Title: Create Complex Shiny Apps More Easily
Version: 0.1.1
Description: Helper and Wrapper functions for making shiny dashboards more easily. Functions are made modular and lower level functions are exported as well, so many use-cases are supported.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: stats, dplyr, magrittr, purrr, rlang, stringr, forcats, ggplot2, ggpubr, scales, ggalluvial, shiny, shinycssloaders, shinydashboard, shinydashboardPlus, shinyWidgets, htmlwidgets, htmltools, plotly, DT
Suggests: RColorBrewer, waiter, spsComps, knitr, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2023-07-19 10:10:59 UTC; user
Author: Corneel Den Hartogh ORCID iD [aut, cre], Tomer Iwan [ctb], VU Analytics [cph]
Maintainer: Corneel Den Hartogh <c.f.den.hartogh@vu.nl>
Repository: CRAN
Date/Publication: 2023-07-19 15:30:02 UTC

vvshiny: Create Complex Shiny Apps More Easily

Description

Helper and Wrapper functions for making shiny dashboards more easily. Functions are made modular and lower level functions are exported as well, so many use-cases are supported.

Author(s)

Maintainer: Corneel Den Hartogh c.f.den.hartogh@vu.nl (ORCID)

Other contributors:

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of calling rhs(lhs).

Create a basic plot using ggplot

Description

This function creates a basic plot with the help of ggplot.

Usage

basic_plot(
  df,
  x,
  y,
  color,
  xlab_setting,
  ylab_setting,
  ggplot_settings = ggplot_basic_settings(),
  legend_position = "none",
  scale_y = NULL
)

Arguments

df

A data frame containing the data to be plotted.

x

A string specifying the column name to be used as the x-axis variable.

y

A string specifying the column name to be used as the y-axis variable.

color

A string specifying the column name to be used as the fill variable.

xlab_setting

ggplot labels settings for x axes.

ylab_setting

ggplot labels settings for y axes.

ggplot_settings

Additional settings for the ggplot.

legend_position

A string specifying the position of the legend.

scale_y

Optional ggplot2 scale function to modify the y axis.

Value

A ggplot plot.

Examples

df <- data.frame(x_var = rnorm(100),
                 y_var = rnorm(100),
                 color_var = sample(c("Red", "Blue"),
                 100,
                 replace = TRUE))
xlab_setting <- ggplot2::xlab("x label")
ylab_setting <- ggplot2::ylab("y label")
ggplot_instellingen <- ggplot2::geom_point()
scale_y <- ggplot2::scale_y_continuous()
basic_plot(df, "x_var", "y_var", "color_var", xlab_setting,
           ylab_setting, ggplot_instellingen, "none", scale_y)

Bind both

Description

This function binds two dataframes row-wise and performs additional manipulations depending on the 'type'. The function also reorders the factor levels of the specified facet variable.

Usage

bind_both(
  dfLeft,
  dfRight,
  id = "bench",
  y_left = NULL,
  y_right = NULL,
  facet_var = rlang::sym("VIS_Groep"),
  facet_name_var = rlang::sym("VIS_Groep_naam")
)

Arguments

dfLeft

A dataframe to be combined

dfRight

A dataframe to be combined

id

An identifier string specifying the type of operation

y_left

A character vector specifying the column to be used for the left dataframe

y_right

A character vector specifying the column to be used for the right dataframe

facet_var

A symbol specifying the variable to be used for faceting

facet_name_var

A symbol specifying the variable to be used for the facet name

Value

A dataframe obtained by binding dfLeft and dfRight, with additional transformations applied

Examples

df1 <- data.frame(x = 1:5, y = rnorm(5), VIS_Groep_naam = "One")
df2 <- data.frame(x = 6:10, y = rnorm(5), VIS_Groep_naam = "Two")
df_both <- bind_both(df1, df2, id = "test",
                     y_left = "y", y_right = "y",
                     facet_var = rlang::sym("x"))

Bind both table

Description

This function joins two summarized dataframes and relocates y_left before y_right. The function also sets the VIS_Groep value to 'left' for the right dataframe.

