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- traits.build paper published in Sep 2024 in Ecological Informatics (DOI: 10.1016/j.ecoinf.2024.102773)
- Minor updates to ontology, which is now version 1.0.0
- Added standard error and standard deviation as value types
Moved functions to austraits package and made austraits package a dependancy
Renamed some of the functions that are now moved to austraits package
bind_databases <-- build_combine
convert_df_to_list <-- util_df_to_list
convert_list_to_df1 <-- util_list_to_df1
convert_list_to_df2 <-- util_list_to_df2
Renamed functions still also assigned their old name, with a deprecation warning indicating the new name
plot_trait_distribution_beeswarm, trait_pivot_longer and trait_pivot_wider had been in both austraits and traits.build packages and have now been removed from traits.build
Import new austraits function flatten_database (had been suggested to be database_create_combined_table)
Refactoring of test functions used by dataset_test
Added tests using the dataset_test function, so it is checked explicitly by traits.build (run on Example datasets)
Minor bug fixes
Issues addressed since last traits.build release:
Issue #150 & Issue #154 (error if no contexts)
Issue #179 (incorrect test written that flagged an error if input/output units identical)
Issue #171 (SE required as a value type)
Issue #131 (remove duplicate functions)
Issue #183 (deprecated function still being used)
Issue #185 (context table columns mis-ordered, dataset test failing)
Issue #143 (add dataset_test tests)
---------
Co-authored-by: Daniel Falster <[email protected]>
Co-authored-by: Fonti Kar <[email protected]>
Co-authored-by: Sophie Yang <[email protected]>- Loading branch information
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Dec 19, 2024
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,7 +1,7 @@ | ||
| Type: Package | ||
| Package: traits.build | ||
| Title: A workflow for harmonising trait data from diverse sources into a documented standard structure | ||
| Version: 1.1.0 | ||
| Version: 2.0.0 | ||
| Maintainer: Daniel Falster <[email protected]> | ||
| Authors@R: c( | ||
| person(given = "Daniel", family = "Falster", role = c("cre", "aut"), email = "[email protected]", comment = c(ORCID = "0000-0002-9814-092X")), | ||
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@@ -22,7 +22,8 @@ Depends: | |
| lubridate, | ||
| readr, | ||
| stringr, | ||
| tidyr | ||
| tidyr, | ||
| austraits | ||
| Imports: | ||
| crayon, | ||
| git2r, | ||
|
|
@@ -38,7 +39,8 @@ Imports: | |
| testthat, | ||
| tibble, | ||
| whisker, | ||
| yaml | ||
| yaml, | ||
| lifecycle | ||
| Suggests: | ||
| furrr, | ||
| remake, | ||
|
|
@@ -57,8 +59,10 @@ Suggests: | |
| zip, | ||
| covr | ||
| Remotes: | ||
| richfitz/remake | ||
| richfitz/remake, | ||
| traitecoevo/austraits | ||
| Encoding: UTF-8 | ||
| VignetteBuilder: knitr | ||
| RoxygenNote: 7.2.3 | ||
| RoxygenNote: 7.3.2 | ||
| Config/testthat/edition: 3 | ||
| Roxygen: list(markdown = TRUE) | ||
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| Original file line number | Diff line number | Diff line change |
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@@ -30,108 +30,3 @@ check_pivot_wider <- function(dataset) { | |
| } | ||
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| } | ||
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| #' @title Pivot long format data into a wide format | ||
| #' | ||
| #' @description `trait_pivot_wider` "widens" long format data ("tidy data"). | ||
| #' | ||
| #' Databases built with `traits.build` are organised in a long format where observations are on different rows and the | ||
| #' type of observation is denoted by various identifying columns (e.g `trait_name`, `dataset_id`, | ||
| #' `observation_id`, etc.). | ||
| #' This function converts the data into wide format so that each trait in its own column. | ||
| #' | ||
| #' @param traits The traits table from database (list object) | ||
| #' @return A tibble in wide format | ||
| #' @details `trait_pivot_wider` will return a single wide tibble; note that some meta-data columns | ||
