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diff --git a/docs/articles/anomaly-and-aggregation_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/anomaly-and-aggregation_files/accessible-code-block-0.0.1/empty-anchor.js
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+// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
+// v0.0.1
+// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
+
+document.addEventListener('DOMContentLoaded', function() {
+ const codeList = document.getElementsByClassName("sourceCode");
+ for (var i = 0; i < codeList.length; i++) {
+ var linkList = codeList[i].getElementsByTagName('a');
+ for (var j = 0; j < linkList.length; j++) {
+ if (linkList[j].innerHTML === "") {
+ linkList[j].setAttribute('aria-hidden', 'true');
+ }
+ }
+ }
+});
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+Anomaly Calculation and Data Aggregation
+ + + Source:vignettes/anomaly-and-aggregation.Rmd
+ anomaly-and-aggregation.Rmd
+
+
+Overview +
+Environmental time series frequently contain missing values, and international standards from organisations like the World Meteorological Organisation (WMO) specify exactly how much missing data is acceptable before an aggregated value should not be reported. er.helpers provides two complementary sets of tools for these situations:
-
+
- +Aggregation with criteria — compute a summary statistic (mean, min, max, sum) only when a dataset meets user-defined missing-data thresholds. +
- +Annual anomaly — express each year’s value relative to a reference period mean, with the same missing-data safeguards applied to the baseline. +
+
+
+
+Aggregating data with missing-value criteria +
+aggregate_with_criteria() returns the requested aggregate, or NA if either the total number of missing values or the length of the longest consecutive run of missing values exceeds the specified thresholds.
Thresholds can be expressed as counts (integers ≥ 1) or proportions (0 < value ≤ 1):
+
+# A month of daily temperatures with a few missing days
+# randomize sequence of daily temperatures and missing values to demonstrate consecutive-missing criteria
+set.seed(42)
+daily_temp <- sample(c(15:25, NA, NA, NA), size = 30, replace = TRUE)
+
+# Allow up to 3 missing days and no more than 2 consecutive missing days
+aggregate_with_criteria(daily_temp, max_missing = 3, max_consecutive = 2)
+#> [1] 19.62963
+
+# Tigthen the criteria: allow at most 2 missing days and no more than 1 consecutive missing day
+aggregate_with_criteria(daily_temp, max_missing = 2, max_consecutive = 1)
+#> [1] NA
+
+
+Convenience wrappers +
+mean_with_criteria(), min_with_criteria(), max_with_criteria(), and sum_with_criteria() call aggregate_with_criteria() with the appropriate fun argument pre-set:
+mean_with_criteria(daily_temp, max_missing = 3, max_consecutive = 2)
+#> [1] 19.62963
+min_with_criteria(daily_temp, max_missing = 3, max_consecutive = 2)
+#> [1] 15
+max_with_criteria(daily_temp, max_missing = 3, max_consecutive = 2)
+#> [1] 25
+sum_with_criteria(daily_temp, max_missing = 3, max_consecutive = 2)
+#> [1] 530
+Applying criteria across groups with
+
+
+
+Applying criteria across groups with dplyr
+
+A common workflow is to compute a monthly aggregate from daily data:
+
+set.seed(42)
+
+daily_data <- tibble(
+ date = seq.Date(as.Date("2020-01-01"),
+ as.Date("2020-12-31"), by = "day"),
+ temperature = runif(366, min = 5, max = 25)) |>
+ # Introduce ~8 % random missing values
+ mutate(temperature = ifelse(runif(n()) < 0.08, NA, temperature))
+
+monthly_means <- daily_data |>
+ mutate(year = format(date, "%Y"),
+ month = format(date, "%m")) |>
+ group_by(year, month) |>
+ summarise(mean_temp = mean_with_criteria(
+ temperature, max_missing = 3, max_consecutive = 2),
+ .groups = "drop")
+
+monthly_means
+#> # A tibble: 12 × 3
+#> year month mean_temp
+#> <chr> <chr> <dbl>
+#> 1 2020 01 17.1
+#> 2 2020 02 NA
+#> 3 2020 03 13.5
+#> 4 2020 04 15.4
+#> 5 2020 05 16.3
+#> 6 2020 06 14.6
+#> 7 2020 07 NA
+#> 8 2020 08 13.7
+#> 9 2020 09 14.3
+#> 10 2020 10 12.9
+#> 11 2020 11 15.1
+#> 12 2020 12 15.6Months where the missing-data criteria are exceeded will show NA, making it easy to flag them downstream.
