Merge pull request #182 from OHI-Science/dev

Dev
OHI-Science · Jun 30, 2016 · 410cebb · 410cebb
2 parents fbf866d + 92839a7
commit 410cebb
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -11,52 +11,38 @@ LazyData: TRUE
 Depends:
     R (>= 3.1.0)
 Imports:
-    RColorBrewer,
-    RJSONIO,
     dplyr (>= 0.3),
     ggplot2 (>= 1.0.0),
-    ggvis (>= 0.4),
-    httr,
-    markdown (>= 0.6.3),
     plyr (>= 1.8.0),
-    rCharts,
     reshape2,
-    shiny (>= 0.10.0),
     stringr,
-    knitr (>= 1.5)
+    git2r,
+    rgdal,
+    sp,
+    methods,
+    tidyr,
+    RColorBrewer,
+    htmlwidgets,
+    plotly
 Suggests:
     testthat
 Collate:
     'CalculateAll.R'
     'CalculateGoalIndex.R'
     'CalculatePressuresAll.R'
-    'CalculatePressuresComponent.R'
-    'CalculatePressuresMatrix.R'
-    'CalculatePressuresScore.R'
     'CalculateResilienceAll.R'
-    'CalculateResilienceComponent.R'
-    'CalculateResilienceScore.R'
-    'CalculateStatusComponent.R'
-    'CalculateSubgoal.R'
     'CheckLayers.R'
     'Conf.R'
     'Layers.R'
     'PlotFlower.R'
     'ScoreScaling.R'
     'SelectLayersData.R'
-    'SpatialSchemes.R'
-    'TransformSpatialScheme.R'
-    'aster.R'
     'compare_scores_df.R'
     'gapfill_georegions.R'
-    'get_scenarios.R'
-    'launch_app.R'
-    'launch_cmp.R'
-    'mapvalues.R'
     'name_to_rgn.R'
     'read_git_csv.R'
     'shp_to_geojson.R'
     'trace_git_csv_value.R'
-    'write_shortcuts.R'
-    'zzz.R'
+    'mapvalues.R'
+    'change_plot.R'
 RoxygenNote: 5.0.1
diff --git a/NAMESPACE b/NAMESPACE
@@ -3,27 +3,15 @@
 export(CalculateAll)
 export(CalculateGoalIndex)
 export(CalculatePressuresAll)
-export(CalculatePressuresComponent)
-export(CalculatePressuresMatrix)
-export(CalculatePressuresScore)
 export(CalculateResilienceAll)
-export(CalculateResilienceComponent)
-export(CalculateResilienceMatrix)
-export(CalculateResilienceScore)
-export(CalculateStatusComponent)
-export(CalculateSubgoal)
 export(CheckLayers)
 export(Conf)
 export(Layers)
 export(PlotFlower)
 export(SelectLayersData)
-export(TransformSpatialScheme)
-export(aster)
+export(change_plot)
 export(compare_scores_df)
 export(gapfill_georegions)
-export(get_scenarios)
-export(launch_app)
-export(launch_cmp)
 export(mapvalues)
 export(name_to_rgn)
 export(read_git_csv)
@@ -32,23 +20,16 @@ export(score.max)
 export(score.rescale)
 export(shp_to_geojson)
 export(trace_git_csv_value)
-export(write_shortcuts)
 exportClasses(Conf)
 exportClasses(Layers)
-exportClasses(SpatialSchemes)
-import(RColorBrewer)
-import(RJSONIO)
 import(dplyr)
 import(ggplot2)
-import(ggvis)
-import(httr)
-import(markdown)
+import(git2r)
+import(htmlwidgets)
+import(plotly)
 import(plyr)
-import(rCharts)
 import(reshape2)
-import(shiny)
-import(stringr)
-import(yaml)
+import(tidyr)
 importFrom(dplyr,arrange)
 importFrom(dplyr,desc)
 importFrom(dplyr,failwith)

