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revised data types
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parent
07e94f4401
commit
aaceb55fe8
5 changed files with 84 additions and 38 deletions
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@ -1 +1 @@
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app_version <- function()'Version: 25.4.3.250415_1627'
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app_version <- function()'Version: 25.4.3.250422'
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@ -155,8 +155,8 @@ overview_vars <- function(data) {
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data <- as.data.frame(data)
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dplyr::tibble(
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icon = data_type(data),
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type = icon,
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icon = get_classes(data),
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class = icon,
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name = names(data),
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n_missing = unname(colSums(is.na(data))),
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p_complete = 1 - n_missing / nrow(data),
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@ -189,6 +189,7 @@ create_overview_datagrid <- function(data,...) {
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std_names <- c(
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"Name" = "name",
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"Icon" = "icon",
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"Class" = "class",
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"Type" = "type",
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"Missings" = "n_missing",
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"Complete" = "p_complete",
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@ -235,7 +236,7 @@ create_overview_datagrid <- function(data,...) {
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grid <- add_class_icon(
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grid = grid,
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column = "icon",
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fun = type_icons
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fun = class_icons
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)
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grid <- toastui::grid_format(
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@ -408,7 +408,7 @@ all_but <- function(data, ...) {
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#'
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#' @examples
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#' default_parsing(mtcars) |> subset_types("ordinal")
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#' default_parsing(mtcars) |> subset_types(c("dichotomous", "ordinal", "categorical"))
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#' default_parsing(mtcars) |> subset_types(c("dichotomous", "categorical"))
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#' #' default_parsing(mtcars) |> subset_types("factor",class)
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subset_types <- function(data, types, type.fun = data_type) {
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data[sapply(data, type.fun) %in% types]
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@ -443,58 +443,58 @@ supported_plots <- function() {
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fun = "plot_hbars",
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descr = "Stacked horizontal bars",
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note = "A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars",
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primary.type = c("dichotomous", "ordinal", "categorical"),
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secondary.type = c("dichotomous", "ordinal", "categorical"),
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primary.type = c("dichotomous", "categorical"),
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secondary.type = c("dichotomous", "categorical"),
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secondary.multi = FALSE,
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tertiary.type = c("dichotomous", "ordinal", "categorical"),
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tertiary.type = c("dichotomous", "categorical"),
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secondary.extra = "none"
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),
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plot_violin = list(
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fun = "plot_violin",
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descr = "Violin plot",
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note = "A modern alternative to the classic boxplot to visualise data distribution",
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primary.type = c("datatime", "continuous", "dichotomous", "ordinal", "categorical"),
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secondary.type = c("dichotomous", "ordinal", "categorical"),
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primary.type = c("datatime", "continuous", "dichotomous", "categorical"),
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secondary.type = c("dichotomous", "categorical"),
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secondary.multi = FALSE,
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secondary.extra = "none",
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tertiary.type = c("dichotomous", "ordinal", "categorical")
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tertiary.type = c("dichotomous", "categorical")
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),
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# plot_ridge = list(
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# descr = "Ridge plot",
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# note = "An alternative option to visualise data distribution",
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# primary.type = "continuous",
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# secondary.type = c("dichotomous", "ordinal" ,"categorical"),
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# tertiary.type = c("dichotomous", "ordinal" ,"categorical"),
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# secondary.type = c("dichotomous" ,"categorical"),
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# tertiary.type = c("dichotomous" ,"categorical"),
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# secondary.extra = NULL
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# ),
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plot_sankey = list(
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fun = "plot_sankey",
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descr = "Sankey plot",
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note = "A way of visualising change between groups",
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primary.type = c("dichotomous", "ordinal", "categorical"),
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secondary.type = c("dichotomous", "ordinal", "categorical"),
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primary.type = c("dichotomous", "categorical"),
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secondary.type = c("dichotomous", "categorical"),
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secondary.multi = FALSE,
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secondary.extra = NULL,
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tertiary.type = c("dichotomous", "ordinal", "categorical")
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tertiary.type = c("dichotomous", "categorical")
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),
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plot_scatter = list(
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fun = "plot_scatter",
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descr = "Scatter plot",
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note = "A classic way of showing the association between to variables",
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primary.type = c("datatime", "continuous"),
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secondary.type = c("datatime", "continuous", "ordinal", "categorical"),
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secondary.type = c("datatime", "continuous", "categorical"),
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secondary.multi = FALSE,
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tertiary.type = c("dichotomous", "ordinal", "categorical"),
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tertiary.type = c("dichotomous", "categorical"),
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secondary.extra = NULL
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),
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plot_box = list(
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fun = "plot_box",
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descr = "Box plot",
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note = "A classic way to plot data distribution by groups",
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primary.type = c("datatime", "continuous", "dichotomous", "ordinal", "categorical"),
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secondary.type = c("dichotomous", "ordinal", "categorical"),
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primary.type = c("datatime", "continuous", "dichotomous", "categorical"),
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secondary.type = c("dichotomous", "categorical"),
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secondary.multi = FALSE,
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tertiary.type = c("dichotomous", "ordinal", "categorical"),
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tertiary.type = c("dichotomous", "categorical"),
