From 2ea0c93e4456b778a3f84ab2268bc656bb61da1f Mon Sep 17 00:00:00 2001 From: "Matthijs S. Berends" Date: Mon, 13 May 2019 16:35:48 +0200 Subject: [PATCH] documentation fix --- R/resistance_predict.R | 63 ++++++++++++++++---------- codecov.yml | 3 ++ docs/reference/resistance_predict.html | 30 ++++++------ man/resistance_predict.Rd | 22 ++++----- 4 files changed, 68 insertions(+), 50 deletions(-) diff --git a/R/resistance_predict.R b/R/resistance_predict.R index b8b159b0..3b3dc83d 100755 --- a/R/resistance_predict.R +++ b/R/resistance_predict.R @@ -22,20 +22,21 @@ #' Predict antimicrobial resistance #' #' 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_ab column name of \code{x} 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, 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. Defaults to a generalised linear regression model with binomial distribution, 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 valid options. -#' @param I_as_R a logical to indicate whether values \code{I} should be treated as \code{R} +#' @param I_as_S a logical to indicate whether values \code{I} should be treated as \code{S} #' @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 +#' @inheritParams first_isolate +#' @inheritParams graphics::plot #' @details Valid options for the statistical model are: #' \itemize{ #' \item{\code{"binomial"} or \code{"binom"} or \code{"logit"}: a generalised linear regression model with binomial distribution} @@ -104,7 +105,7 @@ #' x = "Year") + #' theme_minimal(base_size = 13) #' } -resistance_predict <- function(tbl, +resistance_predict <- function(x, col_ab, col_date = NULL, year_min = NULL, @@ -112,33 +113,46 @@ resistance_predict <- function(tbl, year_every = 1, minimum = 30, model = 'binomial', - I_as_R = TRUE, + I_as_S = TRUE, preserve_measurements = TRUE, - info = TRUE) { + info = TRUE, + ...) { - if (nrow(tbl) == 0) { + if (nrow(x) == 0) { stop('This table does not contain any observations.') } - if (!col_ab %in% colnames(tbl)) { + if (!col_ab %in% colnames(x)) { stop('Column ', col_ab, ' not found.') } + dots <- unlist(list(...)) + if (length(dots) != 0) { + # backwards compatibility with old parameters + dots.names <- dots %>% names() + if ('tbl' %in% dots.names) { + x <- dots[which(dots.names == 'tbl')] + } + if ('I_as_R' %in% dots.names) { + warning("`I_as_R is deprecated - use I_as_S instead.", call. = FALSE) + } + } + # -- date if (is.null(col_date)) { - col_date <- search_type_in_df(tbl = tbl, type = "date") + col_date <- search_type_in_df(tbl = x, type = "date") } if (is.null(col_date)) { stop("`col_date` must be set.", call. = FALSE) } - if (!col_date %in% colnames(tbl)) { + if (!col_date %in% colnames(x)) { stop('Column ', col_date, ' not found.') } - if (n_groups(tbl) > 1) { + if (n_groups(x) > 1) { # no grouped tibbles please, mutate will throw errors - tbl <- base::as.data.frame(tbl, stringsAsFactors = FALSE) + x <- base::as.data.frame(x, stringsAsFactors = FALSE) } year <- function(x) { @@ -149,14 +163,15 @@ resistance_predict <- function(tbl, } } - df <- tbl %>% + df <- x %>% 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 { + if (I_as_S == TRUE) { gsub("I", "S", .) + } else { + # then I as R + gsub("I", "R", .) } )) %>% filter_at(col_ab, all_vars(!is.na(.))) %>% @@ -289,7 +304,7 @@ resistance_predict <- function(tbl, structure( .Data = df_prediction, class = c("resistance_predict", "data.frame"), - I_as_R = I_as_R, + I_as_S = I_as_S, model_title = model, model = model_lm, ab = col_ab @@ -306,10 +321,10 @@ rsi_predict <- resistance_predict #' @importFrom graphics plot axis arrows points #' @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 { + if (attributes(x)$I_as_S == TRUE) { ylab <- "%R" + } else { + ylab <- "%IR" } plot(x = x$year, y = x$value, @@ -352,10 +367,10 @@ ggplot_rsi_predict <- function(x, stop("`x` must be a resistance prediction model created with resistance_predict().") } - if (attributes(x)$I_as_R == TRUE) { - ylab <- "%IR" - } else { + if (attributes(x)$I_as_S == TRUE) { ylab <- "%R" + } else { + ylab <- "%IR" } p <- ggplot2::ggplot(x, ggplot2::aes(x = year, y = value)) + diff --git a/codecov.yml b/codecov.yml index 0a1028a0..b637472b 100644 --- a/codecov.yml +++ b/codecov.yml @@ -14,3 +14,6 @@ coverage: project: no patch: no changes: no + +ignore: + - "R/atc_online.R" diff --git a/docs/reference/resistance_predict.html b/docs/reference/resistance_predict.html index f4da5499..52ee3a39 100644 --- a/docs/reference/resistance_predict.html +++ b/docs/reference/resistance_predict.html @@ -241,15 +241,15 @@ - 
resistance_predict(tbl, col_ab, col_date = NULL, year_min = NULL,
+    
resistance_predict(x, 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)
+  model = "binomial", I_as_S = TRUE, preserve_measurements = TRUE,
+  info = TRUE, ...)
 