Usage

bind_both_table(dfLeft_summ, dfRight_summ, y_left, y_right)

Arguments

dfLeft_summ

A summarized dataframe to be joined

dfRight_summ

A summarized dataframe to be joined

y_left

A character vector specifying the column to be relocated before y_right

y_right

A character vector specifying the column after which y_left will be relocated

Value

A dataframe obtained by joining dfLeft_summ and dfRight_summ, with y_left relocated before y_right

Examples

df1 <- data.frame(
  VIS_Groep = "a",
  x = c("a", "b"),
  y1 = 1:2
)
df2 <- data.frame(
  VIS_Groep = "b",
  x = c("a", "b"),
  y2 = 3:4
)

df_both <- bind_both_table(df1, df2, "y1", "y2")

Clean the legend of a plotly object

Description

This function cleans the legend of a plotly object by removing unnecessary duplication. It is specifically designed to work around a bug that causes facet_wrap to create a separate legend entry for each facet.

Usage

clean_pltly_legend(pltly_obj, new_legend = c())

Arguments

pltly_obj

A plotly object with a legend to be cleaned.

new_legend

An optional vector of strings specifying new legend entries. Default is an empty vector.

Value

The input plotly object with its legend cleaned.

Get a user-friendly display name

Description

This function provides a user-friendly name for a column based on a mapping table, if available.

Usage

display_name(col_name, mapping_table)

Arguments

col_name

A string specifying the name of the column.

mapping_table

A named list with as name the original colum name and as value the display name

Value

A string containing the user-friendly name for the column.

Examples

  mapping <- list(
col1 = "Column 1",
col2 = "Column 2"
)
display_name("col1", mapping)

Description

Dropdown that is actually a link to a tab.

Usage

dropdownTabDirect(
  type = c("messages", "notifications", "tasks"),
  tab_name,
  title,
  icon = NULL,
  .list = NULL,
  header = NULL
)

Arguments

type

A character vector of either "messages", "notifications", "tasks". Default is c("messages", "notifications", "tasks").

tab_name

The name of the tab to link to.

title

The title of the dropdown.

icon

The icon to use in the dropdown. If NULL, defaults will be set based on type.

.list

A list of items to add to the dropdown.

header

The header for the dropdown.

Value

A dropdown menu in the form of an HTML list, where clicking the dropdown directs to a specific tab.

Examples

dropdownTabDirect(type = "messages", tab_name = "Tab1", title = "Interesting tab")

Description

Dropdown that is actually more of a menu with adapted tasks.

Usage

dropdownTabMenu(
  ...,
  type = c("messages", "notifications", "tasks"),
  title = NULL,
  icon = NULL,
  .list = NULL,
  header = NULL
)

Arguments

...

additional arguments.

type

A character vector of either "messages", "notifications", "tasks". Default is c("messages", "notifications", "tasks").

title

The title of the dropdown.

icon

The icon to use in the dropdown. If NULL, defaults will be set based on type.

.list

A list of items to add to the dropdown.

header

The header for the dropdown.

Value

A dropdown menu in the form of an HTML list.

Examples

dropdownTabMenu(type = "messages", title = "Category tab items")

Filter with lists

Description

This function filters a dataframe using a list with column and one or more values.

Usage

filter_with_lists(df, filters)

Arguments

df

A dataframe to be filtered

filters

A list of lists containing column names in the first element and a list their corresponding values for filtering in the second element

Value

A dataframe filtered based on the input filters

Examples

df <- dplyr::tibble(
  VIS_Groep = sample(c("Group1", "Group2", "Group3"), 100, replace = TRUE),
  VIS_Groep_naam = sample(c("Name1", "Name2", "Name3"), 100, replace = TRUE),
  var1 = sample(c("A", "B", "C"), 100, replace = TRUE),
  var2 = rnorm(100),
  color_var = sample(c("Red", "Blue", "Green"), 100, replace = TRUE)
)
filters = list(c("var1", c("A", "B")))
dfFiltered <- filter_with_lists(df, filters)

Gantt Chart Shiny App

Description

Gantt Chart Shiny App

Usage

gantt_app(df, df_config_gantt, id = "gantt")

Arguments

df

Data frame for Gantt chart

df_config_gantt

Config data frame for Gantt chart

id

Module ID for Gantt chart

Value

Shiny app object

Examples

df <- dplyr::tribble( ~OPL_Onderdeel_CROHO_examen, ~OPL_Onderdeel_CROHO_instroom,
~OPL_CBS_Label_rubriek_examen, ~OPL_CBS_Label_rubriek_instroom, "GEDRAG EN MAATSCHAPPIJ",
"GEZONDHEIDSZORG", "sociale geografie", "(huis)arts, specialist, geneeskunde",
"GEDRAG EN MAATSCHAPPIJ", "GEDRAG EN MAATSCHAPPIJ", "sociale geografie", "sociale geografie",
"GEDRAG EN MAATSCHAPPIJ", "RECHT", "sociale geografie", "notariaat", "RECHT", "RECHT",
"notariaat", "notariaat", "TAAL EN CULTUUR", "RECHT", "niet westerse talen en culturen",
"notariaat")

df_config_gantt <- dplyr::tribble( ~Categorie, ~Veldnaam, ~Veldnaam_gebruiker, ~input_var,
~target_var, ~title_start, ~title_end, ~position_y_label, "Doorstroom vanuit B ",
"OPL_Onderdeel_CROHO_examen",	"B Croho sector", "OPL_Onderdeel_CROHO_examen",
"OPL_Onderdeel_CROHO_instroom",	"Waar stromen", "Bachelor gediplomeerden naar toe?",	"right",
"Doorstroom vanuit B ",	"OPL_CBS_Label_rubriek_examen",	"B ISCED-F Rubriek",
"OPL_CBS_Label_rubriek_examen",	"OPL_CBS_Label_rubriek_instroom",	"Waar stromen",
"Bachelor gediplomeerden naar toe?",	"right", "Instroom bij M", "OPL_Onderdeel_CROHO_instroom",
"M Croho sector", "OPL_Onderdeel_CROHO_instroom", "OPL_Onderdeel_CROHO_examen",
"Waarvandaan stromen ", " Master studenten in?", "left", "Instroom bij M",
"OPL_CBS_Label_rubriek_instroom", "M ISCED-F Rubriek", "OPL_CBS_Label_rubriek_instroom",
"OPL_CBS_Label_rubriek_examen", "Waarvandaan stromen ", " Master studenten in?", "left" )

Create a Gantt plot using ggplot and plotly

Description

This function creates a Gantt plot with the help of ggplot and plotly.

Usage

gantt_plot(df, x, xend, split_var, title, position_label_y)

Arguments

df

A data frame containing the data to be plotted.

x

A string specifying the column name to be used as the x-axis variable.

xend

A string specifying the column name to be used as the end of the x-axis variable.

split_var

A string specifying the column name to be used as the splitting variable.

title

A string specifying the title of the plot.

position_label_y

A string specifying the position of y-axis labels.

Value

A Gantt plot.

Set ggplot basic settings

Description

Basic ggplot settings are put in a list and returned

Usage

ggplot_basic_settings()

Value

A list with ggplot settings

Make ggplotly and add legend with color as title

Description

This function creates a Plotly version of a ggplot2 object and adds a legend with the user-friendly name of the color variable as its title.

Usage

ggplotly_with_legend(plot, color, mapping_table)

Arguments

plot

A ggplot object.

color

A string specifying the column name to be used as the color variable.

mapping_table

A named list with as name the original colum name and as value the display name

Value

A plotly object with a formatted legend.

Examples

df <- data.frame(x_var = rnorm(100),
                 y_var = rnorm(100),
                 color_var = sample(c("Red", "Blue"),
                 100,
                 replace = TRUE))
xlab_setting <- ggplot2::xlab("x label")
ylab_setting <- ggplot2::ylab("y label")
ggplot_instellingen <- ggplot2::geom_point()
scale_y <- ggplot2::scale_y_continuous()
plot <- basic_plot(df, "x_var", "y_var", "color_var", xlab_setting,
                   ylab_setting, ggplot_instellingen, "none", scale_y)
mapping_table <- list(color_var = "user friendly name var")
plotly_object <- ggplotly_with_legend(plot, "color_var", mapping_table)

Grid boxplot

Description

Function for creating grid boxplots for either benchmark or comparison across variables.

