Merge branch 'develop' of https://github.com/ropensci/skimr into develop

Conflicts:
	codemeta.json

DESCRIPTION

@@ -87,8 +87,12 @@ Authors@R:
       person(given = "David",
              family = "Zimmermann",
              role = "ctb",
-             email = "david_j_zimmermann@hotmail.com"))
-Description: A simple to use summary function that can be used with pipes
+             email = "david_j_zimmermann@hotmail.com"),
+      person(given = "Kyle", 
+             family = "Butts",
+             role ="ctb",
+             email = ""))
+Description: A simple to use summary function that can be used buttskyle96@gmail.comwith pipes
     and displays nicely in the console. The default summary statistics may
     be modified by the user as can the default formatting.  Support for
     data frames and vectors is included, and users can implement their own

NEWS.md

@@ -3,6 +3,7 @@
 ### MINOR IMPROVEMENTS
 
 *   Add support for lubridate Timespan objects.
+*   Improvements to Supporting Additional Objects vignette.
 
 ### BUG FIXES
 

codemeta.json

@@ -5,7 +5,7 @@
   ],
   "@type": "SoftwareSourceCode",
   "identifier": "skimr",
-  "description": "A simple to use summary function that can be used with pipes\n    and displays nicely in the console. The default summary statistics may\n    be modified by the user as can the default formatting.  Support for\n    data frames and vectors is included, and users can implement their own\n    skim methods for specific object types as described in a vignette.\n    Default summaries include support for inline spark graphs.\n    Instructions for managing these on specific operating systems are\n    given in the \"Using skimr\" vignette and the README.",
+  "description": "A simple to use summary function that can be used buttskyle96@gmail.comwith pipes\n    and displays nicely in the console. The default summary statistics may\n    be modified by the user as can the default formatting.  Support for\n    data frames and vectors is included, and users can implement their own\n    skim methods for specific object types as described in a vignette.\n    Default summaries include support for inline spark graphs.\n    Instructions for managing these on specific operating systems are\n    given in the \"Using skimr\" vignette and the README.",
   "name": "skimr: Compact and Flexible Summaries of Data",
   "codeRepository": "https://github.com/ropensci/skimr",
   "issueTracker": "https://github.com/ropensci/skimr/issues",
@@ -150,6 +150,11 @@
       "givenName": "David",
       "familyName": "Zimmermann",
       "email": "david_j_zimmermann@hotmail.com"
+    },
+    {
+      "@type": "Person",
+      "givenName": "Kyle",
+      "familyName": "Butts"
     }
   ],
   "copyrightHolder": [
@@ -420,7 +425,7 @@
   ],
   "releaseNotes": "https://github.com/ropensci/skimr/blob/master/NEWS.md",
   "readme": "https://github.com/ropensci/skimr/blob/master/README.md",
-  "fileSize": "18346.978KB",
+  "fileSize": "364473.922KB",
   "contIntegration": ["https://travis-ci.org/ropensci/skimr", "https://ci.appveyor.com/project/michaelquinn32/skimr", "https://codecov.io/gh/ropensci/skimr"],
   "review": {
     "@type": "Review",

vignettes/Supporting_additional_objects.Rmd

@@ -24,11 +24,9 @@ involves two required elements and one optional element.
 - if needed, define any custom statistics
 
 If you are adding skim support to a package you will also need to add `skimr`
-to the list of imports. Note that in this vignette the actual analysis will
-not be run because that would require importing the `sf` package just for this
-example.  However to run it on your own you can install `sf` and then run the
-following code.  Note that code in this vignette was not evaluated when
-rendering the vignette in order to avoid forcing installation of sf.
+to the list of imports. Note that to run the code in this vignette you will
+need to install the `sf` package. We suggest not doing that, and instead
+substitute whatever package you are working with.
 
 ```{r}
 library(skimr)
@@ -39,6 +37,8 @@ nc <- st_read(system.file("shape/nc.shp", package = "sf"))
 
 ```{r}
 class(nc)
+
+class(nc$geometry)
 ```
 
 Unlike the example of having a new type of data in a column of a simple data 
@@ -65,11 +65,13 @@ back to treating the type as a character, which isn't necessarily helpful. In
 this case, you're best off adding your data type with `skim_with()`.
 
 Before we begin, we'll be using the following custom summary statistic
-throughout. It's a naive example, but covers the requirements of what we need.
+throughout. The function gets the geometry's crs and combines it into a string.
 
 ```{r}
-funny_sf <- function(x) {
-  length(x) + 1
+get_crs <- function(column) {
+  crs <- sf::st_crs(column)
+
+  paste0("epsg: ", crs[["epsg"]], " proj4string: '", crs[["proj4string"]], "'")
 }
 ```
 
@@ -92,71 +94,41 @@ default `skimr` percentiles are returned by using `quantile()` five
 times.
 
 Next, we create a custom skimming function. To do this, we need to think about
-the many specific classes of data in the `sf` package.  The following example
-will build  support for `sfc_MULTIPOLYGON`, but note that we'll have to
-eventually think about `sfc_LINESTRING`, `sfc_POLYGON`, `sfc_MULTIPOINT` and
-others if we want to fully support `sf`.
+the many specific classes of data in the `sf` package.  From above, you can see 
+the geometry column has two classes: 1st the specific geometry type (e.g. 
+`sfc_MULTIPOLYGON` `sfc_LINESTRING`, `sfc_POLYGON`, `sfc_MULTIPOINT`) and 2nd 
+the general sfc class. Skimr will try to find a sfl() helper function for the
+classes in the order they appear in `class(.)` (see S3 classes for more detail 
+[*Advanced R*](https://adv-r.hadley.nz/s3.html)). The following example will 
+build  support for `sfc`, which encompasses all `sf` objects: `sfc_MULTIPOLYGON` 
+`sfc_LINESTRING`, `sfc_POLYGON`, `sfc_MULTIPOINT`. If we want custom skim_with 
+functions we can write `sfl()` helper functions for the geometry type. 
+
 
