Type:	Package
Title:	High-Level Functions for Tabulating, Charting and Reporting Survey Data
Version:	3.1.0
Date:	2025-04-05
Description:	Craft polished tables and plots in Markdown reports. Simply choose whether to treat your data as counts or metrics, and the package will automatically generate well-designed default tables and plots for you. Boiled down to the basics, with labeling features and simple interactive reports. All functions are 'tidyverse' compatible.
URL:	https://github.com/strohne/volker, https://strohne.github.io/volker/
BugReports:	https://github.com/strohne/volker/issues
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
LazyData:	true
Imports:	stats, utils, rlang, lifecycle, tibble, dplyr, tidyr, tidyselect, ggplot2 (≥ 2.2.1), scales, base64enc, purrr, magrittr, skimr, broom, knitr, kableExtra, rmarkdown, psych, car, effectsize, heplots
Depends:	R (≥ 4.2)
Suggests:	tidyverse, remotes, usethis, testthat (≥ 3.0.0)
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-04-05 20:30:45 UTC; Jakob
Author:	Jakob Jünger [aut, cre, cph], Henrieke Kotthoff [aut, ctb], Chantal Gärtner [ctb]
Maintainer:	Jakob Jünger <jakob.juenger@uni-muenster.de>
Repository:	CRAN
Date/Publication:	2025-04-05 20:50:02 UTC

volker: High-Level Functions for Tabulating, Charting and Reporting Survey Data

Description

Craft polished tables and plots in Markdown reports. Simply choose whether to treat your data as counts or metrics, and the package will automatically generate well-designed default tables and plots for you. Boiled down to the basics, with labeling features and simple interactive reports. All functions are 'tidyverse' compatible.

Author(s)

Maintainer: Jakob Jünger jakob.juenger@uni-muenster.de (ORCID) [copyright holder]

Authors:

• Henrieke Kotthoff henrieke.kotthoff@uni-muenster.de [contributor]

Other contributors:

• Chantal Gärtner chantal.gaertner@uni-muenster.de (ORCID) [contributor]

See Also

Useful links:

• https://github.com/strohne/volker

• https://strohne.github.io/volker/

• Report bugs at https://github.com/strohne/volker/issues

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Add an object to the report list

Description

Add an object to the report list

Usage

.add_to_vlkr_rprt(obj, chunks, tab = NULL)

Arguments

Value

A volker report object.

Insert a name-value-pair into an object attribute

Description

Insert a name-value-pair into an object attribute

Usage

.attr_insert(obj, key, name, value)

Arguments

Value

The object with new attributes.

Transfer attributes from one to another object

Description

Transfer attributes from one to another object

Usage

.attr_transfer(to, from, keys)

Arguments

Value

The target object with the updated attributes.

Get the maximum density value in a density plot

Description

Useful for placing geoms in the center of density plots

Usage

.density_mode(data, col)

Arguments

Value

The maximum density value.

Test whether correlations are different from zero

Description

Test whether correlations are different from zero

Usage

.effect_correlations(data, cols, cross, method = "pearson", labels = TRUE)

Arguments

Value

A tibble with correlation results.

Calculate nmpi

Description

Calculate nmpi

Usage

.effect_npmi(data, col, cross, labels = TRUE, clean = TRUE, smoothing = 0, ...)

Arguments

Value

A volker tibble.

Create a factor vector and preserve all attributes

Description

Create a factor vector and preserve all attributes

Usage

.factor_with_attr(x, levels = NULL)

Arguments

Value

A factor vector with the new levels

Get plot size and resolution for the current output format from the config

Description

Get plot size and resolution for the current output format from the config

Usage

.get_fig_settings()

Value

A list with figure settings

Calculate IQR

Description

Calculate IQR

Usage

.iqr(x)

Arguments

Value

The IQR

Knit volker plots

Description

Automatically calculates the plot height from chunk options and volker options.

Usage

.knit_plot(pl)

Arguments

Details

Presumptions:

• a screen resolution of 72dpi

• a default plot width of 7 inches = 504px

• a default page width of 700px (vignette) or 910px (report)

• an optimal bar height of 40px for 910px wide plots. i.e. a ratio of 0.04

• an offset of one bar above and one bar below

Value

Character string containing a html image tag, including the base64 encoded image.

Prepare markdown content for table rendering

Description

Prepare markdown content for table rendering

Usage

.knit_prepare(x, wrap = FALSE)

Arguments

Value

Markdown text with line breaks and escaped special characters.

Knit volker tables

Description

Knit volker tables

Usage

.knit_table(df, ...)

Arguments

Value

Formatted table produced by kable.

Calculate outliers

Description

Calculate outliers

Usage

.outliers(x, k = 1.5)

Arguments

Value

A list of outliers.

Helper function: plot grouped bar chart

Description

Helper function: plot grouped bar chart

Usage

.plot_bars(
  data,
  category = NULL,
  ci = FALSE,
  scale = NULL,
  limits = NULL,
  numbers = NULL,
  orientation = "horizontal",
  base = NULL,
  title = NULL
)

Arguments

Value

A ggplot object.

Helper function: plot cor and regression outputs

Description

Helper function: plot cor and regression outputs

Usage

.plot_cor(
  data,
  ci = TRUE,
  base = NULL,
  limits = NULL,
  title = NULL,
  label = NULL
)

Arguments

Value

A ggplot object.

Helper function: plot grouped line chart

Description

Helper function: plot grouped line chart

Usage

.plot_lines(data, scale = NULL, base = NULL, limits = NULL, title = NULL)

Arguments

Value

A ggplot object.

Helper function: scree plot

Description

Helper function: scree plot

Usage

.plot_scree(data, k = NULL, lab_x = NULL, lab_y = NULL)

Arguments

Value

A vlkr_plot object

Helper function: plot grouped line chart by summarising values

Description

Helper function: plot grouped line chart by summarising values

Usage

.plot_summary(
  data,
  ci = FALSE,
  scale = NULL,
  base = NULL,
  box = FALSE,
  limits = NULL,
  title = NULL
)

Arguments

Value

A ggplot object.

Generate an cluster table and plot

Description

Generate an cluster table and plot

Usage

.report_cls(
  data,
  cols,
  cross,
  metric = FALSE,
  ...,
  k = 2,
  effect = FALSE,
  title = TRUE
)

Arguments

Value

A list containing a table and a plot volker report chunk.

Generate an factor table and plot

Description

Generate an factor table and plot

Usage

.report_fct(
  data,
  cols,
  cross,
  metric = FALSE,
  ...,
  k = 2,
  effect = FALSE,
  title = TRUE
)

Arguments

Value

A list containing a table and a plot volker report chunk.

Generate an index table and plot

Description

Generate an index table and plot

Usage

.report_idx(
  data,
  cols,
  cross,
  metric = FALSE,
  ...,
  effect = FALSE,
  title = TRUE
)

Arguments

Value

A list containing a table and a plot volker report chunk.

Split a metric column into categories based on the median

Description

Split a metric column into categories based on the median

Usage

.tab_split(data, col, labels = TRUE)

Arguments

Value

A data frame with the specified column converted into categorical labels based on its median value. The split threshold (median) is stored as an attribute of the column.

Add vlkr_df class - that means, the data frame has been prepared

Description

Add vlkr_df class - that means, the data frame has been prepared

Usage

.to_vlkr_df(data, digits = NULL)

Arguments

Value

A tibble of class vlkr_df.

Add vlkr_list class

Description

Used to collect multiple tables in a list, e.g. from regression outputs

Usage

.to_vlkr_list(data, baseline = TRUE)

Arguments

Value

A volker list.

Add the volker class and options

Description

Add the volker class and options

Usage

.to_vlkr_plot(
  pl,
  rows = NULL,
  maxlab = NULL,
  baseline = TRUE,
  theme_options = TRUE
)

Arguments

Value

A ggplot object with vlkr_plt class.

Add the vlkr_rprt class to an object

Description

Adding the class makes sure the appropriate printing function is applied in markdown reports.

Usage

.to_vlkr_rprt(chunks)

Arguments

Value

A volker report object: List of character strings with the vlkr_rprt class containing the parts of the report.

Add vlkr_tbl class

Description

Additionally, removes the skim_df class if present.

