Merge pull request #159 from hugomflavio/issue_156

Replace instances of 'is stored' with 'as stored'

R/explore.R

@@ -734,7 +734,7 @@ printExploreRmd <- function(override.fragment, biometric.fragment, individual.pl
 
 Note:
   : The colouring in these plots will follow that of the individual detection plots, which can be modified using `detections.y.axis`.
-  : The data used for these graphics is stored in the `valid.detections` object.
+  : The data used for these graphics are stored in the `valid.detections` object.
   : You can replicate these graphics and edit them as needed using the `plotSensors()` function.
 
 <center>\n", sensor.plots, "\n</center>")
@@ -841,7 +841,7 @@ Note:
 Note:
   : Coloured lines on the outer circle indicate the mean value for each group and the respective ranges show the standard error of the mean. Each group\'s bars sum to 100%. The number of data points in each group is presented between brackets in the legend of each pannel.
   : You can replicate these graphics and edit them as needed using the `plotTimes()` function.
-  : The data used in these graphics is stored in the `times` object.
+  : The data used in these graphics are stored in the `times` object.
   : To obtain reports with the legacy linear circular scale, run `options(actel.circular.scale = "linear")` before running your analyses.
 
 <center>
@@ -859,7 +859,7 @@ Note:
   : The ', ifelse(detections.y.axis == "stations", 'stations', 'arrays'), ' have been aligned by ', ifelse(detections.y.axis == "stations", 'array', 'section'), ', following the order provided ', ifelse(detections.y.axis == "stations", '', 'either '), 'in the spatial input', ifelse(detections.y.axis == "stations", '.', ' or the `section.order` argument.'), '
   : You can replicate these graphics and edit them as needed using the `plotDetections()` function.
   : You can also see the movement events of multiple tags simultaneously using the `plotMoves()` function.
-  : The data used in these graphics is stored in the `detections` and `movements` objects (and respective valid counterparts).
+  : The data used in these graphics are stored in the `detections` and `movements` objects (and respective valid counterparts).
 
 <center>
 ', individual.plots,'

R/migration.R

@@ -1134,7 +1134,7 @@ printMigrationRmd <- function(override.fragment, biometric.fragment,
                               "  : The colouring in these plots will follow",
                               " that of the individual detection plots, which",
                               " can be modified using `detections.y.axis`.\n",
-                              "  : The data used for these graphics is stored",
+                              "  : The data used for these graphics are stored",
                               " in the `valid.detections` object.\n",
                               "  : You can replicate these graphics and edit",
                               " them as needed using the `plotSensors()`",
@@ -1273,7 +1273,7 @@ printMigrationRmd <- function(override.fragment, biometric.fragment,
     "### Section Survival\n",
     "\n",
     "Note:\n",
-    ": The data used in this table and graphic is stored in the",
+    ": The data used in this table and graphic are stored in the",
     " `section.overview` object.\n",
     "\n",
     paste(knitr::kable(section.overview), collapse = "\n"), "\n",
@@ -1285,7 +1285,7 @@ printMigrationRmd <- function(override.fragment, biometric.fragment,
     "### Last Seen Arrays\n",
     "\n",
     "Note:\n",
-    "  : The data used in this graphic is stored in the `status.df` object",
+    "  : The data used in this graphic are stored in the `status.df` object",
     " ('Very.last.array' column).\n",
     "\n",
     "<center>\n",
@@ -1306,7 +1306,7 @@ printMigrationRmd <- function(override.fragment, biometric.fragment,
                   " [section survival overview](#section-survival) and [last",
                   " seen arrays](#last-seen-arrays) to find out how many",
                   " animals were considered to have disappeared per section.\n",
-                  "  : The data used in this graphic is stored in the",
+                  "  : The data used in this graphic are stored in the",
                   " `overall.CJS` object, and the data used in the tables is",
                   " stored in the `group.overview` object. You can find",
                   " detailed progressions per release site in the",
@@ -1332,7 +1332,7 @@ printMigrationRmd <- function(override.fragment, biometric.fragment,
     " is presented between brackets in the legend of each pannel.\n",
     "  : You can replicate these graphics and edit them as needed using the",
     " `plotTimes()` function.\n",
-    "  : The data used in these graphics is stored in the `times` object.\n",
+    "  : The data used in these graphics are stored in the `times` object.\n",
     "  : To obtain reports with the legacy linear circular scale, run",
     " `options(actel.circular.scale = \"linear\")` before running",
     " your analyses.\n",
@@ -1353,7 +1353,7 @@ printMigrationRmd <- function(override.fragment, biometric.fragment,
     " The columns starting with \"In\" should be read as \"Total time in",
     " ...\". These reductions were made to keep the column headers as short",
     " as possible.\n",
-    "  : The data used in these graphics is stored in the `status.df`",
+    "  : The data used in these graphics are stored in the `status.df`",
     " object.\n",
     "\n",
     "<center>\n",
@@ -1395,7 +1395,7 @@ printMigrationRmd <- function(override.fragment, biometric.fragment,
     " `plotDetections()` function.\n",
     "  : You can also see the movement events of multiple tags simultaneously",
     " using the `plotMoves()` function.\n",
-    "  : The data used in these graphics is stored in the `detections` and",
+    "  : The data used in these graphics are stored in the `detections` and",
     " `movements` objects (and respective valid counterparts).\n",
     "\n",
     "<center>\n",

