unit tests

.github/workflows/check-current.yaml

@@ -58,6 +58,9 @@ jobs:
           - {os: ubuntu-latest,  r: 'release', allowfail: false}
           - {os: windows-latest, r: 'devel',   allowfail: false}
           - {os: windows-latest, r: 'release', allowfail: false}
+          - {os: macOS-latest,   r: '3.6',     allowfail: false}
+          - {os: ubuntu-latest,  r: '3.6',     allowfail: false}
+          - {os: windows-latest, r: '3.6',     allowfail: false}
 
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/check-old.yaml

@@ -59,6 +59,7 @@ jobs:
           - {os: ubuntu-22.04,   r: '4.0',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           - {os: ubuntu-22.04,   r: '3.6',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           # R 3.5 returns a strange GC error when running examples, omit the checks for that
+          # - {os: ubuntu-22.04,   r: '3.5',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           - {os: ubuntu-22.04,   r: '3.4',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           - {os: ubuntu-22.04,   r: '3.3',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           - {os: ubuntu-22.04,   r: '3.2',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 1.8.2.9076
-Date: 2022-12-30
+Version: 1.8.2.9077
+Date: 2023-01-05
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)
   data analysis and to work with microbial and antimicrobial properties by

NEWS.md

@@ -1,4 +1,4 @@
-#  1.8.2.9076
+#  1.8.2.9077
 
 *(this beta version will eventually become v2.0! We're happy to reach a new major milestone soon!)*
 

R/aa_helper_functions.R

@@ -966,7 +966,7 @@ unique_call_id <- function(entire_session = FALSE, match_fn = NULL) {
   # and relevant system call (where 'match_fn' is being called in)
   calls <- sys.calls()
   in_test <- any(as.character(calls[[1]]) %like_case% "run_test_dir|run_test_file|test_all|tinytest|test_package|testthat", na.rm = TRUE)
-  if (!isTRUE(in_test)) {
+  if (!isTRUE(in_test) && !is.null(match_fn)) {
     for (i in seq_len(length(calls))) {
       call_clean <- gsub("[^a-zA-Z0-9_().-]", "", as.character(calls[[i]]), perl = TRUE)
       if (match_fn %in% call_clean || any(call_clean %like% paste0(match_fn, "\\("), na.rm = TRUE)) {
@@ -1262,6 +1262,7 @@ create_pillar_column <- function(x, ...) {
 
 as_original_data_class <- function(df, old_class = NULL) {
   if ("tbl_df" %in% old_class && pkg_is_available("tibble", also_load = FALSE)) {
+    # this will then also remove groups
     fn <- import_fn("as_tibble", "tibble")
   } else if ("tbl_ts" %in% old_class && pkg_is_available("tsibble", also_load = FALSE)) {
     fn <- import_fn("as_tsibble", "tsibble")
@@ -1270,7 +1271,7 @@ as_original_data_class <- function(df, old_class = NULL) {
   } else if ("tabyl" %in% old_class && pkg_is_available("janitor", also_load = FALSE)) {
     fn <- import_fn("as_tabyl", "janitor")
   } else {
-    fn <- base::as.data.frame
+    fn <- function(x) base::as.data.frame(df, stringsAsFactors = FALSE)
   }
   fn(df)
 }

R/ab_property.R

@@ -36,7 +36,7 @@
 #' @param language language of the returned text, defaults to system language (see [get_AMR_locale()]) and can also be set with `getOption("AMR_locale")`. Use `language = NULL` or `language = ""` to prevent translation.
 #' @param administration way of administration, either `"oral"` or `"iv"`
 #' @param open browse the URL using [utils::browseURL()]
-#' @param ... in case of [set_ab_names()] and `data` is a [data.frame]: variables to select (supports tidy selection such as `column1:column4`), otherwise other arguments passed on to [as.ab()]
+#' @param ... in case of [set_ab_names()] and `data` is a [data.frame]: columns to select (supports tidy selection such as `column1:column4`), otherwise other arguments passed on to [as.ab()]
 #' @param data a [data.frame] of which the columns need to be renamed, or a [character] vector of column names
 #' @param snake_case a [logical] to indicate whether the names should be in so-called [snake case](https://en.wikipedia.org/wiki/Snake_case): in lower case and all spaces/slashes replaced with an underscore (`_`)
 #' @param only_first a [logical] to indicate whether only the first ATC code must be returned, with giving preference to J0-codes (i.e., the antimicrobial drug group)

R/bug_drug_combinations.R

@@ -161,7 +161,7 @@ bug_drug_combinations <- function(x,
     out <- run_it(x)
   }
   rownames(out) <- NULL
-  out <- as_original_data_class(out, class(x.bak))
+  out <- as_original_data_class(out, class(x.bak)) # will remove tibble groups
   structure(out, class = c("bug_drug_combinations", ifelse(data_has_groups, "grouped", character(0)), class(out)))
 }
 
@@ -322,7 +322,7 @@ format.bug_drug_combinations <- function(x,
   }
 
   rownames(y) <- NULL
-  as_original_data_class(y, class(x.bak))
+  as_original_data_class(y, class(x.bak)) # will remove tibble groups
 }
 