diff --git a/NEWS.md b/NEWS.md
@@ -1,11 +1,14 @@
 # ohicore 0.1
 
+
 ## Major changes
 
+
 * Added datasets
 
   * georegion_labels
   * georegions
   * sovregion_labels
   * sovregions
   * rgn_synonyms
+  * test
diff --git a/R/CalculateAll.R b/R/CalculateAll.R
@@ -54,6 +54,8 @@
 #' @import reshape2
 #' @import plyr
 #' @import dplyr
+#' @import tidyr
+#' @import git2r
 #' @importFrom dplyr arrange
 #' @importFrom dplyr desc
 #' @importFrom dplyr failwith
@@ -69,7 +71,7 @@
 #' @importFrom plyr rename
 #'
 #' @export
-CalculateAll = function(conf, layers, debug=F){
+CalculateAll = function(conf, layers){
 
   ## Remove global scores
   if (exists('scores', envir=.GlobalEnv)) rm(scores, envir=.GlobalEnv)
@@ -79,11 +81,11 @@ CalculateAll = function(conf, layers, debug=F){
       cat('Running Setup()...\n')
       conf$functions$Setup()
     }
-  
-  ## Pre-Index functions: Status and Trend, by goal
+
+  ## Access Pre-Index functions: Status and Trend, by goal
   goals_X = conf$goals %>%
-    filter(!is.na(preindex_function)) %>%
-    arrange(order_calculate)
+    dplyr::filter(!is.na(preindex_function)) %>%
+    dplyr::arrange(order_calculate)
 
   ## Setup scores variable; many rbinds to follow
   scores = data.frame(
@@ -93,7 +95,7 @@ CalculateAll = function(conf, layers, debug=F){
     score     = numeric())
 
   ## Calculate Status and Trend, all goals
-  for (i in 1:nrow(goals_X)){ # i=3
+  for (i in 1:nrow(goals_X)){ # i=5
     g = goals_X$goal[i]
     cat(sprintf('Calculating Status and Trend for each region for %s...\n', g))
 
@@ -106,26 +108,23 @@ CalculateAll = function(conf, layers, debug=F){
   }
 
   ## Calculate Pressures, all goals
-  cat(sprintf('Calculating Pressures for each region...\n'))
-  scores_P = CalculatePressuresAll(layers, conf, gamma=conf$config$pressures_gamma, debug)
+  scores_P = CalculatePressuresAll(layers, conf)
   scores = rbind(scores, scores_P)
 
   ## Calculate Resilience, all goals
-  cat(sprintf('Calculating Resilience for each region...\n'))
-  cat(sprintf('Note: each goal in resilience_matrix.csv must have at least one resilience field\n'))
-  scores = rbind(scores, CalculateResilienceAll(layers, conf, debug))
+  scores = rbind(scores, CalculateResilienceAll(layers, conf))
   scores = data.frame(scores)
 
   ## Calculate Goal Score and Likely Future, all goals
   goals_G = as.character(unique(subset(scores, dimension=='status', goal, drop=T)))
   for (g in goals_G){ # g = 'FIS'
     cat(sprintf('Calculating Goal Score and Likely Future for each region for %s...\n', g))
-    
+
     ## spread the scores by dimension
     v = scores %>%
-      filter(goal == g) %>%
-      spread(dimension, score)
-    
+      dplyr::filter(goal == g) %>%
+      tidyr::spread(dimension, score)
+
    ## message if missing dimension, assign NA
     for (col in c('status','trend','pressures','resilience')){
       if (!col %in% names(v)){
@@ -150,11 +149,11 @@ CalculateAll = function(conf, layers, debug=F){
     ## Gather to scores format: goal, dimension, region_id, score
     scores_G = x %>%
         dplyr::select(region_id = id,
-                      future    = xF, 
-                      score) %>% 
-      gather(dimension, score, -region_id) %>%
-      mutate(goal = g) %>%
-      select(goal, dimension, region_id, score)
+                      future    = xF,
+                      score) %>%
+      tidyr::gather(dimension, score, -region_id) %>%
+      dplyr::mutate(goal = g) %>%
+      dplyr::select(goal, dimension, region_id, score)
 
