change geom_missing_point to geom_miss_point

NAMESPACE

@@ -8,8 +8,8 @@ S3method(shadow_shift,default)
 S3method(shadow_shift,factor)
 S3method(shadow_shift,numeric)
 export("%>%")
-export(GeomMissingPoint)
-export(StatMissingPoint)
+export(GeomMissPoint)
+export(StatMissPoint)
 export(add_any_miss)
 export(add_label_missings)
 export(add_label_shadow)
@@ -24,7 +24,7 @@ export(cast_shadow_shift)
 export(cast_shadow_shift_label)
 export(draw_key_missing_point)
 export(gather_shadow)
-export(geom_missing_point)
+export(geom_miss_point)
 export(gg_miss_case)
 export(gg_miss_fct)
 export(gg_miss_span)
@@ -51,7 +51,7 @@ export(prop_complete)
 export(prop_miss)
 export(replace_to_na)
 export(shadow_shift)
-export(stat_missing_point)
+export(stat_miss_point)
 export(vis_miss)
 import(ggplot2)
 import(rlang)

NEWS.md

@@ -1,10 +1,14 @@
-# narnia 0.0.9.9300 (2017/07/24)
+# narnia 0.0.9.4000 (2017/07/24)
 
 ## new features
 
 - `replace_to_na` is a complement to `tidyr::replace_na` and replaces a specified
 value from a variable to NA.
 
+## breaking changes
+
+- changed `geom_missing_point()` to `geom_miss_point()`.
+
 # narnia 0.0.9.9201 (2017/07/12)
 
 ## new features

R/geom-missing-point.R

@@ -1,10 +1,10 @@
 #' Plot Missing Data Points
 #'
-#' \code{geom_missing_point} adds a missing data point geometry to ggplot2. This displays missing data points 10% below the minimum value.
+#' \code{ geom_miss_point} adds a missing data point geometry to ggplot2. This displays missing data points 10% below the minimum value.
 #'
 #' @include legend-draw.R
-#' @name geom_missing_point
-#' @title geom_missing_point
+#' @name  geom_miss_point
+#' @title  geom_miss_point
 #' @note Warning message if na.rm = T is supplied.
 #'
 #' @param mapping Set of aesthetic mappings created by \code{\link[ggplot2]{aes}}
@@ -55,14 +55,14 @@
 #'            y = Solar.R)) +
 #' geom_point()
 #'
-#' # using geom_missing_point()
+#' # using  geom_miss_point()
 #' ggplot(airquality,
 #'        aes(x = Ozone,
 #'            y = Solar.R)) +
-#' geom_missing_point()
+#'  geom_miss_point()
 #'
 #' @export
-geom_missing_point <- function(mapping = NULL,
+ geom_miss_point <- function(mapping = NULL,
                                data = NULL,
                                # stat = "identity",
                                position = "identity",
@@ -74,8 +74,8 @@ geom_missing_point <- function(mapping = NULL,
   layer(
     data = data,
     mapping = mapping,
-    stat = StatMissingPoint,
-    geom = GeomMissingPoint,
+    stat = StatMissPoint,
+    geom = GeomMissPoint,
     position = position,
     show.legend = show.legend,
     inherit.aes = inherit.aes,
@@ -91,7 +91,7 @@ geom_missing_point <- function(mapping = NULL,
 #' @format NULL
 #' @usage NULL
 #' @export
-GeomMissingPoint <- ggproto("GeomMissingPoint", GeomPoint,
+GeomMissPoint <- ggproto("GeomMissPoint", GeomPoint,
                             required_aes = c("x", "y"),
                             default_aes = aes(shape = 19,
                                               colour = ..missing..,