Usage

.to_vlkr_tab(data, digits = NULL, caption = NULL, baseline = NULL)

Arguments

Value

A volker tibble.

Calculate lower whisker in a boxplot

Description

Calculate lower whisker in a boxplot

Usage

.whisker_lower(x, k = 1.5)

Arguments

Value

The lower whisker value.

Calculate upper whisker in a boxplot

Description

Calculate upper whisker in a boxplot

Usage

.whisker_upper(x, k = 1.5)

Arguments

Value

The upper whisker value.

Resolution settings for plots

Description

Override with options(vlkr.fig.settings=list(html = list(dpi = 192, scale = 2, width = 910, pxperline = 15))). Add a key for each output format when knitting a document. You can override the width by setting vlkr.fig.width in the chunk options.

Usage

VLKR_FIG_SETTINGS

Format

An object of class list of length 2.

Fill colors

Description

Override with options(vlkr.discrete.fill=list(c("purple"))).

Usage

VLKR_FILLDISCRETE

Format

An object of class list of length 3.

Gradient colors

Description

Override with options(vlkr.gradient.fill=list(c("white","black"))).

Usage

VLKR_FILLGRADIENT

Format

An object of class character of length 5.

Polarized colors

Description

Polarized colors

Usage

VLKR_FILLPOLARIZED

Format

An object of class character of length 5.

Maximum number of distinct values to determine whether a column selection contains only categorical values

Description

Override with options(vlkr.max.categories=10).

Usage

VLKR_MAX_CATEGORIES

Format

An object of class numeric of length 1.

Levels to remove from factors

Description

Override with options(vlkr.na.levels=c("Not answered")).

Usage

VLKR_NA_LEVELS

Format

An object of class character of length 4.

Numbers to remove from vectors

Description

Override with options(vlkr.na.numbers=c(-2,-9)).

Usage

VLKR_NA_NUMBERS

Format

An object of class numeric of length 3.

Output thresholds

Description

Output thresholds

Usage

VLKR_NORMAL_DIGITS

Format

An object of class numeric of length 1.

Wrapping threshold

Description

Override with options(vlkr.wrap.labels=20). Override with options(vlkr.wrap.legend=10). Override with options(vlkr.wrap.scale=10). Override with options(vlkr.angle.value=30). Override with options(vlkr.angle.threshold=10).

Usage

VLKR_PLOT_LABELWRAP

Format

An object of class numeric of length 1.

Alpha values

Description

Alpha values

Usage

VLKR_POINT_ALPHA

Format

An object of class numeric of length 1.

Shapes

Description

Shapes

Usage

VLKR_POINT_MEAN_SHAPE

Format

An object of class numeric of length 1.

Sizes

Description

Sizes

Usage

VLKR_POINT_SIZE

Format

An object of class numeric of length 1.

Word wrap separators

Description

Word wrap separators

Usage

VLKR_WRAP_SEPARATOR

Format

An object of class character of length 1.

Add cluster number to a data frame

Description

Clustering is performed using stats::kmeans.

Usage

add_clusters(data, cols, newcol = NULL, k = 2, method = "kmeans", clean = TRUE)

Arguments

Value

The input tibble with additional column containing cluster values as a factor. The new column is prefixed with "cls_". The new column contains the fit result in the attribute stats.kmeans.fit. The names of the items used for clustering are stored in the attribute stats.kmeans.items. The clustering diagnostics (Within-Cluster and Between-Cluster Sum of Squares) are stored in the attribute stats.kmeans.wss.

Examples

library(volker)
ds <- volker::chatgpt

volker::add_clusters(ds, starts_with("cg_adoption"), k = 3)

Add PCA columns along with summary statistics (KMO and Bartlett test) to a data frame

Description

PCA is performed using psych::pca usind varimax rotation. Bartlett's test for sphericity is calculated with psych::cortest.bartlett. The Kaiser-Meyer-Olkin (KMO) measure is computed using psych::KMO.

Usage

add_factors(data, cols, newcols = NULL, k = 2, method = "pca", clean = TRUE)

Arguments

Value

The input tibble with additional columns containing factor values. The new columns are prefixed with "fct_". The first new column contains the fit result in the attribute psych.pca.fit. The names of the items used for factor analysis are stored in the attribute psych.pca.items. The summary diagnostics (Bartlett test and KMO) are stored in the attribute psych.kmo.bartlett.

Examples

library(volker)
ds <- volker::chatgpt

volker::add_factors(ds, starts_with("cg_adoption"))

Calculate the mean value of multiple items

Description

Usage

add_index(data, cols, newcol = NULL, cols.reverse, clean = TRUE)

Arguments

Value

The input tibble with an additional column that contains the index values. The column contains the result of the alpha calculation in the attribute named "psych.alpha".

Examples

ds <- volker::chatgpt
volker::add_index(ds, starts_with("cg_adoption"))

Get configured na numbers

Description

Retrieves values either from the option or from the constant.

Usage

cfg_get_na_numbers(default = VLKR_NA_NUMBERS)

Arguments

ChatGPT Adoption Dataset CG-GE-APR23

Description

A small random subset of data from a survey about ChatGPT adoption. The survey was conducted in April 2023 within the population of German Internet users.

Usage

chatgpt

Format

chatgpt

A data frame with 101 rows and 19 columns:

case

A running case number

adopter

Adoption groups inspired by Roger's innovator typology.

use_

Columns starting with use contain data about ChatGPT usage in different contexts.

cg_activities

Text answers to the question, what the respondents do with ChatGPT.

cg_adoption_

A scale consisting of items about advantages, fears, and social aspects. The scales match theoretical constructs inspired by Roger's diffusion model and Davis' Technology Acceptance Model

sd_

Columns starting with sd contain sociodemographics of the respondents.

Details

Call codebook(volker::chatgpt) to see the items and answer options.

Source

Communication Department of the University of Münster (gehrau@uni-muenster.de).

Check whether a column exist and stop if not

Description

Check whether a column exist and stop if not

Usage

check_has_column(data, cols, msg = NULL)

Arguments

Value

boolean Whether the column exists.

Check whether a column selection is categorical

Description

Check whether a column selection is categorical

Usage

check_is_categorical(data, cols, msg = NULL)

Arguments

Value

boolean Whether the columns are categorical

Check whether the object is a dataframe

Description

Check whether the object is a dataframe

Usage

check_is_dataframe(obj, msg = NULL, stopit = TRUE)

Arguments

Value

boolean Whether the object is a data.frame object.

Check whether a column selection is numeric

Description

Check whether a column selection is numeric

Usage

check_is_numeric(data, cols, msg = NULL)

Arguments

Value

boolean Whether the columns are numeric.

Check whether a parameter value is from a valid set

Description

Check whether a parameter value is from a valid set

Usage

check_is_param(
  value,
  allowed,
  allownull = FALSE,
  allowmultiple = FALSE,
  stopit = TRUE,
  msg = NULL
)

Arguments

Value

logical whether method is valid.

Get plot for clustering result

Description

Kmeans clustering is performed using add_clusters.

Usage

cluster_plot(
  data,
  cols,
  newcol = NULL,
  k = NULL,
  method = NULL,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Examples

library(volker)
data <- volker::chatgpt

cluster_plot(data, starts_with("cg_adoption"), k = 2)

Get tables for clustering result

Description

Kmeans clustering is performed using add_clusters.

Usage

cluster_tab(
  data,
  cols,
  newcol = NULL,
  k = NULL,
  method = "kmeans",
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker list with with three volker tabs: cluster centers, cluster counts, and clustering diagnostics.

Examples

library(volker)
data <- volker::chatgpt

cluster_tab(data, starts_with("cg_adoption"), k = 2)

Get variable and value labels from a data set

Description

Variable labels are extracted from their comment or label attribute. Variable values are extracted from factor levels, the labels attribute, numeric or boolean attributes.

Usage

codebook(data, cols, values = TRUE)

Arguments

Details

Value

A tibble with the columns:

• item_name: The column name.

• item_group: First part of the column name, up to an underscore.

• item_class: The last class value of an item (e.g. numeric, factor).

• item_label: The comment attribute of the column.

• value_name: In case a column has numeric attributes, the attribute names.