+
+
+
+
+Calculating annual anomalies +
+calc_annual_anomaly() subtracts the mean of a reference period from every observation. The reference period mean is itself computed with mean_with_criteria(), so the same missing-data safeguards apply.
+set.seed(7)
+
+# Simulate 50 years of annual mean temperatures
+temp_series <- tibble(
+ year = 1971:2020,
+ temperature = 12 + cumsum(rnorm(50, mean = 0.03, sd = 0.4)))
+
+# Define the WMO 1981–2010 climate normal as the reference period
+reference_period <- c(1981, 2010)
+temp_series <- temp_series |>
+ mutate(anomaly = calc_annual_anomaly(temperature,
+ year, period = reference_period))
+
+head(temp_series)
+#> # A tibble: 6 × 3
+#> year temperature anomaly
+#> <int> <dbl> <dbl>
+#> 1 1971 12.9 -3.77
+#> 2 1972 12.5 -4.22
+#> 3 1973 12.2 -4.47
+#> 4 1974 12.1 -4.61
+#> 5 1975 11.8 -4.96
+#> 6 1976 11.4 -5.31
+
+
+Visualising anomalies +
+
+library(ggplot2)
+
+ggplot(temp_series, aes(x = year, y = anomaly, fill = anomaly > 0)) +
+ geom_col(show.legend = FALSE) +
+ geom_hline(yintercept = 0, linewidth = 0.4) +
+ scale_fill_manual(values = c("#0D94A3", "#AE4E51")) +
+ labs(title = "Annual temperature anomaly",
+ subtitle = paste0("Relative to ", reference_period[1], "\u2013",
+ reference_period[2], " mean"),
+ x = NULL, y = "\u00b0C") +
+ theme_minimal()
+
+
+
+Handling missing data in the reference period +
+If observations are missing inside the reference period, calc_annual_anomaly() passes max_missing and max_consecutive to mean_with_criteria(). If the reference mean cannot be calculated the function returns NA for every year and issues a warning:
+# Introduce missing values in the reference period
+temp_with_gaps <- temp_series$temperature
+temp_with_gaps[11:15] <- NA # years 1981–1985 are missing
+
+anomalies_strict <- calc_annual_anomaly(
+ x = temp_with_gaps,
+ year = temp_series$year,
+ period = reference_period,
+ max_missing = 0, # zero tolerance for missing baseline data
+ max_consecutive = 0)
+#> Warning in calc_annual_anomaly(x = temp_with_gaps, year = temp_series$year, :
+#> Missing values for the anomaly reference period.
+
+head(anomalies_strict)
+#> [1] NA NA NA NA NA NA
+
+# Relax the constraint: allow up to 20 % missing in the reference period
+anomalies_relaxed <- calc_annual_anomaly(
+ x = temp_with_gaps,
+ year = temp_series$year,
+ period = reference_period,
+ max_missing = 0.20,
+ max_consecutive = NULL # no consecutive-missing constraint
+)
+#> Warning in calc_annual_anomaly(x = temp_with_gaps, year = temp_series$year, :
+#> Missing values for the anomaly reference period.
+
+head(anomalies_relaxed)
+#> [1] -4.203734 -4.652443 -4.900160 -5.035077 -5.393346 -5.742258+
+
+
+
+ Summary of key arguments +
+| Argument | +Description | +Accepts | +
|---|---|---|
max_missing |
+Maximum allowable missing values | +Count (integer) or proportion (0–1) | +
max_consecutive |
+Maximum allowable consecutive missing values | +Count (integer) or proportion (0–1) | +
fun |
+Aggregation function (aggregate_with_criteria only) |
+Any function, e.g. mean, sum
+ |
+
period |
+Start and end year of the anomaly baseline | +
+c(start, end) or NULL for full range |
+
Setting a threshold to NULL or 1 disables that constraint entirely.