R/stat-missing-point.R

@@ -1,6 +1,6 @@
-#' @name stat_missing_point
-#' @title stat_missing_point
-#' @description stat_missing_point adds a geometry for displaying missingness to geom_point
+#' @name stat_miss_point
+#' @title stat_miss_point
+#' @description stat_miss_point adds a geometry for displaying missingness to geom_point
 #'
 #' @note Very first attempt at creating a geom that is compatible with ggplot2.
 #' Data plotting works. Still todo:
@@ -43,7 +43,7 @@
 #' }
 #'
 #' @export
-stat_missing_point <- function(mapping = NULL,
+stat_miss_point <- function(mapping = NULL,
                                data = NULL,
                                geom = "point",
                                position = "identity",
@@ -52,7 +52,7 @@ stat_missing_point <- function(mapping = NULL,
                                inherit.aes = TRUE,
                                ...) {
   ggplot2::layer(
-    stat = StatMissingPoint,
+    stat = StatMissPoint,
     data = data,
     mapping = mapping,
     geom = geom,
@@ -66,7 +66,7 @@ stat_missing_point <- function(mapping = NULL,
 
 #' @rdname narnia-ggproto
 #' @export
-StatMissingPoint <- ggproto("StatMissingPoint", Stat,
+StatMissPoint <- ggproto("StatMissPoint", Stat,
     required_aes = c("x", "y"),
     default_aes = aes(colour = ..missing..),
     setup_data = function(data, params){

README.Rmd

@@ -27,7 +27,7 @@ Currently it provides:
     + `gather_shadow()`
     + `is_na()`
 - Visualisation methods: 
-    + `geom_missing_point()`
+    + `geom_miss__point()`
     + `gg_miss_var()`
     + `gg_miss_case()`
     + `gg_miss_which()`
@@ -66,7 +66,7 @@ __Illustration of data structures for facilitating visualisation of missings and
 
 # Visualising missing data
 
-Visualising missing data might sound a little strange - how do you visualise something that is not there?  One approach to visualising missing data comes from ggobi and manet, where we replace "NA" values with values 10% lower than the minimum value in that variable. This is provided with the `geom_missing_point()` ggplot2 geom, which we can illustrate by exploring the relationship between Ozone and Solar radiation from the airquality dataset.
+Visualising missing data might sound a little strange - how do you visualise something that is not there?  One approach to visualising missing data comes from ggobi and manet, where we replace "NA" values with values 10% lower than the minimum value in that variable. This is provided with the `geom_miss_point()` ggplot2 geom, which we can illustrate by exploring the relationship between Ozone and Solar radiation from the airquality dataset.
 
 
 ```{r regular-geom-point}
@@ -82,7 +82,7 @@ ggplot(data = airquality,
 
 ggplot2 does not handle these missing values, and we get a warning message about the missing values.
 
-We can instead use the `geom_missing_point()` to display the missing data
+We can instead use the `geom_miss_point()` to display the missing data
 
 ```{r geom-missing-point}
 
@@ -91,11 +91,11 @@ library(narnia)
 ggplot(data = airquality,
        aes(x = Ozone,
            y = Solar.R)) +
-  geom_missing_point()
+  geom_miss_point()
 
 ```
 
-`geom_missing_point()` has shifted the missing values to now be 10% below the minimum value. The missing values are a different colour so that missingness becomes pre-attentive.
+`geom_miss_point()` has shifted the missing values to now be 10% below the minimum value. The missing values are a different colour so that missingness becomes pre-attentive.
 
 This plays nicely with other parts of ggplot, like adding transparency
 
@@ -104,7 +104,7 @@ This plays nicely with other parts of ggplot, like adding transparency
 ggplot(data = airquality,
        aes(x = Ozone,
            y = Solar.R)) +
-  geom_missing_point(alpha = 0.5)
+  geom_miss_point(alpha = 0.5)
 
 ```
 
@@ -120,7 +120,7 @@ p1 <-
 ggplot(data = airquality,
        aes(x = Ozone,
            y = Solar.R)) + 
-  geom_missing_point() + 
+  geom_miss_point() + 
   facet_wrap(~Month, ncol = 2) + 
   theme(legend.position = "bottom")
 
@@ -286,7 +286,7 @@ gg_miss_which(airquality)
 
 # Future Work
 
-Other plans to extend the `geom_missing_` family to include:
+Other plans to extend the `geom_miss_` family to include:
 
 - Categorical variables
 - Bivariate plots: Scatterplots, Density overlays.

