merge for dev

Merge branch 'main' of https://github.com/JoFrhwld/densityarea

# Conflicts:
#	DESCRIPTION
#	usethis_hist.R

.Rbuildignore

@@ -10,3 +10,6 @@ usethis_hist.R
 ^_pkgdown\.yml$
 ^docs$
 ^pkgdown$
+^cran-comments\.md$
+.DS_Store
+^CRAN-SUBMISSION$

.gitignore

@@ -4,3 +4,4 @@
 .Ruserdata
 inst/doc
 docs
+.DS_Store

DESCRIPTION

@@ -1,13 +1,13 @@
 Type: Package
 Package: densityarea
-Title: Areas of Bivarate Density Distributions
+Title: Polygons of Bivariate Density Distributions
 Version: 0.1.0.9000
 Authors@R: 
-    person("Josef", "Fruehwald", , "jofrhwld@gmail.com", role = c("aut", "cre"))
-Description: With bivarate data, it is possible to calculate 2-dimensional
-    kernel density estimates that return polygons at given levels of
-    probablity. `densityarea` returns these polygons for analysis,
-    including for calculating their area.
+    person("Josef", "Fruehwald", , "jofrhwld@gmail.com", role = c("aut", "cre", "cph"))
+Description: With bivariate data, it is possible to calculate
+    2-dimensional kernel density estimates that return polygons at given
+    levels of probability. 'densityarea' returns these polygons for
+    analysis, including for calculating their area.
 License: GPL (>= 3)
 URL: https://github.com/JoFrhwld/densityarea,
     https://jofrhwld.github.io/densityarea/

NEWS.md

@@ -0,0 +1,3 @@
+# densityarea 0.1.0
+
+* Initial CRAN submission.

R/data.R

@@ -25,13 +25,13 @@
 #' Amogh Gulati, Coles, A., Veaux, C., Eren, E., Mishra, H., Paweł Potrykus,
 #' Jung, S., Sereda, T., Mestrou, T., Michaelasocolof, & Vannawillerton. (2022).
 #' MontrealCorpusTools/Montreal-Forced-Aligner: Version 2.0.1 (v2.0.1)
-#' [https://doi.org/10.5281/ZENODO.6658586](https://doi.org/10.5281/ZENODO.6658586)
+#' \doi{10.5281/ZENODO.6658586}
 
 #'
 #' @source Pitt, M. A., Dilley, L., Johnson, K., Kiesling, S., Raymond, W.,
 #' Hume, E., & Fosler-Lussier, E. (2007). Buckeye Corpus of Conversational
 #' Speech (2nd release). Department of Psychology, Ohio State University.
-#' [www.buckeyecorpus.osu.edu](www.buckeyecorpus.osu.edu)
+#' [https://buckeyecorpus.osu.edu/](https://buckeyecorpus.osu.edu/)
 #'
 #' @source Rosenfelder, I., Fruehwald, J., Brickhouse, C., Evanini, K.,
 #' Seyfarth, S., Gorman, K., Prichard, H., & Yuan, J. (2022).

R/density_area.R

@@ -289,6 +289,27 @@ density_polygons <- function(x,
 #' If only one or the other of `rangex` and `rangey` are defined, `range_mult`
 #' will be used to produce the range of the undefined one.
 #'
+#' @returns A list of data frames, if `as_list=TRUE`, or just a data frame,
+#' if `as_list=FALSE`.
+#'
+#' ## Data frame output
+#'
+#' If `as_sf=FALSE`, the data frame has the following columns:
+#' \describe{
+#'  \item{level_id}{An integer id for each probability level}
+#'  \item{prob}{The probability level (originally passed to `probs`)}
+#'  \item{area}{The area of the HDR polygon}
+#' }
+#'
+#' ## sf output
+#' If `as_sf=TRUE`, the data frame has the following columns:
+#' \describe{
+#'  \item{level_id}{An integer id for each probability level}
+#'  \item{prob}{The probability level (originally passed to `probs`)}
+#'  \item{geometry}{The `sf::st_polygon()` of the HDR}
+#'  \item{area}{The area of the HDR polygon}
+#' }
+#'
 #' @example inst/examples/density_area_example.R
 #'
 #' @importFrom dplyr .data

README.Rmd

@@ -21,11 +21,12 @@ knitr::opts_chunk$set(
 
 <!-- badges: start -->
 [![R-CMD-check](https://github.com/JoFrhwld/densityarea/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/JoFrhwld/densityarea/actions/workflows/R-CMD-check.yaml)
+[![CRAN status](https://www.r-pkg.org/badges/version/densityarea)](https://CRAN.R-project.org/package=densityarea)
 <!-- badges: end -->
 