+
+
+
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+
diff --git a/docs/articles/palettes.html b/docs/articles/palettes.html
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+ Articles
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+ All vignettes
+ + +
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diff --git a/docs/articles/palettes_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/palettes_files/accessible-code-block-0.0.1/empty-anchor.js
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+// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
+// v0.0.1
+// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
+
+document.addEventListener('DOMContentLoaded', function() {
+ const codeList = document.getElementsByClassName("sourceCode");
+ for (var i = 0; i < codeList.length; i++) {
+ var linkList = codeList[i].getElementsByTagName('a');
+ for (var j = 0; j < linkList.length; j++) {
+ if (linkList[j].innerHTML === "") {
+ linkList[j].setAttribute('aria-hidden', 'true');
+ }
+ }
+ }
+});
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+Overview +
+er.helpers provides two families of colour palettes tailored for environmental reporting at Statistics New Zealand:
| Family | +Prefix | +Use case | +
|---|---|---|
| Statistics NZ web | +pal_snz_* |
+StatsNZ website publications | +
| EA19 report | +pal_ea19_* |
+Environment Aotearoa 2019 print report | +
| Map / point | +pal_point_* |
+Spatial maps and scatter plots | +
Within each family there are palettes for different situations:
+| Suffix | +Purpose | +
|---|---|
| (none) | +Nominal (unordered) categorical variables | +
_alpha2 |
+Comparing a current year to one past year | +
_trend2 |
+Two-level ordinal trend (e.g. improving / worsening) | +
_trend3 |
+Three-level ordinal trend (e.g. improving / indeterminate / worsening) | +
_trend5 |
+Five-level ordinal trend | +
_nztcs_c |
+NZ Threat Classification System — categories | +
_nztcs_s |
+NZ Threat Classification System — sub-categories (SNZ only) | +
An additional signed_sqrt_trans() scale transformation is useful for visualising trend magnitudes that span both negative and positive values.
+
+
+
+
+
+
+Viewing palettes +
+Use scales::show_col() to display any palette:
+scales::show_col(pal_snz, borders = NA)
+scales::show_col(pal_ea19, borders = NA)
+scales::show_col(pal_point_set1, borders = NA)
+
+
+
+
+Nominal palettes (unordered categories) +
+Use the base pal_snz / pal_ea19 palettes with scale_fill_manual() or scale_colour_manual() for unordered categorical variables:
+set.seed(1)
+indicator_data <- tibble(
+ indicator = paste0("Indicator ", 1:7),
+ value = runif(7, 50, 150)
+)
+
+ggplot(indicator_data, aes(x = reorder(indicator, value), y = value,
+ fill = indicator)) +
+ geom_col(show.legend = FALSE) +
+ scale_fill_manual(values = pal_snz) +
+ coord_flip() +
+ labs(title = "pal_snz — nominal palette", x = NULL, y = "Value") +
+ theme_minimal()
+
+
+Trend palettes (ordinal categories) +
+Trend palettes are designed to pair with order_likelihood_levels() so that colours align with the direction and strength of a trend.
+
+
+Two levels +
+
+trend2_df <- tibble(
+ trend = factor(c("Improving", "Worsening")),
+ n = c(12, 8)
+)
+
+ggplot(trend2_df, aes(x = trend, y = n, fill = trend)) +
+ geom_col(show.legend = FALSE) +
+ scale_fill_manual(values = pal_snz_trend2) +
+ labs(title = "pal_snz_trend2", x = NULL, y = "Count") +
+ theme_minimal()
+
+
+
+Five levels — full likelihood scale +
+
+set.seed(7)
+p_values <- runif(50)
+
+trend5_df <- tibble(
+ p_value = p_values,
+ likelihood = get_likelihood_category(p_value, term_type = "increasing-decreasing") |>
+ order_likelihood_levels()
+) |>
+ count(likelihood)
+
+ggplot(trend5_df, aes(x = likelihood, y = n, fill = likelihood)) +
+ geom_col(show.legend = FALSE) +
+ scale_fill_manual(values = pal_snz_trend5) +
+ labs(title = "pal_snz_trend5 — five-level trend palette",
+ x = NULL, y = "Count") +
+ theme_minimal() +
+ theme(axis.text.x = element_text(angle = 30, hjust = 1))
+
+
+
+
+Year-comparison palettes +
+pal_snz_alpha2 and pal_ea19_alpha2 use a tinted base colour for the earlier year and the full base colour for the current year:
+comparison_df <- tibble(
+ year = factor(c(2019, 2023), levels = c(2019, 2023)),
+ value = c(82, 91)
+)
+
+ggplot(comparison_df, aes(x = year, y = value, fill = year)) +
+ geom_col(show.legend = FALSE, width = 0.5) +
+ scale_fill_manual(values = pal_snz_alpha2) +
+ labs(title = "pal_snz_alpha2 — year comparison palette",
+ x = NULL, y = "Value") +
+ theme_minimal()
+
+
+
+
+NZ Threat Classification System palettes +
+pal_snz_nztcs_c (four categories) and pal_snz_nztcs_s (nine sub-categories) are named vectors, so they map automatically to factor levels:
+nztcs_df <- tibble(
