fixed links

Author: earowang

.Rbuildignore

@@ -14,3 +14,4 @@
 ^cran-comments\.md$
 ^\.github$
 ^\.github/workflows/R-CMD-check\.yaml$
+^CRAN-SUBMISSION$

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: sugrrants
 Title: Supporting Graphs for Analysing Time Series
-Version: 0.2.8
+Version: 0.2.9
 Authors@R: 
     c(person(given = "Earo",
              family = "Wang",
@@ -46,4 +46,4 @@ Encoding: UTF-8
 Language: en-GB
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.1.1
+RoxygenNote: 7.2.3

R/prettify.R

@@ -7,7 +7,7 @@
 #' "text" are used. If "text2" is specified for the "monthly" calendar only,
 #' it will add day of month to the `ggplot` object.
 #' @param locale ISO 639 language code. The default is "en" (i.e. US English).
-#' For other languages support, package **readr** needs to be installed. 
+#' For other languages support, package **readr** needs to be installed.
 #' See [readr::locale] for more details.
 #' @param abbr Logical to specify if the abbreviated version of label should be
 #' used.
@@ -48,11 +48,11 @@ prettify.plotly <- function(plot, label = c("label", "text"), locale, abbr = TRU
   label <- lst$label
   text <- lst$text
   if ("label" %in% label_arg) {
-    plot <- plot %>% 
+    plot <- plot %>%
       plotly::add_text(x = ~ x, y = ~ y, text = ~ label, data = label, ...)
   }
   if ("text" %in% label_arg) {
-    plot <- plot %>% 
+    plot <- plot %>%
       plotly::add_text(x = ~ x, y = ~ y - 0.03, text = ~ label, data = text, ...)
   }
   if ("text2" %in% label_arg) {
@@ -90,6 +90,7 @@ prettify.ggplot <- function(plot, label = c("label", "text"), locale, abbr = TRU
     text_param <- text2_param <- param_list[which(names_param %in% text_all)]
   }
 
+  label_arg <- c("label", "text")
   if ("label" %in% label_arg) {
     label_param$data <- label
     label_param$mapping <- aes(x, y, label = label)

README.Rmd

@@ -16,7 +16,6 @@ options(tibble.print_min = 5)
 # sugrrants
 
 [![R build status](https://github.com/earowang/sugrrants/workflows/R-CMD-check/badge.svg)](https://github.com/earowang/sugrrants/actions?workflow=R-CMD-check)
-[![Coverage Status](https://codecov.io/gh/earowang/sugrrants/branch/master/graph/badge.svg)](https://codecov.io/github/earowang/sugrrants?branch=master)
 [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/sugrrants)](https://cran.r-project.org/package=sugrrants)
 [![Downloads](http://cranlogs.r-pkg.org/badges/sugrrants?color=brightgreen)](https://cran.r-project.org/package=sugrrants)
 
@@ -75,7 +74,7 @@ This package is part of the project---[Tidy data structures and visual methods t
 
 A new function `frame_calendar()` [[here](https://github.com/earowang/sugrrants/blob/master/R/frame-calendar.R) and [here](https://github.com/earowang/sugrrants/blob/master/R/calendar-fun.R)] in the **sugrrants** package has been developed and documented for calendar-based graphics. I have also written a vignette [[source](https://github.com/earowang/sugrrants/blob/master/vignettes/frame-calendar.Rmd) and [reader view](https://pkg.earo.me/sugrrants/articles/frame-calendar.html)], which introduces and demonstrates the usage of the `frame_calendar()` function. [Many unit tests](https://github.com/earowang/sugrrants/blob/master/tests/testthat/test-calendar.R) have been carried out to ensure the expected performance of this function. The function implements non-standard evaluation and highlights the [tidy evaluation](https://rlang.r-lib.org) in action. The initial release (v0.1.0) of the package has been published on [CRAN](https://CRAN.R-project.org/package=sugrrants) during the gsoc summer time.
 
-I have initialised a new R package [**tsibble**](https://github.com/earowang/tsibble) for tidy temporal data, as part of the project. The `tsibble()` function constructs a new `tbl_ts` class for temporal data, and the `as_tsibble()` helps to convert a few `ts` objects into the `tbl_ts` class. Some key verbs (generics) from the **dplyr** package, such as `mutate()`, `summarise()`, `filter()`, have been defined and developed for the `tbl_ts` data class. The **tsibble** package was highly experimental over the period of the gsoc [[commits](https://github.com/earowang/tsibble/commit/aba1cfc2eec88966c43232fe5d249522f88e1e27)], and these functions are very likely to be changed or improved in the future.
+I have initialised a new R package [**tsibble**](https://github.com/tidyverts/tsibble) for tidy temporal data, as part of the project. The `tsibble()` function constructs a new `tbl_ts` class for temporal data, and the `as_tsibble()` helps to convert a few `ts` objects into the `tbl_ts` class. Some key verbs (generics) from the **dplyr** package, such as `mutate()`, `summarise()`, `filter()`, have been defined and developed for the `tbl_ts` data class. The **tsibble** package was highly experimental over the period of the gsoc [[commits](https://github.com/tidyverts/tsibble/commit/aba1cfc2eec88966c43232fe5d249522f88e1e27)], and these functions are very likely to be changed or improved in the future.
 
