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Add
austraits functions to dataset_test and allow trait values to…
… be excluded in `exclude_observations` (#123) - Moved austraits functions `trait_pivot_wider` and `trait_pivot_longer` to traits.build in `pivot.R` and added testing within `dataset_test` - Moved code for testing pivot wider into standalone function called `check_pivot_wider()` - Allowed trait values to be excluded in `exclude_observations`, where previously only metadata fields/locations, etc. could be excluded - Added extra tests in `dataset_test` for `exclude_observations` and `taxonomic_updates` sections - Replaced `testthat` functions with own functions to enable customisation of failure messages - Regenerated `taxon_list.csv` for test datasets and documented method
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| #' @title Test whether a dataset can pivot wider | ||
| #' | ||
| #' @description Test whether the traits table of a dataset can pivot wider with the minimum required columns. | ||
| #' | ||
| #' @param dataset Built dataset with `test_build_dataset` | ||
| #' | ||
| #' @return Number of rows with duplicates preventing pivoting wider | ||
| #' @export | ||
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| check_pivot_wider <- function(dataset) { | ||
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| duplicates <- dataset$traits %>% | ||
| select( | ||
| dplyr::all_of(c("dataset_id", "trait_name", "value", "observation_id", "value_type", | ||
| "repeat_measurements_id", "method_id", "method_context_id")) | ||
| ) %>% | ||
| tidyr::pivot_wider(names_from = "trait_name", values_from = "value", values_fn = length) %>% | ||
| tidyr::pivot_longer(cols = 7:ncol(.)) %>% | ||
| dplyr::rename(dplyr::all_of(c("trait_name" = "name", "number_of_duplicates" = "value"))) %>% | ||
| select( | ||
| dplyr::all_of(c("dataset_id", "trait_name", "number_of_duplicates", "observation_id", | ||
| "value_type")), everything() | ||
| ) %>% | ||
| filter(.data$number_of_duplicates > 1) %>% | ||
| nrow() | ||
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| if (duplicates == 0) { | ||
| invisible(TRUE) | ||
| } else { | ||
| invisible(FALSE) | ||
| } | ||
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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$traits %>% filter(dataset_id == "Falster_2003") | ||
| #' data # Long format | ||
| #' traits_wide <- trait_pivot_wider(data) | ||
| #' traits_wide # Wide format | ||
| #' } | ||
| #' @author Daniel Falster - daniel.falster@unsw.edu.au | ||
| #' @export | ||
| db_traits_pivot_wider <- function(traits) { | ||
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| metadata_cols <- c("unit", "replicates", "measurement_remarks", "basis_of_value") | ||
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| # A check for if there are more than 1 value_type for a given taxon_name, observation_id and method | ||
| check_value_type <- traits %>% | ||
| select(dplyr::all_of(c( | ||
| "trait_name", "value", "dataset_id", "observation_id", "method_id", "method_context_id", | ||
| "repeat_measurements_id", "value_type"))) %>% | ||
| dplyr::group_by( | ||
| .data$dataset_id, .data$observation_id, .data$method_id, | ||
| .data$method_context_id, .data$repeat_measurements_id) %>% | ||
| dplyr::summarise(n_value_type = length(unique(.data$value_type))) %>% | ||
| arrange(.data$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 - daniel.falster@unsw.edu.au | ||
| #' @author Fonti Kar - fonti.kar@unsw.edu.au | ||
| #' @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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