 The package `{ggdensity}`[^1] allows for plotting interpretable bivariate densities by using highest density ranges (HDRs). For example:
 
-[^1]: Otto J, Kahle D (2023). *ggdensity: Interpretable Bivariate Density Visualization with 'ggplot2'*. <https://jamesotto852.github.io/ggdensity/,> <https://github.com/jamesotto852/ggdensity/.>
+[^1]: Otto J, Kahle D (2023). *ggdensity: Interpretable Bivariate Density Visualization with 'ggplot2'*. <https://jamesotto852.github.io/ggdensity/> <https://github.com/jamesotto852/ggdensity/>
 
 ```{r}
 #| fig-width: 4
@@ -50,7 +51,12 @@ ggplot(df, aes(x,y))+
 
 ## Installation
 
-You can install the development version of `{densityarea}` from [GitHub](https://github.com/) with:
+You can install `{densityarea}` from CRAN with:
+```r
+install.packages("densityarea")
+```
+
+Or you can install the development version from [GitHub](https://github.com/) with:
 
 ``` r
 # install.packages("devtools")

README.md

@@ -6,6 +6,8 @@
 <!-- badges: start -->
 
 [![R-CMD-check](https://github.com/JoFrhwld/densityarea/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/JoFrhwld/densityarea/actions/workflows/R-CMD-check.yaml)
+[![CRAN
+status](https://www.r-pkg.org/badges/version/densityarea)](https://CRAN.R-project.org/package=densityarea)
 <!-- badges: end -->
 
 The package `{ggdensity}`[^1] allows for plotting interpretable
@@ -34,7 +36,13 @@ analysis.
 
