new unit tests for ggplot, small fixes

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
 Version: 0.2.0.9023
-Date: 2018-08-11
+Date: 2018-08-12
 Title: Antimicrobial Resistance Analysis
 Authors@R: c(
     person(
@@ -57,14 +57,14 @@ Imports:
     Rcpp (>= 0.12.14),
     readr,
     rvest (>= 0.3.2),
-    tibble,
+    tibble
-    ggplot2
 Suggests:
     testthat (>= 1.0.2),
     covr (>= 3.0.1),
     rmarkdown,
     rstudioapi,
-    tidyr
+    tidyr,
+    ggplot2
 VignetteBuilder: knitr
 URL: https://github.com/msberends/AMR
 BugReports: https://github.com/msberends/AMR/issues

R/classes.R

@@ -102,9 +102,14 @@ print.rsi <- function(x, ...) {
 #' @noRd
 summary.rsi <- function(object, ...) {
   x <- object
-  lst <- c('rsi', sum(is.na(x)), sum(x == "S"), sum(x %in% c("I", "R")), sum(x == "R"), sum(x == "I"))
+  c(
-  names(lst) <- c("Mode", "<NA>", "Sum S", "Sum IR", "-Sum R", "-Sum I")
+    "Mode" = 'rsi',
-  lst
+    "<NA>" = sum(is.na(x)),
+    "Sum S" = sum(x == "S", na.rm = TRUE),
+    "Sum IR" = sum(x %in% c("I", "R"), na.rm = TRUE),
+    "-Sum R" = sum(x == "R", na.rm = TRUE),
+    "-Sum I" = sum(x == "I", na.rm = TRUE)
+  )
 }
 
 #' @exportMethod plot.rsi

R/ggplot_rsi.R

@@ -18,7 +18,7 @@
 
 #' AMR bar plots with \code{ggplot}
 #'
-#' Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link{ggplot}} functions.
+#' Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link[ggplot2]{ggplot}} functions.
 #' @param data a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}})
 #' @param position position adjustment of bars, either \code{"stack"} (default) or \code{"dodge"}
 #' @param x parameter to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"}
@@ -28,13 +28,13 @@
 #' \strong{The functions}\cr
 #' \code{geom_rsi} will take any variable from the data that has an \code{rsi} class (created with \code{\link{as.rsi}}) using \code{\link{portion_df}} and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
 #'
-#' \code{facet_rsi} creates 2d plots (at default based on S/I/R) using \code{\link{facet_wrap}}.
+#' \code{facet_rsi} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2]{facet_wrap}}.
 #'
 #' \code{scale_y_percent} transforms the y axis to a 0 to 100% range.
 #'
 #' \code{scale_rsi_colours} sets colours to the bars: green for S, yellow for I and red for R.
 #'
-#' \code{theme_rsi} is a \code{\link{theme}} with minimal distraction.
+#' \code{theme_rsi} is a \code{\link[ggplot2]{theme}} with minimal distraction.
 #'
 #' \code{ggplot_rsi} is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (\code{\%>\%}). See Examples.
 #' @rdname ggplot_rsi
@@ -67,6 +67,11 @@
 ggplot_rsi <- function(data,
                        x = "Antibiotic",
                        facet = NULL) {
+
+  if (!"ggplot2" %in% rownames(installed.packages())) {
+    stop('this function requires the ggplot2 package.', call. = FALSE)
+  }
+
   p <- ggplot2::ggplot(data = data) +
     geom_rsi(x = x) +
     scale_y_percent() +

R/n_rsi.R

@@ -35,7 +35,7 @@
 #'             combination_n = n_rsi(cipr, gent))
 n_rsi <- function(ab1, ab2 = NULL) {
   if (NCOL(ab1) > 1) {
-    stop('`ab` must be a vector of antimicrobial interpretations', call. = FALSE)
+    stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
   }
   if (!is.rsi(ab1)) {
     ab1 <- as.rsi(ab1)

R/portion.R

@@ -176,6 +176,32 @@ portion_S <- function(ab1,
            as_percent = as_percent)
 }
 