Usage

grid_boxplots(
  df,
  x,
  color,
  y,
  id,
  y_left = NULL,
  y_right = NULL,
  facet_var = rlang::sym("VIS_Groep"),
  facet_name_var = rlang::sym("VIS_Groep_naam")
)

Arguments

df

The data frame used to create the plot.

x

The variable used on the x-axis of the plot.

color

The variable used to color the points or bars in the plot.

y

The variable used on the y-axis of the plot.

id

The identifier for selecting the data frame source.

y_left

The variable used on the left y-axis when creating a comparative plot.

y_right

The variable used on the right y-axis when creating a comparative plot.

facet_var

The variable used for facet wrapping.

facet_name_var

The name of the variable used for facet wrapping.

Value

A ggplot object.

Grid histogram

Description

Function for creating grid histograms.

Usage

grid_histograms(
  df,
  x,
  color,
  y,
  id,
  y_left = NULL,
  y_right = NULL,
  facet_var = rlang::sym("VIS_Groep"),
  facet_name_var = rlang::sym("VIS_Groep_naam")
)

Arguments

df

The data frame used to create the plot.

x

The variable used on the x-axis of the plot.

color

The variable used to color the points or bars in the plot.

y

The variable used on the y-axis of the plot.

id

The identifier for selecting the data frame source.

y_left

The variable used on the left y-axis when creating a comparative plot.

y_right

The variable used on the right y-axis when creating a comparative plot.

facet_var

The variable used for facet wrapping.

facet_name_var

The name of the variable used for facet wrapping.

Value

A list of ggplot objects.

Keep only relevant values

Description

Filters out only relevant values based on the provided filters

Usage

keep_only_relevant_values(lFilters, sVariable, dfFilters)

Arguments

lFilters

List of filters to be applied on the data.

sVariable

The variable for which relevant values are to be retrieved.

dfFilters

Dataframe with the possible filters and values for this dataset

Details

This function removes null elements from the filter list, transforms filter list into elements suitable for filtering, and retrieves relevant values from the data.

Value

A list of relevant values for the specified variable.

Examples

dfFilters <- dplyr::tibble(
  var1 = sample(c("A", "B", "C"), 100, replace = TRUE),
  var2 = sample(c("D", "E", "F"), 100, replace = TRUE),
  var3 = sample(c("G", "H", "I"), 100, replace = TRUE)
)
filters <- list("D;var2")
relevant_values <- keep_only_relevant_values(filters, "var1", dfFilters)

# Check if the relevant values are only from the rows where var2 is "D" or "E"
expected_values <- dfFilters$var1[dfFilters$var2 %in% c("D")] %>%
  purrr::set_names(.) %>%
  purrr::map(~paste0(.x, ";var1"))

Keep values

Description

This function extracts values before the semicolon from a ";"-separated string.

Usage

keep_values(input)

Arguments

input

A character vector with ";"-separated strings

Value

A list of values before the semicolon in the input

Examples

input = c("A;var1", "B;var1", "C;var1")
values = keep_values(input)

pickerGanttValues function

Description

Function to filter the values in the Gantt chart.

Usage

pickerGanttValues(id, filter_var, df_doorstroom_gantt)

Arguments

id

A string representing the id of the input element.

filter_var

A string representing the variable to filter.

df_doorstroom_gantt

A data frame containing the Gantt chart data.

Value

A pickerInput object.

pickerGanttVar function

Description

Function to pick a variable to show values in a Gantt chart.

Usage

pickerGanttVar(id, element, df_config_gantt, input_var_value = NULL)

Arguments

id

A string representing the id of the input element.

element

A string representing the element.

df_config_gantt

A data frame containing the Gantt configuration.

input_var_value

A variable value from the input. Default is NULL.

pickerSankeyValues function

Description

Function to pick values for transition of the two Sankey states.

Usage

pickerSankeyValues(id, filter_var, df_sankey, side)

Arguments

id

A string representing the id of the input element.

filter_var

A string representing the variable to filter.

df_sankey

A data frame containing the Sankey diagram data.

side

A string representing the side of the Sankey diagram.

Value

A pickerInput object.

pickerSankeyVar function

Description

Function to pick variables for state in a Sankey diagram.

Usage

pickerSankeyVar(id, df_sankey, df_config_sankey, state = "left_var")

Arguments

id

A string representing the id of the input element.

df_sankey

A data frame containing the Sankey diagram data.

df_config_sankey

A data frame containing the Sankey configuration.

state

A string representing the state of the variable. Default is "left_var".