• value_label: In case a column has numeric attributes or T/F-attributes, the attribute values. In case a column has a levels attribute, the levels.

Examples

volker::codebook(volker::chatgpt)

Convert numeric values to string

Description

Convert numeric values to string

Usage

data_cat(data, cols)

Arguments

Value

A data frame with the converted values

Prepare dataframe for the analysis

Description

Depending on the selected cleaning plan, for example, recodes residual values to NA.

Usage

data_clean(data, plan = "default", ...)

Arguments

Details

The tibble remembers whether it was already cleaned and the cleaning plan is only applyed once in the first call.

Value

Cleaned data frame with vlkr_df class.

Examples

ds <- volker::chatgpt
ds <- data_clean(ds)

Prepare data originating from SoSci Survey or SPSS

Description

Preparation steps:

• Remove the avector class from all columns (comes from SoSci and prevents combining vectors)

• Recode residual factor values to NA (e.g. "NA nicht beantwortet")

• Recode residual numeric values to NA (e.g. -9)

Usage

data_clean_default(data, remove.na.levels = TRUE, remove.na.numbers = TRUE)

Arguments

Details

The tibble remembers whether it was already prepared and the operations are only performed once in the first call.

Value

Data frame with vlkr_df class (the class is used to prevent double preparation).

Examples

ds <- volker::chatgpt
ds <- data_clean_default(ds)

Convert values to numeric values

Description

Convert values to numeric values

Usage

data_num(data, cols)

Arguments

Value

A data frame with the converted values

Prepare data for calculation

Description

Clean data, check column selection, remove cases with missing values

Usage

data_prepare(
  data,
  cols,
  cross,
  cols.categorical,
  cols.numeric,
  cols.reverse,
  clean = TRUE
)

Arguments

Value

Prepared data frame.

Examples

data <- volker::chatgpt
data_prepare(data, sd_age, sd_gender)

Reverse item values

Description

Reverse item values

Usage

data_rev(data, cols)

Arguments

Value

A data frame with the specified items reversed.

Remove missings and output a message

Description

Remove missings and output a message

Usage

data_rm_missings(data, cols)

Arguments

Value

Data frame.

Remove NA levels

Description

Remove NA levels

Usage

data_rm_na_levels(data, na.levels = TRUE, default = VLKR_NA_LEVELS)

Arguments

Value

Data frame

Remove NA numbers

Description

Remove NA numbers

Usage

data_rm_na_numbers(
  data,
  na.numbers = TRUE,
  check.labels = TRUE,
  default = VLKR_NA_NUMBERS
)

Arguments

Value

Data frame

Remove negatives and output a warning

Description

Remove negatives and output a warning

Usage

data_rm_negatives(data, cols)

Arguments

Value

Data frame

Remove zero values, drop missings and output a message

Description

Remove zero values, drop missings and output a message

Usage

data_rm_zeros(data, cols)

Arguments

Value

Data frame.

Output effect sizes and test statistics for count data

Description

The type of effect size depends on the number of selected columns:

• One categorical column: see effect_counts_one

• Multiple categorical columns: see effect_counts_items

Cross tabulations:

• One categorical column and one grouping column: see effect_counts_one_grouped

• Multiple categorical columns and one grouping column: see effect_counts_items_grouped (not yet implemented)

• Multiple categorical columns and multiple grouping columns: effect_counts_items_grouped_items (not yet implemented)

By default, if you provide two column selections, the second column is treated as categorical. Setting the metric-parameter to TRUE will call the appropriate functions for correlation analysis:

• One categorical column and one metric column: see effect_counts_one_cor (not yet implemented)

• Multiple categorical columns and one metric column: see effect_counts_items_cor (not yet implemented)

• Multiple categorical columns and multiple metric columns:effect_counts_items_cor_items (not yet implemented)

Usage

effect_counts(data, cols, cross = NULL, metric = FALSE, clean = TRUE, ...)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

effect_counts(data, sd_gender, adopter)

Test homogeneity of category shares for multiple items

Description

Performs a goodness-of-fit test and calculates the Gini coefficient for each item. The goodness-of-fit-test is calculated using stats::chisq.test.

Usage

effect_counts_items(data, cols, labels = TRUE, clean = TRUE, ...)

Arguments

Value

A volker tibble with the following statistical measures:

• Gini coefficient: Gini coefficient, measuring inequality.

• n: Number of cases the calculation is based on.

• Chi-squared: Chi-Squared test statistic.

• p: p-value for the statistical test.

• stars: Significance stars based on p-value (*, **, ***).

Examples

library(volker)
data <- volker::chatgpt

effect_counts_items(data, starts_with("cg_adoption_adv"))

Correlate the values in multiple items with one metric column and output effect sizes and tests

Description

Not yet implemented. The future will come.

Usage

effect_counts_items_cor(data, cols, cross, clean = TRUE, ...)

Arguments

Value

A volker tibble.

Correlate the values in multiple items with multiple metric columns and output effect sizes and tests

Description

Not yet implemented. The future will come.

Usage

effect_counts_items_cor_items(data, cols, cross, clean = TRUE, ...)

Arguments

Value

A volker tibble.

Effect size and test for comparing multiple variables by a grouping variable

Description

Not yet implemented. The future will come.

Usage

effect_counts_items_grouped(data, cols, cross, clean = TRUE, ...)

Arguments

Value

A volker tibble.

Effect size and test for comparing multiple variables by multiple grouping variables

Description

Not yet implemented. The future will come.

Usage

effect_counts_items_grouped_items(data, cols, cross, clean = TRUE, ...)

Arguments

Value

A volker tibble.

Test homogeneity of category shares

Description

Performs a goodness-of-fit test and calculates the Gini coefficient. The goodness-of-fit-test is calculated using stats::chisq.test.

Usage

effect_counts_one(data, col, clean = TRUE, ...)

Arguments

Value

A volker tibble with the following statistical measures:

• Gini coefficient: Gini coefficient, measuring inequality.

• n: Number of cases the calculation is based on.

• Chi-squared: Chi-Squared test statistic.

• p: p-value for the statistical test.

• stars: Significance stars based on p-value (*, **, ***).

Examples

library(volker)
data <- volker::chatgpt

data |>
  filter(sd_gender != "diverse") |>
  effect_counts_one(sd_gender)

Output test statistics and effect size from a logistic regression of one metric predictor

Description

Not yet implemented. The future will come.

Usage

effect_counts_one_cor(data, col, cross, clean = TRUE, labels = TRUE, ...)

Arguments

Value

A volker tibble.

Output test statistics and effect size for contingency tables

Description

Chi squared is calculated using stats::chisq.test. If any cell contains less than 5 observations, the exact-parameter is set.

Usage

effect_counts_one_grouped(data, col, cross, clean = TRUE, ...)

Arguments

Details

Phi is derived from the Chi squared value by sqrt(fit$statistic / n). Cramer's V is derived by sqrt(phi / (min(dim(contingency)[1], dim(contingency)[2]) - 1)).

Value

A volker tibble with the following statistical measures:

• Cramer's V: Effect size measuring the association between two variables.

• n: Number of cases the calculation is based on.

• Chi-squared: Chi-Squared test statistic.

• df: Degrees of freedom.

• p: p-value for the statistical test.

• stars: Significance stars based on p-value (*, **, ***).

Examples

library(volker)
data <- volker::chatgpt

effect_counts_one_grouped(data, adopter, sd_gender)

Output effect sizes and test statistics for metric data

Description

The calculations depend on the number of selected columns:

• One metric column: see effect_metrics_one

• Multiple metric columns: see effect_metrics_items

Group comparisons:

• One metric column and one grouping column: see effect_metrics_one_grouped

• Multiple metric columns and one grouping column: see effect_metrics_items_grouped

• Multiple metric columns and multiple grouping columns: not yet implemented

By default, if you provide two column selections, the second column is treated as categorical. Setting the metric-parameter to TRUE will call the appropriate functions for correlation analysis:

• Two metric columns: see effect_metrics_one_cor

• Multiple metric columns and one metric column: see effect_metrics_items_cor

• Two metric column selections: see effect_metrics_items_cor_items

Usage

effect_metrics(data, cols, cross = NULL, metric = FALSE, clean = TRUE, ...)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

effect_metrics(data, sd_age, sd_gender)

Test whether a distribution is normal for each item

Description

The test is calculated using stats::shapiro.test.

