Merge pull request #81 from sinhrks/v010

REL: v0.1.0
sinhrks · Nov 30, 2015 · d3cf5fb · d3cf5fb
2 parents 7f06d89 + 3851ed8
commit d3cf5fb
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: ggfortify
 Type: Package
 Title: Data Visualization Tools for Statistical Analysis Results
-Version: 0.0.4.9100
-Date: 2015-10-03
+Version: 0.1.0
+Date: 2015-11-30
 Author: Masaaki Horikoshi <[email protected]> and Yuan Tang
     <[email protected]>
 Maintainer: Masaaki Horikoshi <[email protected]>

diff --git a/NEWS.rst b/NEWS.rst
@@ -1,8 +1,8 @@
 NEWS
 =====================
 
-ggfortify dev version
----------------------
+ggfortify v0.1.0
+----------------
 
 - Compatible with ggplot2 v1.1.0
 - Support glmnet

diff --git a/R/fortify_stats.R b/R/fortify_stats.R
@@ -186,8 +186,10 @@ fortify.factanal <- function(model, data = NULL, ...) {
 #' @inheritParams fortify_base
 #' @return data.frame
 #' @examples
+#' \dontrun{
 #' model <- lfda::lfda(iris[, -5], iris[, 5], 3, metric = "plain")
 #' fortify(model)
+#' }
 #' @export
 fortify.lfda <- function(model, data = NULL, ...) {
 

diff --git a/R/geom.R b/R/geom.R
@@ -8,7 +8,7 @@
 #' @param ... other arguments passed to methods
 geom_confint <- function (mapping = NULL, data = NULL, stat = "identity",
                           position = "identity", na.rm = FALSE, ...) {
-  if (packageVersion("ggplot2") <= '1.0.1') {
+  if (utils::packageVersion("ggplot2") <= '1.0.1') {
     GeomConfint$new(mapping = mapping, data = data, stat = stat,
                     position = position, na.rm = na.rm, ...)
   } else {
@@ -21,7 +21,7 @@ geom_confint <- function (mapping = NULL, data = NULL, stat = "identity",
   }
 }
 
-if (packageVersion("ggplot2") <= '1.0.1') {
+if (utils::packageVersion("ggplot2") <= '1.0.1') {
   # mostly derived from ggplot2
   # Licensed under GPL-2
   # Link - https://github.com/hadley/ggplot2/blob/master/DESCRIPTION

diff --git a/R/plotlib.R b/R/plotlib.R
@@ -386,7 +386,7 @@ post_autoplot <- function(p, xlim = c(NA, NA), ylim = c(NA, NA), log = "",
 #' @return logical
 support_autoplot <- function(obj) {
   maybe_autoplot <- paste0('autoplot.', class(obj))
-  return(any(sapply(maybe_autoplot, function(x) x %in% methods('autoplot'))))
+  return(any(sapply(maybe_autoplot, function(x) x %in% utils::methods('autoplot'))))
 }
 
 

diff --git a/README.rst b/README.rst
@@ -13,7 +13,7 @@ ggfortify
 
 Define ``fortify`` and ``autoplot`` functions to allow ``ggplot2`` to handle some popular R packages.
 
-**NOTE** For functions which returns ``list``, ``ggfortify`` tries to infer a background class using its attributes. Such functions are marked as "(inference)".
+**NOTE** For functions which returns ``list``, ``ggfortify`` tries to infer a background class using its attribute names. Such functions are marked as "(inference)".
 
 
 This covers following classes:
@@ -44,7 +44,7 @@ This covers following classes:
 - ``stats::acf``
 - ``stats::ar``
 - ``stats::Arima``
-- ``stats::cmdscale`` (inference, see the `doc <http://rpubs.com/sinhrks/plot_mds>`_)
+- ``stats::cmdscale`` (inference)
 - ``stats::decomposed.ts``
 - ``stats::density``
 - ``stats::factanal``

diff --git a/man/fortify.lfda.Rd b/man/fortify.lfda.Rd
diff --git a/tests/testthat/test-stats.R b/tests/testthat/test-stats.R
@@ -118,25 +118,7 @@ test_that('fortify.princomp works for iris', {
   pcs <- c('Comp.1', 'Comp.2', 'Comp.3', 'Comp.4')
   expected_names <- c(names(df), pcs)
 