Value

A pickerInput object.

pickerSplitVar function

Description

Function to create a picker input for splitting variables.

Usage

pickerSplitVar(
  id,
  variable = "INS_Splits_variabele",
  name = "color",
  label = "Kleur",
  df
)

Arguments

id

A string representing the id of the input element.

variable

A string representing the variable to split. Default is "INS_Splits_variabele".

name

A string representing the name. Default is "color".

label

A string representing the label of the input. Default is "Kleur".

df

A data frame containing the data. Default is dfCombi_geaggregeerd.

Value

A pickerInput object.

pickerValues function

Description

Function to create a picker input for filtering value.

Usage

pickerValues(
  id,
  df,
  variable = "faculty",
  role = "left",
  selected = "All",
  multiple = TRUE
)

Arguments

id

A string representing the id of the input element.

df

A data frame containing the data.

variable

A string representing the variable to filter. Default is "faculty".

role

A string representing the role. Default is "left".

selected

The selected value. Default is "All".

multiple

A boolean indicating whether multiple selections are allowed. Default is TRUE.

Value

A pickerInput object.

pickerVar function

Description

Function to generate a picker input element based on given id and element.

Usage

pickerVar(id, element, df_categories, label = NULL)

Arguments

id

A string representing the id of the input element.

element

A string representing the element.

df_categories

A dataframe with metadata about the available categories per picker element

label

A string representing the label of the input. Default is NULL.

Value

A pickerInput object.

Prepare a dataframe

Description

Prepares a dataframe based on provided filters and naming options

Usage

prep_df(
  lFilters,
  lValues_for_naming,
  df,
  color_var,
  facet = "left",
  facet_var = rlang::sym("VIS_Groep"),
  facet_name_var = rlang::sym("VIS_Groep_naam")
)

Arguments

lFilters

List of filters to be applied on the dataframe.

lValues_for_naming

List of values used for naming.

df

Dataframe to be processed.

color_var

Variable used for coloring.

facet

Facet grid side ("left" by default).

facet_var

Variable used for facet grid ("VIS_Groep" by default).

facet_name_var

Variable used for facet grid naming ("VIS_Groep_naam" by default).

Details

This function collapses values from the naming list into a single string, removes null elements from the filter list, transforms filter list into elements suitable for filtering, applies filters, adds new columns, and casts var used as color to factor.

Value

A prepared dataframe with applied filters and new columns.

Examples

df <- dplyr::tibble(
  VIS_Groep = sample(c("Group1", "Group2", "Group3"), 100, replace = TRUE),
  VIS_Groep_naam = sample(c("Name1", "Name2", "Name3"), 100, replace = TRUE),
  var1 = sample(c("A", "B", "C"), 100, replace = TRUE),
  var2 = rnorm(100),
  color_var = sample(c("Red", "Blue", "Green"), 100, replace = TRUE)
)
lFilters <- list("A;var1")
lValues_for_naming = list("Name1;VIS_Groep_naam", "Name2;VIS_Groep_naam")
color_var = "color_var"
dfPrepared <- prep_df(lFilters, lValues_for_naming, df, color_var, facet = "right")

Prepare summarized dataframe

Description

This function prepares a summarized dataframe based on provided variables and a y-variable. The function groups the dataframe by the provided variables, summarizes the y-variable, and counts the number of observations per group.

Usage

prep_df_summ(df, variables, y)

Arguments

df

A dataframe to be summarized

variables

A character vector specifying the columns to be grouped by

y

A character vector specifying the column to be summarized

Value

A summarized dataframe

Examples

df <- data.frame(
id = c(1, 1, 2, 2),
group = c("A", "A", "B", "B"),
value = c(2, 4, 6, 8)
)
df_summ <- prep_df_summ(df, c("id", "group"), "value")

Prepare summarized and aggregated dataframe

Description

This function prepares a summarized dataframe based on provided variables, y-variable, color, and total count. The function groups the dataframe by the provided variables, calculates the weighted mean for the y-variable, sums up total count per group, and arranges by color.

