bump version and release v0.0.1 on Github

Author: earowang

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: sugrrants
 Title: Supporting Graphics with R for Analysing Time Series
-Version: 0.0.0.9000
+Version: 0.0.1
 Authors@R: c(
     person("Earo", "Wang", email = "earo.wang@gmail.com", role = c("aut", "cre")),
     person("Di", "Cook", role = "aut"),

NEWS.md

@@ -1,4 +1,4 @@
-# sugrrants 0.0.0.9000
+# sugrrants 0.0.1
 
 * Added a `NEWS.md` file to track changes to the package.
 * Added `frame_calendar` function to compute calendar grids for visual representation of temporal data, coupled with a vignette.

R/draw-key.R

@@ -4,8 +4,12 @@
 #'
 #' @return A grid grob
 #'
+#' @keywords internal
+#' @name draw-key
+NULL
+
+#' @rdname draw-key
 #' @export
-#' 
 draw_key_acf <- function(data, params, size) {
   grobTree(
     draw_key_rect(data, params),

R/geom-acf.R

@@ -2,7 +2,7 @@
 #'
 #' @description Since the data input is `data.frame`, it's better to sort
 #'  the date-times from early to recent and make implicit missing values explicit
-#'  before using `geom_acfbar`.
+#'  before using `geom_acf`.
 #'  
 #' @inheritParams ggplot2::geom_bar
 #' @param lag.max An integer indicating the maximum lag at which to calculate the
@@ -14,7 +14,6 @@
 #'    default is the "correlation" and other options are "covariance" and "partial".
 #'
 #' @author Earo Wang
-#' 
 #' @rdname sugrrants-geom
 #'
 #' @examples
@@ -28,7 +27,6 @@
 #'      geom_acf()
 #'
 #' @export
-#'
 geom_acf <- function(mapping = NULL, data = NULL, 
   position = "identity", na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, 
   lag.max = NULL, type = "correlation", level = 0.95, ...) {

R/stat-acf.R

@@ -26,7 +26,6 @@
 #'      stat_acf(geom = "bar")
 #'
 #' @export
-#'
 stat_acf <- function(mapping = NULL, data = NULL, geom = "bar",
   position = "identity", na.rm = FALSE, show.legend = NA, 
   inherit.aes = TRUE, lag.max = NULL, type = "correlation", level = 0.95, ...) {

README.Rmd

@@ -23,11 +23,11 @@ The goal of *sugrrants* is to provide supporting graphics with R for analysing t
 
 ## Installation
 
-You could install the development version from Github using
+The recommended development version is **v0.0.1** and you could install the version from Github using
 
 ```r
 # install.packages("devtools")
-devtools::install_github("earowang/sugrrants", build_vignettes = TRUE)
+devtools::install_github("earowang/sugrrants@v0.0.1", build_vignettes = TRUE)
 ```
 
 ## Usage

README.md

@@ -13,11 +13,11 @@ The goal of *sugrrants* is to provide supporting graphics with R for analysing t
 Installation
 ------------
 
-You could install the development version from Github using
+The recommended development version is **v0.0.1** and you could install the version from Github using
 
 ``` r
 # install.packages("devtools")
-devtools::install_github("earowang/sugrrants", build_vignettes = TRUE)
+devtools::install_github("earowang/sugrrants@v0.0.1", build_vignettes = TRUE)
 ```
 
 Usage

_pkgdown.yml

@@ -10,6 +10,26 @@ authors:
   Rob Hyndman:
     href: http://robjhyndman.com
 
+reference:
+- title: Calendar based graphics
+  desc: >
+    Rearrange `data.frame` or `tibble` into a calendar layout using linear
+    algebra tools for plotting via `ggplot2`.
+  contents:
+    - frame_calendar
+
+- title: Geoms and stats extension to temporal-context data
+  contents:
+    - starts_with("geom_")
+    - starts_with("stat_")
+
+- title: Handle with temporal components
+  desc: >
+    These functions provides tools to handle with temporal components.
+  contents:
+    - au_holiday
+    - wday2
+
 navbar:
   type: default
   left: