark)), ")")) %>% 
-    select(ab, ab_txt, mo, txt) %>% 
-    spread(mo, txt) %>%
+    select(ab, ab_txt, mo, txt) %>%
+    arrange(mo) %>% 
+    pivot_wider(names_from = mo, values_from = txt) %>% 
     mutate_all(~ifelse(is.na(.), "", .)) %>% 
     mutate(ab_group = ab_group(ab, language = language),
            ab_txt) %>% 

R/resistance_predict.R

@@ -29,12 +29,13 @@
 #' @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. This could be a generalised linear regression model with binomial distribution (i.e. using \code{\link{glm}(..., family = \link{binomial})}), assuming that a period of zero resistance was followed by a period of increasing resistance leading slowly to more and more resistance. See Details for all valid options.
-#' @param I_as_S a logical to indicate whether values \code{I} should be treated as \code{S} (will otherwise be treated as \code{R})
+#' @param I_as_S a logical to indicate whether values \code{I} should be treated as \code{S} (will otherwise be treated as \code{R}). The default, \code{TRUE}, follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. 
 #' @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.
 #' @param main title of the plot
 #' @param ribbon a logical to indicate whether a ribbon should be shown (default) or error bars
 #' @param ... parameters passed on to functions
+#' @inheritSection as.rsi Interpretation of S, I and R
 #' @inheritParams first_isolate
 #' @inheritParams graphics::plot
 #' @details Valid options for the statistical model are:
@@ -59,6 +60,7 @@
 #' @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 n_groups transmute ungroup
+#' @importFrom tidyr pivot_wider
 #' @inheritSection AMR Read more on our website!
 #' @examples
 #' x <- resistance_predict(example_isolates, col_ab = "AMX", year_min = 2010, model = "binomial")
@@ -161,6 +163,7 @@ resistance_predict <- function(x,
   }
 
   year <- function(x) {
+    # don't depend on lubridate or so, would be overkill for only this function
     if (all(grepl("^[0-9]{4}$", x))) {
       x
     } else {
@@ -192,9 +195,12 @@ resistance_predict <- function(x,
   }
 
   colnames(df) <- c("year", "antibiotic", "observations")
+
   df <- df %>%
     filter(!is.na(antibiotic)) %>%
-    tidyr::spread(antibiotic, observations, fill = 0) %>%
+    pivot_wider(names_from = antibiotic,
+                values_from = observations,
+                values_fill = list(observations = 0)) %>% 
     filter((R + S) >= minimum)
   df_matrix <- df %>%
     ungroup() %>%

R/rsi_calc.R

@@ -167,8 +167,8 @@ rsi_calc <- function(...,
   }
 }
 
-#' @importFrom dplyr %>% summarise_if mutate select everything bind_rows
-#' @importFrom tidyr gather
+#' @importFrom dplyr %>% summarise_if mutate select everything bind_rows arrange
+#' @importFrom tidyr pivot_longer
 rsi_calc_df <- function(type, # "proportion" or "count"
                         data,
                         translate_ab = "name",
@@ -247,12 +247,13 @@ rsi_calc_df <- function(type, # "proportion" or "count"
   }
   
   res <- res %>%
-    gather(antibiotic, value, -interpretation, -data.groups) %>%
-    select(antibiotic, everything())
+    pivot_longer(-c(interpretation, data.groups), names_to = "antibiotic") %>% 
+    select(antibiotic, everything()) %>% 
+    arrange(antibiotic, interpretation)
   
   if (!translate_ab == FALSE) {
     res <- res %>% mutate(antibiotic = AMR::ab_property(antibiotic, property = translate_ab, language = language))
   }
   
-  res
+  as.data.frame(res, stringsAsFactors = FALSE)
 }