     ## bind to other scores
     scores = rbind(scores, scores_G)
@@ -163,7 +162,7 @@ CalculateAll = function(conf, layers, debug=F){
   ## Post-Index functions: supragoals
   goals_Y = subset(conf$goals, !is.na(postindex_function))
   supragoals = subset(conf$goals, is.na(parent), goal, drop=T); supragoals
-  
+
   for (i in 1:nrow(goals_Y)){ # i = 1
 
     cat(sprintf('Calculating post-Index function for each region for %s...\n', goals_Y$goal[i]))
@@ -177,42 +176,42 @@ CalculateAll = function(conf, layers, debug=F){
   cat(sprintf('Calculating Index score for each region for supragoals using goal weights...\n'))
 
   # calculate weighted-mean Index scores from goal scores and rbind to 'scores' variable
-  scores = 
-    rbind(scores, 
+  scores =
+    rbind(scores,
           scores %>%
-            
+
             # filter only supragoal scores, merge with supragoal weightings
-            filter(dimension=='score',  goal %in% supragoals) %>%
+            dplyr::filter(dimension=='score',  goal %in% supragoals) %>%
             merge(conf$goals %>%
                     select(goal, weight)) %>%
-            
+
             # calculate the weighted mean of supragoals, add goal and dimension column
-            group_by(region_id) %>%
+            dplyr::group_by(region_id) %>%
             dplyr::summarise(score = weighted.mean(score, weight, na.rm=T)) %>%
-            mutate(goal      = 'Index',
+            dplyr::mutate(goal      = 'Index',
                    dimension = 'score') %>%
             data.frame())
-      
+
 
   ## Calculate Region Likely Future State Scores using goal weights
   cat(sprintf('Calculating Likely Future State for each region for supragoals using goal weights...\n'))
- 
+
   # calculate weighted-mean Likely Future State scores and rbind to 'scores' variable
-  scores = 
-    rbind(scores, 
+  scores =
+    rbind(scores,
           scores %>%
-            
+
             # filter only supragoal scores, merge with supragoal weightings
-            filter(dimension=='future',  goal %in% supragoals) %>%
+            dplyr::filter(dimension=='future',  goal %in% supragoals) %>%
             merge(conf$goals %>%
                     select(goal, weight)) %>%
-            
+
             # calculate the weighted mean of supragoals, add goal and dimension column
-            group_by(region_id) %>%
+            dplyr::group_by(region_id) %>%
             dplyr::summarise(score = weighted.mean(score, weight, na.rm=T)) %>%
-            mutate(goal      = 'Index',
+            dplyr::mutate(goal      = 'Index',
                    dimension = 'future') %>%
-            data.frame()) 
+            data.frame())
 
   ## Post-process scores, but pre-global calculation: for global assessment only
   if ('PreGlobalScores' %in% ls(conf$functions)){
@@ -222,25 +221,24 @@ CalculateAll = function(conf, layers, debug=F){
 
   ## Assessment Areas (sometimes known as 'global', region_id-0) scores by area weighting
   cat(sprintf('Calculating scores for ASSESSMENT AREA (region_id=0) by area weighting...\n'))
-  
-  ## Calculate area-weighted Assessment Area scores and rbind to all scores 
+
+  ## Calculate area-weighted Assessment Area scores and rbind to all scores
   scores = rbind(
     scores,
     scores %>%
-      
+
       # filter only score, status, future dimensions, merge to the area (km2) of each region
-      filter(dimension %in% c('score','status','future')) %>%
+      dplyr::filter(dimension %in% c('score','status','future')) %>%
       merge(SelectLayersData(layers, layers=conf$config$layer_region_areas, narrow=T) %>%
               dplyr::select(region_id = id_num,
                             area      = val_num)) %>%
-      
+
       # calculate weighted mean by area
-      group_by(goal, dimension) %>%
-      summarise(score = weighted.mean(score, area, na.rm=T),
+      dplyr::group_by(goal, dimension) %>%
+      dplyr::summarise(score = weighted.mean(score, area, na.rm=T),
                 region_id = 0) %>%
-      ungroup()) 
-
-
+      ungroup())
+
   ## post-process
   if ('FinalizeScores' %in% ls(conf$functions)){
     cat(sprintf('Calculating FinalizeScores function...\n'))