R/print.R

@@ -543,7 +543,7 @@ printEfficiency <- function(CJS = NULL, efficiency = NULL, intra.CJS, type = c("
       efficiency.fragment <- paste0('
 Note:
   : These efficiency calculations **do not account for** backwards movements. This implies that the total number of animals to have been **last seen** at a given array **may be lower** than the displayed below. Please refer to the [section survival overview](#section-survival) and [last seen arrays](#last-seen-arrays) to find out how many animals were considered to have disappeared per section.
-  : The data used in the tables below is stored in the `overall.CJS` object. Auxiliary information can also be found in the `matrices` and `arrays` objects.
+  : The data used in the tables below are stored in the `overall.CJS` object. Auxiliary information can also be found in the `matrices` and `arrays` objects.
   : These efficiency values are estimated using the analytical equations provided in the paper "Using mark-recapture models to estimate survival from telemetry data" by [Perry et al. (2012)](<https://www.researchgate.net/publication/256443823_Using_mark-recapture_models_to_estimate_survival_from_telemetry_data>). In some situations, more advanced efficiency estimation methods may be required.
   : You can try running `advEfficiency(results$overall.CJS)` to obtain beta-drawn efficiency distributions (replace `results` with the name of the object where you saved the analysis).
 
@@ -579,7 +579,7 @@ Note:
       efficiency.fragment <- paste0('
 Note:
   : More information on the differences between "Known missed events" and "Potentially missed events" can be found in the package vignettes.
-  : The data used in this table is stored in the `efficiency` object.
+  : The data used in this table are stored in the `efficiency` object.
   : These efficiency values are estimated using a simple step-by-step method (described in the package vignettes). In some situations, more advanced efficiency estimation methods may be required.
   : You can try running `advEfficiency(results$efficiency)` to obtain beta-drawn efficiency distributions (replace `results` with the name of the object where you saved the analysis).
 
@@ -601,7 +601,7 @@ Note:
 #### Intra array efficiency estimates
 
 Note:
-  : The data used in the table(s) below is stored in the `intra.array.CJS` object. Auxiliary information can also be found in the `intra.array.matrices` object.
+  : The data used in the table(s) below are stored in the `intra.array.CJS` object. Auxiliary information can also be found in the `intra.array.matrices` object.
   : These efficiency values are estimated using the analytical equations provided in the paper "Using mark-recapture models to estimate survival from telemetry data" by [Perry et al. (2012)](<https://www.researchgate.net/publication/256443823_Using_mark-recapture_models_to_estimate_survival_from_telemetry_data>). In some situations, more advanced efficiency estimation methods may be required.
   : You can try running `advEfficiency(results$intra.array.CJS$', names(intra.CJS)[1], ')` to obtain beta-drawn efficiency distributions (replace `results` with the name of the object where you saved the analysis).
 ')

R/residency.R

@@ -937,7 +937,7 @@ printResidencyRmd <- function(
 
 Note:
   : The colouring in these plots will follow that of the individual detection plots, which can be modified using `detections.y.axis`.
-  : The data used for these graphics is stored in the `valid.detections` object.
+  : The data used for these graphics are stored in the `valid.detections` object.
   : You can replicate these graphics and edit them as needed using the `plotSensors()` function.
 
 <center>\n", sensor.plots, "\n</center>")
@@ -1045,7 +1045,7 @@ Note:
 ### Last seen
 
 Note:
-  : The data used in this table and graphic is stored in the `last.seen` object.
+  : The data used in this table and graphic are stored in the `last.seen` object.
 
