add barplots

2024-12-25 20:06:12 +01:00 · 2018-03-13 14:34:10 +01:00 · 2018-03-13 14:34:10 +01:00 · 04fc3573ec
commit 04fc3573ec
parent e4b371d30a
8 changed files with 86 additions and 35 deletions
--- a/2
+++ b/2
@ -1,6 +1,6 @@
 Package: AMR
 Version: 0.1.1
-Date: 2018-02-26
+Date: 2018-03-13
 Title: Antimicrobial Resistance Analysis
 Authors@R: c(
    person(
--- a/4
+++ b/4
@ -3,6 +3,8 @@
 S3method(as.double,mic)
 S3method(as.integer,mic)
 S3method(as.numeric,mic)
+S3method(barplot,mic)
+S3method(barplot,rsi)
 S3method(plot,mic)
 S3method(plot,rsi)
 S3method(print,mic)
@ -32,6 +34,8 @@ export(semi_join_bactlist)
 exportMethods(as.double.mic)
 exportMethods(as.integer.mic)
 exportMethods(as.numeric.mic)
+exportMethods(barplot.mic)
+exportMethods(barplot.rsi)
 exportMethods(plot.mic)
 exportMethods(plot.rsi)
 exportMethods(print.mic)
--- a/R/classes.R
+++ b/R/classes.R
@ -125,7 +125,7 @@ summary.rsi <- function(object, ...) {

 #' @exportMethod plot.rsi
 #' @export
-#' @importFrom dplyr %>% group_by summarise filter mutate if_else
+#' @importFrom dplyr %>% group_by summarise filter mutate if_else n_distinct
 #' @importFrom graphics plot text
 #' @noRd
 plot.rsi <- function(x, ...) {
@ -150,12 +150,43 @@ plot.rsi <- function(x, ...) {
       ylab = 'Percentage',
       xlab = 'Antimicrobial Interpretation',
       main = paste('Susceptibilty Analysis of', x_name),
+       axes = FALSE,
       ...)
+  # x axis
+  axis(side = 1, at = 1:n_distinct(data$x), labels = levels(data$x), lwd = 0)
+  # y axis, 0-100%
+  axis(side = 2, at = seq(0, 100, 5))
+  
  text(x = data$x,
-       y = data$s + 5,
+       y = data$s + 4,
       labels = paste0(data$s, '% (n = ', data$n, ')'))
 }

+
+#' @exportMethod barplot.rsi
+#' @export
+#' @importFrom dplyr %>% group_by summarise filter mutate if_else n_distinct
+#' @importFrom graphics plot text
+#' @noRd
+barplot.rsi <- function(x, ...) {
+  x_name <- deparse(substitute(x))
+  
+  data <- data.frame(rsi = x, cnt = 1) %>%
+    group_by(rsi) %>%
+    summarise(cnt = sum(cnt)) %>%
+    droplevels()
+
+  barplot(table(rsi_data),
+          col = c('green3', 'orange2', 'red3'),
+          xlab = 'Antimicrobial Interpretation',
+          main = paste('Susceptibilty Analysis of', x_name),
+          ylab = 'Frequency',
+          axes = FALSE,
+          ...)
+  # y axis, 0-100%
+  axis(side = 2, at = seq(0, max(data$cnt) + max(data$cnt) * 1.1, by = 25))
+}
+
 #' Class 'mic'
 #'
 #' This transforms a vector to a new class\code{mic}, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
@ -169,6 +200,7 @@ plot.rsi <- function(x, ...) {
 #' mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
 #' is.mic(mic_data)
 #' plot(mic_data)
+#' barplot(mic_data)
 #' 
 #' \donttest{
 #' library(dplyr)
@ -357,21 +389,29 @@ summary.mic <- function(object, ...) {
 #' @noRd
 plot.mic <- function(x, ...) {
  x_name <- deparse(substitute(x))
-  
+  create_barplot_mic(x, x_name, ...)
+}
+
+#' @exportMethod barplot.mic
+#' @export
+#' @importFrom dplyr %>% group_by summarise
+#' @importFrom graphics plot text
+#' @noRd
+barplot.mic <- function(x, ...) {
+  x_name <- deparse(substitute(x))
+  create_barplot_mic(x, x_name, ...)
+}
+
+create_barplot_mic <- function(x, x_name, ...) {
  data <- data.frame(mic = x, cnt = 1) %>%
    group_by(mic) %>%
    summarise(cnt = sum(cnt)) %>%
    droplevels()
-
-  plot(x = data$mic,
-       y = data$cnt,
-       lwd = 2,
-       ylim = c(-0.5, max(5, max(data$cnt))),
-       ylab = 'Frequency',
-       xlab = 'MIC value',
-       main = paste('MIC values of', x_name),
-       ...)
-  text(x = data$mic,
-       y = -0.5,
-       labels = paste('n =', data$cnt))
+  barplot(table(droplevels(x)),
+          ylab = 'Frequency',
+          xlab = 'MIC value',
+          main = paste('MIC values of', x_name), 
+          axes = FALSE,
+          ...)
+  axis(2, seq(0, max(data$cnt)))
 }
--- a/README.md
+++ b/README.md
@ -13,22 +13,26 @@ AMR can also be predicted for the forthcoming years with the `rsi_predict` funct
 It also contains functions to translate antibiotic codes from the lab (like `"AMOX"`) or the [WHO](https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no) (like `"J01CA04"`) to trivial names (like `"amoxicillin"`) and vice versa.

 ## How to get it?
-[![CRAN_Badge](http://www.r-pkg.org/badges/version/AMR)](http://cran.r-project.org/package=AMR)[![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](http://cran.r-project.org/package=AMR)[![Travis_Build](https://travis-ci.org/msberends/AMR.svg?branch=master)](https://travis-ci.org/msberends/AMR)
+This package is available on CRAN and also here on GitHub.