 ```{r}
 skim_sf <- skim_with(
-  sfc_MULTIPOLYGON = sfl(
+  sfc = sfl(
     n_unique = n_unique,
     valid = ~ sum(sf::st_is_valid(.)),
-    funny = funny_sf
+    crs = get_crs
   )
 )
 ```
 
 The example above creates a new *function*, and you can call that function on
-a specific column with `sfc_MULTIPOLYGON` data to get the appropriate summary 
-statistics.
+a specific column with `sfc` data to get the appropriate summary 
+statistics. The `skim_with` factory also uses the default skimrs for things 
+like factors, characters, and numerics. Therefore our `skim_sf` is like the regular
+`skim` function with the added ability to summarize `sfc` columns.
 
 ```{r}
 skim_sf(nc$geometry)
 ```
 
-Creating a function that is a method of the skim_by_type generic
-for the data type allows skimming of an entire data frame that contains some 
-columns of that type.
-
-```{r}
-skim_by_type.sfc_MULTIPOLYGON <- function(mangled, columns, data) {
-  skimmed <- dplyr::summarize_at(data, columns, mangled$funs)
-  build_results(skimmed, columns, NULL)
-}
-```
-
-```{r}
-skim_sf(nc)
-```
-
-
-Sharing these functions within a separate package requires an export. 
-The simplest way to do this is with Roxygen.
-
-```{r}
-#' Skimming functions for `sfc_MULTIPOLYGON` objects.
-#' @export
-skim_sf <- skim_with(
-  sfc_MULTIPOLYGON = sfl(
-    missing = n_missing,
-    n = length,
-    n_unique = n_unique,
-    valid = ~ sum(sf::st_is_valid(.)),
-    funny = funny_sf
-  )
-)
-
-#' A skim_by_type function for `sfc_MULTIPOLYGON` objects.
-#' @export
-skim_by_type.sfc_MULTIPOLYGON <- function(mangled, columns, data) {
-  skimmed <- dplyr::summarize_at(data, columns, mangled$funs)
-  skimr::build_results(skimmed, columns, NULL)
-}
-```
 
-While this works within any package, there is an even better approach in this
-case. To take full advantage of `skimr`, we'll dig a bit into its API.
+While this works for any data type and you can also include it within any 
+package (assuming your users load skimr), there is an even better approach in 
+this case. To take full advantage of `skimr`, we'll dig a bit into its API.
 
 ## Adding new methods
 
@@ -165,21 +137,25 @@ find default summary functions for each class. This is based on the S3 class
 system. You can learn more about it in
 [*Advanced R*](https://adv-r.hadley.nz/s3.html).
 
+This requires that you add `skimr` to your list of dependencies.
+
 To export a new set of defaults for a data type, create a method for the generic
 function `get_skimmers`. Each of those methods returns an `sfl`, a `skimr`
 function list. This is the same list-like data structure used in the
 `skim_with()` example above. But note! There is one key difference. When adding
-a generic we also want to identify the `skim_type` in the `sfl`.
+a generic we also want to identify the `skim_type` in the `sfl`. You will
+probably want to use `skimr::get_skimmers.sfc()` but that will not work in a
+vignette.
 
 ```{r}
 #' @importFrom skimr get_skimmers
 #' @export
-get_skimmers.sfc_MULTIPOLYGON <- function(column) {
+get_skimmers.sfc <- function(column) {
   sfl(
-    skim_type = "sfc_MULTIPOLYGON",
+    skim_type = "sfc",
     n_unique = n_unique,
     valid = ~ sum(sf::st_is_valid(.)),
-    funny = funny_sf
+    crs = get_crs
   )
 }
 ```
@@ -190,32 +166,27 @@ The same strategy follows for other data types.
 * return an `sfl`
 * make sure that the `skim_type` is there
 
-```{r}
-#' @export
-get_skimmers.sfc_POINT <- function(column) {
-  sfl(
-    skim_type = "sfc_POINT",
-    n_unique = n_unique,
-    valid = ~ sum(sf::st_is_valid(.))
-  )
-}
-```
-
-Users of your package should load `skimr` to get the `skim()` function. Once
+Users of your package should load `skimr` to get the `skim()` function 
+(although you could import and reexport it). Once
 loaded, a call to `get_default_skimmer_names()` will return defaults for your
-data types as well!
+data types as well! 
 
 ```{r}
 get_default_skimmer_names()
 ```
 
+They will then be able to use `skim()` directly.
+
+```{r}
+skim(nc)
 ```
 
+
 ## Conclusion
 
-This is a very simple example. For a package such as sf the custom statistics
+This is a very simple example. For a package such as `sf` the custom statistics
 will likely  be much more complex. The flexibility of `skimr` allows you to
 manage that.
 
-Thanks to Jakub Nowosad, Tiernan Martin, Edzer Pebesma and Michael Sumner for
-inspiring and  helping with the development of this code.
+Thanks to Jakub Nowosad, Tiernan Martin, Edzer Pebesma, Michael Sumner, and 
+Kyle Butts for inspiring and helping with the development of this code.