Usage

effect_metrics_items(data, cols, labels = TRUE, clean = TRUE, ...)

Arguments

Value

A volker table containing itemwise statistics:

• skewness: Measure of asymmetry in the distribution. A value of 0 indicates perfect symmetry.

• kurtosis: Measure of the "tailedness" of the distribution.

• W: W-statistic from the Shapiro-Wilk normality test.

• p: p-value for the statistical test.

• stars: Significance stars based on p-value (*, **, ***).

• normality: Interpretation of normality based on Shapiro-Wilk test.

Examples

library(volker)
data <- volker::chatgpt

effect_metrics_items(data, starts_with("cg_adoption"))

Output correlation coefficients for items and one metric variable

Description

The correlation is calculated using stats::cor.test.

Usage

effect_metrics_items_cor(
  data,
  cols,
  cross,
  method = "pearson",
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker table containing itemwise correlations:

If method = "pearson":

• R-squared: Coefficient of determination.

• n: Number of cases the calculation is based on.

• Pearson's r: Correlation coefficient.

• ci low / ci high: Lower and upper bounds of the 95% confidence interval.

• df: Degrees of freedom.

• t: t-statistic.

• p: p-value for the statistical test, indicating whether the correlation differs from zero.

• stars: Significance stars based on the p-value (*, **, ***).

If method = "spearman":

• Spearman's rho is displayed instead of Pearson's r.

• S-statistic is used instead of the t-statistic.

Examples

library(volker)
data <- volker::chatgpt

effect_metrics_items_cor(
  data, starts_with("cg_adoption_adv"), sd_age
)

Output correlation coefficients for multiple items

Description

The correlation is calculated using stats::cor.test.

Usage

effect_metrics_items_cor_items(
  data,
  cols,
  cross,
  method = "pearson",
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker table containing correlations.

If method = "pearson":

• R-squared: Coefficient of determination.

• n: Number of cases the calculation is based on.

• Pearson's r: Correlation coefficient.

• ci low / ci high: Lower and upper bounds of the 95% confidence interval.

• df: Degrees of freedom.

• t: t-statistic.

• p: p-value for the statistical test, indicating whether the correlation differs from zero.

• stars: Significance stars based on the p-value (*, **, ***).

If method = "spearman":

• Spearman's rho is displayed instead of Pearson's r.

• S-statistic is used instead of the t-statistic.

Examples

library(volker)
data <- volker::chatgpt

effect_metrics_items_cor_items(
  data,
  starts_with("cg_adoption_adv"),
  starts_with("use"),
  metric = TRUE
)

Compare groups for each item by calculating F-statistics and effect sizes

Description

The models are fitted using stats::lm. ANOVA of type II is computed for each fitted model using car::Anova. Eta Squared is calculated for each ANOVA result using effectsize::eta_squared.

Usage

effect_metrics_items_grouped(
  data,
  cols,
  cross,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble with the following statistical measures:

• Eta-squared: Effect size indicating the proportion of variance in the dependent variable explained by the predictor.

• Eta: Root of Eta-squared, a standardized effect size.

• n: Number of cases the calculation is based on.

• F: F-statistic from the linear model.

• p: p-value for the statistical test.

• stars: Significance stars based on p-value (*, **, ***).

Examples

library(volker)
data <- volker::chatgpt

effect_metrics(data, starts_with("cg_adoption_"), adopter)

Compare groups for each item with multiple target items by calculating F-statistics and effect sizes

Description

Not yet implemented. The future will come.

Usage

effect_metrics_items_grouped_items(data, cols, cross, clean = TRUE, ...)

Arguments

Value

A volker tibble.

Test whether a distribution is normal

Description

The test is calculated using stats::shapiro.test.

Usage

effect_metrics_one(data, col, labels = TRUE, clean = TRUE, ...)

Arguments

Value

A volker list object with the following statistical measures:

• skewness: Measure of asymmetry in the distribution. A value of 0 indicates perfect symmetry.

• kurtosis: Measure of the "tailedness" of the distribution.

• W: W-statistic from the Shapiro-Wilk normality test.

• p: p-value for the statistical test.

• stars: Significance stars based on p-value (*, **, ***).

• normality: Interpretation of normality based on Shapiro-Wilk test.

Examples

library(volker)
data <- volker::chatgpt

effect_metrics_one(data, sd_age)

Test whether the correlation is different from zero

Description

The correlation is calculated using stats::cor.test.

Usage

effect_metrics_one_cor(
  data,
  col,
  cross,
  method = "pearson",
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker table containing the requested statistics.

If method = "pearson":

• R-squared: Coefficient of determination.

• n: Number of cases the calculation is based on.

• Pearson's r: Correlation coefficient.

• ci low / ci high: Lower and upper bounds of the 95% confidence interval.

• df: Degrees of freedom.

• t: t-statistic.

• p: p-value for the statistical test, indicating whether the correlation differs from zero.

• stars: Significance stars based on the p-value (*, **, ***).

If method = "spearman":

• Spearman's rho is displayed instead of Pearson's r.

• S-statistic is used instead of the t-statistic.

Examples

library(volker)
data <- volker::chatgpt

effect_metrics_one_cor(data, sd_age, use_private, metric = TRUE)

Output a regression table with estimates and macro statistics

Description

The regression output comes from stats::lm. T-test is performed using stats::t.test. Normality check is performed using stats::shapiro.test. Equality of variances across groups is assessed using car::leveneTest. Cohen's d is calculated using effectsize::cohens_d.

Usage

effect_metrics_one_grouped(
  data,
  col,
  cross,
  method = "lm",
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker list object containing volker tables with the requested statistics.

Regression table:

• estimate: Regression coefficient (unstandardized).

• ci low / ci high: lower and upper bound of the 95% confidence interval.

• se: Standard error of the estimate.

• t: t-statistic.

• p: p-value for the statistical test.

• stars: Significance stars based on p-value (*, **, ***).

Macro statistics:

• Adjusted R-squared: Adjusted coefficient of determination.

• F: F-statistic for the overall significance of the model.

• df: Degrees of freedom for the model.

• residual df: Residual degrees of freedom.

• p: p-value for the statistical test.

• stars: Significance stars based on p-value (*, **, ***).

If method = t.test:

Shapiro-Wilk test (normality check):

• W: W-statistic from the Shapiro-Wilk normality test.

• p: p-value for the test.

• normality: Interpretation of the Shapiro-Wilk test.

Levene test (equality of variances):

• F: F-statistic from the Levene test for equality of variances between groups.

• p: p-value for Levene's test.

• variances: Interpretation of the Levene test.

Cohen's d (effect size):

• d: Standardized mean difference between the two groups.

• ci low / ci high: Lower and upper bounds of the 95% confidence interval.

t-test

• method: Type of t-test performed (e.g., "Two Sample t-test").

• difference: Observed difference between group means.

• ci low / ci high: Lower and upper bounds of the 95% confidence interval.

• se: Estimated standard error of the difference.

• df: Degrees of freedom used in the t-test.

• t: t-statistic.

• p: p-value for the t-test.

• stars: Significance stars based on p-value (*, ⁠**⁠, ⁠***⁠).

Examples

library(volker)
data <- volker::chatgpt

effect_metrics_one_grouped(data, sd_age, sd_gender)

Select variables by their postfix

Description

See tidyselect::ends_with for details.

Get plot with factor analysis result

Description

PCA is performed using add_factors.

Usage

factor_plot(
  data,
  cols,
  newcols = NULL,
  k = 2,
  method = "pca",
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Examples

library(volker)
ds <- volker::chatgpt

volker::factor_plot(ds, starts_with("cg_adoption"), k = 3)

Get tables with factor analysis results

Description

PCA is performed using add_factors.

Usage

factor_tab(
  data,
  cols,
  newcols = NULL,
  k = 2,
  method = "pca",
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

        If the first column already contains a pca result from \link{add_factors},
        the result is used. Other parameters are ignored.

        If there is no pca result yet, it is calculated by \link{add_factors} first.

Value

A volker list with with three volker tabs: loadings, variances and diagnostics.