 #' @method print bug_drug_combinations

R/custom_eucast_rules.R

@@ -77,7 +77,7 @@
 #'
 #' ### Using taxonomic properties in rules
 #'
-#' There is one exception in variables used for the rules: all column names of the [microorganisms] data set can also be used, but do not have to exist in the data set. These column names are: `r vector_and(colnames(microorganisms), sort = FALSE)`. Thus, this next example will work as well, despite the fact that the `df` data set does not contain a column `genus`:
+#' There is one exception in columns used for the rules: all column names of the [microorganisms] data set can also be used, but do not have to exist in the data set. These column names are: `r vector_and(colnames(microorganisms), sort = FALSE)`. Thus, this next example will work as well, despite the fact that the `df` data set does not contain a column `genus`:
 #'
 #' ```r
 #' y <- custom_eucast_rules(TZP == "S" & genus == "Klebsiella" ~ aminopenicillins == "S",

R/eucast_rules.R

@@ -1035,7 +1035,7 @@ eucast_rules <- function(x,
 
   # Return data set ---------------------------------------------------------
   if (isTRUE(verbose)) {
-    as_original_data_class(verbose_info, old_attributes$class)
+    as_original_data_class(verbose_info, old_attributes$class) # will remove tibble groups
   } else {
     # x was analysed with only unique rows, so join everything together again
     x <- x[, c(cols_ab, ".rowid"), drop = FALSE]
@@ -1043,8 +1043,9 @@ eucast_rules <- function(x,
     x.bak <- x.bak %pm>%
       pm_left_join(x, by = ".rowid")
     x.bak <- x.bak[, old_cols, drop = FALSE]
-    # reset original attributes, no need for as_original_data_class() here
+    # reset original attributes
     attributes(x.bak) <- old_attributes
+    x.bak <- as_original_data_class(x.bak, old_class = class(x.bak)) # will remove tibble groups
     x.bak
   }
 }

R/join_microorganisms.R

@@ -185,5 +185,5 @@ join_microorganisms <- function(type, x, by, suffix, ...) {
     warning_("in `", type, "_microorganisms()`: the newly joined data set contains ", nrow(joined) - nrow(x), " rows more than the number of rows of `x`.")
   }
 
-  as_original_data_class(joined, class(x.bak))
+  as_original_data_class(joined, class(x.bak))  # will remove tibble groups
 }

R/mean_amr_distance.R

@@ -30,30 +30,40 @@
 #' Calculate the Mean AMR Distance
 #'
 #' Calculates a normalised mean for antimicrobial resistance between multiple observations, to help to identify similar isolates without comparing antibiograms by hand.
-#' @param x a vector of class [rsi][as.rsi()], [rsi][as.rsi()] or [rsi][as.rsi()], or a [data.frame] containing columns of any of these classes
+#' @param x a vector of class [rsi][as.rsi()], [mic][as.mic()] or [disk][as.disk()], or a [data.frame] containing columns of any of these classes
 #' @param ... variables to select (supports [tidyselect language][tidyselect::language] such as `column1:column4` and `where(is.mic)`, and can thus also be [antibiotic selectors][ab_selector()]
 #' @param combine_SI 	a [logical] to indicate whether all values of S and I must be merged into one, so the input only consists of S+I vs. R (susceptible vs. resistant), defaults to `TRUE`
-#' @details The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand. For common numeric data this distance is equal to [Z scores](https://en.wikipedia.org/wiki/Standard_score) (the number of standard deviations from the mean).
+#' @details The mean AMR distance is effectively [the Z-score](https://en.wikipedia.org/wiki/Standard_score); a normalised numeric value to compare AMR test results which can help to identify similar isolates, without comparing antibiograms by hand.
 #'
-#' MIC values (see [as.mic()]) are transformed with [log2()] first; their distance is calculated as `(log2(x) - mean(log2(x))) / sd(log2(x))`.
+#' MIC values (see [as.mic()]) are transformed with [log2()] first; their distance is thus calculated as `(log2(x) - mean(log2(x))) / sd(log2(x))`.
 #'
 #' R/SI values (see [as.rsi()]) are transformed using `"S"` = 1, `"I"` = 2, and `"R"` = 3. If `combine_SI` is `TRUE` (default), the `"I"` will be considered to be 1.
 #'
-#' For data sets, the mean AMR distance will be calculated per variable, after which the mean of all columns will returned per row (using [rowMeans()]), see *Examples*.
+#' For data sets, the mean AMR distance will be calculated per column, after which the mean per row will be returned, see *Examples*.
 #'
 #' Use [amr_distance_from_row()] to subtract distances from the distance of one row, see *Examples*.
 #' @section Interpretation:
 #' Isolates with distances less than 0.01 difference from each other should be considered similar. Differences lower than 0.025 should be considered suspicious.
 #' @export
 #' @examples
-#' x <- random_mic(10)
-#' x
-#' mean_amr_distance(x)
+#' rsi <- random_rsi(10)
+#' rsi
+#' mean_amr_distance(rsi)
+#' 
+#' mic <- random_mic(10)
+#' mic
+#' mean_amr_distance(mic)
+#' # equal to the Z-score of their log2:
+#' (log2(mic) - mean(log2(mic))) / sd(log2(mic))
+#' 
+#' disk <- random_disk(10)
+#' disk
+#' mean_amr_distance(disk)
 #'
 #' y <- data.frame(
 #'   id = LETTERS[1:10],
-#'   amox = random_mic(10, ab = "amox", mo = "Escherichia coli"),
-#'   cipr = random_mic(10, ab = "cipr", mo = "Escherichia coli"),
+#'   amox = random_rsi(10, ab = "amox", mo = "Escherichia coli"),
+#'   cipr = random_disk(10, ab = "cipr", mo = "Escherichia coli"),
 #'   gent = random_mic(10, ab = "gent", mo = "Escherichia coli"),
 #'   tobr = random_mic(10, ab = "tobr", mo = "Escherichia coli")
 #' )
@@ -65,7 +75,7 @@
 #' if (require("dplyr")) {
 #'   y %>%
 #'     mutate(
-#'       amr_distance = mean_amr_distance(., where(is.mic)),
+#'       amr_distance = mean_amr_distance(y),
 #'       check_id_C = amr_distance_from_row(amr_distance, id == "C")
 #'     ) %>%
 #'     arrange(check_id_C)
@@ -76,8 +86,8 @@
 #'     filter(mo_genus() == "Enterococcus" & mo_species() != "") %>%
 #'     select(mo, TCY, carbapenems()) %>%
 #'     group_by(mo) %>%
-#'     mutate(d = mean_amr_distance(., where(is.rsi))) %>%
-#'     arrange(mo, d)
+#'     mutate(dist = mean_amr_distance(.)) %>%
+#'     arrange(mo, dist)
 #' }
 mean_amr_distance <- function(x, ...) {
   UseMethod("mean_amr_distance")
@@ -87,6 +97,7 @@ mean_amr_distance <- function(x, ...) {
 #' @export
 mean_amr_distance.default <- function(x, ...) {
   x <- as.double(x)
+  # calculate z-score
   (x - mean(x, na.rm = TRUE)) / stats::sd(x, na.rm = TRUE)
 }
 