 A new package [**rwalkr**](https://github.com/earowang/rwalkr) has been created and released on [CRAN](https://cran.r-project.org/package=rwalkr) during the gsoc summer. This package provides API to Melbourne pedestrian sensor data and arrange the data in tidy temporal data form. Two functions including [`walk_melb()`](https://github.com/earowang/rwalkr/blob/master/R/scrape.R) and [`shine_melb()`](https://github.com/earowang/rwalkr/blob/master/R/shiny.R), have been written and documented as the v0.1.0 and v0.2.0 releases on CRAN. The majority of the code for the function [`run_melb()`](https://github.com/earowang/rwalkr/blob/master/R/soda.R) has been done, but the interface needs improving after the gsoc.
 </details>

README.md

@@ -1,49 +1,50 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-sugrrants
-=========
+# sugrrants
 
 [![R build
 status](https://github.com/earowang/sugrrants/workflows/R-CMD-check/badge.svg)](https://github.com/earowang/sugrrants/actions?workflow=R-CMD-check)
-[![Coverage
-Status](https://codecov.io/gh/earowang/sugrrants/branch/master/graph/badge.svg)](https://codecov.io/github/earowang/sugrrants?branch=master)
-[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/sugrrants)](https://cran.r-project.org/package=sugrrants)
+[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/sugrrants)](https://cran.r-project.org/package=sugrrants)
 [![Downloads](http://cranlogs.r-pkg.org/badges/sugrrants?color=brightgreen)](https://cran.r-project.org/package=sugrrants)
 
 The goal of *sugrrants* is to provide supporting graphs with R for
 analysing time series data. It aims to fit into the *tidyverse* and
 grammar of graphics framework for handling temporal data.
 
-Installation
-------------
+## Installation
 
 You could install the stable version on CRAN:
 
-    install.packages("sugrrants")
+``` r
+install.packages("sugrrants")
+```
 
 You could also install the development version from Github using:
 
-    # install.packages("remotes")
-    remotes::install_github("earowang/sugrrants")
+``` r
+# install.packages("remotes")
+remotes::install_github("earowang/sugrrants")
+```
 
-Usage
------
+## Usage
 
 ### Calendar-based graphics
 
 The fully-fledged faceting calendar `facet_calendar()` unlocks
 day-to-day stories.
 
-    library(dplyr)
-    library(sugrrants)
-    hourly_peds %>%
-      filter(Date < as.Date("2016-05-01")) %>% 
-      ggplot(aes(x = Time, y = Hourly_Counts, colour = Sensor_Name)) +
-      geom_line() +
-      facet_calendar(~ Date) + # a variable contains dates
-      theme_bw() +
-      theme(legend.position = "bottom")
+``` r
+library(dplyr)
+library(sugrrants)
+hourly_peds %>%
+  filter(Date < as.Date("2016-05-01")) %>% 
+  ggplot(aes(x = Time, y = Hourly_Counts, colour = Sensor_Name)) +
+  geom_line() +
+  facet_calendar(~ Date) + # a variable contains dates
+  theme_bw() +
+  theme(legend.position = "bottom")
+```
 
 ![](man/figures/facet-calendar-1.png)<!-- -->
 
@@ -52,19 +53,20 @@ re-structuring the data into a compact calendar layout, without using
 the faceting method. It is fast and light-weight, although it does not
 preserve the values.
 
-    p <- hourly_peds %>%
-      filter(Sensor_ID == 9, Year == 2016) %>%
-      mutate(Weekend = if_else(Day %in% c("Saturday", "Sunday"), "Weekend", "Weekday")) %>%
-      frame_calendar(x = Time, y = Hourly_Counts, date = Date) %>% 
-      ggplot(aes(x = .Time, y = .Hourly_Counts, group = Date, colour = Weekend)) +
-      geom_line() +
-      theme(legend.position = "bottom")
-    prettify(p)
+``` r
+p <- hourly_peds %>%
+  filter(Sensor_ID == 9, Year == 2016) %>%
+  mutate(Weekend = if_else(Day %in% c("Saturday", "Sunday"), "Weekend", "Weekday")) %>%
+  frame_calendar(x = Time, y = Hourly_Counts, date = Date) %>% 
+  ggplot(aes(x = .Time, y = .Hourly_Counts, group = Date, colour = Weekend)) +
+  geom_line() +
+  theme(legend.position = "bottom")
+prettify(p)
+```
 
 ![](man/figures/calendar-plot-1.png)<!-- -->
 
-Google Summer of Code 2017
---------------------------
+## Google Summer of Code 2017
 
 <details>
 
@@ -94,15 +96,15 @@ release (v0.1.0) of the package has been published on
 summer time.
 