+ status = factor(names(pal_snz_nztcs_c),
+ levels = names(pal_snz_nztcs_c)),
+ n = c(45, 120, 18, 310)
+)
+
+ggplot(nztcs_df, aes(x = status, y = n, fill = status)) +
+ geom_col(show.legend = FALSE) +
+ scale_fill_manual(values = pal_snz_nztcs_c) +
+ labs(title = "pal_snz_nztcs_c — NZ Threat Classification",
+ x = NULL, y = "Number of species") +
+ theme_minimal() +
+ theme(axis.text.x = element_text(angle = 15, hjust = 1))
+
+
+
+
+Signed square-root scale transformation +
+When a continuous variable spans a wide range of both positive and negative values, a standard linear scale compresses small values near zero. The signed_sqrt_trans() transformation stretches both tails symmetrically:
+set.seed(22)
+slopes_df <- tibble(
+ station = paste0("S", 1:20),
+ slope = c(rnorm(10, mean = -2, sd = 0.5),
+ rnorm(10, mean = 8, sd = 3))
+)
+
+ggplot(slopes_df, aes(x = reorder(station, slope), y = slope,
+ fill = slope > 0)) +
+ geom_col(show.legend = FALSE) +
+ scale_y_continuous(trans = signed_sqrt_trans()) +
+ scale_fill_manual(values = c("#0D94A3", "#AE4E51")) +
+ labs(title = "Signed square-root y-axis",
+ x = NULL, y = "Sen's slope (signed\u221a)") +
+ theme_minimal() +
+ theme(axis.text.x = element_text(angle = 45, hjust = 1))
+
+
+ Quick reference +
+#> # A tibble: 17 × 3
+#> Object Colours Description
+#> <chr> <int> <chr>
+#> 1 pal_snz 9 Nominal — StatsNZ web
+#> 2 pal_snz_alpha2 2 Year comparison — StatsNZ web
+#> 3 pal_snz_trend2 2 Trend 2-level — StatsNZ web
+#> 4 pal_snz_trend3 3 Trend 3-level — StatsNZ web
+#> 5 pal_snz_trend5 5 Trend 5-level — StatsNZ web
+#> 6 pal_snz_nztcs_c 4 NZ TCS categories — StatsNZ web
+#> 7 pal_snz_nztcs_s 9 NZ TCS sub-categories — StatsNZ web
+#> 8 pal_ea19 9 Nominal — EA19 report
+#> 9 pal_ea19_alpha2 2 Year comparison — EA19 report
+#> 10 pal_ea19_trend2 2 Trend 2-level — EA19 report
+#> 11 pal_ea19_trend3 3 Trend 3-level — EA19 report
+#> 12 pal_ea19_trend5 5 Trend 5-level — EA19 report
+#> 13 pal_ea19_nztcs_c 4 NZ TCS categories — EA19 report
+#> 14 pal_point_set1 9 Nominal — maps/points
+#> 15 pal_point_trend2 2 Trend 2-level — maps/points
+#> 16 pal_point_trend3 3 Trend 3-level — maps/points
+#> 17 pal_point_trend5 5 Trend 5-level — maps/points
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+
Helper functions commonly used in Environmental Reporting at Stats NZ • er.helpers
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Add a row for each unused factor level to ensure plotly displays all levels in the legend. — add_unused_levels • er.helpers
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diff --git a/docs/reference/aggregate_with_criteria.html b/docs/reference/aggregate_with_criteria.html
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-Calculate an aggregated value taking into account certain exclusion cirteria — aggregate_with_criteria • er.helpers
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+ +
+
+
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+
+ Citation
+ Source:DESCRIPTION
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Fernando Cagua. Author, maintainer. -
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- -
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David Hodge. Author. -
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- -
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Bonnie Farant. Author. -
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- -
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Statistics New Zealand. Copyright holder. -
-
-
Cagua F, Hodge D, Farant B, Robinson N (2026). +er.helpers: Helper functions commonly used in Environmental Reporting at Stats NZ. +R package version 1.4.1, https://statisticsnz.github.io/er.helpers. +
+@Manual{,
+ title = {er.helpers: Helper functions commonly used in Environmental Reporting at Stats NZ},
+ author = {Fernando Cagua and David Hodge and Bonnie Farant and Nestor Robinson},
+ year = {2026},
+ note = {R package version 1.4.1},
+ url = {https://statisticsnz.github.io/er.helpers},
+}
Authors
-
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diff --git a/docs/reference/add_unused_levels.html b/docs/reference/add_unused_levels.html
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+++ b/docs/reference/add_unused_levels.html
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diff --git a/docs/reference/a4_width_mm.html b/docs/reference/a4_width_mm.html
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diff --git a/docs/news/index.html b/docs/news/index.html
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+++ b/docs/news/index.html
@@ -1,66 +1,12 @@
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--Introduction
+
+
+
+
-Introduction +
er.helpers is an R package that facilitates the analysis and reporting of environmental indicators.
