Inital CRAN Release

DESCRIPTION

@@ -5,7 +5,7 @@ Date: 2019-04-29
 Authors@R: c(
     person("Carson", "Sievert", role = "aut"),
     person("Roy", "Mendelssohn", role = c("aut", "ctb", "cre"), email = "[email protected]"))
-Description: Easily visualize 'tabledap' and 'griddap' objects obtained via the rerddap package.
+Description: Easily visualize and animate 'tabledap' and 'griddap' objects obtained via the 'rerddap' package in a simple one-line command,  using either base graphics or 'ggplot2' graphics. 'plotdap' handles extracting and reshaping the data,  map projections and continental outlines.  Optionally the data can be animated through time using the 'gganmiate' package.
 License: MIT + file LICENSE
 URL: https://github.com/ropensci/plotdap
 BugReports: https://github.com/ropensci/plotdap/issues

R/add_griddap.R

@@ -31,39 +31,42 @@
 #' # base plotting tends to be faster,
 #' # but is less extensible plotdap("base")
 #'
-#' # code to obtain datasets using `rerddap` given as comment
 #' # actual datasets in data folder to meet execution timings
 #'
-#' # murSST <- griddap(
-#'   # 'jplMURSST41', latitude = c(35, 40), longitude = c(-125, -1205),
-#'   # time = c('last', 'last'), fields = 'analysed_sst'
-#'  # )
+#'\donttest{
+#'  murSST <- griddap(
+#'    'jplMURSST41', latitude = c(35, 40), longitude = c(-125, -1205),
+#'    time = c('last', 'last'), fields = 'analysed_sst'
+#'   )
+#'
+#'  QMwind <- griddap(
+#'    'erdQMwindmday', time = c('2016-11-16', '2017-01-16'),
+#'    latitude = c(30, 50), longitude = c(210, 240),
+#'    fields = 'x_wind'
+#'  )
+#'
+#' p <- plotdap(crs = "+proj=robin")
+#' add_griddap(p, murSST, ~analysed_sst)
+#'
+#' p <- plotdap(mapTitle = "Average wind over time")
+#' add_griddap(p, QMwind, ~x_wind)
+#'
+#'}
+#'
+#
 #' p <- plotdap("base", crs = "+proj=robin")
 #' p <- add_griddap(p, murSST, ~analysed_sst)
 #'
 #' # layer tables on top of grids
+#' require(magrittr)
 #' p <- plotdap("base") %>%
 #'   add_griddap(murSST, ~analysed_sst) %>%
 #'   add_tabledap(sardines, ~subsample_count)
 #'
 #' # multiple time periods
-#' # wind <- griddap(
-#'   # 'erdQMwindmday', time = c('2016-11-16', '2017-01-16'),
-#'   # latitude = c(30, 50), longitude = c(210, 240),
-#'   # fields = 'x_wind'
-#' # )
 #' p <- plotdap("base", mapTitle = "Average wind over time")
 #' p <- add_griddap(p, QMwind, ~x_wind)
 #'
-#'\donttest{
-#' # ggplot2 add_griddap functions take too long for CRAN
-#' # but here are the same examples from above
-#' p <- plotdap(crs = "+proj=robin")
-#' add_griddap(p, murSST, ~analysed_sst)
-#'
-#' p <- plotdap(mapTitle = "Average wind over time")
-#' add_griddap(p, QMwind, ~x_wind)
-#'}
 
 add_griddap <- function(plot, grid, var, fill = "viridis",
                         maxpixels = 10000, time = mean, animate = FALSE,

R/add_tabledap.R

@@ -26,20 +26,22 @@
 #' # base plotting tends to be faster,
 #' # but is less extensible plotdap("base")
 #'
-#' # code to obtain datasets using `rerddap` given as comment
-#' # actual datasets in data folder to meet execution timings
+#' # test datasets in data folder to meet execution timings
+#' # code given to extract the data
+#'
+#'\donttest{
+#' sardines <- tabledap(
+#'  'FRDCPSTrawlLHHaulCatch',
+#'  fields = c('latitude',  'longitude', 'time',
+#'           'scientific_name', 'subsample_count'),
+#'   'time>=2010-01-01', 'time<=2012-01-01',
+#'   scientific_name="Sardinops sagax"'
+#'   )
+#'}
 #'
-#' # tabledap examples
-#' # sardines <- tabledap(
-#'  # 'FRDCPSTrawlLHHaulCatch',
-#'  # fields = c('latitude',  'longitude', 'time',
-#'           # 'scientific_name', 'subsample_count'),
-#'   # 'time>=2010-01-01', 'time<=2012-01-01',
-#'  # 'scientific_name="Sardinops sagax"'
-#'  # )
 #' p <- plotdap()
-#' p <- add_tabledap(p, sardines, ~subsample_count)
-#' p1 <- add_tabledap(p, sardines, ~log2(subsample_count))
+#' p1 <- add_tabledap(p, sardines, ~subsample_count)
+#' p2 <- add_tabledap(p, sardines, ~log2(subsample_count))
 #'
 #' # using base R plotting
 #' p <- plotdap("base")