Examples

library(volker)
ds <- volker::chatgpt

volker::factor_tab(ds, starts_with("cg_adoption"), k = 3)

Filter function

Description

See dplyr::filter for details.

Get number of items and Cronbach's alpha of a scale added by add_index()

Description

TODO: Rename to index_tab, return volker list as in factor_tab()

Usage

get_alpha(data)

Arguments

Value

A named list with with the keys "items" and "alpha".

Angle labels

Description

Calculate angle for label adjustment based on character length.

Usage

get_angle(
  labels,
  threshold = VLKR_PLOT_ANGLE_THRESHOLD,
  angle = VLKR_PLOT_ANGLE_VALUE
)

Arguments

Value

A single angle value.

Get a formatted baseline for removed zero, negative, and missing cases and include focus category information if present

Description

Get a formatted baseline for removed zero, negative, and missing cases and include focus category information if present

Usage

get_baseline(obj)

Arguments

Value

A formatted message or NULL if missings and focus attributes are not present.

Calculate ci values to be used for error bars on a plot

Description

Calculate ci values to be used for error bars on a plot

Usage

get_ci(x, conf = 0.95)

Arguments

Value

A named list with values for y, ymin, and ymax.

Detect whether a scale is a numeric sequence

Description

From all values in the selected columns, the numbers are extracted. If no numeric values can be found, returns 0. Otherwise, if any positive values form an ascending sequence, returns -1. In all other cases, returns 1.

Usage

get_direction(data, cols, extract = TRUE)

Arguments

Value

0 = an undirected scale, -1 = descending values, 1 = ascending values.

Calculate Eta squared

Description

Calculate Eta squared

Usage

get_etasq(fit)

Arguments

Value

A data frame with at least the column Eta2

Calculate the Gini coefficient

Description

Calculate the Gini coefficient

Usage

get_gini(x)

Arguments

Value

The gini coefficient

Get the labels of values from a codebook

Description

Get the labels of values from a codebook

Usage

get_labels(codes, values)

Arguments

Value

The labels. If the values are not present in the codebook, returns the values.

Get the numeric range from the labels

Description

Gets the range of all values in the selected columns by the first successful of the following methods:

Usage

get_limits(data, cols, negative = TRUE)

Arguments

Details

• Inspect the limits column attribute.

• Lookup the value names in the codebook.

• Calculate the range from all values in the columns.

Value

A list or NULL.

Get the common prefix of character values

Description

Helper function taken from the biobase package. Duplicated here instead of loading the package to avoid overhead. See https://github.com/Bioconductor/Biobase

Usage

get_prefix(x, ignore.case = FALSE, trim = FALSE, delimiters = c(":", "\n"))

Arguments

"Usage: in "

Value

The longest common prefix of the strings.

Get significance stars from p values

Description

Get significance stars from p values

Usage

get_stars(x)

Arguments

Value

A character vector with significance stars.

Get a common title for a column selection

Description

Get a common title for a column selection

Usage

get_title(data, cols, default = NULL)

Arguments

Value

A character string.

Volker style HTML document format

Description

Based on the standard theme, tweaks the pill navigation to switch between tables and plots. To use the format, in the header of your Markdown document, set output: volker::html_report.

Usage

html_report(...)

Arguments

Value

R Markdown output format.

Examples

## Not run: 
# Add `volker::html_report` to the output options of your Markdown document:
#
# ```
# ---
# title: "How to create reports?"
# output: volker::html_report
# ---
# ```

## End(Not run)

Deprecated Alias for add_index

Description

idx_add() was renamed to add_index().

Usage

idx_add(data, cols, newcol = NULL, reverse = NULL, clean = TRUE)

Details

This function is a deprecated alias for add_index.

Printing method for volker plots when knitting

Description

Printing method for volker plots when knitting

Usage

## S3 method for class 'vlkr_plt'
knit_print(x, ...)

Arguments

Value

Knitr asis output

Examples

library(volker)
data <- volker::chatgpt

pl <- plot_metrics(data, sd_age)
print(pl)

Wrap labels in plot scales

Description

Wrap labels in plot scales

Usage

label_scale(x, scale)

Arguments

Value

A vevtor of wrapped labels.

Set column and value labels

Description

Usage

labs_apply(data, codes = NULL, cols = NULL, items = TRUE, values = TRUE)

Arguments

Details

You can either provide a data frame in codebook format to the codes-parameter or provide named lists to the items- or values-parameter.

When working with a codebook in the codes-parameter:

• Change column labels by providing the columns item_name and item_label in the codebook. Set the items-parameter to TRUE (the default setting).

• Change value labels by providing the columns value_name and value_label in the codebook. To tell which columns should be changed, you can either use the item_name column in the codebook or use the cols-parameter. For factor values, the levels and their order are retrieved from the value_label column. For coded values, labels are retrieved from both the columns value_name and value_label.

When working with lists in the items- or values-parameter:

• Change column labels by providing a named list to the items-parameter. The list contains labels named by the columns. Set the parameters codes and cols to NULL (their default value).

• Change value labels by providing a named list to the values-parameter. The list contains labels named by the values. Provide the column selection in the cols-parameter. Set the codes-parameter to NULL (its default value).

Value

A tibble containing the dataset with new labels.

Examples

library(volker)

# Set column labels using the items-parameter
volker::chatgpt %>%
  labs_apply(
   items = list(
     "cg_adoption_advantage_01" = "Allgemeine Vorteile",
     "cg_adoption_advantage_02" = "Finanzielle Vorteile",
     "cg_adoption_advantage_03" = "Vorteile bei der Arbeit",
     "cg_adoption_advantage_04" = "Macht mehr Spaß"
   )
 ) %>%
 tab_metrics(starts_with("cg_adoption_advantage_"))

# Set value labels using the values-parameter
 volker::chatgpt %>%
   labs_apply(
     cols=starts_with("cg_adoption"),
     values = list(
       "1" = "Stimme überhaupt nicht zu",
       "2" = "Stimme nicht zu",
       "3" = "Unentschieden",
       "4" = "Stimme zu",
       "5" =  "Stimme voll und ganz zu"
     )
   ) %>%
   plot_metrics(starts_with("cg_adoption"))

Remove all comments from the selected columns

Description

Usage

labs_clear(data, cols, labels = NULL)

Arguments

Value

A tibble with comments removed.

Examples

library(volker)
volker::chatgpt |>
  labs_clear()

Add missing residual labels in numeric columns that have at least one labeled value

Description

Add missing residual labels in numeric columns that have at least one labeled value

Usage

labs_impute(data)

Arguments

Value

A tibble with added value labels

Replace item value names in a column by their labels

Description

Replace item value names in a column by their labels

Usage

labs_replace(
  data,
  col,
  codes,
  col_from = "value_name",
  col_to = "value_label",
  na.missing = FALSE
)

Arguments

Value

Tibble with new labels.

Restore labels from the codebook store in the codebook attribute.

Description

Usage

labs_restore(data, cols = NULL)

Arguments

Details

You can store labels before mutate operations by calling labs_store.

Value

A data frame.

Examples

library(dplyr)
library(volker)

volker::chatgpt |>
  labs_store() |>
  mutate(sd_age = 2024 - sd_age) |>
  labs_restore() |>
  tab_metrics(sd_age)

Get the current codebook and store it in the codebook attribute.

Description

Usage

labs_store(data)

Arguments

Details

You can restore the labels after mutate operations by calling labs_restore.

Value

A data frame.

Examples

library(dplyr)
library(volker)

volker::chatgpt |>
  labs_store() |>
  mutate(sd_age = 2024 - sd_age) |>
  labs_restore() |>
  tab_metrics(sd_age)

Plot regression coefficients

Description

The regression output comes from stats::lm.

Usage

model_metrics_plot(
  data,
  col,
  categorical,
  metric,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker list object containing volker plots

Examples

library(volker)
data <- volker::chatgpt

data |>
  filter(sd_gender != "diverse") |>
  model_metrics_plot(use_work, categorical = c(sd_gender, adopter), metric = sd_age)

Output a regression table with estimates and macro statistics for multiple categorical or metric independent variables

Description

The regression output comes from stats::lm.

Usage

model_metrics_tab(
  data,
  col,
  categorical,
  metric,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker list object containing volker tables with the requested statistics.