Usage

prep_df_summ_aggr(
  df,
  variables,
  y,
  color,
  total_n_var = rlang::sym("INS_Aantal_eerstejaars"),
  aggr_split_value_var = rlang::sym("INS_Splits_variabele_waarde")
)

Arguments

df

A dataframe to be summarized

variables

A character vector specifying the columns to be grouped by

y

A character vector specifying the column to be summarized

color

A character vector specifying the column to be used for color arrangement

total_n_var

A symbol specifying the variable to be used for total count calculation

aggr_split_value_var

A symbol specifying the variable to be used for color assignment

Value

A summarized and aggregated dataframe arranged by color

Examples

df <- data.frame( split_var_value = c("male", "male", "female", "female", "dutch", "dutch",
"EER", "EER", "Outside EER", "Outside EER"), other_var = c("Early", "Late", "Early", "Late",
"Early", "Late", "Early", "Late", "Early", "Late"), value = c(2, 4, 6, 8, 10, 2, 4, 6, 8, 10),
total = c(10, 10, 20, 20, 30, 30, 40, 40, 50, 50), split_var = c("gender", "gender", "gender",
"gender", "background", "background", "background", "background", "background", "background") )

Prepare a data table for displaying

Description

This function prepares a data table for displaying by providing user-friendly names, removing unneeded variables, and formatting percentages.

Usage

prep_table(
  y,
  df,
  df_summarized,
  id,
  y_right = NULL,
  facet_var = rlang::sym("VIS_Groep"),
  facet_name_var = rlang::sym("VIS_Groep_naam"),
  table_type = c("basic", "advanced"),
  rownames = FALSE,
  extensions = c("Buttons"),
  options_DT = basic_options(),
  limit_width = "values",
  ...
)

Arguments

y

A string specifying the column name to be used as the y-axis variable.

df

A data frame containing the raw data.

df_summarized

A data frame containing the summarized data.

id

A string specifying the ID associated with the data.

y_right

An optional string specifying the column name to be used as the second y-axis variable. Default is NULL.

facet_var

A symbol specifying the column to be used for faceting. Default is 'VIS_Groep'.

facet_name_var

A symbol specifying the column to be used for faceting names. Default is 'VIS_Groep_naam'.

table_type

Choose from basic for a simple datatable and advanced for more buttons etc.

rownames

A logical value indicating whether to display row names.

extensions

A character vector specifying the DataTables extensions to be used.

options_DT

A list of DataTables options.

limit_width

A character string indicating how to limit column width.

...

Further arguments passed on to the 'make_basic_table' function.

Value

A DT::datatable object ready for displaying.

Examples

df <- data.frame(VIS_Groep = c("Group1", "Group1", "Group2", "Group2"),
                VIS_Groep_naam = c("Name1", "Name1", "Name2", "Name2"),
                y = c(TRUE, TRUE, FALSE, FALSE), z = c(TRUE, FALSE, TRUE, FALSE))
df_summarized <- df %>%
 dplyr::group_by(VIS_Groep, VIS_Groep_naam) %>%
 dplyr::summarise(
   y = mean(y),
   z = mean(z)
 ) %>%
 dplyr::ungroup()
 id <- "id"
output <- prep_table("y", df, df_summarized, id = id)

present_and_correct function

Description

Function to check if the column is present and correctly formed based on the element type.

Usage

present_and_correct(column_name, element = NA, df)

Arguments

column_name

A string representing the column name.

element

A string representing the element. Default is NA.

df

A data frame for which to check the column. Default is dfCombi_geaggregeerd.

Value

A boolean indicating whether the column is present and correctly formed.

Quietly run a function

Description

This function is a wrapper that allows a function to be run quietly without the need to create a separate quiet function.

Usage

quietly_run(func, ...)

Arguments

func

The function to be run.

...

Optional further arguments passed to the 'func' function.

Value

The list result of the 'func' function with messages, warnings, and output captured.

Examples

warning_func_arugment <- function(info) {
  warning(info)
  return("Complete")
}
result <- quietly_run(warning_func_arugment, "Just checking")

Create a Sankey plot using ggplot and ggalluvial

Description

This function creates a Sankey plot with the help of ggplot and ggalluvial.