 ## Installation
 
-You can install the development version of `{densityarea}` from
+You can install `{densityarea}` from CRAN with:
+
+``` r
+install.packages("densityarea")
+```
+
+Or you can install the development version from
 [GitHub](https://github.com/) with:
 
 ``` r
@@ -98,5 +106,5 @@ information on using spatial polygons, see `vignette("sf-operations")`.
 
 [^1]: Otto J, Kahle D (2023). *ggdensity: Interpretable Bivariate
     Density Visualization with ‘ggplot2’*.
-    <https://jamesotto852.github.io/ggdensity/,>
-    <https://github.com/jamesotto852/ggdensity/.>
+    <https://jamesotto852.github.io/ggdensity/>
+    <https://github.com/jamesotto852/ggdensity/>

cran-comments.md

@@ -0,0 +1,41 @@
+## Resubmission
+
+This is a resubmission. In this version I have:
+
+* written the package name in single quotes in the description.
+* There are no references in the description field to format.
+
+## R CMD check results
+
+0 errors | 0 warnings | 1 note
+
+* This is a new release.
+
+## Included data license information
+
+* `s01.txt` is "supplemental annotation" of Buckeye Corpus content, which
+licensees are permitted to distribute.
+
+The relevant section of the Buckeye Corpus License reads:
+
+> Licensee may augment the Content with supplemental
+annotations. Such annotations shall not constitute a
+derivative work or Improvement of the Content.
+Furthermore, these supplemental annotations may be
+distributed by the Licensee as long as the other
+provisions of this Agreement are satisfied, including but
+not limited to the prohibition on the creation of
+derivative works or the distribution of the Content by the
+Licensee.
+
+## Description refrerences
+
+There are no references in my package description.
+
+## Spellcheck
+
+Words flagged as possibly misspelled are not misspelled:
+
+* bivariate
+* densityarea
+

inst/examples/density_area_example.R

@@ -1,9 +1,9 @@
 library(densityarea)
-library(ggplot2)
 library(dplyr)
-library(tidyr)
 library(sf)
 
+ggplot2_inst <- require(ggplot2)
+
 # basic usage
 
 set.seed(10)
@@ -18,9 +18,11 @@ density_area(x,
 head(poly_areas_df)
 
 # Plotting the relationship between probability level and area
-ggplot(poly_areas_df,
-       aes(prob, area)) +
-  geom_line()
+if(ggplot2_inst){
+  ggplot(poly_areas_df,
+         aes(prob, area)) +
+    geom_line()
+}
 
 # Tidyverse usage
 
@@ -42,9 +44,11 @@ s01 |>
                        probs = ppoints(10))) ->
   s01_areas_df
 
-s01_areas_df |>
-  ggplot(aes(prob, area)) +
-  geom_line()
+if(ggplot2_inst){
+  s01_areas_df |>
+    ggplot(aes(prob, area)) +
+    geom_line()
+}
 
 ### Including sf output
 
@@ -57,7 +61,9 @@ s01 |>
   st_sf() ->
   s01_areas_sf
 
-s01_areas_sf |>
-  arrange(desc(prob)) |>
-  ggplot() +
-  geom_sf(aes(fill = area))
+if(ggplot2_inst){
+  s01_areas_sf |>
+    arrange(desc(prob)) |>
+    ggplot() +
+    geom_sf(aes(fill = area))
+}

inst/examples/density_polygon_example.R

@@ -1,10 +1,11 @@
 library(densityarea)
-library(ggplot2)
 library(dplyr)
 library(purrr)
-library(tidyr)
 library(sf)
 
+ggplot2_inst <- require(ggplot2)
+tidyr_inst <- require(tidyr)
+
 set.seed(10)
 x <- c(rnorm(100))
 y <- c(rnorm(100))
@@ -18,10 +19,11 @@ head(poly_df)
 
 # It's necessary to specify a grouping factor that combines `level_id` and `id`
 # for cases of multimodal density distributions
-ggplot(poly_df, aes(x, y)) +
-  geom_path(aes(group = paste0(level_id, id),
-                color = prob))
-
+if(ggplot2_inst){
+  ggplot(poly_df, aes(x, y)) +
+    geom_path(aes(group = paste0(level_id, id),
+                  color = prob))
+}
 
 # sf output
 poly_sf <- density_polygons(x,
@@ -32,11 +34,12 @@ poly_sf <- density_polygons(x,
 head(poly_sf)
 
 # `geom_sf()` is from the `{sf}` package.
-poly_sf |>
-  arrange(desc(prob)) |>
-  ggplot() +
-  geom_sf(aes(fill = prob))
-
+if(ggplot2_inst){
+  poly_sf |>
+    arrange(desc(prob)) |>
+    ggplot() +
+    geom_sf(aes(fill = prob))
+}
 
 # Tidyverse usage
 
@@ -56,11 +59,13 @@ s01 |>
                            probs = ppoints(5))) ->
   speaker_poly_df
 
-speaker_poly_df |>
-  ggplot(aes(log_F2, log_F1)) +
-  geom_path(aes(group = paste0(level_id, id),
-                color = prob)) +
-  coord_fixed()
+if(ggplot2_inst){
+  speaker_poly_df |>
+    ggplot(aes(log_F2, log_F1)) +
+    geom_path(aes(group = paste0(level_id, id),
+                  color = prob)) +
+    coord_fixed()
+}
 
 ### sf output
 s01 |>
@@ -72,34 +77,39 @@ s01 |>
   st_sf() ->
   speaker_poly_sf
 
-speaker_poly_sf |>
-  ggplot() +
-  geom_sf(aes(color = prob),
-          fill = NA)
+if(ggplot2_inst){
+  speaker_poly_sf |>
+    ggplot() +
+    geom_sf(aes(color = prob),
+            fill = NA)
+}
 
 ## basic usage with dplyr::summarise()
 ### data frame output
 
-s01 |>
-  group_by(name) |>
-  summarise(poly = density_polygons(log_F2,
-                                    log_F1,
-                                    probs = ppoints(5),
-                                    as_list = TRUE)) |>
-  unnest(poly) ->
-  speaker_poly_df
-
+if(tidyr_inst){
+  s01 |>
+    group_by(name) |>
+    summarise(poly = density_polygons(log_F2,
+                                      log_F1,
+                                      probs = ppoints(5),
+                                      as_list = TRUE)) |>
+    unnest(poly) ->
+    speaker_poly_df
+}
 ### sf output
 
-s01 |>
-  group_by(name) |>
-  summarise(poly = density_polygons(
-    log_F2,
-    log_F1,
-    probs = ppoints(5),
-    as_list = TRUE,
-    as_sf = TRUE
-  )) |>
-  unnest(poly) |>
-  st_sf() ->
-  speaker_poly_sf
+if(tidyr_inst){
+  s01 |>
+    group_by(name) |>
+    summarise(poly = density_polygons(
+      log_F2,
+      log_F1,
+      probs = ppoints(5),
+      as_list = TRUE,
+      as_sf = TRUE
+    )) |>
+    unnest(poly) |>
+    st_sf() ->
+    speaker_poly_sf
+}