Examples

library(volker)
data <- volker::chatgpt

data |>
  filter(sd_gender != "diverse") |>
  model_metrics_tab(use_work, categorical = c(sd_gender, adopter), metric = sd_age)

Mutate function

Description

See dplyr::mutate for details.

Convert a named vector to a list

Description

Convert a named vector to a list

Usage

named.to.list(x)

Arguments

Value

Lists are returned as is. Vectors are converted to lists with names as list names.

Volker style PDF document format

Description

Based on the standard theme, tweaks tex headers. To use the format, in the header of your Markdown document, set output: volker::pdf_report.

Usage

pdf_report(...)

Arguments

Value

R Markdown output format.

Examples

## Not run: 
# Add `volker::pdf_report` to the output options of your Markdown document:
#
# ```
# ---
# title: "How to create reports?"
# output: volker::pdf_report
# ---
# ```

## End(Not run)

Output a frequency plot

Description

The type of frequency plot depends on the number of selected columns:

• One categorical column: see plot_counts_one

• Multiple categorical columns: see plot_counts_items

Cross tabulations:

• One categorical column and one grouping column: see plot_counts_one_grouped

• Multiple categorical columns and one grouping column: see plot_counts_items_grouped

• Two categorical column selections: see plot_counts_items_grouped_items (not yet implemented)

By default, if you provide two column selections, the second selection is treated as categorical. Setting the metric-parameter to TRUE will call the appropriate functions for correlation analysis:

• One categorical column and one metric column: see plot_counts_one_cor

• Multiple categorical columns and one metric column: see plot_counts_items_cor

• Multiple categorical columns and multiple metric columns: see plot_counts_items_cor_items (not yet implemented)

Parameters that may be passed to the count functions (see the respective function help):

• ci: Add confidence intervals to proportions.

• ordered: The values of the cross column can be nominal (0), ordered ascending (1), or ordered descending (-1). The colors are adjusted accordingly.

• category: When you have multiple categories in a column, you can focus one of the categories to simplify the plots. By default, if a column has only TRUE and FALSE values, the outputs focus the TRUE category.

• prop: For stacked bar charts, displaying row percentages instead of total percentages gives a direct visual comparison of groups.

• limits: The scale limits are automatically guessed by the package functions (work in progress). Use the limits-parameter to manually fix any misleading graphs.

• title: All plots usually get a title derived from the column attributes or column names. Set to FALSE to suppress the title or provide a title of your choice as a character value.

• labels: Labels are extracted from the column attributes. Set to FALSE to output bare column names and values.

• numbers: Set the numbers parameter to “n” (frequency), “p” (percentage) or c(“n”,“p”). To prevent cluttering and overlaps, numbers are only plotted on bars larger than 5%.

• width: When comparing groups by row of column percentages, by default, the bar or column width reflects the number of cases. You can disable this behavior by setting width to FALSE.

Usage

plot_counts(data, cols, cross = NULL, metric = FALSE, clean = TRUE, ...)

Arguments

Value

A ggplot2 plot object.

Examples

library(volker)
data <- volker::chatgpt

plot_counts(data, sd_gender)

Output frequencies for multiple variables

Description

Output frequencies for multiple variables

Usage

plot_counts_items(
  data,
  cols,
  category = NULL,
  ordered = NULL,
  ci = FALSE,
  limits = NULL,
  numbers = NULL,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Examples

library(volker)
data <- volker::chatgpt

plot_counts_items(data, starts_with("cg_adoption_"))

Plot percent shares of multiple items compared by a metric variable split into groups

Description

Plot percent shares of multiple items compared by a metric variable split into groups

Usage

plot_counts_items_cor(
  data,
  cols,
  cross,
  category = NULL,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Examples

library(volker)
data <- volker::chatgpt

plot_counts_items_cor(
  data, starts_with("cg_adoption_"), sd_age,
  category=c("agree","strongly agree")
)

plot_counts_items_cor(
  data, starts_with("cg_adoption_"), sd_age,
  category=c(4,5)
)

Correlation of categorical items with metric items

Description

Not yet implemented. The future will come.

Usage

plot_counts_items_cor_items(
  data,
  cols,
  cross,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Plot percent shares of multiple items compared by groups

Description

Plot percent shares of multiple items compared by groups

Usage

plot_counts_items_grouped(
  data,
  cols,
  cross,
  category = NULL,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Examples

library(volker)
data <- volker::chatgpt
plot_counts_items_grouped(
  data, starts_with("cg_adoption_"), adopter,
  category=c("agree","strongly agree")
)

plot_counts_items_grouped(
  data, starts_with("cg_adoption_"), adopter,
  category=c(4,5)
)

Correlation of categorical items with categorical items

Description

Not yet implemented. The future will come.

Usage

plot_counts_items_grouped_items(
  data,
  cols,
  cross,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Plot the frequency of values in one column

Description

Plot the frequency of values in one column

Usage

plot_counts_one(
  data,
  col,
  category = NULL,
  ci = FALSE,
  limits = NULL,
  numbers = NULL,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Examples

library(volker)
data <- volker::chatgpt

plot_counts_one(data, sd_gender)

Plot frequencies cross tabulated with a metric column that will be split into groups

Description

Plot frequencies cross tabulated with a metric column that will be split into groups

Usage

plot_counts_one_cor(
  data,
  col,
  cross,
  category = NULL,
  prop = "total",
  limits = NULL,
  ordered = NULL,
  numbers = NULL,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Examples

library(volker)
data <- volker::chatgpt

plot_counts_one_cor(data, adopter, sd_age)

Plot frequencies cross tabulated with a grouping column

Description

Plot frequencies cross tabulated with a grouping column

Usage

plot_counts_one_grouped(
  data,
  col,
  cross,
  category = NULL,
  prop = "total",
  width = NULL,
  limits = NULL,
  ordered = NULL,
  numbers = NULL,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Examples

library(volker)
data <- volker::chatgpt

plot_counts_one_grouped(data, adopter, sd_gender)

Output a plot with distribution parameters such as the mean values

Description

The plot type depends on the number of selected columns:

• One metric column: see plot_metrics_one

• Multiple metric columns: see plot_metrics_items

Group comparisons:

• One metric column and one grouping column: see plot_metrics_one_grouped

• Multiple metric columns and one grouping column: see plot_metrics_items_grouped

• Multiple metric columns and multiple grouping columns: see plot_metrics_items_grouped_items (not yet implemented)

By default, if you provide two column selections, the second selection is treated as categorical. Setting the metric-parameter to TRUE will call the appropriate functions for correlation analysis:

• Two metric columns: see plot_metrics_one_cor

• Multiple metric columns and one metric column : see plot_metrics_items_cor

• Two metric column selections: see plot_metrics_items_cor_items

Parameters that may be passed to the metric functions (see the respective function help):

• ci: Plot confidence intervals for means or correlation coefficients.

• box: Visualise the distribution by adding boxplots.

• log: In scatter plots, you can use a logarithmic scale. Be aware, that zero values will be omitted because their log value is undefined.

• method: By default, correlations are calculated using Pearson’s R. You can choose Spearman’s Rho with the methods-parameter.

• limits: The scale limits are automatically guessed by the package functions (work in progress). Use the limits-parameter to manually fix any misleading graphs.

• title: All plots usually get a title derived from the column attributes or column names. Set to FALSE to suppress the title or provide a title of your choice as a character value.

• labels: Labels are extracted from the column attributes. Set to FALSE to output bare column names and values.

• numbers: Controls whether to display correlation coefficients on the plot.

Usage

plot_metrics(data, cols, cross = NULL, metric = FALSE, clean = TRUE, ...)

Arguments

Value

A ggplot object.