Usage

sankey_plot(
  df,
  left_var,
  right_var,
  xlab_setting,
  ylab_setting,
  name_left,
  name_right,
  title,
  title_size = 20,
  title_font = "verdana"
)

Arguments

df

A data frame containing the data to be plotted.

left_var

A string specifying the column name to be used as the left variable.

right_var

A string specifying the column name to be used as the right variable.

xlab_setting

ggplot labels settings for x axes.

ylab_setting

ggplot labels settings for y axes.

name_left

A string specifying the name for the left side of the plot.

name_right

A string specifying the name for the right side of the plot.

title

A string specifying the title of the plot.

title_size

Numeric value specifying the size of the title.

title_font

A string specifying the font of the title.

Value

A Sankey plot.

UI function for single module dashboard

Description

UI function for single module dashboard

Usage

single_module_ui(request, id, tab_item)

Arguments

request

shiny request object

id

Module id

tab_item

Tab item UI

Value

dashboardPage UI

Stacked bar chart

Description

Create a stacked bar chart, with optional settings for percentage (or not) and wrap (or not) modes.

Usage

stacked_composition_bar_chart(
  df,
  x,
  color,
  id,
  facet_name_var = rlang::sym("VIS_Groep_naam"),
  percentage = FALSE,
  wrap = FALSE
)

Arguments

df

The data frame used to create the plot.

x

The variable used on the x-axis of the plot.

color

The variable used to color the points or bars in the plot.

id

The identifier for selecting the data frame source.

facet_name_var

The name of the variable used for facet wrapping.

percentage

Logical indicating whether to create a plot in percentage mode.

wrap

Logical indicating whether to use facet wrapping.

Value

A ggplot object.

tabTableOne function

Description

Function to create a tab panel with one table.

Usage

tabTableOne(id, table_one)

Arguments

id

A string representing the id.

table_one

A string representing the table.

Value

A tab panel with one table.

Examples

dummy_data <- data.frame(
  A = 1:5,
  B = letters[1:5]
)
dummy_dt <- DT::datatable(dummy_data)
tabTableOne("dummy_id", dummy_dt)

tabTableTwo function

Description

Function to create a tab panel with two tables.

Usage

tabTableTwo(id, table_one, table_two)

Arguments

id

A string representing the id.

table_one

A string representing the first table.

table_two

A string representing the second table.

Value

A tab panel with two tables.

Examples

dummy_data1 <- data.frame(
  A = 1:5,
  B = letters[1:5]
)
dummy_dt1 <- DT::datatable(dummy_data1)
dummy_data2 <- data.frame(
X = 6:10,
Y = letters[6:10]
)
dummy_dt2 <- DT::datatable(dummy_data2)
tabTableTwo("dummy_id", dummy_dt1, dummy_dt2)

taskItemTab function

Description

Item for above dropdownActionMenu function.

Usage

taskItemTab(text, tab_name = NULL, href = NULL, tabSelect = FALSE)

Arguments

text

The text to display for the item.

tab_name

The name of the tab to link to. Default is NULL.

href

The href link for the item. If NULL, it defaults to "#".

tabSelect

A boolean indicating whether to select the tab. Default is FALSE.

Value

An HTML list item.

Examples

taskItemTab(text = "Selected tab", tab_name = "Tab1", tabSelect = TRUE)
taskItemTab(text = "Other tab", tab_name = "Tab2", tabSelect = FALSE)

Transform input

Description

This function transforms a list of inputs into a column and value for filtering.

Usage

transform_input(input)

Arguments

input

A list of inputs to be transformed

Value

A list containing a column and its corresponding value for filtering

Examples

input = list("A;var1", "B;var1", "C;var1")
filter_element = transform_input(input)

Wrapped chart

Description

Wrapped chart function for creating a plot based on the provided dataframe and variables.

Usage

wrapped_chart(
  df,
  x,
  y,
  color,
  id = "bench",
  df_original,
  y_left = NULL,
  y_right = NULL,
  facet_var = rlang::sym("VIS_Groep"),
  facet_name_var = rlang::sym("VIS_Groep_naam")
)

Arguments

df

The data frame used to create the plot.

x

The variable used on the x-axis of the plot.

y

The variable used on the y-axis of the plot.

color

The variable used to color the points or bars in the plot.

id

The identifier for selecting the data frame source.

df_original

The original dataframe before summarization

y_left

The variable used on the left y-axis when creating a comparative plot.

y_right

The variable used on the right y-axis when creating a comparative plot.

facet_var

The variable used for facet wrapping.

facet_name_var

The name of the variable used for facet wrapping.

Value

A ggplot object.