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Replace instances of 'is stored' with 'as stored' #159

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Dec 10, 2024
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Replace instances of 'is stored' with 'as stored' #159

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6 changes: 3 additions & 3 deletions R/explore.R
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Original file line number Diff line number Diff line change
Expand Up @@ -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>")
Expand Down Expand Up @@ -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>
Expand All @@ -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,'
Expand Down
14 changes: 7 additions & 7 deletions R/migration.R
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Original file line number Diff line number Diff line change
Expand Up @@ -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()`",
Expand Down Expand Up @@ -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",
Expand All @@ -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",
Expand All @@ -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",
Expand All @@ -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",
Expand All @@ -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",
Expand Down Expand Up @@ -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",
Expand Down
6 changes: 3 additions & 3 deletions R/print.R
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Original file line number Diff line number Diff line change
Expand Up @@ -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).

Expand Down Expand Up @@ -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).

Expand All @@ -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).
')
Expand Down
20 changes: 10 additions & 10 deletions R/residency.R
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -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>")
Expand Down Expand Up @@ -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"), '

Expand All @@ -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>
Expand All @@ -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% }
Expand All @@ -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>
Expand All @@ -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% }
Expand All @@ -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>
Expand All @@ -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()`.
Expand All @@ -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>
Expand All @@ -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,'
Expand Down
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