Examples

library(volker)
data <- volker::chatgpt

plot_metrics(data, sd_age)

Output averages for multiple variables

Description

Output averages for multiple variables

Usage

plot_metrics_items(
  data,
  cols,
  ci = FALSE,
  box = FALSE,
  limits = NULL,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Examples

library(volker)
data <- volker::chatgpt

plot_metrics_items(data, starts_with("cg_adoption_"))

Multiple items correlated with one metric variable

Description

Multiple items correlated with one metric variable

Usage

plot_metrics_items_cor(
  data,
  cols,
  cross,
  ci = FALSE,
  method = "pearson",
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Examples

library(volker)
data <- volker::chatgpt

plot_metrics_items_cor(data, starts_with("use_"), sd_age)

Heatmap for correlations between multiple items

Description

Heatmap for correlations between multiple items

Usage

plot_metrics_items_cor_items(
  data,
  cols,
  cross,
  method = "pearson",
  numbers = FALSE,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Examples

library(volker)
data <- volker::chatgpt

plot_metrics_items_cor_items(data, starts_with("cg_adoption_adv"), starts_with("use_"))

Output averages for multiple variables compared by a grouping variable

Description

Output averages for multiple variables compared by a grouping variable

Usage

plot_metrics_items_grouped(
  data,
  cols,
  cross,
  limits = NULL,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Examples

library(volker)
data <- volker::chatgpt

plot_metrics_items_grouped(data, starts_with("cg_adoption_"), sd_gender)

Correlation of metric items with categorical items

Description

Not yet implemented. The future will come.

Usage

plot_metrics_items_grouped_items(
  data,
  cols,
  cross,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Output a density plot for a single metric variable

Description

Output a density plot for a single metric variable

Usage

plot_metrics_one(
  data,
  col,
  ci = FALSE,
  box = FALSE,
  limits = NULL,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Examples

library(volker)
data <- volker::chatgpt

plot_metrics_one(data, sd_age)

Correlate two items

Description

Correlate two items

Usage

plot_metrics_one_cor(
  data,
  col,
  cross,
  limits = NULL,
  log = FALSE,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Examples

library(volker)
data <- volker::chatgpt

plot_metrics_one_cor(data, use_private, sd_age)

Output averages for multiple variables

Description

Output averages for multiple variables

Usage

plot_metrics_one_grouped(
  data,
  col,
  cross,
  ci = FALSE,
  box = FALSE,
  limits = NULL,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A ggplot object.

Examples

library(volker)
data <- volker::chatgpt

plot_metrics_one_grouped(data, sd_age, sd_gender)

Prepare the scale attribute values

Description

Prepare the scale attribute values

Usage

prepare_scale(data)

Arguments

Value

A named list or NULL.

Printing method for volker lists

Description

Printing method for volker lists

Usage

## S3 method for class 'vlkr_list'
print(x, ...)

Arguments

Value

No return value.

Examples

library(volker)
data <- volker::chatgpt

rp <- report_metrics(data, sd_age, sd_gender, effect = TRUE)
print(rp)

Printing method for volker plots

Description

Printing method for volker plots

Usage

## S3 method for class 'vlkr_plt'
print(x, ...)

## S3 method for class 'vlkr_plt'
plot(x, ...)

Arguments

Value

No return value.

Examples

library(volker)
data <- volker::chatgpt

pl <- plot_metrics(data, sd_age)
print(pl)

Printing method for volker reports

Description

Printing method for volker reports

Usage

## S3 method for class 'vlkr_rprt'
print(x, ...)

Arguments

Value

No return value.

Examples

library(volker)
data <- volker::chatgpt

rp <- report_metrics(data, sd_age)
print(rp)

Printing method for volker tables.

Description

Printing method for volker tables.

Usage

## S3 method for class 'vlkr_tbl'
print(x, ...)

Arguments

Value

No return value.

Examples

library(volker)
data <- volker::chatgpt

tb <- tab_metrics(data, sd_age)
print(tb)

Create table and plot for categorical variables

Description

Depending on your column selection, different types of plots and tables are generated. See plot_counts and tab_counts.

Usage

report_counts(
  data,
  cols,
  cross = NULL,
  metric = FALSE,
  index = FALSE,
  effect = FALSE,
  numbers = NULL,
  title = TRUE,
  close = TRUE,
  clean = TRUE,
  ...
)

Arguments

Details

For item batteries, an index is calculated and reported. When used in combination with the Markdown-template "html_report", the different parts of the report are grouped under a tabsheet selector.

Value

A volker report object.

Examples

library(volker)
data <- volker::chatgpt

report_counts(data, sd_gender)

Create table and plot for metric variables

Description

Depending on your column selection, different types of plots and tables are generated. See plot_metrics and tab_metrics.

Usage

report_metrics(
  data,
  cols,
  cross = NULL,
  metric = FALSE,
  ...,
  index = FALSE,
  factors = FALSE,
  clusters = FALSE,
  effect = FALSE,
  title = TRUE,
  close = TRUE,
  clean = TRUE
)

Arguments

Details

For item batteries, an index is calculated and reported. When used in combination with the Markdown-template "html_report", the different parts of the report are grouped under a tabsheet selector.

Value

A volker report object.

Examples

library(volker)
data <- volker::chatgpt

report_metrics(data, sd_age)

Select function

Description

See dplyr::select for details.

A skimmer for boxplot generation

Description

Returns a five point summary, mean and sd, items count and alpha for scales added by add_index(). Additionally, the whiskers defined by the minimum respective maximum value within 1.5 * iqr are calculated. Outliers are returned in a list column.

Usage

skim_boxplot(data, ..., .data_name = NULL)

Calculate a metric by groups

Description

Calculate a metric by groups

Usage

skim_grouped(data, cols, cross, value = "numeric.mean", labels = TRUE)

Arguments

Value

A tibble with each item in a row, a total column and columns for all groups.

A reduced skimmer for metric variables Returns a five point summary, mean and sd, items count and alpha for scales added by add_index()

Description

A reduced skimmer for metric variables Returns a five point summary, mean and sd, items count and alpha for scales added by add_index()

Usage

skim_metrics(data, ..., .data_name = NULL)

Value

A skimmer, see skim_with

Examples

library(volker)
data <- volker::chatgpt

skim_metrics(data)

Select variables by their prefix

Description

See tidyselect::starts_with for details.

Output a frequency table

Description

The type of frequency table depends on the number of selected columns:

• One categorical column: see tab_counts_one

• Multiple categorical columns: see tab_counts_items

Cross tabulations:

• One categorical column and one grouping column: see tab_counts_one_grouped

• Multiple categorical columns and one grouping column: see tab_counts_items_grouped

• Multiple categorical columns and multiple grouping columns: see tab_counts_items_grouped_items (not yet implemented)

By default, if you provide two column selections, the second column is treated as categorical. Setting the metric-parameter to TRUE will call the appropriate functions for correlation analysis:

• One categorical column and one metric column: see tab_counts_one_cor

• Multiple categorical columns and one metric column: see tab_counts_items_cor

• Multiple categorical columns and multiple metric columns: tab_counts_items_cor_items (not yet implemented)

Parameters that may be passed to specific count functions:

• ci: Add confidence intervals to proportions.

• percent: Frequency tables show percentages by default. Set to FALSE to get raw proportions.

• prop: For cross tables you can choose between total, row or column percentages.

• values: The values to output: n (frequency) or p (percentage) or both (the default).

• category: When you have multiple categories in a column, you can focus one of the categories to simplify the plots. By default, if a column has only TRUE and FALSE values, the outputs focus the TRUE category.

• labels: Labels are extracted from the column attributes. Set to FALSE to output bare column names and values.

Usage

tab_counts(data, cols, cross = NULL, metric = FALSE, clean = TRUE, ...)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

tab_counts(data, sd_gender)

Output frequencies for multiple variables

Description

Output frequencies for multiple variables

Usage

tab_counts_items(
  data,
  cols,
  ci = FALSE,
  percent = TRUE,
  values = c("n", "p"),
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

tab_counts_items(data, starts_with("cg_adoption_"))

Compare the values in multiple items by a metric column that will be split into groups

Description

Compare the values in multiple items by a metric column that will be split into groups

Usage

tab_counts_items_cor(
  data,
  cols,
  cross,
  category = NULL,
  split = NULL,
  percent = TRUE,
  values = c("n", "p"),
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt
tab_counts_items_cor(
  data, starts_with("cg_adoption_"), sd_age,
  category=c("agree", "strongly agree")
)

Correlation of categorical items with metric items

Description

Not yet implemented. The future will come.

Usage

tab_counts_items_cor_items(
  data,
  cols,
  cross,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble.