We can group these helper functions into anomaly calculation, data aggregation, data acquisition, likelihood category estimation, precipitation indices, RShiny development, seasonality calculations, data summarisation and visualisation, and trend calculation.
-
@@ -106,69 +115,92 @@
Grids: A function to make grids from meshblock data, and standard hexagonal grids for use.
-
+
-Installation
-# install.packages("remotes") -remotes::install_github("StatisticsNZ/er.helpers", build_vignettes = TRUE)
+
-Installation +
+
+# install.packages("remotes")
+remotes::install_github("StatisticsNZ/er.helpers", build_vignettes = TRUE)
-
-Documentation
+
+
Documentation +
Webpage URL:
-https://statisticsnz.github.io/er.helpers/
+https://statisticsnz.github.io/er.helpers/
Copyright and Licensing
The package is Crown copyright (c) 2020, Statistics New Zealand on behalf of the New Zealand Government, and is licensed under the MIT License.
-
This document is Crown copyright (c) 2020, Statistics New Zealand on behalf of the New Zealand Government, and is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
This document is Crown copyright (c) 2020, Statistics New Zealand on behalf of the New Zealand Government, and is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
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diff --git a/docs/pkgdown.css b/docs/pkgdown.css
index c01e592..80ea5b8 100644
--- a/docs/pkgdown.css
+++ b/docs/pkgdown.css
@@ -56,8 +56,10 @@ img.icon {
float: right;
}
-img {
+/* Ensure in-page images don't run outside their container */
+.contents img {
max-width: 100%;
+ height: auto;
}
/* Fix bug in bootstrap (only seen in firefox) */
@@ -78,11 +80,10 @@ dd {
/* Section anchors ---------------------------------*/
a.anchor {
- margin-left: -30px;
- display:inline-block;
- width: 30px;
- height: 30px;
- visibility: hidden;
+ display: none;
+ margin-left: 5px;
+ width: 20px;
+ height: 20px;
background-image: url(./link.svg);
background-repeat: no-repeat;
@@ -90,17 +91,15 @@ a.anchor {
background-position: center center;
}
-.hasAnchor:hover a.anchor {
- visibility: visible;
-}
-
-@media (max-width: 767px) {
- .hasAnchor:hover a.anchor {
- visibility: hidden;
- }
+h1:hover .anchor,
+h2:hover .anchor,
+h3:hover .anchor,
+h4:hover .anchor,
+h5:hover .anchor,
+h6:hover .anchor {
+ display: inline-block;
}
-
/* Fixes for fixed navbar --------------------------*/
.contents h1, .contents h2, .contents h3, .contents h4 {
@@ -244,14 +243,14 @@ nav[data-toggle='toc'] .nav .nav > .active:focus > a {
.ref-index th {font-weight: normal;}
-.ref-index td {vertical-align: top;}
+.ref-index td {vertical-align: top; min-width: 100px}
.ref-index .icon {width: 40px;}
.ref-index .alias {width: 40%;}
.ref-index-icons .alias {width: calc(40% - 40px);}
.ref-index .title {width: 60%;}
.ref-arguments th {text-align: right; padding-right: 10px;}
-.ref-arguments th, .ref-arguments td {vertical-align: top;}
+.ref-arguments th, .ref-arguments td {vertical-align: top; min-width: 100px}
.ref-arguments .name {width: 20%;}
.ref-arguments .desc {width: 80%;}
@@ -264,31 +263,26 @@ table {
/* Syntax highlighting ---------------------------------------------------- */
-pre {
- word-wrap: normal;
- word-break: normal;
- border: 1px solid #eee;
-}
-
-pre, code {
+pre, code, pre code {
background-color: #f8f8f8;
color: #333;
}
+pre, pre code {
+ white-space: pre-wrap;
+ word-break: break-all;
+ overflow-wrap: break-word;
+}
-pre code {
- overflow: auto;
- word-wrap: normal;
- white-space: pre;
+pre {
+ border: 1px solid #eee;
}
-pre .img {
+pre .img, pre .r-plt {
margin: 5px 0;
}
-pre .img img {
+pre .img img, pre .r-plt img {
background-color: #fff;
- display: block;
- height: auto;
}
code a, pre a {
@@ -305,9 +299,8 @@ a.sourceLine:hover {
.kw {color: #264D66;} /* keyword */
.co {color: #888888;} /* comment */
-.message { color: black; font-weight: bolder;}
-.error { color: orange; font-weight: bolder;}
-.warning { color: #6A0366; font-weight: bolder;}
+.error {font-weight: bolder;}
+.warning {font-weight: bolder;}
/* Clipboard --------------------------*/
@@ -365,3 +358,27 @@ mark {
content: "";
}
}
+
+/* Section anchors ---------------------------------