-  fortified <- ggplot2::fortify(stats::princomp(df, center = TRUE, scale = TRUE))
-  expect_equal(is.data.frame(fortified), TRUE)
-  expect_equal(names(fortified), expected_names)
-  expect_equal(data.frame(fortified[c(1, 2, 3, 4)]), df)
-  expect_equal(rownames(fortified), rownames(df))
-
-  fortified <- ggplot2::fortify(stats::princomp(df, center = FALSE, scale = TRUE))
-  expect_equal(is.data.frame(fortified), TRUE)
-  expect_equal(names(fortified), expected_names)
-  expect_equal(data.frame(fortified[c(1, 2, 3, 4)]), df)
-  expect_equal(rownames(fortified), rownames(df))
-
-  fortified <- ggplot2::fortify(stats::princomp(df, center = TRUE, scale = FALSE))
-  expect_equal(is.data.frame(fortified), TRUE)
-  expect_equal(names(fortified), expected_names)
-  expect_equal(data.frame(fortified[c(1, 2, 3, 4)]), df)
-  expect_equal(rownames(fortified), rownames(df))
-
-  fortified <- ggplot2::fortify(stats::princomp(df, center = FALSE, scale = FALSE))
+  fortified <- ggplot2::fortify(stats::princomp(df))
   expect_equal(is.data.frame(fortified), TRUE)
   expect_equal(names(fortified), expected_names)
   expect_equal(data.frame(fortified[c(1, 2, 3, 4)]), df)
@@ -207,25 +189,7 @@ test_that('fortify.princomp works for USArrests', {
   pcs <- c('Comp.1', 'Comp.2', 'Comp.3', 'Comp.4')
   expected_names <- c(names(USArrests), pcs)
 
-  fortified <- ggplot2::fortify(stats::princomp(USArrests, center = TRUE, scale = TRUE))
-  expect_equal(is.data.frame(fortified), TRUE)
-  expect_equal(names(fortified), expected_names)
-  expect_equal(data.frame(fortified[c(1, 2, 3, 4)]), USArrests)
-  expect_equal(rownames(fortified), rownames(USArrests))
-
-  fortified <- ggplot2::fortify(stats::princomp(USArrests, center = FALSE, scale = TRUE))
-  expect_equal(is.data.frame(fortified), TRUE)
-  expect_equal(names(fortified), expected_names)
-  expect_equal(data.frame(fortified[c(1, 2, 3, 4)]), USArrests)
-  expect_equal(rownames(fortified), rownames(USArrests))
-
-  fortified <- ggplot2::fortify(stats::princomp(USArrests, center = TRUE, scale = FALSE))
-  expect_equal(is.data.frame(fortified), TRUE)
-  expect_equal(names(fortified), expected_names)
-  expect_equal(data.frame(fortified[c(1, 2, 3, 4)]), USArrests)
-  expect_equal(rownames(fortified), rownames(USArrests))
-
-  fortified <- ggplot2::fortify(stats::princomp(USArrests, center = FALSE, scale = FALSE))
+  fortified <- ggplot2::fortify(stats::princomp(USArrests))
   expect_equal(is.data.frame(fortified), TRUE)
   expect_equal(names(fortified), expected_names)
   expect_equal(data.frame(fortified[c(1, 2, 3, 4)]), USArrests)
@@ -246,30 +210,30 @@ test_that('fortify.dist works for eurodist', {
 })
 
 test_that('fortify.lfda works for iris', {
-  library(lfda)
-  k <- iris[,-5]
-  y <- iris[,5]
-  r <- 3
-  model <- lfda(k, y, r, metric = "plain")
-  fortified <- ggplot2::fortify(model)
-  expect_equal(is.data.frame(fortified), TRUE)
-
-  model <- klfda(kmatrixGauss(k), y, r, metric = "plain")
-  fortified <- ggplot2::fortify(model)
-  expect_equal(is.data.frame(fortified), TRUE)
-
-  model <- self(k, y, beta=0.1, r, metric = "plain")
-  fortified <- ggplot2::fortify(model)
-  expect_equal(is.data.frame(fortified), TRUE)
+    skip_on_cran()
+    library(lfda)
+    k <- iris[,-5]
+    y <- iris[,5]
+    r <- 3
+    model <- lfda(k, y, r, metric = "plain")
+    fortified <- ggplot2::fortify(model)
+    expect_equal(is.data.frame(fortified), TRUE)
+    model <- klfda(kmatrixGauss(k), y, r, metric = "plain")
+    fortified <- ggplot2::fortify(model)
+    expect_equal(is.data.frame(fortified), TRUE)
+    model <- self(k, y, beta=0.1, r, metric = "plain")
+    fortified <- ggplot2::fortify(model)
+    expect_equal(is.data.frame(fortified), TRUE)
 })
 