Compare the values in multiple items by a grouping column

Description

Compare the values in multiple items by a grouping column

Usage

tab_counts_items_grouped(
  data,
  cols,
  cross,
  category = NULL,
  percent = TRUE,
  values = c("n", "p"),
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt
tab_counts_items_grouped(
  data, starts_with("cg_adoption_"), adopter,
  category=c("agree", "strongly agree")
)

Correlation of categorical items with categorical items

Description

Not yet implemented. The future will come.

Usage

tab_counts_items_grouped_items(
  data,
  cols,
  cross,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble.

Output a frequency table for the values in one column

Description

Output a frequency table for the values in one column

Usage

tab_counts_one(
  data,
  col,
  ci = FALSE,
  percent = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

tab_counts_one(data, sd_gender)

Count values by a metric column that will be split into groups

Description

Count values by a metric column that will be split into groups

Usage

tab_counts_one_cor(
  data,
  col,
  cross,
  prop = "total",
  percent = TRUE,
  values = c("n", "p"),
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

tab_counts_one_cor(data, adopter, sd_age)

Output frequencies cross tabulated with a grouping column

Description

Output frequencies cross tabulated with a grouping column

Usage

tab_counts_one_grouped(
  data,
  col,
  cross,
  prop = "total",
  percent = TRUE,
  values = c("n", "p"),
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

tab_counts_one_grouped(data, adopter, sd_gender)

Output a table with distribution parameters

Description

The table type depends on the number of selected columns:

• One metric column: see tab_metrics_one

• Multiple metric columns: see tab_metrics_items

Group comparisons:

• One metric column and one grouping column: see tab_metrics_one_grouped

• Multiple metric columns and one grouping column: see tab_metrics_items_grouped

• Multiple metric columns and multiple grouping columns: see tab_metrics_items_grouped_items (not yet implemented)

By default, if you provide two column selections, the second column is treated as categorical. Setting the metric-parameter to TRUE will call the appropriate functions for correlation analysis:

• Two metric columns: see tab_metrics_one_cor

• Multiple metric columns and one metric column: see tab_metrics_items_cor

• Two metric column selections: see tab_metrics_items_cor_items

Parameters that may be passed to specific metric functions:

• ci: Add confidence intervals for means or correlation coefficients.

• values: The output metrics, mean (m), the standard deviation (sd) or both (the default).

• digits: Tables containing means and standard deviations by default round values to one digit. Increase the number to show more digits

• method: By default, correlations are calculated using Pearson’s R. You can choose Spearman’s Rho with the methods-parameter.

• labels: Labels are extracted from the column attributes. Set to FALSE to output bare column names and values.

Usage

tab_metrics(data, cols, cross = NULL, metric = FALSE, clean = TRUE, ...)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

tab_metrics(data, sd_age)

Output a five point summary table for multiple items

Description

Output a five point summary table for multiple items

Usage

tab_metrics_items(
  data,
  cols,
  ci = FALSE,
  digits = 1,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

tab_metrics_items(data, starts_with("cg_adoption_"))

Output a correlation table for item battery and one metric variable

Description

Usage

tab_metrics_items_cor(
  data,
  cols,
  cross,
  method = "pearson",
  digits = 2,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

tab_metrics_items_cor(
  data,
  starts_with("cg_adoption_adv"),
  sd_age,
  metric = TRUE
)

Output a correlation table for item battery and item battery

Description

Usage

tab_metrics_items_cor_items(
  data,
  cols,
  cross,
  method = "pearson",
  digits = 2,
  ci = FALSE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

tab_metrics_items_cor_items(
  data,
  starts_with("cg_adoption_adv"),
  starts_with("use"),
  metric = TRUE
)

Output the means for groups in one or multiple columns

Description

Output the means for groups in one or multiple columns

Usage

tab_metrics_items_grouped(
  data,
  cols,
  cross,
  digits = 1,
  values = c("m", "sd"),
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

tab_metrics_items_grouped(data, starts_with("cg_adoption_"), sd_gender)

Correlation of metric items with categorical items

Description

Not yet implemented. The future will come.

Usage

tab_metrics_items_grouped_items(
  data,
  cols,
  cross,
  title = TRUE,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble.

Output a five point summary table for the values in multiple columns

Description

Output a five point summary table for the values in multiple columns

Usage

tab_metrics_one(
  data,
  col,
  ci = FALSE,
  digits = 1,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

tab_metrics_one(data, sd_age)

Correlate two columns

Description

Correlate two columns

Usage

tab_metrics_one_cor(
  data,
  col,
  cross,
  method = "pearson",
  ci = FALSE,
  digits = 2,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

tab_metrics_one_cor(data, use_private, sd_age)

Output a five point summary for groups

Description

Output a five point summary for groups

Usage

tab_metrics_one_grouped(
  data,
  col,
  cross,
  ci = FALSE,
  digits = 1,
  labels = TRUE,
  clean = TRUE,
  ...
)

Arguments

Value

A volker tibble.

Examples

library(volker)
data <- volker::chatgpt

tab_metrics_one_grouped(data, sd_age, sd_gender)

Get, set, and modify the active ggplot theme

Description

See ggplot2::theme_set for details.

Define a default theme for volker plots

Description

Set ggplot colors, sizes and layout parameters.

Usage

theme_vlkr(
  base_size = 11,
  base_color = "black",
  base_fill = VLKR_FILLDISCRETE,
  base_gradient = VLKR_FILLGRADIENT
)

Arguments

Details

Value

A theme function.

Examples

library(volker)
library(ggplot2)
data <- volker::chatgpt

theme_set(theme_vlkr(base_size=15, base_fill = list("red")))
plot_counts(data, sd_gender)

Tidy tibbles

Description

See tibble::tibble for details.

Tidy lm results, replace categorical parameter names by their levels and add the reference level

Description

Tidy lm results, replace categorical parameter names by their levels and add the reference level

Usage

tidy_lm_levels(fit)

Arguments

Value

A tibble with regression parameters.

Author(s)

Created with the help of ChatGPT.

Tidy tribbles

Description

See tibble::tribble for details.

Remove trailing zeros and trailing or leading whitespaces, colons, hyphens and underscores

Description

Remove trailing zeros and trailing or leading whitespaces, colons, hyphens and underscores

Usage

trim_label(x)

Arguments

Value

The trimmed character value.

Remove a prefix from a character vector or a factor

Description

If the resulting character values would be empty, the prefix is returned. At the end, all items in the vector are trimmed using trim_label.

Usage

trim_prefix(x, prefix = TRUE)

Arguments

Details

If x is a factor, the order of factor levels is retained.

Value

The trimmed character or factor vector.

Truncate labels

Description

Truncate labels that exceed a specified maximum length.

Usage

trunc_labels(x, max_length = 20)

Arguments

Value

A character vector with truncated labels.

Interpolate an alpha value based on case numbers

Description

Interpolate an alpha value based on case numbers

Usage

vlkr_alpha_interpolated(
  n,
  n_min = 20,
  n_max = 100,
  alpha_min = VLKR_POINT_ALPHA,
  alpha_max = 1
)

Arguments

Value

A value between the minimum and the maximum alpha value

Get colors for discrete scales

Description

If the option ggplot2.discrete.fill is set, gets color values from the first list item that has enough colors and reverses them to start filling from the left in grouped bar charts.

Usage

vlkr_colors_discrete(n)

Arguments

Details

Falls back to scale_fill_hue().

Value

A vector of colors.

Get colors for polarized scales

Description

Creates a gradient scale based on VLKR_FILLPOLARIZED.

Usage

vlkr_colors_polarized(n = NULL)

Arguments

Value

A vector of colors.

Get colors for sequential scales

Description

Creates a gradient scale based on VLKR_FILLGRADIENT.

Usage

vlkr_colors_sequential(n = NULL)

Arguments

Value

A vector of colors.

Wrap a string

Description

Wrap a string

Usage

wrap_label(x, width = 40)

Arguments

Value

A character vector with wrapped strings.

Combine two identically shaped data frames by adding values of each column from the second data frame into the corresponding column in the first dataframe using parentheses

Description

Combine two identically shaped data frames by adding values of each column from the second data frame into the corresponding column in the first dataframe using parentheses

Usage

zip_tables(x, y, newline = TRUE, brackets = FALSE)

Arguments

Value

A combined data frame.