+ Added in pandoc 2.11: https://github.com/jgm/pandoc-templates/commit/9904bf71
+*/
+
+div.csl-bib-body { }
+div.csl-entry {
+ clear: both;
+}
+.hanging-indent div.csl-entry {
+ margin-left:2em;
+ text-indent:-2em;
+}
+div.csl-left-margin {
+ min-width:2em;
+ float:left;
+}
+div.csl-right-inline {
+ margin-left:2em;
+ padding-left:1em;
+}
+div.csl-indent {
+ margin-left: 2em;
+}
diff --git a/docs/pkgdown.js b/docs/pkgdown.js
index 7e7048f..6f0eee4 100644
--- a/docs/pkgdown.js
+++ b/docs/pkgdown.js
@@ -80,7 +80,7 @@
$(document).ready(function() {
var copyButton = "";
- $(".examples, div.sourceCode").addClass("hasCopyButton");
+ $("div.sourceCode").addClass("hasCopyButton");
// Insert copy buttons:
$(copyButton).prependTo(".hasCopyButton");
@@ -91,7 +91,7 @@
// Initialize clipboard:
var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', {
text: function(trigger) {
- return trigger.parentNode.textContent;
+ return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, "");
}
});
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index e109665..73d3cca 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -1,6 +1,7 @@
-pandoc: 2.3.1
-pkgdown: 1.5.0
+pandoc: 2.9.2.1
+pkgdown: 2.2.0
pkgdown_sha: ~
-articles: []
-last_built: 2022-02-24T02:13Z
-
+articles:
+ anomaly-and-aggregation: anomaly-and-aggregation.html
+ palettes: palettes.html
+last_built: 2026-04-22T10:31Z
diff --git a/docs/reference/a4_height_mm.html b/docs/reference/a4_height_mm.html
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- -er.helpers 1.3.3
--
-
- remove simplevis and gridify_mb_data functions, so that it can be installed. -
-
-
-
-
- -er.helpers 1.3.0
--
-
- Added add_unused_levels function to assist with fixing a plotly legend bug. -
-
- -er.helpers 1.2.6
--
-
- Updated read_csv_datalake to use readr::write_excel_csv, so that macrons are preserved. -
-
-er.helpers 1.2.5
--
-
- Bug fix: updated ggplot functions per updates to simplevis. +
- Removed search_data_lake() function. Added separate round() function to overwrite base round as doesn’t behave how expected.
- Updated read_csv_datalake to use readr::write_excel_csv, so that macrons are preserved.
- Bug fix: updated ggplot functions per updates to simplevis.
- New feature: added a4 dimensions. -
- New feature: added ggplot hbar functions that work with x_na_inf for APS data. -
- New feature: added read_from_datalake, get_metadata and write_rds_datalake functions. -
- Bug fix: ungrouped output from gridify_mb_data, so that an sf object is returned. -
- New feature: expand gridify_mb_data function to support the gridifying of more than 1 linecode. -
- New feature: add gridify_mb_data function to create a grid from meshblock data. -
- New feature: added the sankey_build_data function to support networkD3 sankey charts. -
- Improvement: darkened the shade of grey in pal_point_trend3 and pal_point_trend5. -
- Bug fix: aggregation functions now return NA as expected when all values are missing. Even if it passes other requirements. -
- New feature: Added all palettes from simplevis, plus a signed square root trans function scales. +
- New feature: added read_from_datalake, get_metadata and write_rds_datalake functions.
- Bug fix: ungrouped output from gridify_mb_data, so that an sf object is returned.
- New feature: expand gridify_mb_data function to support the gridifying of more than 1 linecode.
- New feature: added the sankey_build_data function to support networkD3 sankey charts.
- Improvement: darkened the shade of grey in pal_point_trend3 and pal_point_trend5.
- Bug fix: aggregation functions now return NA as expected when all values are missing. Even if it passes other requirements.
- New feature: Added all palettes from simplevis, plus a signed square root trans function scales.
- New feature: Added a signed square root transformation function scale. -
- Improvement: Order of season levels changed such that the annual category goes before the other seasons. -
- New feature: Added
linear_model()as another option for trend estimation
+ - Improvement: Order of season levels changed such that the annual category goes before the other seasons.
