From 3d3d32432238f6f3568e5b936a3f384bc2b62f07 Mon Sep 17 00:00:00 2001 From: hugomflavio Date: Mon, 9 Dec 2024 22:20:10 -0400 Subject: [PATCH] replace instances of 'is stored' with 'as stored' closes #156 --- R/explore.R | 6 +++--- R/migration.R | 14 +++++++------- R/print.R | 6 +++--- R/residency.R | 20 ++++++++++---------- 4 files changed, 23 insertions(+), 23 deletions(-) diff --git a/R/explore.R b/R/explore.R index 3112a75..c085eb7 100644 --- a/R/explore.R +++ b/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.
\n", sensor.plots, "\n
") @@ -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.
@@ -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).
', individual.plots,' diff --git a/R/migration.R b/R/migration.R index a637d41..ad2bcd4 100644 --- a/R/migration.R +++ b/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", "
\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", "
\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", "
\n", diff --git a/R/print.R b/R/print.R index 389286e..c874d33 100644 --- a/R/print.R +++ b/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)](). 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)](). 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). ') diff --git a/R/residency.R b/R/residency.R index 7cda510..6ac4d4b 100644 --- a/R/residency.R +++ b/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.
\n", sensor.plots, "\n
") @@ -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.
@@ -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.
![](', 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.
@@ -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.
![](', 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.
@@ -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.
@@ -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).
', individual.detection.plots,'