 test_that('autoplot.lfda works for iris', {
-  k <- iris[,-5]
-  y <- iris[,5]
-  r <- 4
-  model <- lfda::lfda(k,y,r,metric="plain")
-  p <- autoplot(model, data=iris, frame = TRUE, frame.colour='Species')
-  expect_true(is(p, 'ggplot'))
+    skip_on_cran()
+    k <- iris[,-5]
+    y <- iris[,5]
+    r <- 4
+    model <- lfda::lfda(k,y,r,metric="plain")
+    p <- autoplot(model, data=iris, frame = TRUE, frame.colour='Species')
+    expect_true(is(p, 'ggplot'))
 })
 
 test_that('autoplot.acf works', {

diff --git a/vignettes/intro_Chinese.Rmd b/vignettes/intro_Chinese.Rmd
@@ -7,7 +7,7 @@ output: word_document
 
 <!--
 %\VignetteEngine{knitr::knitr}
-%\VignetteIndexEntry{Introduction to ggfortify package}
+%\VignetteIndexEntry{Introduction to ggfortify package (Chinese)}
 -->
 
 [ggfortify](https://github.com/sinhrks/ggfortify) 有着简单易用的统一的界面来用一行代码来对许多受欢迎的R软件包结果进行二维可视化的一个R工具包。这让许多的统计学家以及数据科学家省去了许多繁琐和重复的过程，不用对结果进行任何处理就能以 `{ggplot}` 的风格画出好看的图，大大地提高了工作的效率。

diff --git a/vignettes/plot_dist.Rmd b/vignettes/plot_dist.Rmd
@@ -5,7 +5,7 @@ output: pdf_document
 
 <!--
 %\VignetteEngine{knitr::knitr}
-%\VignetteIndexEntry{Introduction to ggfortify package}
+%\VignetteIndexEntry{Plotting Probability Distributions}
 -->
 
 ```{r global_options, include=FALSE}

diff --git a/vignettes/plot_lm.Rmd b/vignettes/plot_lm.Rmd
@@ -5,7 +5,7 @@ output: html_document
 
 <!--
 %\VignetteEngine{knitr::knitr}
-%\VignetteIndexEntry{Introduction to ggfortify package}
+%\VignetteIndexEntry{Plotting Diagnostics for LM and GLM}
 -->
 
 ```{r global_options, include=FALSE}

diff --git a/vignettes/plot_pca.Rmd b/vignettes/plot_pca.Rmd
@@ -5,7 +5,7 @@ output: html_document
 
 <!--
 %\VignetteEngine{knitr::knitr}
-%\VignetteIndexEntry{Introduction to ggfortify package}
+%\VignetteIndexEntry{Plotting PCA, clustering, LFDA and MDS results}
 -->
 
 ```{r global_options, include=FALSE}
@@ -104,7 +104,7 @@ autoplot(pam(iris[-5], 3), frame = TRUE, frame.type = 'norm')
 
 # Plotting Local Fisher Discriminant Analysis with ``{lfda}`` package
 
-[``{lfda}``](https://cran.r-project.org/web/packages/lfda/index.html) package supports a set of Local Fisher Discriminant Analysis methods. You can use ``autoplot`` to plot the analysis result as the same manner as PCA.
+``{lfda}`` package supports a set of Local Fisher Discriminant Analysis methods. You can use ``autoplot`` to plot the analysis result as the same manner as PCA.
 
 Thanks to the kind contribution of [Yuan Tang](https://github.com/terrytangyuan), the author of ``{lfda}`` package. 
 

diff --git a/vignettes/plot_surv.Rmd b/vignettes/plot_surv.Rmd
@@ -5,7 +5,7 @@ output: html_document
 
 <!--
 %\VignetteEngine{knitr::knitr}
-%\VignetteIndexEntry{Introduction to ggfortify package}
+%\VignetteIndexEntry{Plotting Survival Curves}
 -->
 
 ```{r global_options, include=FALSE}

diff --git a/vignettes/plot_ts.Rmd b/vignettes/plot_ts.Rmd
@@ -5,7 +5,7 @@ output: html_document
 
 <!--
 %\VignetteEngine{knitr::knitr}
-%\VignetteIndexEntry{Introduction to ggfortify package}
+%\VignetteIndexEntry{Plotting Time Series}
 -->
 
 ```{r global_options, include=FALSE}