-rsi_predict(tbl, col_ab, col_date = NULL, year_min = NULL,
+rsi_predict(x, 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)
+  model = "binomial", I_as_S = TRUE, preserve_measurements = TRUE,
+  info = TRUE, ...)
 
 # S3 method for resistance_predict
 plot(x,
@@ -261,9 +261,13 @@
     
Arguments

     
+    
+      
+      
+    
-      
+      
@@ -290,8 +294,8 @@
       
-      
-      
+      
+      
@@ -302,17 +306,13 @@
       
-      
-      
+      
+      
-    
-      
-      
-    
diff --git a/man/resistance_predict.Rd b/man/resistance_predict.Rd
index 4048bc05..7d79f451 100644
--- a/man/resistance_predict.Rd
+++ b/man/resistance_predict.Rd
@@ -7,15 +7,15 @@
 \alias{ggplot_rsi_predict}
 \title{Predict antimicrobial resistance}
 \usage{
-resistance_predict(tbl, col_ab, col_date = NULL, year_min = NULL,
+resistance_predict(x, 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)
+  model = "binomial", I_as_S = TRUE, preserve_measurements = TRUE,
+  info = TRUE, ...)
 
-rsi_predict(tbl, col_ab, col_date = NULL, year_min = NULL,
+rsi_predict(x, 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)
+  model = "binomial", I_as_S = TRUE, preserve_measurements = TRUE,
+  info = TRUE, ...)
 
 \method{plot}{resistance_predict}(x,
   main = paste("Resistance prediction of", attributes(x)$ab), ...)
@@ -24,7 +24,9 @@ ggplot_rsi_predict(x, main = paste("Resistance prediction of",
   attributes(x)$ab), ribbon = TRUE, ...)
 }
 \arguments{
-\item{col_ab}{column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})}
+\item{x}{a \code{data.frame} containing isolates.}
+
+\item{col_ab}{column name of \code{x} 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, defaults to the first column of with a date class}
 
@@ -38,18 +40,16 @@ ggplot_rsi_predict(x, main = paste("Resistance prediction of",
 
 \item{model}{the statistical model of choice. Defaults to a generalised linear regression model with binomial distribution, 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 valid options.}
 
-\item{I_as_R}{a logical to indicate whether values \code{I} should be treated as \code{R}}
+\item{I_as_S}{a logical to indicate whether values \code{I} should be treated as \code{S}}
 
 \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}{a \code{data.frame} containing isolates.}
+\item{...}{parameters passed on to functions}
 
 \item{main}{title of the plot}
 
-\item{...}{parameters passed on to the \code{first_isolate} function}
-
 \item{ribbon}{a logical to indicate whether a ribbon should be shown (default) or error bars}
 }
 \value{


     
x
a data.frame containing isolates.

     

       col_ab
column name of tbl with antimicrobial interpretations (R, I and S)
column name of x with antimicrobial interpretations (R, I and S)

     

     

       col_date
the statistical model of choice. Defaults to a generalised linear regression model with binomial distribution, 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 valid options.

     

     
I_as_R
a logical to indicate whether values I should be treated as R
I_as_S
a logical to indicate whether values I should be treated as S

     

     

       preserve_measurements
a logical to indicate whether textual analysis should be printed with the name and summary of the statistical model.

     

     
x
a data.frame containing isolates.
...
parameters passed on to functions

     

     

       main
       
title of the plot

     
...
parameters passed on to the first_isolate function

     

       ribbon
       
a logical to indicate whether a ribbon should be shown (default) or error bars