@@ -120,6 +131,7 @@ mean_amr_distance.data.frame <- function(x, ..., combine_SI = TRUE) {
   if (is_null_or_grouped_tbl(df)) {
     df <- get_current_data("x", -2)
   }
+  df <- as.data.frame(df, stringsAsFactors = FALSE)
   if (tryCatch(length(list(...)) > 0, error = function(e) TRUE)) {
     out <- tryCatch(suppressWarnings(c(...)), error = function(e) NULL)
     if (!is.null(out)) {
@@ -128,13 +140,18 @@ mean_amr_distance.data.frame <- function(x, ..., combine_SI = TRUE) {
       df <- pm_select(df, ...)
     }
   }
+  df_classes <- colnames(df)[vapply(FUN.VALUE = logical(1), df, function(x) is.disk(x) | is.mic(x) | is.disk(x), USE.NAMES = FALSE)]
+  df_antibiotics <- unname(get_column_abx(df, info = FALSE))
+  df <- df[, colnames(df)[colnames(df) %in% union(df_classes, df_antibiotics)], drop = FALSE]
+  
   stop_if(ncol(df) < 2,
     "data set must contain at least two variables",
     call = -2
   )
   if (message_not_thrown_before("mean_amr_distance", "groups")) {
-    message_("Calculating mean AMR distance based on columns ", vector_and(colnames(df)))
+    message_("Calculating mean AMR distance based on columns ", vector_and(colnames(df), sort = FALSE))
   }
+  
   res <- vapply(
     FUN.VALUE = double(nrow(df)),
     df,
@@ -149,7 +166,7 @@ mean_amr_distance.data.frame <- function(x, ..., combine_SI = TRUE) {
     }
   }
   res <- rowMeans(res, na.rm = TRUE)
-  res[is.infinite(res)] <- 0
+  res[is.infinite(res) | is.nan(res)] <- 0
   res
 }
 

R/resistance_predict.R

@@ -274,7 +274,7 @@ resistance_predict <- function(x,
   df_prediction$value <- ifelse(df_prediction$value > 1, 1, pmax(df_prediction$value, 0))
   df_prediction <- df_prediction[order(df_prediction$year), , drop = FALSE]
 
-  out <- as_original_data_class(df_prediction, class(x.bak))
+  out <- as_original_data_class(df_prediction, class(x.bak))  # will remove tibble groups
   structure(out,
     class = c("resistance_predict", class(out)),
     I_as_S = I_as_S,

R/rsi_calc.R

@@ -371,6 +371,6 @@ rsi_calc_df <- function(type, # "proportion", "count" or "both"
   }
 
   rownames(out) <- NULL
-  out <- as_original_data_class(out, class(data.bak))
+  out <- as_original_data_class(out, class(data.bak)) # will remove tibble groups
   structure(out, class = c("rsi_df", class(out)))
 }