 I have initialised a new R package
-[**tsibble**](https://github.com/earowang/tsibble) for tidy temporal
+[**tsibble**](https://github.com/tidyverts/tsibble) for tidy temporal
 data, as part of the project. The `tsibble()` function constructs a new
 `tbl_ts` class for temporal data, and the `as_tsibble()` helps to
 convert a few `ts` objects into the `tbl_ts` class. Some key verbs
 (generics) from the **dplyr** package, such as `mutate()`,
 `summarise()`, `filter()`, have been defined and developed for the
 `tbl_ts` data class. The **tsibble** package was highly experimental
 over the period of the gsoc
-\[[commits](https://github.com/earowang/tsibble/commit/aba1cfc2eec88966c43232fe5d249522f88e1e27)\],
+\[[commits](https://github.com/tidyverts/tsibble/commit/aba1cfc2eec88966c43232fe5d249522f88e1e27)\],
 and these functions are very likely to be changed or improved in the
 future.
 
@@ -120,8 +122,7 @@ CRAN. The majority of the code for the function
 has been done, but the interface needs improving after the gsoc.
 </details>
 
-Miscellaneous
--------------
+## Miscellaneous
 
 The acronym of *sugrrants* is **SU**pporting **GR**aphs with **R** for
 **AN**alysing **T**ime **S**eries, pronounced as “sugar ants” that are a

cran-comments.md

@@ -1,8 +1,8 @@
-This is a resubmission, which fixed (possibly) invalid URLs.
+This is a submission to fix cran errors
 
 ## Test environments
 
-* local OS X install, R 4.0.2
+* local OS X install, R 4.3.0
 * github ci
 
 ## R CMD check results

inst/CITATION

@@ -1,6 +1,6 @@
 citHeader("To cite calendar-based graphics in publications, please use:")
 
-citEntry(entry = "article",
+bibentry(bibtype = "Article",
   author = "Earo Wang and Dianne Cook and Rob J Hyndman",
   title = "Calendar-based graphics for visualizing people's daily schedules",
   journal = "Journal of Computational and Graphical Statistics",

man/facet-calendar.Rd

@@ -3,7 +3,6 @@ Version: 1.0
 RestoreWorkspace: No
 SaveWorkspace: No
 AlwaysSaveHistory: Default
-QuitChildProcessesOnExit: Default
 
 EnableCodeIndexing: Yes
 UseSpacesForTab: Yes

man/figures/calendar-plot-1.png

@@ -25,7 +25,7 @@ options(tibble.print_min = 5)
 
 Calendar-based graphics turn out to be a useful tool for visually unfolding people's daily schedules in detail, such as hourly foot traffic in the CBD, daily residential electricity demand and etc. It arranges the values according to the corresponding dates into a calendar layout, which is comprised of weekdays in columns and weeks of a month in rows for a common monthly calendar. The idea originates from @van_wijk_cluster_1999 and is implemented in a couple of R packages ([ggTimeSeries](https://github.com/AtherEnergy/ggTimeSeries) and [ggcal](https://github.com/jayjacobs/ggcal)), yet they all are a variant of heatmap in temporal context. We extend the calendar-based graphics to a broader range of applications using linear algebra tools. For example, (1) it not only handles the data of daily intervals but also of higher frequencies like hourly data; (2) it is no longer constrained to a heatmap but can be used with other types of *Geoms*; (3) the built-in calendars include *monthly*, *weekly*, and *daily* types for the purpose of comparison between different temporal components. The `frame_calendar()` function returns the computed calendar grids as a data frame or a tibble according to its data input, and *ggplot2* takes care of the plotting as you usually do with a data frame.
 
-We are going to use Melbourne pedestrian data (shipped with the package) as an example throughout the vignette, which is sourced from [Melbourne Open Data Portal](http://www.pedestrian.melbourne.vic.gov.au). The subset of the data contains 7 sensors counting foot traffic at hourly intervals across the city of Melbourne from January to April in 2017.
+We are going to use Melbourne pedestrian data (shipped with the package) as an example throughout the vignette, which is sourced from Melbourne Open Data Portal. The subset of the data contains 7 sensors counting foot traffic at hourly intervals across the city of Melbourne from January to April in 2017.
 
 ```{r load}
 library(tidyr)