- New feature: Added
linear_model()as another option for trend estimation - Breaking changes: The slope calculated using
sen_slope()is now returned in a data frame with a column called “slope” instead of “sen_slope”. This is for consistency with the slope calculated usinglinear_model()
- - New feature: Added
sum_with_criteria()to collection of aggregation functions
+ - New feature: Added
sum_with_criteria()to collection of aggregation functions - Bug fix: Fixed a bug in
aggregate_with_criteria()that meant that the mean was calculated regardless of the function indicated
- - Bug fix: Fixed bug in precipitation functions that prevented them from being calculated properly. -
- Improvement:
search_data_lake()has been deprecated in favour ofsearch_datalake()to be consistent with naming
+ - Bug fix: Fixed bug in precipitation functions that prevented them from being calculated properly. +
- Improvement:
search_data_lake()has been deprecated in favour ofsearch_datalake()to be consistent with naming - Improvement:
search_datalake()now uses AND instead of OR when filtering keys. - Improvement:
search_datalake()now returns a tibble - Improvement: Using MIT License -
- Bug fix: Fix documentation warning generated when installing the package.
- Bug fix: Functions used to calculate rainfall indices use wet days only (days > 1mm precipitation by default).
- Improvement: Deprecated
get_reference_rainfall()andrainfall_above_reference()in favour ofget_reference_precipitation()andprecipitation_above_reference()in order to adopt WMO names.
- - Improvement: We use exclusion criteria on missing values to calculate anoamliesS +
- Improvement: We use exclusion criteria on missing values to calculate anoamliesS
- Bug fix: When aggregation criteria were equal to 1 it correctly assume they are given as proportions
- Improvement: Aggregation with criteria functions now have default values for maximum number of missing values and maximum number of consecutive missing values. -
- New feature: A set of functions (
aggragate_with_criteria()) to calculate aggregations using criteria for missing data.
+ - New feature: A set of functions (
aggragate_with_criteria()) to calculate aggregations using criteria for missing data. - New feature: A function to print the range of a numeric vector in a “pretty way”. -
- Bug fix: When searching for a key in the data lake it looks for it in all the keys, not the first 1000 -
- Bug fix: Return a p-value of 0.5 when all values are tied in Mann-Kendall test -
- Bug fix: Trend functions
sen_slope()andmann_kendall()return their method regardless of wether the trend estimation is successful or not.
- - Improvement: Trend functions
sen_slope()andmann_kendall()return NA if there are NAs in the input data.
- - Bug fix: Correcting spelling of autumn in
order_season_levels()-
- - New feature: Added
order_season_levels()to automatically order season as a factor for pretty plotting and data standarisation
+ - Bug fix: When searching for a key in the data lake it looks for it in all the keys, not the first 1000
- Bug fix: Trend functions
sen_slope()andmann_kendall()return their method regardless of wether the trend estimation is successful or not. - Improvement: Trend functions
sen_slope()andmann_kendall()return NA if there are NAs in the input data. - Bug fix: Correcting spelling of autumn in
order_season_levels()+ - New feature: Added
order_season_levels()to automatically order season as a factor for pretty plotting and data standarisation - Bug fix: Corrected example that were not working in
round_preserve_sum()
- - Bug fix:
er.helpers:::likelihood_termsinternal data was not being saved properly.
- - New feature: Added
simplifY_likelihood_levels()which collapses levels in likelihood category factors
+ - Bug fix:
er.helpers:::likelihood_termsinternal data was not being saved properly. - New feature: Added
simplifY_likelihood_levels()which collapses levels in likelihood category factors - New feature: Added
round_preserve_sum()which rounds a set of number while maintaining the total. Used, for example, so that rounded percentages still add to 100.
- - New feature:Added
get_likelihood_category()which given a probabiliy p it returns the term used to describe the category this probability belongs to. It also ensures the levels of the output are ordered appropietly.