+#' @rdname portion
+#' @importFrom dplyr bind_cols summarise_if mutate
+#' @export
+portion_df <- function(data, translate = getOption("get_antibiotic_names", TRUE)) {
+  resS <- bind_cols(data.frame(Interpretation = "S", stringsAsFactors = FALSE),
+                    summarise_if(.tbl = data,
+                                 .predicate = is.rsi,
+                                 .funs = portion_S))
+  resI <- bind_cols(data.frame(Interpretation = "I", stringsAsFactors = FALSE),
+                    summarise_if(.tbl = data,
+                                 .predicate = is.rsi,
+                                 .funs = portion_I))
+  resR <- bind_cols(data.frame(Interpretation = "R", stringsAsFactors = FALSE),
+                    summarise_if(.tbl = data,
+                                 .predicate = is.rsi,
+                                 .funs = portion_R))
+
+  res <- bind_rows(resS, resI, resR) %>%
+    mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
+    tidyr::gather(Antibiotic, Percentage, -Interpretation)
+  if (translate == TRUE) {
+    res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = "official"))
+  }
+  res
+}
+
 rsi_calc <- function(type,
                      ab1,
                      ab2,
@@ -244,29 +270,3 @@ rsi_calc <- function(type,
     found / total
   }
 }
-
-#' @rdname portion
-#' @importFrom dplyr bind_cols summarise_if mutate
-#' @export
-portion_df <- function(data, translate = getOption("get_antibiotic_names", TRUE)) {
-  resS <- bind_cols(data.frame(Interpretation = "S", stringsAsFactors = FALSE),
-                    summarise_if(.tbl = data,
-                                 .predicate = is.rsi,
-                                 .funs = portion_S))
-  resI <- bind_cols(data.frame(Interpretation = "I", stringsAsFactors = FALSE),
-                    summarise_if(.tbl = data,
-                                 .predicate = is.rsi,
-                                 .funs = portion_I))
-  resR <- bind_cols(data.frame(Interpretation = "R", stringsAsFactors = FALSE),
-                    summarise_if(.tbl = data,
-                                 .predicate = is.rsi,
-                                 .funs = portion_R))
-
-  res <- bind_rows(resS, resI, resR) %>%
-    mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
-    tidyr::gather(Antibiotic, Percentage, -Interpretation)
-  if (translate == TRUE) {
-    res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = "official"))
-  }
-  res
-}

README.md

@@ -136,43 +136,61 @@ guess_bactid("VRSA") # Vancomycin Resistant S. aureus
 This package contains two new S3 classes: `mic` for MIC values (e.g. from Vitek or Phoenix) and `rsi` for antimicrobial drug interpretations (i.e. S, I and R). Both are actually ordered factors under the hood (an MIC of `2` being higher than `<=1` but lower than `>=32`, and for class `rsi` factors are ordered as `S < I < R`). 
 Both classes have extensions for existing generic functions like `print`, `summary` and `plot`.
 
-```r
-# Transform values to new classes
-mic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))
-rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
-```
 These functions also try to coerce valid values.
 
-Quick overviews with `summary`:
+#### RSI
+The `septic_patients` data set comes with antimicrobial results of more than 40 different drugs. For example, columns `amox` and `cipr` contain results of amoxicillin and ciprofloxacin, respectively.
 ```r
-summary(rsi_data)
+summary(septic_patients[, c("amox", "cipr")])
-#  Mode  :rsi  
+#      amox          cipr     
-#  <NA>  :0    
+#  Mode  :rsi    Mode  :rsi   
-#  Sum S :474  
+#  <NA>  :1002   <NA>  :596   
-#  Sum IR:406  
+#  Sum S :336    Sum S :1108  
-#  -Sum R:370  
+#  Sum IR:662    Sum IR:296   
-#  -Sum I:36
+#  -Sum R:659    -Sum R:227   
+#  -Sum I:3      -Sum I:69  
+```
+
+You can use the `plot` function from base R:
+```r
+plot(septic_patients$cipr)
+```
+
+![example_1_rsi](man/figures/rsi_example1.png)
+
+Or use the `ggplot2` and `dplyr` packages to create more appealing plots:
+```r
+septic_patients %>%
+  select(amox, cipr) %>%
+  ggplot_rsi()
+```
+
+![example_2_rsi](man/figures/rsi_example2.png)
+
+```r
+septic_patients %>%
+    select(amox, cipr) %>%
+    ggplot_rsi(x = "Interpretation", facet = "Antibiotic")
+```
+
+![example_3_rsi](man/figures/rsi_example3.png)
+
+
+#### MIC
+
+```r
+# Transform values to new class
+mic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))
 
 summary(mic_data)
 #  Mode:mic      
 #  <NA>:0        
 #  Min.:<=0.128  
 #  Max.:>=32 
-```
 
-A plot of `rsi_data`:
-```r
-plot(rsi_data)
-```
-
-![example1](man/figures/rsi_example.png)
-
-A plot of `mic_data` (defaults to bar plot):
-```r
 plot(mic_data)
 ```
-
+![example_mic](man/figures/mic_example.png)
-![example2](man/figures/mic_example.png)
 