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secondary.extra = "none"
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),
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plot_euler = list(
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@ -505,7 +505,7 @@ supported_plots <- function() {
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secondary.type = "dichotomous",
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secondary.multi = TRUE,
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secondary.max = 4,
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tertiary.type = c("dichotomous", "ordinal", "categorical"),
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tertiary.type = c("dichotomous", "categorical"),
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secondary.extra = NULL
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)
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)
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44
R/helpers.R
44
R/helpers.R
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@ -258,16 +258,22 @@ default_parsing <- function(data) {
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#' @export
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#'
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#' @examples
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#' ds <- mtcars |> lapply(\(.x) REDCapCAST::set_attr(.x, label = NA, attr = "label")) |> dplyr::bind_cols()
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#' ds <- mtcars |>
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#' lapply(\(.x) REDCapCAST::set_attr(.x, label = NA, attr = "label")) |>
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#' dplyr::bind_cols()
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#' ds |>
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#' remove_empty_attr() |>
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#' str()
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#' mtcars |> lapply(\(.x) REDCapCAST::set_attr(.x, label = NA, attr = "label")) |> remove_empty_attr() |>
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#' mtcars |>
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#' lapply(\(.x) REDCapCAST::set_attr(.x, label = NA, attr = "label")) |>
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#' remove_empty_attr() |>
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#' str()
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#'
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remove_empty_attr <- function(data) {
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if (is.data.frame(data)) {
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data |> lapply(remove_empty_attr) |> dplyr::bind_cols()
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data |>
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lapply(remove_empty_attr) |>
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dplyr::bind_cols()
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} else if (is.list(data)) {
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data |> lapply(remove_empty_attr)
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} else {
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@ -387,7 +393,7 @@ data_description <- function(data, data_text = "Data") {
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#' }
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data_type_filter <- function(data, type) {
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## Please ensure to only provide recognised data types
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assertthat::assert_that(all(type %in% data_types()))
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assertthat::assert_that(all(type %in% names(data_types())))
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if (!is.null(type)) {
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out <- data[data_type(data) %in% type]
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@ -616,3 +622,33 @@ append_column <- function(data, column, name, index = "right") {
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) |>
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dplyr::bind_cols()
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}
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#' Test if element is identical to the previous
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#'
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#' @param data data. vector, data.frame or list
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#' @param no.name logical to remove names attribute before testing
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#'
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#' @returns logical vector
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#' @export
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#'
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#' @examples
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#' c(1, 1, 2, 3, 3, 2, 4, 4) |> is_identical_to_previous()
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#' mtcars[c(1, 1, 2, 3, 3, 2, 4, 4)] |> is_identical_to_previous()
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#' list(1, 1, list(2), "A", "a", "a") |> is_identical_to_previous()
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is_identical_to_previous <- function(data, no.name = TRUE) {
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if (is.data.frame(data)) {
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lagged <- data.frame(FALSE, data[seq_len(length(data) - 1)])
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} else {
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lagged <- c(FALSE, data[seq_len(length(data) - 1)])
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}
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vapply(seq_len(length(data)), \(.x){
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if (isTRUE(no.name)) {
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identical(unname(lagged[.x]), unname(data[.x]))
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} else {
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identical(lagged[.x], data[.x])
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}
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}, FUN.VALUE = logical(1))
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}
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@ -279,11 +279,11 @@ data_type <- function(data) {
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if (identical("logical", cl_d) | length(unique(data)) == 2) {
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out <- "dichotomous"
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} else {
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if (is.ordered(data)) {
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out <- "ordinal"
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} else {
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# if (is.ordered(data)) {
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# out <- "ordinal"
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# } else {
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out <- "categorical"
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}
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# }
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}
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} else if (identical(cl_d, "character")) {
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out <- "text"
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@ -310,7 +310,16 @@ data_type <- function(data) {
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#' @examples
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#' data_types()
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data_types <- function() {
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c("dichotomous", "ordinal", "categorical", "datatime", "continuous", "text", "empty", "monotone", "unknown")
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list(
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"empty" = list(descr="Variable of all NAs",classes="Any class"),
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"monotone" = list(descr="Variable with only one unique value",classes="Any class"),
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"dichotomous" = list(descr="Variable with only two unique values",classes="Any class"),
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"categorical"= list(descr="Factor variable",classes="factor (ordered or unordered)"),
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"text"= list(descr="Character variable",classes="character"),
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"datetime"= list(descr="Variable of time, date or datetime values",classes="hms, Date, POSIXct and POSIXt"),
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"continuous"= list(descr="Numeric variable",classes="numeric, integer or double"),
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"unknown"= list(descr="Anything not falling within the previous",classes="Any other class")
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)
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}
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@ -351,7 +360,7 @@ supported_functions <- function() {
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polr = list(
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descr = "Ordinal logistic regression model",
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design = "cross-sectional",
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out.type = c("ordinal", "categorical"),
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out.type = c("categorical"),
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fun = "MASS::polr",
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args.list = list(
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Hess = TRUE,
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