| #' (unit, replicates, measurement_remarks, basis_of_record, basis_of_value) will be excluded to | ||
| #' produce a useful wide tibble. | ||
| #' @examples | ||
| #' \dontrun{ | ||
| #' data <- austraits$traits %>% filter(dataset_id == "Falster_2003") | ||
| #' data # Long format | ||
| #' traits_wide <- trait_pivot_wider(data) | ||
| #' traits_wide # Wide format | ||
| #' } | ||
| #' @author Daniel Falster - [email protected] | ||
| #' @export | ||
| db_traits_pivot_wider <- function(traits) { | ||
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| metadata_cols <- c("unit", "replicates", "measurement_remarks", "basis_of_value") | ||
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| # A check for if there are more than 1 value_type for a given taxon_name, observation_id and method | ||
| check_value_type <- traits %>% | ||
| dplyr::select(dplyr::all_of(c( | ||
| "trait_name", "value", "dataset_id", "observation_id", "method_id", "method_context_id", | ||
| "repeat_measurements_id", "value_type"))) %>% | ||
| dplyr::group_by( | ||
| .data$dataset_id, .data$observation_id, .data$method_id, | ||
| .data$method_context_id, .data$repeat_measurements_id) %>% | ||
| dplyr::summarise(n_value_type = length(unique(.data$value_type))) %>% | ||
| dplyr::arrange(.data$observation_id) %>% | ||
| dplyr::filter(.data$n_value_type > 1) | ||
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| if (nrow(check_value_type) > 1) { | ||
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| traits %>% | ||
| tidyr::pivot_wider( | ||
| names_from = "trait_name", | ||
| values_from = "value", | ||
| id_cols = -dplyr::all_of(metadata_cols) | ||
| ) | ||
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| } else { | ||
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| metadata_cols <- c(metadata_cols, "value_type") | ||
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| traits %>% | ||
| tidyr::pivot_wider( | ||
| names_from = "trait_name", | ||
| values_from = "value", | ||
| id_cols = -dplyr::all_of(metadata_cols) | ||
| ) | ||
| } | ||
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| } | ||
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| #' @title Pivot wide format data into a long format | ||
| #' | ||
| #' @description `trait_pivot_longer` "gathers" wide format data into a "tidy" format. | ||
| #' | ||
| #' This function converts the data into long format where observations are on different rows and the type of | ||
| #' observation is denoted by the `trait_name` column. | ||
| #' In other words, `trait_pivot_longer` reverts the actions of `trait_pivot_wider`. | ||
| #' @param wide_data Output from `trait_pivot_wider` (a tibble of wide data) | ||
| #' @return A tibble in long format | ||
| #' @details | ||
| #' `trait_pivot_longer` will return a tibble with fewer columns than the original traits table | ||
| #' The excluded columns include: "unit", "replicates", "measurement_remarks", "basis_of_record", | ||
| #' "basis_of_value" # Double check #TODO | ||
| #' | ||
| #' @examples | ||
| #' \dontrun{ | ||
| #' data <- austraits$traits %>% | ||
| #' filter(dataset_id == "Falster_2003") | ||
| #' data # Long format | ||
| #' traits_wide <- trait_pivot_wider(data) | ||
| #' traits_wide # Wide format | ||
| #' | ||
| #' values_long <- trait_pivot_longer(traits_wide) | ||
| #' } | ||
| #' @author Daniel Falster - [email protected] | ||
| #' @author Fonti Kar - [email protected] | ||
| #' @export | ||
| db_traits_pivot_longer <- function(wide_data) { | ||
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| # The start of the trait columns is after `original_name` | ||
| start_of_trait_cols <- which(names(wide_data) == "original_name") + 1 | ||
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| wide_data %>% | ||
| tidyr::pivot_longer( | ||
| cols = start_of_trait_cols:ncol(.), | ||
| names_to = "trait_name", | ||
| values_drop_na = TRUE | ||
| ) | ||
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| } | ||
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