 Other epidemiological functions:
 

man/ggplot_rsi.Rd

@@ -31,7 +31,7 @@ theme_rsi()
 \item{position}{position adjustment of bars, either \code{"stack"} (default) or \code{"dodge"}}
 }
 \description{
-Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link{ggplot}} functions.
+Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link[ggplot2]{ggplot}} functions.
 }
 \details{
 At default, the names of antibiotics will be shown on the plots using \code{\link{abname}}. This can be set with the option \code{get_antibiotic_names} (a logical value), so change it e.g. to \code{FALSE} with \code{options(get_antibiotic_names = FALSE)}.
@@ -39,13 +39,13 @@ At default, the names of antibiotics will be shown on the plots using \code{\lin
 \strong{The functions}\cr
 \code{geom_rsi} will take any variable from the data that has an \code{rsi} class (created with \code{\link{as.rsi}}) using \code{\link{portion_df}} and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
 
-\code{facet_rsi} creates 2d plots (at default based on S/I/R) using \code{\link{facet_wrap}}.
+\code{facet_rsi} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2]{facet_wrap}}.
 
 \code{scale_y_percent} transforms the y axis to a 0 to 100% range.
 
 \code{scale_rsi_colours} sets colours to the bars: green for S, yellow for I and red for R.
 
-\code{theme_rsi} is a \code{\link{theme}} with minimal distraction.
+\code{theme_rsi} is a \code{\link[ggplot2]{theme}} with minimal distraction.
 
 \code{ggplot_rsi} is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (\code{\%>\%}). See Examples.
 }

tests/testthat/test-ggplot_rsi.R

@@ -0,0 +1,35 @@
+context("ggplot_rsi.R")
+
+test_that("ggplot_rsi works", {
+
+  skip_if_not("ggplot2" %in% rownames(installed.packages()))
+
+  library(dplyr)
+  library(ggplot2)
+
+  # data should be equal
+  expect_equal(
+    (septic_patients %>% select(amcl, cipr) %>% ggplot_rsi())$data %>%
+      summarise_all(portion_IR) %>% as.double(),
+    septic_patients %>% select(amcl, cipr) %>%
+      summarise_all(portion_IR) %>% as.double()
+  )
+
+  expect_equal(
+    (septic_patients %>% select(amcl, cipr) %>% ggplot_rsi(x = "Interpretation", facet = "Antibiotic"))$data %>%
+      summarise_all(portion_IR) %>% as.double(),
+    septic_patients %>% select(amcl, cipr) %>%
+      summarise_all(portion_IR) %>% as.double()
+  )
+
+  expect_equal(
+    (septic_patients %>% select(amcl, cipr) %>% ggplot_rsi(x = "Antibiotic", facet = "Interpretation"))$data %>%
+      summarise_all(portion_IR) %>% as.double(),
+    septic_patients %>% select(amcl, cipr) %>%
+      summarise_all(portion_IR) %>% as.double()
+  )
+
+  expect_error(geom_rsi(x = "test"))
+  expect_error(facet_rsi(facet = "test"))
+
+})

tests/testthat/test-portion.R

@@ -1,6 +1,6 @@
 context("portion.R")
 
-test_that("resistance works", {
+test_that("portions works", {
   # amox resistance in `septic_patients`
   expect_equal(portion_R(septic_patients$amox), 0.6603, tolerance = 0.0001)
   expect_equal(portion_I(septic_patients$amox), 0.0030, tolerance = 0.0001)
@@ -46,6 +46,9 @@ test_that("resistance works", {
   expect_warning(portion_S(as.character(septic_patients$amcl)))
   expect_warning(portion_S(as.character(septic_patients$amcl,
                                              septic_patients$gent)))
+  expect_equal(n_rsi(as.character(septic_patients$amcl,
+                                  septic_patients$gent)),
+               1570)
 
 
   # check for errors
@@ -59,6 +62,9 @@ test_that("resistance works", {
   expect_error(portion_S("test", as_percent = "test"))
   expect_error(portion_S(septic_patients %>% select(amox, amcl)))
   expect_error(portion_S("R", septic_patients %>% select(amox, amcl)))
+  expect_error(n_rsi(septic_patients %>% select(amox, amcl)))
+  expect_error(n_rsi(septic_patients$amox, septic_patients %>% select(amox, amcl)))
+
 
   # check too low amount of isolates
   expect_identical(portion_R(septic_patients$amox, minimum = nrow(septic_patients) + 1),
@@ -102,6 +108,15 @@ test_that("old rsi works", {
                            combination_n = n_rsi(cipr, gent)) %>%
                  pull(combination_n),
                c(202, 482, 201, 499))
+
+  # portion_df
+  expect_equal(
+    septic_patients %>% select(amox) %>% portion_df(TRUE) %>% pull(Percentage),
+    c(septic_patients$amox %>% portion_S(),
+      septic_patients$amox %>% portion_I(),
+      septic_patients$amox %>% portion_R())
+  )
+
 })
 
 test_that("prediction of rsi works", {