-This package is available on CRAN (latest stable version) and also here on GitHub (latest development version).
+### From CRAN (recommended, latest stable version)
+[![CRAN_Badge](https://img.shields.io/cran/v/AMR.svg?label=CRAN&colorB=3679BC)](http://cran.r-project.org/package=AMR)
+[![CRAN_Downloads](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](http://cran.r-project.org/package=AMR)

-### Latest stable version from CRAN (recommended)
-For RStudio:
-  Click on `Tools` and then `Install Packages...`
-  Type in `AMR` and press <kbd>Install</kbd>
+- RStudio:
+  - Click on `Tools` and then `Install Packages...`
+  -  Type in `AMR` and press <kbd>Install</kbd>
+
+- R console:
+  - `install.packages("AMR")`
+
+### From GitHub (latest development version)
+[![Travis_Build](https://travis-ci.org/msberends/AMR.svg?branch=master)](https://travis-ci.org/msberends/AMR)
+[![Since_Release](https://img.shields.io/github/commits-since/msberends/AMR/latest.svg?colorB=3679BC)](https://github.com/msberends/AMR/releases)
+[![Last_Commit](https://img.shields.io/github/last-commit/msberends/AMR.svg?colorB=3679BC)](https://github.com/msberends/AMR/commits/master)

-Or in the R console:
-```r
-install.packages("AMR")
-```
-
-### Latest development version from GitHub
 ```r
+install.packages("devtools")
 devtools::install_github("msberends/AMR")
 ```

@ -155,6 +159,8 @@ bactlist      # A tibble: 2,507 x 10
 <sup>2</sup> Department of Medical, Market and Innovation (MMI), Certe Medische diagnostiek & advies, Groningen, the Netherlands

 ## Copyright
+[![License](https://img.shields.io/github/license/msberends/AMR.svg?colorB=3679BC)](https://github.com/msberends/AMR/blob/master/LICENSE)
+
 This R package is licensed under the [GNU General Public License (GPL) v2.0](https://github.com/msberends/AMR/blob/master/LICENSE). In a nutshell, this means that this package:

 - May be used for commercial purposes
--- a/man/ablist.Rd
+++ b/man/ablist.Rd
@ -12,9 +12,9 @@
  \item{\code{official}}{Official name by the WHO, like \code{"amoxicillin and enzyme inhibitor"}}
  \item{\code{official_nl}}{Official name in the Netherlands, like \code{"Amoxicilline met enzymremmer"}}
  \item{\code{trivial_nl}}{Trivial name in Dutch, like \code{"Amoxicilline/clavulaanzuur"}}
-  \item{\code{oral_ddd}}{Daily Defined Dose (DDD) according to the WHO, oral treatment}
+  \item{\code{oral_ddd}}{Defined Daily Dose (DDD) according to the WHO, oral treatment}
  \item{\code{oral_units}}{Units of \code{ddd_units}}
-  \item{\code{iv_ddd}}{Daily Defined Dose (DDD) according to the WHO, parenteral treatment}
+  \item{\code{iv_ddd}}{Defined Daily Dose (DDD) according to the WHO, parenteral treatment}
  \item{\code{iv_units}}{Units of \code{iv_ddd}}
  \item{\code{atc_group1}}{ATC group in Dutch, like \code{"Macroliden, lincosamiden en streptograminen"}}
  \item{\code{atc_group2}}{Subgroup of \code{atc_group1} in Dutch, like \code{"Macroliden"}}
--- a/man/as.mic.Rd
+++ b/man/as.mic.Rd
@ -18,12 +18,13 @@ is.mic(x)
 New class \code{mic}
 }
 \description{
-This transforms a vector to a new class\code{mic}, which is an ordered factor valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
+This transforms a vector to a new class\code{mic}, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
 }
 \examples{
 mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
 is.mic(mic_data)
 plot(mic_data)
+barplot(mic_data)

 \donttest{
 library(dplyr)
--- a/man/atc_property.Rd
+++ b/man/atc_property.Rd
@ -20,7 +20,7 @@ atc_property(atc_code, property, administration = "O",
 \item{url}{url of website of the WHO. The sign \code{\%s} can be used as a placeholder for ATC codes.}
 }
 \description{
-Gets data from the WHO to determine properties of an ATC of e.g. an antibiotic. \strong{This function requires an internet connection.}
+Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic) like name, defined daily dose (DDD) or standard unit. \strong{This function requires an internet connection.}
 }
 \details{
 Abbreviations for the property \code{"Adm.R"} (parameter \code{administration}):
@ -51,7 +51,7 @@ Abbreviations for the property \code{"U"} (unit):
 }
 \examples{
 \donttest{
-atc_property("J01CA04", "DDD", "O") # oral DDD of amoxicillin
-atc_property("J01CA04", "DDD", "P") # parenteral DDD of amoxicillin
+atc_property("J01CA04", "DDD", "O") # oral DDD (Defined Daily Dose) of amoxicillin
+atc_property("J01CA04", "DDD", "P") # parenteral DDD (Defined Daily Dose) of amoxicillin
 }
 }
--- a/man/first_isolate.Rd
+++ b/man/first_isolate.Rd
@ -53,7 +53,7 @@ Determine first (weighted) isolates of all microorganisms of every patient per e
 \strong{Why this is so important} \cr
    To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.

-    \strong{\code{points_threshold}} \cr
+    \strong{Using parameter \code{points_threshold}} \cr
    To compare key antibiotics, the difference between antimicrobial interpretations will be measured. A difference from I to S|R (or vice versa) means 0.5 points. A difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate.
 }
 \examples{