 ', paste(knitr::kable(last.seen), collapse = "\n"), '
 
@@ -1059,7 +1059,7 @@ Note:
 Note:
   : Coloured lines on the outer circle indicate the mean value for each group and the respective ranges show the standard error of the mean. Each group\'s bars sum to 100%. The number of data points in each group is presented between brackets in the legend of each pannel.
   : You can replicate these graphics and edit them as needed using the `plotTimes()` function.
-  : The data used in these graphics is stored in the `array.times` object.
+  : The data used in these graphics are stored in the `array.times` object.
   : To obtain reports with the legacy linear circular scale, run `options(actel.circular.scale = "linear")` before running your analyses.
 
 <center>
@@ -1071,7 +1071,7 @@ Note:
 #### Arrival days at each section
 
 Note:
-  : The data used in these graphics is stored in the `section.times$arrival` object.
+  : The data used in these graphics are stored in the `section.times$arrival` object.
 
 <center>
 ![](', work.path, 'arrival_days.png){ width=95% }
@@ -1082,7 +1082,7 @@ Note:
 Note:
   : Coloured lines on the outer circle indicate the mean value for each group and the respective ranges show the standard error of the mean. Each group\'s bars sum to 100%. The number of data points in each group is presented between brackets in the legend of each pannel.
   : You can replicate these graphics and edit them as needed using the `plotTimes()` function.
-  : The data used in these graphics is stored in the `section.times$arrival` object.
+  : The data used in these graphics are stored in the `section.times$arrival` object.
   : To obtain reports with the legacy linear circular scale, run `options(actel.circular.scale = "linear")` before running your analyses.
 
 <center>
@@ -1092,7 +1092,7 @@ Note:
 #### Departure days at each section
 
 Note:
-  : The data used in these graphics is stored in the `section.times$departure` object.
+  : The data used in these graphics are stored in the `section.times$departure` object.
 
 <center>
 ![](', work.path, 'departure_days.png){ width=95% }
@@ -1103,7 +1103,7 @@ Note:
 Note:
   : Coloured lines on the outer circle indicate the mean value for each group and the respective ranges show the standard error of the mean. Each group\'s bars sum to 100%. The number of data points in each group is presented between brackets in the legend of each pannel.
   : You can replicate these graphics and edit them as needed using the `plotTimes()` function.
-  : The data used in these graphics is stored in the `section.times$departure` object.
+  : The data used in these graphics are stored in the `section.times$departure` object.
   : To obtain reports with the legacy linear circular scale, run `options(actel.circular.scale = "linear")` before running your analyses.
 
 <center>
@@ -1113,7 +1113,7 @@ Note:
 ### Global residency
 
 Note:
-  : The data used in these graphics is stored in the `global.ratios` and `time.positions` objects.
+  : The data used in these graphics are stored in the `global.ratios` and `time.positions` objects.
   : You can replicate these graphics and edit them as needed using the `plotRatios()` function.
   : This data is also available split by group in the `group.ratios`object.
   : You can plot these results by group using the \'group\' argument in `plotRatios()`.
@@ -1134,7 +1134,7 @@ Note:
 ### Individual residency plots
 
 Note:
-  : The data used in these graphics is stored in the `time.ratios` object (one table per tag). More condensed information can be found in the `section.movements` object.
+  : The data used in these graphics are stored in the `time.ratios` object (one table per tag). More condensed information can be found in the `section.movements` object.
   : You can replicate these graphics and edit them as needed using the `plotResidency()` function.
 
 <center>
@@ -1152,7 +1152,7 @@ Note:
   : The ', ifelse(detections.y.axis == "stations", 'stations', 'arrays'), ' have been aligned by ', ifelse(detections.y.axis == "stations", 'array', 'section'), ', following the order provided ', ifelse(detections.y.axis == "stations", '', 'either '), 'in the spatial input', ifelse(detections.y.axis == "stations", '.', ' or the `section.order` argument.'), '
   : You can replicate these graphics and edit them as needed using the `plotDetections()` function.
   : You can also see the movement events of multiple tags simultaneously using the `plotMoves()` function.
-  : The data used in these graphics is stored in the `detections` and `movements` objects (and respective valid counterparts).
+  : The data used in these graphics are stored in the `detections` and `movements` objects (and respective valid counterparts).
 
 <center>
 ', individual.detection.plots,'