+ - New feature:Added
get_likelihood_category()which given a probabiliy p it returns the term used to describe the category this probability belongs to. It also ensures the levels of the output are ordered appropietly. - New feature:Added
order_likelihood_levels()which orders the levels of a likelihood category factor - New feature:Added the datasets
ipcc_likelihood_scaleandstatsnz_likelihood_scalewhich contain the Intergovernmental Panel on Climate Change (IPCC) and the Stats NZ likelihood scales respectively - Improvement: Started using automatic package testing before things get out of hand -
- Improvement: Trend functions warn when number of values is small or when all values are tied in the data -
- Bug fix: Reading data frames with a rowname X1 column doesn’t fail anymore -
- New feature: Added function to calculate trends using Sen’s slope and Mann-Kendall test +
- Improvement: Trend functions warn when number of values is small or when all values are tied in the data
- New feature: Added function to calculate trends using Sen’s slope and Mann-Kendall test
- Improvement: When downloading a csv from the data lake remove the X1 column -
- Improvement: Added contribution section to README
- Improvement: Improved documentation
- Improvement: Added CHANGELOG -
+
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+ er.helpers 1.4.2
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+ er.helpers 1.2.6
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+
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- -er.helpers 1.2.3
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- -er.helpers 1.2.2
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- -er.helpers 1.2.1
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--
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- -er.helpers 1.1.2
--
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- -er.helpers 1.1.1
--
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+
+
+ -er.helpers 1.1.0
--
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+ er.helpers 1.2.2
+
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+ er.helpers 1.2.1
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+ er.helpers 1.1.8
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+ er.helpers 1.1.3
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+ er.helpers 1.1.2
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+ er.helpers 1.1.1
+
+
- er.helpers 1.1.0
+
-
- -er.helpers 1.0.1
--
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+ -er.helpers 1.0.0
--
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+ er.helpers 1.0.1
+
+
- er.helpers 1.0.0
+
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+ -er.helpers 0.12.0
--
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- er.helpers 0.12.0
+
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+ -er.helpers 0.11.3
--
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+ er.helpers 0.11.3
+
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- er.helpers 0.11.2
+
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+ -er.helpers 0.11.1
--
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- er.helpers 0.11.1
+
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+ -er.helpers 0.11.0
--
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- er.helpers 0.11.0
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- -er.helpers 0.10.6
--
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- -er.helpers 0.10.5
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- -er.helpers 0.10.3
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- -er.helpers 0.10.2
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- -er.helpers 0.10.1
--
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+ -er.helpers 0.10.0
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+ er.helpers 0.10.6
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+ er.helpers 0.10.3
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+ er.helpers 0.10.2
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+ er.helpers 0.10.1
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- er.helpers 0.10.0
+
-
- -er.helpers 0.9.1
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+ -er.helpers 0.9.0
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+ er.helpers 0.9.1
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- er.helpers 0.9.0
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- -er.helpers 0.5.1
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@@ -120,50 +65,48 @@
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+
Add a row for each unused factor level to ensure plotly displays all levels in the legend.
- Source:R/add_unused_levels.R
+ Source: R/add_unused_levels.R
add_unused_levels.RdAdd a row for each unused factor level to ensure plotly displays all levels
Add a row for each unused factor level to ensure plotly displays all levels in the legend.
add_unused_levels(data, var)+
+
+
+
+ add_unused_levels(data, var)
+
+ Arguments
+ + +
+
Value
A tibble, dataframe or sf object. Required input.
+
-
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@@ -128,79 +73,76 @@
-
+
Calculate an aggregated value taking into account certain exclusion cirteria
- Source:R/aggregation.R
+ Source: R/aggregation.R
aggregate_with_criteria.RdCalculate an aggregated value taking into account certain exclusion cirteria monthly value by specifing these thresholds
aggregate_with_criteria(x, max_missing = 0, max_consecutive = 0, fun = mean) +++ ++aggregate_with_criteria(x, max_missing = 0, max_consecutive = 0, fun = mean) + +mean_with_criteria(x, max_missing = 0, max_consecutive = 0) + +min_with_criteria(x, max_missing = 0, max_consecutive = 0) + +max_with_criteria(x, max_missing = 0, max_consecutive = 0) + +sum_with_criteria(x, max_missing = 0, max_consecutive = 0)++ +Arguments
-mean_with_criteria(x, max_missing = 0, max_consecutive = 0) -min_with_criteria(x, max_missing = 0, max_consecutive = 0) +
- x
+- -max_with_criteria(x, max_missing = 0, max_consecutive = 0) -sum_with_criteria(x, max_missing = 0, max_consecutive = 0) +
Numeric values
- max_missing
+- -
Numeric. Maximum number or proportion of missing values in +x. If any number of missing values is allowed set to 1 (100%) or NULL.
Arguments
-Value
+- fun
+- +
Function. Function used to aggregate values usually, mean, min, +max, or sum
+Functions
- --
+- -
mean_with_criteria: Mean with criteria- -
min_with_criteria: Minimum with criteria- -
max_with_criteria: Maximum with criteria- -
sum_with_criteria: Sum with criteria
- +
mean_with_criteria(): Mean with criteria- +
min_with_criteria(): Minimum with criteria- +
max_with_criteria(): Maximum with criteria- +
sum_with_criteria(): Sum with criteria