change name from ggmissing to naniar

Author: njtierney

DESCRIPTION

@@ -1,10 +1,10 @@
-Package: ggmissing
+Package: naniar
 Type: Package
-Title: enables ggplot to plot missing data
+Title: Enables ggplot to plot missing data
 Version: 0.2.9000
 Author: Nicholas Tierney, Miles McBain, Di Cook,
 Maintainer: <nicholas.tierney@gmail.com>
-Description: ggmissing helps display missing data in ggplot
+Description: naniar helps display missing data in ggplot
 License: MIT + file LICENSE
 LazyData: TRUE
 Suggests:

R/geom_missing_histogram.R

@@ -2,7 +2,7 @@
 #' @name geom_missing_histogram
 #' @description geom_missing_histogram adds a point geometry for displaying missingness.
 #' @note Very first attempt at creating a geom that is compatible with ggplot2.
-#' 
+#'
 #' Data plotting works. Still todo:
 #' manipulate the colour aes so that the colours and legend appear.
 #' fix awful default point sizes.
@@ -39,10 +39,10 @@ geom_missing_histogram <- function(mapping = NULL,
       ...
     )
   )
-  
+
 }
 
-#' @rdname ggmissing-ggproto
+#' @rdname naniar-ggproto
 #' @export
 GeomMissingHistogram <- ggproto("GeomMissingHistogram", GeomRect,
 required_aes = "x",

R/geom_missing_point.R

@@ -11,7 +11,7 @@
 #'
 # this code messes up the documentation
 # library(ggplot2)
-# library(ggmissing)
+# library(naniar)
 #
 #  ggplot(data = brfss,
 #         aes(x = PHYSHLTH,
@@ -50,7 +50,7 @@ geom_missing_point <- function(mapping = NULL,
 
 }
 
-#' @rdname ggmissing-ggproto
+#' @rdname naniar-ggproto
 #' @export
 GeomMissingPoint <- ggproto("GeomMissingPoint", GeomPoint,
                             required_aes = c("x", "y"),

R/ggmissing-ggproto.R

@@ -13,7 +13,7 @@
 #'
 #' @examples
 #'
-#' library(ggmissing)
+#' library(naniar)
 #' percent_missing_df(airquality)
 #'
 percent_missing_df <- function(dat){
@@ -36,7 +36,7 @@ percent_missing_df <- function(dat){
 #'
 #' @examples
 #'
-#' library(ggmissing)
+#' library(naniar)
 #'
 #' percent_missing_var(airquality)
 #'
@@ -65,7 +65,7 @@ percent_missing_var <- function(dat){
 #'
 #' @examples
 #'
-#' library(ggmissing)
+#' library(naniar)
 #' percent_missing_case(airquality)
 #'
 percent_missing_case <- function(dat){

R/missing_data_tidiers.R

@@ -0,0 +1,6 @@
+#' @name naniar-ggproto
+#' @title naniar-ggroto
+#'
+#' @description These are the stat and geom overrides using ggproto from ggplot2 that make naniar work.
+#'
+NULL

R/naniar-ggproto.R

@@ -0,0 +1,18 @@
+# derive_shadows:
+# helpers to clear up common representations of missing values
+# such as "NA", "N/A", etc.
+# and might allow for an easier way for users to describe different
+# missing data codes, such as -99, which might indicate missing, but
+# some other kind of missing value, perhaps a different mechanism of
+# missingness
+# ideas for function names:
+# narify (play on clarify)
+# darken ()
+# shade
+# refract
+
+# Other commands that might be useful?
+# `is_na`
+# `fill_na`
+# `drop_na`
+# `is_null`

R/ggmissing-package.R R/naniar-package.RR/ggmissing-package.R renamed to R/naniar-package.R

@@ -0,0 +1,87 @@
+
+# give NAs a more meaningful label
+is_na <- function(x) {
+  factor(is.na(x), levels = c(FALSE, TRUE), labels = c("!NA", "NA"))
+}
+
+# append some shadow cols
+
+# return a tibble that is a shadow matrix form.
+
+as_shadow <- function(data){
+
+  data_shadow <- purrr::map_df(data, is_na)
+
+  names(data_shadow) <- paste0(names(data),"_NA")
+
+  data_shadow
+
+}
+
+# as_shadow(airquality)
+
+bind_shadow <- function(data){
+
+  # data_shadow <- map_df(data, is_na)
+  # names(data_shadow) <- paste0(names(data),"_NA")
+  data_shadow <- as_shadow(data)
+
+  bound_shadow <- tibble::as_tibble(dplyr::bind_cols(data, data_shadow))
+
+  bound_shadow
+
+}
+
+ggplot(data = bind_shadow(airquality),
+       aes(x = Ozone)) +
+  geom_histogram() +
+  facet_wrap(~Solar.R_NA,
+             ncol = 1)
+#
+ggplot(data = airquality,
+       aes(x = Ozone)) +
+  geom_histogram() +
+  facet_wrap(~is_na(Solar.R),
+             ncol = 1)
+#
+# ggplot(bind_shadow(airquality),
+#        aes(x = Ozone,
+#            colour = Solar.R_NA)) +
+#   geom_density() +
+#   geom_rug()
+#
+# ggplot(airquality,
+#        aes(x = Ozone,
+#            colour = is_na(Solar.R))) +
+#   geom_density() +
+#   geom_rug()
+
+# other "long" shadow format
+
+# shadow_join exists as we want to include this extra metadata about the rows that have missing data, but I also wanted to include some extra information about the class of the data, in case we need to gather the data back into a wider, rather than long, format. Here it takes a function `visdat`, `visdat:::fingerprint`, which is currently not a particularly complex function.
+
+gather_shadow <- function(df){
+
+  df_val_type <- df %>%
+    as_data_frame() %>%
+    purrr::dmap(visdat:::fingerprint) %>%
+    mutate(rows = row_number()) %>%
+    tidyr::gather_(key_col = "variable",
+                   value_col = "valueType",
+                   gather_cols = names(.)[-length(.)])
+
+  # df_shadow
+  df_shadow <- df %>%
+    as_data_frame() %>%
+    mutate(rows = row_number()) %>%
+    gather(key = variable,
+           value = value,
+           -rows) %>%
+    mutate(shadow_matrix = is_na(value)) %>%
+    left_join(df_val_type)
+
+  # perhaps define some attributes
+
+}
+
+aq_shadow <- gather_shadow(airquality)

R/revealers.R

@@ -29,11 +29,11 @@ stat_missing_histogram <- function(mapping = NULL,
     inherit.aes = inherit.aes,
     params = list(na.rm = na.rm, ...)
   )
-  
+
 }
 
 
-#' @rdname ggmissing-ggproto
+#' @rdname naniar-ggproto
 #' @export
 StatMissingHistogram <- ggproto("StatMissingHistogram", Stat,
                             required_aes = c("x"),