_pkgdown.yml

@@ -7,8 +7,7 @@ reference:
     desc: >
       Visualise missing data
     contents:
-      - geom_missing_point
-      - stat_missing_point
+      - geom_miss_point
       - starts_with("gg_miss_")
   - title: "Numerical Summaries"
     desc: >

inst/shadow-mechanics.Rmd

@@ -326,7 +326,7 @@ airquality %>%
 
 Which is rather involved, and not ideal, because you are no longer trying to explore missing data, you have now spent time working out how to correctly shift missing values, keep track of what was missing, and then plot this, then add colour, and identify which values are missing.
 
-To make this simpler, you use the ggplot2 geom `geom_missing_point()`. This `geom` allows for missing values to be displayed, and also works with the rest of ggplot2 - themes, and facets as well.
+To make this simpler, you use the ggplot2 geom `geom_miss_point()`. This `geom` allows for missing values to be displayed, and also works with the rest of ggplot2 - themes, and facets as well.
 
 **What is imputed, stays imputed**
 

tests/testthat/test-plots.R

@@ -1,14 +1,14 @@
 context("narnia-plots")
 
 library(ggplot2)
-geom_missing_point_plot <-
+geom_miss_point_plot <-
   ggplot(airquality,
          aes(x = Solar.R,
              y = Ozone)) +
-  geom_missing_point()
+  geom_miss_point()
 
 test_that("geom_miss_point works",{
-  vdiffr::expect_doppelganger("geom_missing_point", geom_missing_point_plot)
+  vdiffr::expect_doppelganger("geom_miss_point", geom_miss_point_plot)
 })
 
 

vignettes/getting-started-w-narnia.Rmd

@@ -111,7 +111,7 @@ ggplot(airquality,
 
 The problem with this is that ggplot does not handle missings be default, and removes the missing values. This makes them hard to explore.
 
-Visualising missing data might sound a little strange - how do you visualise something that is not there?  One approach to visualising missing data comes from ggobi and manet, where we replace "NA" values with values 10% lower than the minimum value in that variable. This is provided with the `geom_missing_point()` ggplot2 geom, which we can illustrate by exploring the relationship between Ozone and Solar radiation from the airquality dataset.
+Visualising missing data might sound a little strange - how do you visualise something that is not there?  One approach to visualising missing data comes from ggobi and manet, where we replace "NA" values with values 10% lower than the minimum value in that variable. This is provided with the `geom_miss_point()` ggplot2 geom, which we can illustrate by exploring the relationship between Ozone and Solar radiation from the airquality dataset.
 
 ```{r echo = TRUE, out.width = "50%", fig.show='hold'}
 
@@ -125,7 +125,7 @@ library(narnia)
 ggplot(airquality, 
        aes(x = Solar.R, 
            y = Ozone)) + 
-  geom_missing_point()
+  geom_miss_point()
 
 ```
 
@@ -136,7 +136,7 @@ ggplot(airquality,
 ggplot(airquality, 
        aes(x = Solar.R, 
            y = Ozone)) + 
-  geom_missing_point() + 
+  geom_miss_point() + 
   facet_wrap(~Month)
 
 ```
@@ -148,7 +148,7 @@ ggplot(airquality,
 ggplot(airquality, 
        aes(x = Solar.R, 
            y = Ozone)) + 
-  geom_missing_point() + 
+  geom_miss_point() + 
   facet_wrap(~Month) + 
   theme_dark()
 
@@ -410,12 +410,12 @@ The tools in `narnia` help us identify where missingness is, while maintaining a
 
 - Continue making `narnia` play well with `dplyr` and `tidyr`.
 - Make `narnia` work with big data tools like `sparklyr`, and `sparklingwater`.
-- Expand ggplot `geom_missing_*` family.
+- Expand ggplot `geom_miss_*` family.
 - Further develop methods for handling and visualising imputations, and multiple imputation.
 
 # Thank you
 
-A huge thanks of course to Di Cook and Miles McBain for their assistance on this project, and its development. In particular, I thank Di for providing the large conceptual ideas of how to display missings, and the use of the shadow matrix. I also thank Miles for his instrumental work in getting `stat_missing_point` and `geom_missing_point()` working as they do now.
+A huge thanks of course to Di Cook and Miles McBain for their assistance on this project, and its development. In particular, I thank Di for providing the large conceptual ideas of how to display missings, and the use of the shadow matrix. I also thank Miles for his instrumental work in getting `stat_missing_point` and `geom_miss_point()` working as they do now.
 
 # References