rename vector2ratio to ratio

NAMESPACE

@@ -46,13 +46,13 @@ export(left_join_microorganisms)
 export(mo_property)
 export(n_rsi)
 export(p.symbol)
+export(ratio)
 export(right_join_microorganisms)
 export(rsi)
 export(rsi_df)
 export(rsi_predict)
 export(semi_join_microorganisms)
 export(top_freq)
-export(vector2ratio)
 exportMethods(as.double.mic)
 exportMethods(as.integer.mic)
 exportMethods(as.numeric.mic)

NEWS.md

@@ -5,12 +5,12 @@
 * Possibility to globally set the default for the amount of items to print in frequency tables (`freq` function), with `options(max.print.freq = n)`
 * Functions `clipboard_import` and `clipboard_export` as helper functions to quickly copy and paste from/to software like Excel and SPSS
 * Function `g.test` to perform the Χ<sup>2</sup> distributed [*G*-test](https://en.wikipedia.org/wiki/G-test)
-* Function `p.symbol` to transform p value to their related symbol: `0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1`
-* Function `vector2ratio` to transform a vector of values to a preset ratio. For example:
+* Function `ratio` to transform a vector of values to a preset ratio (convenient to use with `g.test`). For example:
 ```r
-vector2ratio(c(772, 1611, 737), ratio = "1:2:1")
+ratio(c(772, 1611, 737), ratio = "1:2:1")
 # [1]  780 1560  780
 ```
+* Function `p.symbol` to transform p value to their related symbol: `0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1`
 
 #### Changed
 * Frequency tables (function `freq`) now supports quasiquotation: `freq(mydata, mycolumn)`, or `mydata %>% freq(mycolumn)`

R/g.test.R

@@ -20,7 +20,7 @@
 #'
 #' A \emph{G}-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a \strong{\emph{G}-test of goodness-of-fit}), or to see whether the proportions of one variable are different for different values of the other variable (called a \strong{\emph{G}-test of independence}).
 #' @param x numeric vector or matrix
-#' @param y expected value of \code{x}. Leave empty to determine automatically. This can also be ratios of \code{x}, e.g. calculated with \code{\link{vector2ratio}}.
+#' @param y expected value of \code{x}. Leave empty to determine automatically. This can also be ratios of \code{x}, e.g. calculated with \code{\link{ratio}}.
 #' @param alpha value to test the p value against
 #' @param info logical to determine whether the analysis should be printed
 #' @param minimum the test with fail if any of the observed values is below this value. Use \code{minimum = 30} for microbial epidemiology, to prevent calculating a p value when less than 30 isolates are available.
@@ -70,7 +70,7 @@
 #' # ratio.
 #'
 #' x <- c(772, 1611, 737)
-#' x.expected <- vector2ratio(x, ratio = "1:2:1")
+#' x.expected <- ratio(x, "1:2:1")
 #' x.expected
 #' # 780 1560 780
 #'
@@ -90,7 +90,7 @@
 #' # observed 1752 right-billed and 1895 left-billed crossbills.
 #'
 #' x <- c(1752, 1895)
-#' x.expected <- vector2ratio(x, ratio = c(1, 1))
+#' x.expected <- ratio(x, ratio = c(1, 1))
 #' x.expected
 #' # 1823.5 1823.5
 #'
@@ -215,7 +215,7 @@ g.test <- function(x,
 #' # ratio.
 #'
 #' x <- c(772, 1611, 737)
-#' x.expected <- vector2ratio(x, ratio = "1:2:1")
+#' x.expected <- ratio(x, "1:2:1")
 #' x.expected
 #' # 780 1560 780
 #'
@@ -235,7 +235,7 @@ g.test <- function(x,
 #' # observed 1752 right-billed and 1895 left-billed crossbills.
 #'
 #' x <- c(1752, 1895)
-#' x.expected <- vector2ratio(x, ratio = c(1, 1))
+#' x.expected <- ratio(x, ratio = c(1, 1))
 #' x.expected
 #' # 1823.5 1823.5
 #'
@@ -245,7 +245,7 @@ g.test <- function(x,
 #' # There is a significant difference from a 1:1 ratio.
 #' # Meaning: there are significantly more left-billed birds.
 #'
-vector2ratio <- function(x, ratio) {
+ratio <- function(x, ratio) {
   if (!all(is.numeric(x))) {
     stop('`x` must be a vector of numeric values.')
   }

man/g.test.Rd

@@ -9,7 +9,7 @@ g.test(x, y = NULL, alpha = 0.05, info = TRUE, minimum = 0)
 \arguments{
 \item{x}{numeric vector or matrix}
 
-\item{y}{expected value of \code{x}. Leave empty to determine automatically. This can also be ratios of \code{x}, e.g. calculated with \code{\link{vector2ratio}}.}
+\item{y}{expected value of \code{x}. Leave empty to determine automatically. This can also be ratios of \code{x}, e.g. calculated with \code{\link{ratio}}.}
 
 \item{alpha}{value to test the p value against}
 
@@ -68,7 +68,7 @@ If there are more than two categories and you want to find out which ones are si
 # ratio.
 
 x <- c(772, 1611, 737)
-x.expected <- vector2ratio(x, ratio = "1:2:1")
+x.expected <- ratio(x, "1:2:1")
 x.expected
 # 780 1560 780
 
@@ -88,7 +88,7 @@ g.test(x, x.expected)
 # observed 1752 right-billed and 1895 left-billed crossbills.
 
 x <- c(1752, 1895)
-x.expected <- vector2ratio(x, ratio = c(1, 1))
+x.expected <- ratio(x, ratio = c(1, 1))
 x.expected
 # 1823.5 1823.5
 

man/ratio.Rd

@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/g.test.R
-\name{vector2ratio}
-\alias{vector2ratio}
+\name{ratio}
+\alias{ratio}
 \title{Transform vector to ratio}
 \usage{
-vector2ratio(x, ratio)
+ratio(x, ratio)
 }
 \arguments{
 \item{x}{vector of values}
@@ -25,7 +25,7 @@ Transform vector to ratio
 # ratio.
 
 x <- c(772, 1611, 737)
-x.expected <- vector2ratio(x, ratio = "1:2:1")
+x.expected <- ratio(x, "1:2:1")
 x.expected
 # 780 1560 780
 
@@ -45,7 +45,7 @@ g.test(x, x.expected)
 # observed 1752 right-billed and 1895 left-billed crossbills.
 
 x <- c(1752, 1895)
-x.expected <- vector2ratio(x, ratio = c(1, 1))
+x.expected <- ratio(x, ratio = c(1, 1))
 x.expected
 # 1823.5 1823.5
 

tests/testthat/test-g.test.R

@@ -6,14 +6,14 @@ test_that("G-test works", {
 
   # example 1: clearfield rice vs. red rice
   x <- c(772, 1611, 737)
-  x.expected <- vector2ratio(x, ratio = "1:2:1")
+  x.expected <- ratio(x, ratio = "1:2:1")
   expect_equal(g.test(x, x.expected),
                expected = 0.12574,
                tolerance = 0.00001)
 
   # example 2: red crossbills
   x <- c(1752, 1895)
-  x.expected <- vector2ratio(x, ratio = c(1, 1))
+  x.expected <- ratio(x, ratio = c(1, 1))
   expect_equal(g.test(x, x.expected),
                expected = 0.01787343,
                tolerance = 0.00000001)